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13.1: An Introduction to Research and Development (R&D)

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Learning Objectives

• Know what constitutes research and development (R&D).
• Understand the importance of R&D to corporations.
• Recognize the role government plays in R&D.

Research and development (R&D) refers to two intertwined processes of research (to identify new knowledge and ideas) and development (turning the ideas into tangible products or processes). Companies undertake R&D in order to develop new products, services, or procedures that will help them grow and expand their operations. Corporate R&D began in the United States with Thomas Edison and the Edison General Electric Company he founded in 1890 (which is today’s GE). Edison is credited with 1,093 patents, but it’s actually his invention of the corporate R&D lab that made all those other inventions possible.Andrea Meyer, “High-Value Innovation: Innovating the Management of Innovation,” Working Knowledge (blog), August 20, 2009, accessed February 22, 2011, http://workingknowledge.com/blog/?p=594 . Edison was the first to bring management discipline to R&D, which enabled a much more powerful method of invention by systematically harnessing the talent of many individuals. Edison’s 1,093 patents had less to do with individual genius and more to do with management genius: creating and managing an R&D lab that could efficiently and effectively crank out new inventions. For fifty years following the early twentieth century, GE was awarded more patents than any other firm in America.Gary Hamel, “The Why, What and How of Management Innovation,” Harvard Business Review , February 2006, accessed February 24, 2011, http://hbr.org/2006/02/the-why-what-and-how-of-management-innovation/ar/1 .

Edison is known as an inventor, but he was also a great innovator. Here’s the difference: an invention brings an idea into tangible reality by embodying it as a product or system. An innovation converts a new idea into revenues and profits. Inventors can get patents on original ideas, but those inventions may not make money. For an invention to become an innovation, people must be willing to buy it in high enough numbers that the firm benefits from making it.A. G. Lafley and Ram Charan, The Game-Changer (New York: Crown Publishing Group, 2008), 21.

Edison wanted his lab to be a commercial success. “Anything that won’t sell, I don’t want to invent. Its sale is proof of utility and utility is success,”A. G. Lafley and Ram Charan, The Game-Changer (New York: Crown Publishing Group, 2008), 25. Edison said. Edison’s lab in Menlo Park, New Jersey, was an applied research lab, which is a lab that develops and commercializes its research findings. As defined by the National Science Foundation, applied research is “systematic study to gain knowledge or understanding necessary to determine the means by which a recognized and specific need may be met.”National Science Foundation, “Definitions of Research and Development,” Office of Management and Budget Circular A-11, accessed March 5, 2011, http://www.nsf.gov/statistics/randdef/fedgov.cfm . In contrast, basic research advances the knowledge of science without an explicit, anticipated commercial outcome.

History and Importance

From Edison’s lab onward, companies learned that a systematic approach to research could provide big competitive advantages. Companies could not only invent new products, but they could also turn those inventions into innovations that launched whole new industries. For example, the radio, wireless communications, and television industry grew out of early-twentieth-century research by General Electric and American Telephone and Telegraph (AT&T, which founded Bell Labs).

The heyday of American R&D labs came in the 1950s and early 1960s, with corporate institutions like Bell Labs, RCA labs, IBM’s research centers, and government institutions such as NASA and DARPA. These labs funded both basic and applied research, giving birth to the transistor, long-distance TV transmission, photovoltaic solar cells, the UNIX operating system, and cellular telephony, each of which led to the creation of not just hundreds of products but whole industries and millions of jobs.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm . DARPA’s creation of the Internet (known at its inception as ARPAnet) and Xerox PARC’s Ethernet and graphical-user interface (GUI) laid the foundations for the PC revolution.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm .

Companies invest in R&D to gain a pipeline of new products. For a high-tech company like Apple, it means coming up with new types of products (e.g., the iPad) as well as newer and better versions of its existing computers and iPhones. For a pharmaceutical company, it means coming out with new drugs to treat diseases. Different parts of the world have different diseases or different forms of known diseases. For example, diabetes in China has a different molecular structure than diabetes elsewhere in the world, and pharmaceutical company Eli Lilly’s new R&D center in Shanghai will focus on this disease variant.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . Even companies that sell only services benefit from innovation and developing new services. For example, MasterCard Global Service started providing customers with emergency cash advances, directions to nearby ATMs, and emergency card replacements.Lance Bettencourt, Service Innovation (New York: McGraw-Hill, 2010), 99.

Innovation also includes new product and service combinations. For example, heavy-equipment manufacturer John Deere created a product and service combination by equipping a GPS into one of its tractors. The GPS keeps the tractor on a parallel path, even under hands-free operation, and keeps the tractor with only a two-centimeter overlap of those parallel lines. This innovation helps a farmer increase the yield of the field and complete passes over the field in the tractor more quickly. The innovation also helps reduce fuel, seed, and chemical costs because there is little overlap and waste of the successive parallel passes.Lance Bettencourt, Service Innovation (New York: McGraw-Hill, 2010), 110.

Did You Know?

Appliance maker Whirlpool has made innovation a strategic priority in order to stay competitive. Whirlpool has an innovation pipeline that currently numbers close to 1,000 new products. On average, Whirlpool introduces one hundred new products to the market each year. “Every month we report pipeline size measured by estimated sales, and our goal this year is $4 billion,” said Moises Norena, director of global innovation at Whirlpool. With Whirlpool’s 2008 revenue totaling $18.9 billion, that means roughly 20 percent of sales would be from new products.Jessie Scanlon, “How Whirlpool Puts New Ideas through the Wringer,” BusinessWeek , August 3, 2009, accessed January 17, 2011, http://www.businessweek.com/innovate/content/aug2009/id2009083_452757.htm .

Not only do companies benefit from investing in R&D, but the nation’s economy benefits as well, as Massachusetts Institute of Technology (MIT) professor Robert Solow discovered. Solow showed mathematically that, in the long run, growth in gross national product per worker is due more to technological progress than to mere capital investment. Solow won a Nobel Prize for his research, and investment in corporate R&D labs grew.

Although R&D has its roots in national interests, it has become globalized. Most US and European Fortune 1000 companies have R&D centers in Asia.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . You’ll see the reasons for the globalization of R&D in Section 13.3 .

The Role of Government

Governments have played a large role in the inception of R&D, mainly to fund research for military applications for war efforts. Today, governments still play a big role in innovation because of their ability to fund R&D. A government can fund R&D directly, by offering grants to universities and research centers or by offering contracts to corporations for performing research in a specific area.

Governments can also provide tax incentives for companies that invest in R&D. Countries vary in the tax incentives that they give to corporations that invest in R&D. By giving corporations a tax credit when they invest in R&D, governments encourage corporations to invest in R&D in their countries. For example, Australia gave a 125 percent tax deduction for R&D expenses. The Australian government’s website noted, “It’s little surprise then, that many companies from around the world are choosing to locate their R&D facilities in Australia.” The government also pointed out that “50 percent of the most innovative companies in Australia are foreign-based.”Committee on Prospering in the Global Economy of the 21st Century (U.S.), Committee on Science, Engineering, and Public Policy (U.S.), Rising Above the Gathering Storm (Washington, DC: National Academies Press, 2007), 195.

Finally, governments can promote innovation through investments in infrastructure that will support new technology and by committing to buy the new technology. China is doing this in a big way, and it is thus influencing the course of many companies around the world. Since 2000, China has had a policy in place “to encourage tech transfer from abroad and to force foreign companies to transfer their R&D operations to China in exchange for access to China’s large volume markets,” reported R&D Magazine in its 2010 review of global R&D.“2011 Global R&D Funding Forecast,” R&D Magazine , December 2010, accessed February 27, 2011, www.battelle.org/aboutus/rd/2011.pdf . For example, any automobile manufacturer that wants to sell cars in China must enter into a partnership with a Chinese company. As a result, General Motors (GM), Daimler, Hyundai, Volkswagen (VW), and Toyota have all formed joint ventures with Chinese companies. General Motors and Volkswagen, for example, have both formed joint ventures with the Chinese company Shanghai Automotive Industry Corporation (SAIC), even though SAIC also sells cars under its own brand.Brian Dumaine, “China Charges into Electric Cars,” Fortune , November 1, 2010, 140. The Chinese government made another strategic decision influencing innovation in the automobile industry. Because no Chinese company is a leader in internal combustion engines, the government decided to leapfrog the technology and focus on becoming a leader in electric cars.Bill Russo, Tao Ke, Edward Tse, and Bill Peng, China’s Next Revolution: Transforming The Global Auto Industry , Booz & Company report, 2010, accessed February 27, 2011, www.booz.com/media/file/China’s_Next_Revolution_en.pdf . “Beijing has pledged that it will do whatever it takes to help the Chinese car industry take the lead in electric vehicles,” notes industry watcher Brian Dumaine. Brian Dumaine, “China Charges into Electric Cars,” Fortune , November 1, 2010, 140. That includes allocating $8 billion in R&D funds as well as another $10 billion in infrastructure (e.g., installing charging stations).Gordon Orr, “Unleashing Innovation in China,” McKinsey Quarterly , January 2011, accessed January 2, 2011, www.mckinseyquarterly.com/Strategy/Innovation/Unleashing_innovation_in_China_2725 . The government will also subsidize the purchase of electric cars by consumers and has committed to buying electric cars for government fleets, thus guaranteeing that there will be buyers for the new electric vehicles that companies invent and develop.

Another role of government is to set high targets that require innovation. In the 1960s, the US Apollo space program launched by President John F. Kennedy inspired US corporations to work toward putting a man on the moon. The government’s investments in the Apollo program sped up the development of computer and communications technology and also led to innovations in fuel cells, water purification, freeze-drying food, and digital image processing now used in medical products for CAT scans and MRIs.Adrian Slywotzky, “How Science Can Create Millions of New Jobs,” BusinessWeek , September 7, 2009, accessed May 11, 2011, http://www.businessweek.com/magazine/content/09_36/b4145036678131.htm . Today, government policies coming from the European Union mandate ambitious environmental targets, such as carbon-neutral fuels and energy, which are driving global R&D to achieve environmental goals the way the Apollo program drove R&D in the 1960s.Martin Grueber and Tim Studt, “A Battelle Perspective on Investing in International R&D,” R&D Magazine , December 22, 2009, http://www.rdmag.com/Featured-Articles/2009/12/Global-Funding-Forecast-A-Battelle-Perspective-International-R-D .

After the 1990s, US investment in R&D declined, especially in basic research. Governments in other countries, however, continue to invest. New government-corporate partnerships are developing around the world. IBM, which for years closely guarded its R&D labs (even IBM employees were required to have special badges to enter the R&D area), is now setting up “collaboratories” around the world. These collaboratories are partnerships between IBM researchers and outside experts from government, universities, and even other companies. “The world is our lab now,” says John E. Kelly III, director of IBM Research.Steve Hamm, “How Big Blue Is Forging Cutting-edge Partnerships around the World,” BusinessWeek , August 27, 2009, accessed January 2, 2010, http://www.businessweek.com/print/magazine/content/09_36/b4145040683083.htm . IBM has deals for six future collaboratories in China, Ireland, Taiwan, Switzerland, India, and Saudi Arabia.

The reason for the collaboratory strategy is to share R&D costs—IBM’s partners must share 50 percent of the funding costs, which means that together the partners can participate in a large-scale effort that they’d be hard pressed to fund on their own. An example is IBM’s research partnership with the state-funded Swiss university ETH Zurich. The two are building a $70 million semiconductor lab for nanotech research with the goal of identifying a replacement for the current semiconductor-switch technology.Steve Hamm, “How Big Blue Is Forging Cutting-Edge Partnerships around the World,” BusinessWeek , August 27, 2009, accessed January 2, 2010, http://www.businessweek.com/print/magazine/content/09_36/b4145040683083.htm . Such a breakthrough could harken the creation of a whole new industry.

Of all the countries in the world, the United States remains the largest investor in R&D. One-third of all spending on R&D comes from the United States. Just one government agency—the Department of Defense—provides more funding than all the nations of the world except China and Japan. Nonetheless, other countries are increasing the amounts of money they spend on R&D. Their governments are funding R&D at higher levels and are giving more attractive tax incentives to firms that spend on R&D.

Governments can also play a big role in the protection of intellectual property rights, as you’ll see in Section 13.2 .

KEY TAKEAWAYS

• R&D refers to two intertwined processes of research (to identify new facts and ideas) and development (turning the ideas into tangible products and services.) Companies undertake R&D to get a pipeline of new products. Breakthrough innovations can create whole new industries, which can provide thousands of jobs.
• Invention is the creation of a new idea embodied in a product or process, while innovation takes that new idea and commercializes it in a way that enables a company to generate revenue from it.
• Government support of R&D plays a significant role in innovation. It has been generally accepted that it’s desirable to encourage R&D for reasons of economic growth as well as national security. This has resulted in massive support from public funds for many sorts of laboratories. Governments influence R&D not only by providing direct funding but also by providing tax incentives to companies that invest in R&D. Governments also stimulate innovation through supporting institutions such as education and providing reliable infrastructure.

(AACSB: Reflective Thinking, Analytical Skills)

• What benefits does a company get by investing in R&D?
• Why do organizations make a distinction between basic research and applied research?
• Describe three ways in which government can influence R&D.

by Gary Anderson and Audrey Kindlon [1]

Small businesses are often incubators of new technologies that will be important to future economic growth. Indeed, research shows that among companies engaged in research and development or in patenting, small and young firms are more innovative, more productive R&D performers, and perform research that is more radical [2] (Akcigit and Kerr 2018, Knott and Vieregger 2017).

This InfoBrief presents R&D data by company size for the years 2008–15. [3] The data are from the Business R&D and Innovation Survey (BRDIS), an annual survey of U.S.-based businesses with five or more employees that is developed and cosponsored by the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation and by the Census Bureau. Rausch (2010) presents similar data for the years 2003–07 from the Survey of Industrial R&D, which preceded BRDIS. Rausch found that smaller firms performed an increasing share of business R&D between 2003 and 2007, had greater R&D intensity (i.e., R&D/sales), and had a greater proportion of employees who are scientists and engineers.

Such data have long been of interest to researchers and policymakers. Using NCSES microdata, Knott and Vieregger (2017) found that large and small firms differ in terms of the type of R&D performed and R&D productivity. Their paper is the most recent contribution to research showing that radical innovation decreases with firm size (Mansfield 1981), the likelihood of performing process R&D increases with firm size (Scherer 1991), and R&D productivity itself varies by firm size (Acs and Audretsch 1988 and 1990, Knott and Vieregger 2017).

In 2015, following international guidance (OECD 2015), NCSES implemented an updated size classification structure based on reported employment for business R&D. This revision is consistent with the size classification used to analyze micro enterprises (5–9 employees), and it allows additional detailed statistics for small and medium enterprises (10–49 and 50–249 employees, respectively). This InfoBrief presents data [4] for 2008–15 using this updated classification structure. This international classification scheme differs considerably from that used by the U.S. Small Business Administration (SBA), which classifies businesses with fewer than 500 employees as small.

Indicators of R&D Performance by Size of Company

In 2015, U.S. companies performed nearly $356 billion in R&D. Large companies (those with 250 or more employees) accounted for 88% of this total. Micro and small companies (5–49 employees) accounted for just 5% of this total. Medium-sized companies (50–249 employees) accounted for the remaining 7% ( table 1 ). Using the SBA definition of small business, these data indicate that companies with fewer than 500 employees accounted for 16% of business R&D in 2015. Rausch (2010) showed that companies with fewer than 500 employees accounted for 19% of the total industrial R&D in 2007. BRDIS data show that companies with fewer than 500 employees accounted for 20% of the total 2008 business R&D. Time series data for 2008–15 indicate changes in the level of R&D performed by particular size classes as well as in the distribution of R&D across size classes.

As the economy began to recover from the Great Recession in 2009, R&D performance at large companies did as well. Over the 2009–15 time period, inflation-adjusted R&D performed by large companies (250 or more employees) grew at a rate of 3% per year. However, R&D performance diverged over this time period between micro, small, and medium companies (5–249 employees) and two classes of large companies (1,000–4,999 and 10,000–24,999 employees) ( figure 1 ). In 2008, slightly less than $50 billion in R&D was performed by micro, small, and medium companies combined and by each of these two classes of large companies. Following a period of decline, the recovery [5] of R&D performance began in 2011 for these two classes of large companies as well as for all large companies. For micro, small, and medium companies, the effects of the recession were more persistent and recovery further delayed. By 2012, R&D performance for micro, small, and medium companies had fallen to $37 billion, and recovery did not take hold until 2014. By 2015, companies with 10,000–24,999 employees performed $54 billion in R&D, yet micro, small, and medium companies performed just over $40 billion in R&D and had not recovered beyond 2009 levels.

SOURCE: National Science Foundation, National Center for Science and Engineering Statistics, Business R&D and Innovation Survey.

Figure 1 Source Data: Excel file

The data in figure 2 indicate that between 2009 and 2015, corresponding to the recovery from the Great Recession, [6] different size classes of businesses have fared differently with respect to inflation-adjusted R&D performance. Micro enterprises continued to perform significantly less R&D in 2015 than in 2009. Small and medium companies have not surpassed 2009 R&D performance. Large companies overall, as well as each size class of large companies other than 250–499, performed significantly more R&D in 2015 than 2009. The trends over 2008–15 for both the growth rate and share of R&D performance by micro, small, and medium companies stand in stark contrast to the 2003–07 trends. For 2003–07, Rausch (2010) showed small firms had higher growth rates in R&D performance than larger companies and an increasing share of business R&D performance.

Figure 2 Source Data: Excel file

Indicators of R&D Intensity of Small Businesses

Another perspective on business R&D performance is revealed by looking at the degree to which company revenues from sales are spent on R&D activities. This ratio, often termed the R&D intensity, is an indication of the firm's commitment to and focus on R&D activities.

The 2008–15 data presented in table 2 show findings that are similar to those presented in Rausch (2010). For both these and earlier data, R&D intensity decreases with company size. R&D intensity, as measured by R&D as a percentage of sales, was nearly 11% for micro companies in 2015. For the largest of companies (25,000 or more employees), the R&D intensity was just over 3%.

Employment of R&D workers by Small Businesses

An indicator of innovative activity by companies, in particular smaller companies, is the proportion of employees that are working on R&D. According to BRDIS, the total number of employees at R&D-performing companies was 18.9 million in 2015 ( table 3 ). In 2015, these same companies employed 1.5 million scientists, engineers, technicians, and support staff working on R&D, an 8% increase from 2008. In 2015, 15% of all employees from micro, small, or medium businesses were working on R&D, which is nearly identical to 16% in 2008 and results in no significant change during the 2008–15 time period.

After the Great Recession, R&D workforce indicators at micro, small, and medium companies differ from those at large companies. In 2009, micro, small, and medium companies employed 2.3 million people, including 387,000 employees working in R&D. In 2015, these firms employed 2.3 million, 350,000 of whom worked on R&D. In contrast, both total employment and the number of R&D employees by large firms increased by 8% and 15%, respectively, between 2009 and 2015, the post-recession years ( table 3 ).

A decrease in R&D employment was most acutely experienced among the smallest firms ( figure 3 ). During the post-recession years of 2009 to 2015, the number of personnel working on R&D at microbusinesses decreased by 40%. This compares with an 8% increase in R&D employment in all companies.

NOTE: R&D employment includes all scientists, engineers, technicians, and support staff working on R&D.

Figure 3 Source Data: Excel file

Conclusions

Generally speaking, when it comes to investing in R&D, smaller companies have not weathered the after effects of the recession as well as larger companies. From 2009, when the economy began to recover from the Great Recession, until 2015, micro, small, and medium companies showed decreased R&D performance and employment, whereas large companies demonstrated a return to growth. The R&D paid for and the sales generated by domestic R&D performers decreased for micro, small, and medium companies whereas large companies experienced growth in both areas. In addition, from 2009 to 2015, the number and the proportion of employees working on R&D decreased among smaller companies.

Data Sources and Limitations

The samples for each year of BRDIS were selected to represent all for-profit, nonfarm companies that are publicly or privately held and have five or more employees in the United States. Estimates produced from the survey and presented in this InfoBrief are restricted to companies that perform or fund R&D, either domestically or abroad. Because the statistics from the survey are based on a sample, they are subject to both sampling and nonsampling errors (see technical notes in the data table reports at https://www.nsf.gov/statistics/industry/ ).

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[1] Gary Anderson ( [email protected] , 703-292-8572) and Audrey Kindlon ( [email protected] , 703-292-2332), Research and Development Statistics Program, National Center for Science and Engineering Statistics, National Science Foundation, 2415 Eisenhower Avenue, Suite W14200, Alexandria, VA 22314.

[2] Akcigit and Kerr (2018) use prior art citations in patents to characterize the novelty of the research that resulted in the patented inventions. More radical, or exploration, research does not contain any prior art citations to earlier patents held by the assignee. In contrast, in exploitation research the majority of prior art patent citations are to earlier patents held by the assignee.

[3] This InfoBrief uses constant dollars when discussing trend data. Current dollars are used for all other amounts and calculations.

[4] Although NCSES does release limited revised statistics that include adjustments based on information obtained after the original statistics were prepared, these data reflect the information available at the time of original release.

[5] Recovery of R&D performance is indicated by a statistically significant increase measured from the post-recession minimum annual performance.

[6] The National Bureau of Economic Research (NBER) dates the end of the 2007 recession as June 2009, which occurred during the 2009 BRDIS calendar year reporting period. Given that BRDIS collects annual data and 2009 corresponds to both the minimum annual real gross domestic product (GDP) over the 2007–15 period and the NBER recession date, we measure the recovery period relative to this trough.

Research and Development (R&D) Investment

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R&D investment reflects an organization’s willingness to invest in discovery and commercialization of new technologies in the form of products and processes as well as refinement of existing technologies. Investments in R&D have historically been made within firm boundaries and have been associated positively with firm innovation and performance. The uncertainty associated with returns to R&D investments and the increasingly distributed nature of knowledge has created an impetus for firms to invest in external sources of innovation to supplement their internal R&D efforts.

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Pakes, A., and Z. Griliches. 1984. Patents and R&D at the firm level: A first look. In R&D, patents, and productivity , ed. Z. Griliches. Chicago: University of Chicago Press.

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Wadhwa, A., and S.B. Kotha. 2006. Knowledge creation through external venturing: Evidence from the telecommunications equipment manufacturing industry. Academy of Management Journal 49: 819–835.

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Wadhwa, A. (2018). Research and Development (R&D) Investment. In: Augier, M., Teece, D.J. (eds) The Palgrave Encyclopedia of Strategic Management. Palgrave Macmillan, London. https://doi.org/10.1057/978-1-137-00772-8_795

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Why You Should Invest in Research and Development (R&D)

Research and development (R&D) is the part of a company's operations that seeks knowledge to develop, design, and enhance its products, services, technologies, or processes. Along with creating new products and adding features to old ones, investing in research and development connects various parts of a company's strategy and business plan.

According to the latest Business Enterprise Research and Development survey by the National Center for Science and Engineering and the U.S. Census Bureau, businesses spent $32.5 billion to support their R&D activities in 2020.

Here are some reasons your business should invest in research and development.

Key Takeaways

• Research and development (R&D) is an essential driver of economic growth as it spurs innovation, invention, and progress.
• R&D spending can lead to breakthroughs that can drive profits and well-being for consumers.
• Today, R&D is present in nearly every business sector as companies jockey for position in their respective markets.
• Smaller firms engaged in R&D can offset some of these costs and attract investors thanks to a federal tax break.

Investing in Research and Development (R&D)

The Internal Revenue Service's definition of research and development is investigative activities that a person or business chooses to do with the desired result of a discovery that will create an entirely new product, product line, or service.

However, the activities don't only need to be for disovering new products or services—this is only for tax purposes.

R&D isn’t just about creating new products; it can be used to strengthen an existing product or service with additional features.

Research refers to any new science or thinking that will result in a new product or new features for an existing product. Research can be broken down into either basic research or applied research. Basic research seeks to delve into scientific principles from an academic standpoint, while applied research aims to use that basic research in a real-world setting.

The development portion refers to the actual application of the new science or thinking so that a new or increasingly better product or service can begin to take shape.

Research and development is essentially the first step in developing a new product, but product development is not exclusively research and development. An offshoot of R&D, product development can refer to the entire product life cycle , from conception to sale to renovation to retirement.

R&D Offers Productivity, Product Differentiation

Firms gain a competitive advantage by performing in some way that their rivals cannot easily replicate. If R&D efforts lead to an improved type of business process—cutting marginal costs or increasing marginal productivity—it is easier to outpace competitors.

R&D often leads to a new type of product or service—for example, without research and development, cell phones or other mobile devices would never have been created. The internet, and even how people live today, would be completely different if businesses had not conducted R&D in the past.

Research results give businesses a means to find issues people have and ways to address them, and development allows companies to find unique and different ways to fix the problems.

This leads to many different product and service variations, which gives consumers choices and keeps the markets competitive. Some examples of companies that carry out R&D activities are auto manufacturers, software creators, cutting-edge tech companies, and pharmaceutical firms.

The R&D Tax Credit

In 1981, the IRS started offering tax breaks for companies to spend money and hire employees for research and development. Qualifying companies include startups and other small ventures with qualified research expenses. Such expenses can be used to offset tax liabilities , along with an impressive 20-year carry-forward provision for the credit.

Many entrepreneurs and small businesses have made a large sum of money in a short time by selling good ideas to established firms with many resources. Buyouts are particularly common with online companies, but they can be seen wherever there is a lot of incentive to innovate.

Research and development can help your ideas or business become more attractive to investors and other companies looking to expand.

Advertising and Marketing R&D Benefits

Advertising is full of claims about revolutionary new techniques or never-before-seen products and technologies. Consumers demand new and improved products, sometimes simply because they are new. R&D departments can act as advertising wings in the right market.

R&D strategies let companies create highly effective marketing strategies around releasing a new or existing product with new features. A company can create marketing campaigns to match innovative products and market participation.

What Are the Reasons for R&D?

Research and development keep your business competitive. Without R&D, you risk losing your competitive advantage and falling behind other companies researching and developing new products in your industry.

Why Is R&D Important for Startups?

R&D is essential because it helps you keep your business momentum going. New products and services help you attract more customers, make sales, and give you something to talk about with your investors.

What Factors are Essential in Successful R&D?

Successful research and development depend on many factors, but the most important is a strong interest from your customer base and investors. If you spend money and time researching and developing something no one wants, it's being wasted.

Increased market participation, cost management benefits, advancements in marketing abilities, and trend-matching are all reasons companies invest in R&D. R&D can help a company follow or stay ahead of market trends and keep the company relevant.

Although resources must be allocated to R&D, the innovations gained through this research can actually work to reduce costs through more efficient production processes or more efficient products. R&D efforts can also reduce corporate income tax, thanks to the deductions and credits they generate.

National Center for Science and Engineering. " Businesses Invested $32.5 Billion in Assets to Support Their R&D Activities in the United States in 2020 ."

Tax Foundation. " Reviewing the Federal Tax Treatment of Research & Development Expenses ."

Internal Revenue Service. " About Form 6765, Credit for Increasing Research Activities ."

Internal Revenue Service. " Instructions for Form 3800 (2022) ," Page 2.

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Understanding Research and Development in Business: A Comprehensive Guide

Learn about the crucial role of Research and Development (R&D) in business and how it can drive innovation, growth, and competitive advantage.

Barrie Dowsett

Chief Executive Officer

10 minute read

Research and Development (R&D) is a key driver of innovative and competitive business practices, as it enables companies to stay ahead of the curve in industries that are constantly evolving. Companies in Ireland recognise this and have been investing more in R&D over the past decade, with funding from government grants and tax incentives such as the R&D tax credit .

R&D encompasses a variety of activities aimed at generating new and improved products and services, as well as enhancing existing ones. It involves systematic investigation, experimentation, and exploration to discover new knowledge and improve existing products, processes, or services. R&D takes place in various forms, such as design, testing, prototyping, and scaling up, and the outcomes can range from products, inventions, patents, or knowledge.

There are many benefits to investing in R&D for Irish businesses. For one, R&D can help improve their market position, generate revenue and enhance their competitive advantage. By innovating through R&D, companies can offer unique, high-quality products or services that can set them apart from their competitors. Moreover, companies can develop new technologies, insights and knowledge that can be leveraged to create future products and services.

However, R&D is not without its challenges. Investing in R&D can be costly and uncertain, and it can take time to see the return on investment. Furthermore, companies need to ensure that they have the right talent, resources, and infrastructure in place to support their R&D initiatives.

In this blog, we will provide a comprehensive guide to understanding R&D for Irish businesses. We will explore the various forms of R&D, its importance for business growth and innovation, and how to implement effective R&D strategies in your business. Whether you are a startup or an established business, this guide will provide you with valuable insights to help you succeed through R&D.

Benefits of R&D in Business

One significant benefit of R&D is its ability to drive innovation and growth by bringing new products and services to the market. Through R&D, companies can better understand their customers' needs, identify new market opportunities, and develop new technologies and products that meet those needs. This not only diversifies a company's product offerings but also strengthens their market position by increasing their revenue streams and expanding their customer base.

Furthermore, R&D enables businesses to gain a competitive advantage by developing unique products and services that differentiate them from their competitors. Companies that invest in R&D can develop and maintain a strong intellectual property portfolio that can prevent competitors from copying their innovations. R&D also helps companies stay ahead of the curve in terms of technology, enabling them to leverage new advancements and establish themselves as leaders within their industries.

In addition, R&D can lead to significant improvements in product quality and performance by allowing companies to develop and test new materials, techniques, and designs. This, in turn, can increase customer satisfaction and loyalty, as well as drive sales and create long-term value for the business.

Finally, R&D can help companies reduce costs and increase efficiency by identifying ways to streamline processes or develop new technologies that are more cost-effective. This can include developing more efficient supply chains or reducing waste through sustainable production methods.

Overall, companies that invest in R&D can reap significant benefits in terms of innovation, growth, and competitive advantage. By continually developing new products and services that meet evolving customer needs and redefining industry standards, these businesses can establish themselves as leaders in their fields and remain sustainable for years to come.

Types of R&D Activities

Research and Development (R&D) activities are broadly categorized into three types: basic research, applied research, and development. Basic research is conducted to gain knowledge about a certain field, phenomenon, or theory without any immediate application. For instance, a pharmaceutical company conducting research on the molecular structure of a protein without any specific drug in mind. Basic research often leads to the discovery of new ideas, theories, and inventions that can eventually be applied in various industries.

Applied research, on the other hand, aims to solve a specific problem or answer a particular question in a practical setting. It involves taking information generated from the basic research and using it to develop useful solutions for specific needs. Applied research is often conducted by companies to improve their products, enhance their services, or identify new opportunities. For instance, a cosmetic company conducting market research to identify customer preferences and develop new products accordingly.

Development involves the translation of applied research findings into a practical form. It involves creating prototypes or models that can be tested and modified to ensure that they meet the desired goals of the project. Development activities can involve various stages, including design, testing, and modification, until the final product or service is produced. For instance, an engineering firm conducting research and development to design and build a new piece of equipment for a specific application.

In summary, basic research provides foundational knowledge and understanding of a certain field, applied research applies this knowledge to address specific problems, and development uses applied research to design and develop new products and services. Each type of R&D plays a crucial role in driving innovation and achieving competitive advantages in the business world. By understanding the different types of R&D activities, businesses can better leverage them to drive growth and success.

Steps Involved in Effective R&D

Effective Research and Development (R&D) requires specific steps to ensure that it addresses the business's needs and goals. The first step is defining the research problem, which means identifying what issues the company wants to address through research. After that, market research must be conducted to gather information about the target market and its needs, preferences, and behaviours. This information is essential in formulating research objectives, which are clear and concise statements about what research aims to achieve. The objectives should align with the research problem identified earlier and should be specific, measurable, achievable, relevant, and time-bound (SMART).

Developing a research plan follows, which involves deciding on the research design, sample size, and data collection methods. The research design could either be qualitative or quantitative, depending on the research question and objectives. The next step is conducting experiments and collecting data using the research plan developed, which could be primary or secondary data. Primary data involves collecting original data through surveys, interviews or observations, while secondary data includes using data that is previously collected and publicly available.

After collecting data, the next step is analysing and interpreting it. In analysing, the data collected should be organised, cleaned, and summarised, after which it can be subjected to various statistical tests to draw conclusions. When interpreting, the findings from the tests are used to answer the research question and match the research objectives. A clear communication mechanism of the findings and recommendations to the relevant stakeholders is essential, and it should be precise, concise, and in a format that is understandable to the recipients.

Finally, R&D should be seen as an ongoing process aimed at improvement and innovation in a business. Continuously redefining research objectives, updating research plans and implementing effective R&D strategies is critical in staying ahead of the competition, remaining relevant, and meeting the evolving needs of the market. Companies should embrace R&D practices as a strategic tool for long-term success, growth and profitability.

Implementing R&D Strategies in Business

Creating an r&d budget.

Creating an R&D budget is a critical step in implementing an effective R&D strategy. Setting aside a budget for R&D activities enables businesses to allocate resources towards research and experimentation that can lead to breakthrough innovations. In general, it is recommended that companies allocate anywhere from 5% to 15% of their revenue towards R&D. However, the exact amount will depend on the industry, the size of the company, and its growth goals.

Building an R&D team

Building an R&D team is also crucial to the success of any R&D strategy. Skilled and diverse R&D teams can bring a range of perspectives and expertise to the table. This can lead to cross-functional collaborations that can uncover new ideas and solutions. Depending on the nature and scale of the R&D activities, companies can consider hiring full-time R&D staff, collaborating with academics or other external experts, or engaging with freelancers or contract workers.

Establishing an R&D process

Establishing an R&D process can help ensure that R&D activities are conducted systematically and efficiently. This can include defining clear research objectives, identifying target markets and customers, conducting feasibility studies, developing prototypes, and conducting trials and tests. Companies can also consider adopting agile methodologies or lean startup principles to foster iterative and lean R&D processes.

Leveraging technology and partnerships

Leveraging technology and partnerships can also help boost the effectiveness of R&D strategies. Technology tools such as virtual and augmented reality simulators, 3D printing, and artificial intelligence can facilitate faster and more cost-effective R&D processes. Partnerships with other companies, suppliers, startups, or research institutions can provide access to complementary expertise, technologies, and markets. Collaborating with customers can also yield valuable insights into their needs and preferences, and can help companies create customer-centric solutions.

Implementing effective R&D strategies requires careful planning, investment, and execution. By creating a dedicated R&D budget, building a skilled and diverse R&D team, establishing clear R&D processes, and leveraging technology and partnerships, Irish businesses can unlock the full potential of R&D to drive innovation, growth, and competitive advantage.

Challenges of R&D in Business

While Research and Development (R&D) is critical for innovation and growth in a business, it is not without its challenges. One challenge that businesses face is cost and resource constraints. R&D requires substantial investment in terms of time, money, and skilled employees. Small and medium-sized businesses (SMEs), in particular, may struggle to allocate sufficient resources towards R&D due to budgetary constraints. They may have limited funds to invest in R&D, which can hamper innovation and limit the scope of the research. Similarly, larger corporations may have the funds to invest in R&D, but may struggle with resource constraints such as a shortage of skilled employees, a lack of facilities or equipment, or finding the right partners for collaboration.

Another challenge of R&D is the inherent uncertainty and risk associated with research activities. The outcome of R&D efforts is often uncertain, and there is no guarantee that the research will lead to a successful innovation. Additionally, R&D can be a lengthy process, and businesses may not see returns on their investment for months or even years. This can make it difficult to justify investment in R&D, especially for SMEs that need to show immediate returns in cash flow.

Finally, there is the challenge of protecting intellectual property. R&D often leads to the creation of intellectual property, such as patents, trademarks, and copyrights. Protecting this intellectual property is crucial, as it can give businesses a competitive advantage and help generate revenue. However, protecting intellectual property can be a time-consuming and expensive process, and businesses must be vigilant in monitoring their intellectual property and taking legal action against infringement.

Cost and resource constraints, uncertainty and risk, and intellectual property protection are just some of the challenges that businesses must navigate to successfully implement effective R&D strategies. Nonetheless, with careful planning, investment, and a reliable R&D process, businesses can successfully leverage R&D to drive innovation, growth, and competitive advantage.

In conclusion, Research and Development (R&D) is a critical tool for businesses looking to drive innovation, growth, and competitive advantage. Without R&D, businesses risk stagnation and getting left behind in a rapidly evolving marketplace. In this blog, we have discussed the importance of R&D in business and how it can drive success.

Businesses looking to implement effective R&D strategies should focus on building a culture of innovation, investing in the right people and resources, and staying up to date with the latest technological advancements. Effective R&D strategies involve identifying market gaps and customer needs, experimenting with new ideas, and using data-driven insights to make informed decisions. Looking to the future, R&D will continue to be a key driver of growth and success for businesses, especially in an increasingly digital and globalized economy.

As market competition grows, businesses will need to invest more in R&D to stay ahead and achieve long-term success. By continuing to innovate and work towards new and exciting breakthroughs, businesses can unlock new opportunities and realize their full potential in today's fast-paced business landscape.

Bring in the experts

Whether you’ve already got a comprehensive R&D strategy in progress or you’re just starting out from scratch, the experts at Myriad Associates are here to help.

We work across the field of innovation funding, with years of experience in  R&D Tax Credits  and  R&D Grants  specifically. We will work alongside you for as long as you need us, whether you require support in creating an effective R&D department, or in planning a particular R&D project. We’ll also be able to discuss which funding options would best suit your needs, helping your R&D bring you the best return on investment.

Contact us today

If you wish to discuss anything we’ve discussed in this article, or about R&D funding for Irish companies, simply call us on call us on +353 1 566 2001 or  send us a message . We’re working remotely during this time and will be pleased to assist you.

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Research and Development (R&D)

Step-by-Step Guide to Understanding Research and Development (R&D) Expense

Learn Online Now

What is R&D?

The Research and Development (R&D) expense refers to spending related to funding internal initiatives around introducing new products or further developing their existing offerings.

Table of Contents

What Does R&D Stand For?

Research and development (r&d): definition in business, r&d expense: operating expense on income statement, how to forecast r&d expense in financial modeling, what is a good r&d spending ratio by industry.

R&D is an abbreviation for “research and development,” and represents the costs associated with product innovation and the introduction of new products/services.

By re-investing a certain amount of earnings into R&D efforts, a company can remain ahead of its competition and thereby fend off any external threats (i.e. shifting industry trends).

Hence, it is crucial for such companies to avoid being blindsided by new disruptive technologies that serve as headwinds to the company.

While R&D costs can easily accumulate over time (and often not create any results of any significance), the R&D can pay off if there is a breakthrough that can directly lead to long-term profitability and a sustainable competitive advantage.

For instance, R&D spending can lead to defensible market positioning through the following:

• Intellectual Property (IP)
• Technological Systems

The definition of research and development, or “R&D”, is as follows:

• Research → The strategic pursuit of obtaining new knowledge and findings that are applicable to the company’s operations. The long-term objective is for the compiled knowledge to be actionable and have a tangible impact in the development of new products or services, which will benefit the company’s existing offerings or be a new separate offering.
• Development → The application of the findings gathered from the research, where the company designs and tests new prototypes, or attempts to modify or implement new processes.

So, is the research and development (R&D) expense capitalized or expensed on the income statement?

Under U.S. GAAP, the majority of research and development costs (R&D) must be expensed in the current period due to the uncertainty surrounding any future economic benefit.

However, companies can opt to capitalize software costs in certain scenarios (e.g. software development).

Since R&D tends to operate on a longer-term time horizon, these investments are not anticipated to generate immediate benefits.

R&D spending is treated as an expense – i.e. expensed on the income statement on the date incurred – rather than as a long-term investment.

There is some controversy, however, regarding whether this approach is the correct classification given the duration of the benefits.

Considering how long-term the expected economic benefits could be, one could make the case that all R&D should instead be capitalized rather than treated as an expense.

In terms of how research and development expenses are projected in financial models , R&D is typically tied to revenue .

To forecast R&D, the first step would be to calculate the historical R&D as a % of revenue for recent years, followed by the continuation of the trend to project future R&D spending or an average of the past couple of years.

The intuition is that the more revenue growth there is, the more capital could be allocated towards R&D – much like the relationship between revenue and discretionary capital expenditures (Capex) .

Considering the growth of software across the past two decades, the number of industries prone to disruption has increased substantially, especially with the increased amount of capital available in the private markets to fund these high-growth start-ups.

From a broad perspective, consistent R&D spending enables a company to stay ahead of the curve, while anticipating changes in customer demands or upcoming trends.

As a general rule of thumb, the more technical the industry’s products/services are, the more outsized R&D spending will be.

Depending on the specific sector, the standard spending on R&D will be different, but the industries known for being the most R&D intensive are generally the following:

• Pharmaceuticals
• Semiconductors
• Technology/Software

For many of these companies, R&D becomes the core of their business model, as the continuous development and roll-out of newer and more advanced products/services is essential for their continued positive trajectory.

In the sectors mentioned above, R&D shapes the corporate strategy and is how companies provide differentiated offerings.

Given the rate of technological advancement, particularly in countries like the U.S. and China, R&D is integral for companies to stay competitive and create products that are difficult for their competitors to replicate.

McKinsey Insights “While the pharmaceutical sector garners much attention due to its high R&D spending as a percentage of revenues, a comparison based on industry profits shows that several industries, ranging from high tech to automotive to consumer, are putting more than 20 percent of earnings before interest, taxes, depreciation, and amortization (EBITDA) back into innovation research.” R&D Spending % EBITDA by Industry (Source: McKinsey )

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Research Laboratory  

by Daniel Watch and Deepa Tolat Perkins + Will

Within This Page

Building attributes, emerging issues, relevant codes and standards, additional resources.

Research Laboratories are workplaces for the conduct of scientific research. This WBDG Building Type page will summarize the key architectural, engineering, operational, safety, and sustainability considerations for the design of Research Laboratories.

The authors recognize that in the 21st century clients are pushing project design teams to create research laboratories that are responsive to current and future needs, that encourage interaction among scientists from various disciplines, that help recruit and retain qualified scientists, and that facilitates partnerships and development. As such, a separate WBDG Resource Page on Trends in Lab Design has been developed to elaborate on this emerging model of laboratory design.

A. Architectural Considerations

Over the past 30 years, architects, engineers, facility managers, and researchers have refined the design of typical wet and dry labs to a very high level. The following identifies the best solutions in designing a typical lab.

Lab Planning Module

The laboratory module is the key unit in any lab facility. When designed correctly, a lab module will fully coordinate all the architectural and engineering systems. A well-designed modular plan will provide the following benefits:

Flexibility —The lab module, as Jonas Salk explained, should "encourage change" within the building. Research is changing all the time, and buildings must allow for reasonable change. Many private research companies make physical changes to an average of 25% of their labs each year. Most academic institutions annually change the layout of 5 to 10% of their labs. See also WBDG Productive—Design for the Changing Workplace .

• Expansion —The use of lab planning modules allows the building to adapt easily to needed expansions or contractions without sacrificing facility functionality.

A common laboratory module has a width of approximately 10 ft. 6 in. but will vary in depth from 20–30 ft. The depth is based on the size necessary for the lab and the cost-effectiveness of the structural system. The 10 ft. 6 in. dimension is based on two rows of casework and equipment (each row 2 ft. 6 in. deep) on each wall, a 5 ft. aisle, and 6 in. for the wall thickness that separates one lab from another. The 5 ft. aisle width should be considered a minimum because of the requirements of the Americans with Disabilities Act (ADA) .

Two-Directional Lab Module —Another level of flexibility can be achieved by designing a lab module that works in both directions. This allows the casework to be organized in either direction. This concept is more flexible than the basic lab module concept but may require more space. The use of a two-directional grid is beneficial to accommodate different lengths of run for casework. The casework may have to be moved to create a different type or size of workstation.

Three-Dimensional Lab Module —The three-dimensional lab module planning concept combines the basic lab module or a two-directional lab module with any lab corridor arrangement for each floor of a building. This means that a three-dimensional lab module can have a single-corridor arrangement on one floor, a two-corridor layout on another, and so on. To create a three-dimensional lab module:

• A basic or two-directional lab module must be defined.
• All vertical risers must be fully coordinated. (Vertical risers include fire stairs, elevators, restrooms, and shafts for utilities.)
• The mechanical, electrical, and plumbing systems must be coordinated in the ceiling to work with the multiple corridor arrangements.

Lab Planning Concepts

The relationship of the labs, offices, and corridor will have a significant impact on the image and operations of the building. See also WBDG Functional—Account for Functional Needs .

Do the end users want a view from their labs to the exterior, or will the labs be located on the interior, with wall space used for casework and equipment?

Some researchers do not want or cannot have natural light in their research spaces. Special instruments and equipment, such as nuclear magnetic resonance (NMR) apparatus, electron microscopes, and lasers cannot function properly in natural light. Natural daylight is not desired in vivarium facilities or in some support spaces, so these are located in the interior of the building.

Zoning the building between lab and non-lab spaces will reduce costs. Labs require 100% outside air while non-lab spaces can be designed with re-circulated air, like an office building .

Adjacencies with corridors can be organized with a single, two corridor (racetrack), or a three corridor scheme. There are number of variations to organize each type. Illustrated below are three ways to organize a single corridor scheme:

Single corridor lab design with labs and office adjacent to each other.

Single corridor lab design with offices clustered together at the end and in the middle.

Single corridor lab design with office clusters accessing main labs directly.

• Open labs vs. closed labs. An increasing number of research institutions are creating "open" labs to support team-based work. The open lab concept is significantly different from that of the "closed" lab of the past, which was based on accommodating the individual principle investigator. In open labs, researchers share not only the space itself but also equipment, bench space, and support staff. The open lab format facilitates communication between scientists and makes the lab more easily adaptable for future needs. A wide variety of labs—from wet biology and chemistry labs, to engineering labs, to dry computer science facilities—are now being designed as open labs.

Flexibility

In today's lab, the ability to expand, reconfigure, and permit multiple uses has become a key concern. The following should be considered to achieve this:

Flexible Lab Interiors

Equipment zones—These should be created in the initial design to accommodate equipment, fixed, or movable casework at a later date.

Generic labs

Mobile casework—This can be comprised of mobile tables and mobile base cabinets. It allows researchers to configure and fit out the lab based on their needs as opposed to adjusting to pre-determined fixed casework.

Mobile casework

Mobile base cabinet Photo Credit: Kewaunee Scientific Corp.

Flexible partitions—These can be taken down and put back up in another location, allowing lab spaces to be configured in a variety of sizes.

Overhead service carriers—These are hung from the ceiling. They can have utilities like piping, electric, data, light fixtures, and snorkel exhausts. They afford maximum flexibility as services are lifted off the floor, allowing free floor space to be configured as needed.

Flexible Engineering Systems

Lab designed with overhead connects and disconnects allow for flexibility and fast hook up of equipment.

Labs should have easy connects/disconnects at walls and ceilings to allow for fast and affordable hook up of equipment. See also WBDG Productive—Integrate Technological Tools .

The Engineering systems should be designed such that fume hoods can be added or removed.

Space should be allowed in the utility corridors, ceilings, and vertical chases for future HVAC, plumbing, and electric needs.

Building Systems Distribution Concepts

Interstitial space.

An interstitial space is a separate floor located above each lab floor. All services and utilities are located here where they drop down to service the lab below. This system has a high initial cost but it allows the building to accommodate change very easily without interrupting the labs.

Conventional design vs. interstitial design Image Credit: Zimmer, Gunsul, Frasca Partnership

Service Corridor

Lab spaces adjoin a centrally located corridor where all utility services are located. Maintenance personnel are afforded constant access to main ducts, shutoff valves, and electric panel boxes without having to enter the lab. This service corridor can be doubled up as an equipment/utility corridor where common lab equipment like autoclaves, freezer rooms, etc. can be located.

B. Engineering Considerations

Typically, more than 50% of the construction cost of a laboratory building is attributed to engineering systems. Hence, the close coordination of these ensures a flexible and successfully operating lab facility. The following engineering issues are discussed here: structural systems, mechanical systems, electrical systems, and piping systems. See also WBDG Functional—Ensure Appropriate Product/Systems Integration .

Structural Systems

Once the basic lab module is determined, the structural grid should be evaluated. In most cases, the structural grid equals 2 basic lab modules. If the typical module is 10 ft. 6 in. x 30 ft., the structural grid would be 21 ft. x 30 ft. A good rule of thumb is to add the two dimensions of the structural grid; if the sum equals a number in the low 50's, then the structural grid would be efficient and cost-effective.

Typical lab structural grid.

Key design issues to consider in evaluating a structural system include:

• Framing depth and effect on floor-to-floor height;
• Ability to coordinate framing with lab modules;
• Ability to create penetrations for lab services in the initial design as well as over the life of the building;
• Potential for vertical or horizontal expansion;
• Vibration criteria; and

Mechanical Systems

The location of main vertical supply/exhaust shafts as well as horizontal ductwork is very crucial in designing a flexible lab. Key issues to consider include: efficiency and flexibility, modular design, initial costs , long-term operational costs , building height and massing , and design image .

The various design options for the mechanical systems are illustrated below:

Shafts in the middle of the building

Shafts at the end of the building

Exhaust at end and supply in the middle

Multiple internal shafts

Shafts on the exterior

See also WBDG High Performance HVAC .

Electrical Systems

Three types of power are generally used for most laboratory projects:

Normal power circuits are connected to the utility supply only, without any backup system. Loads that are typically on normal power include some HVAC equipment, general lighting, and most lab equipment.

Emergency power is created with generators that will back up equipment such as refrigerators, freezers, fume hoods, biological safety cabinets, emergency lighting, exhaust fans, animal facilities, and environmental rooms. Examples of safe and efficient emergency power equipment include distributed energy resources (DER) , microturbines , and fuel cells .

An uninterruptible power supply (UPS) is used for data recording, certain computers, microprocessor-controlled equipment, and possibly the vivarium area. The UPS can be either a central unit or a portable system, such as distributed energy resources (DER) , microturbines , fuel cells , and building integrated photovoltaics (BIPV) .

See also WBDG Productive—Assure Reliable Systems and Spaces .

The following should be considered:

• Load estimation
• Site distribution
• Power quality
• Management of electrical cable trays/panel boxes
• User expectations
• Illumination levels
• Lighting distribution-indirect, direct, combination
• Luminaire location and orientation-lighting parallel to casework and lighting perpendicular to casework
• Telephone and data systems

Piping Systems

There are several key design goals to strive for in designing laboratory piping systems:

• Provide a flexible design that allows for easy renovation and modifications.
• Provide appropriate plumbing systems for each laboratory based on the lab programming.
• Provide systems that minimize energy usage .
• Provide equipment arrangements that minimize downtime in the event of a failure.
• Locate shutoff valves where they are accessible and easily understood.
• Accomplish all of the preceding goals within the construction budget.

C. Operations and Maintenance

Cost savings.

The following cost saving items can be considered without compromising quality and flexibility:

• Separate lab and non-lab zones.
• Try to design with standard building components instead of customized components. See also WBDG Functional—Ensure Appropriate Product/Systems Integration .
• Identify at least three manufacturers of each material or piece of equipment specified to ensure competitive bidding for the work.
• Locate fume hoods on upper floors to minimize ductwork and the cost of moving air through the building.
• Evaluate whether process piping should be handled centrally or locally. In many cases it is more cost-effective to locate gases, in cylinders, at the source in the lab instead of centrally.
• Create equipment zones to minimize the amount of casework necessary in the initial construction.
• Provide space for equipment (e.g., ice machine) that also can be shared with other labs in the entry alcove to the lab. Shared amenities can be more efficient and cost-effective.
• Consider designating instrument rooms as cross-corridors, saving space as well as encouraging researchers to share equipment.
• Design easy-to-maintain, energy-efficient building systems. Expose mechanical, plumbing, and electrical systems for easy maintenance access from the lab.
• Locate all mechanical equipment centrally, either on a lower level of the building or on the penthouse level.
• Stack vertical elements above each other without requiring transfers from floor to floor. Such elements include columns, stairs, mechanical closets, and restrooms.

D. Lab and Personnel Safety and Security

Protecting human health and life is paramount, and safety must always be the first concern in laboratory building design. Security-protecting a facility from unauthorized access-is also of critical importance. Today, research facility designers must work within the dense regulatory environment in order to create safe and productive lab spaces. The WBDG Resource Page on Security and Safety in Laboratories addresses all these related concerns, including:

• Laboratory classifications: dependent on the amount and type of chemicals in the lab;
• Containment devices: fume hoods and bio-safety cabinets;
• Levels of bio-safety containment as a design principle;
• Radiation safety;
• Employee safety: showers, eyewashes, other protective measures; and
• Emergency power.

See also WBDG Secure / Safe Branch , Threat/Vulnerability Assessments and Risk Analysis , Balancing Security/Safety and Sustainability Objectives , Air Decontamination , and Electrical Safety .

E. Sustainability Considerations

The typical laboratory uses far more energy and water per square foot than the typical office building due to intensive ventilation requirements and other health and safety concerns. Therefore, designers should strive to create sustainable , high performance, and low-energy laboratories that will:

• Minimize overall environmental impacts;
• Protect occupant safety ; and
• Optimize whole building efficiency on a life-cycle basis.

For more specific guidance, see WBDG Sustainable Laboratory Design ; EPA and DOE's Laboratories for the 21st Century (Labs21) , a voluntary program dedicated to improving the environmental performance of U.S. laboratories; WBDG Sustainable Branch and Balancing Security/Safety and Sustainability Objectives .

F. Three Laboratory Sectors

There are three research laboratory sectors. They are academic laboratories, government laboratories, and private sector laboratories.

• Academic labs are primarily teaching facilities but also include some research labs that engage in public interest or profit generating research.
• Government labs include those run by federal agencies and those operated by state government do research in the public interest.
• Design of labs for the private sector , run by corporations, is usually driven by the need to enhance the research operation's profit making potential.

G. Example Design and Construction Criteria

For GSA, the unit costs for this building type are based on the construction quality and design features in the following table   . This information is based on GSA's benchmark interpretation and could be different for other owners.

LEED® Application Guide for Laboratory Facilities (LEED-AGL)—Because research facilities present a unique challenge for energy efficiency and sustainable design, the U.S. Green Building Council (USGBC) has formed the LEED-AGL Committee to develop a guide that helps project teams apply LEED credits in the design and construction of laboratory facilities. See also the WBDG Resource Page Using LEED on Laboratory Projects .

The following agencies and organizations have developed codes and standards affecting the design of research laboratories. Note that the codes and standards are minimum requirements. Architects, engineers, and consultants should consider exceeding the applicable requirements whenever possible.

• 29 CFR 1910.1450: OSHA "Occupational Exposures to Hazardous Chemicals in Laboratories"
• ANSI/ASSE/AIHA Z9.5 Laboratory Ventilation
• ANSI/ISEA Z358.1 Emergency Eyewash and Shower Equipment
• Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC) Standards
• Biosafety in Microbiological and Biomedical Laboratories (BMBL) 5th Edition , Department of Health and Human Services, Centers for Disease Control and Prevention and National Institutes of Health.
• GSA PBS-P100 Facilities Standards for the Public Buildings Service
• Guidelines for the Laboratory Use of Chemical Carcinogens , Pub. No. 81-2385. National Institutes of Health
• NIH Design Requirements Manual , National Institutes of Health
• NFPA 30 Flammable and Combustible Liquids Code
• NFPA 45 Fire Protection for Laboratories using Chemical
• Unified Facilities Guide Specifications (UFGS) —organized by MasterFormat™ divisions, are for use in specifying construction for the military services. Several UFGS exist for safety-related topics.

Publications

• Building Type Basics for Research Laboratories , 2nd Edition by Daniel Watch. New York: John Wiley & Sons, Inc., 2008. ISBN# 978-0-470-16333-7.
• CRC Handbook of Laboratory Safety , 5th ed. by A. Keith Furr. CRC Press, 2000.
• Design and Planning of Research and Clinical Laboratory Facilities by Leonard Mayer. New York, NY: John Wiley & Sons, Inc., 1995.
• Design for Research: Principals of Laboratory Architecture by Susan Braybrooke. New York, NY: John Wiley & Sons, Inc., 1993.
• Guidelines for Laboratory Design: Health and Safety Considerations , 4th Edition by Louis J. DiBerardinis, et al. New York, NY: John Wiley & Sons, Inc., 2013.
• Guidelines for Planning and Design of Biomedical Research Laboratory Facilities by The American Institute of Architects, Center for Advanced Technology Facilities Design. Washington, DC: The American Institute of Architects, 1999.
• Handbook of Facilities Planning, Vol. 1: Laboratory Facilities by T. Ruys. New York, NY: Van Nostrand Reinhold, 1990.
• Laboratories, A Briefing and Design Guide by Walter Hain. London, UK: E & FN Spon, 1995.
• Laboratory by Earl Walls Associates, May 2000.
• Laboratory Design from the Editors of R&D Magazine.
• Laboratory Design, Construction, and Renovation: Participants, Process, and Product by National Research Council, Committee on Design, Construction, and Renovation of Laboratory Facilities. Washington, DC: National Academy Press, 2000.
• Planning Academic Research Facilities: A Guidebook by National Science Foundation. Washington, DC: National Science Foundation, 1992.
• Research and Development in Industry: 1995-96 by National Science Foundation, Division of Science Resources Studies. Arlington, VA: National Science Foundation, 1998.
• Science and Engineering Research Facilities at Colleges and Universities by National Science Foundation, Division of Science Resources Studies. Arlington, VA, 1998.
• Laboratories for the 21st Century (Labs21) —Sponsored by the U.S. Environmental Protection Agency and the U.S. Department of Energy, Labs21 is a voluntary program dedicated to improving the environmental performance of U.S. laboratories.

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Research And Development (R&D) Analytics Market

Research And Development Analytics Market by End Use, Enterprise Size & Region | Forecast 2022 to 2032.

Research And Development Analytics Deployment rising amid Surging Technological Advancements across major Industries

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Research And Development Analytics Market Snapshot (2022 to 2032)

Global Research And Development (R&D) Analytics Market demand is anticipated to be valued at US$ 2,025.0 Million in 2022, forecast a CAGR of 12.1% to be valued at US$ 6,366.6 Million from 2022 to 2032. Growth is attributed to the evolving need in end-use industries. From 2016 to 2021 a CAGR of 9.1% was registered for the Research And Development  Analytics Market.

Revenue growth for any institution depends on the investment made in  Research and Development organizations, there is a need to take several vital decisions with regard to allocations of funds, monitor the recent technology trends and assess the risks and also manage talent. Much of these are done through experience and the expertise of the organization which is more of an art than science in which they have devised their own methods.

For short-term projects, these methods might be beneficial but for long-term Research and Development projects adoption of analytics has to be done in industries as there is a need to take decisions regarding what products to develop, competition landscape, intellectual property, patent data, market segmentation, etc.

The use of analytics in  Research and Development can increase revenue and lower cost, improve accuracy, save time, and meet customers’ ever-increasing demand. There is a large volume of unstructured or unorganized data in this complex business environment, so there is a need to optimize this data using analytics in order to improve their return on investment in Research and Development.

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Which are Some Prominent Drivers Spearheading Research And Development (R&D) Analytics Market Growth?

Increasing Usage in Large Enterprises to Drive the Market Growth

There is a large increase in the amount of data and there is a need to effectively manage the database with appropriate tools for converting them to valuable and structured data for accelerating the growth of the Research and Development organizations. There is a need to take decisions regarding what products to develop, customer behavior, competition landscape, intellectual property, patent data, spending, and revenue return analysis.

The increasing adoption of analytics tools assists in precise and customer-focused businesses and offers accurate data which helps in tracking achievements and goals from campaigns. This will continue to fuel the demand for Research and Development analytics solutions across different industry verticals.

Large organizations are finding it difficult to analyze these large sets of unstructured data, so here advanced Research and Development analytics tools come in to extract the relevant and appropriate information. Data being stored is growing exponentially everywhere in many formats which need to be organized and utilized appropriately.

What are the Challenges Faced by the Research and Development Analytics Industry?

The Data Security Concern May Impede the Market Growth

Although the Research And Development (R&D) Analytics Market has numerous end-uses, there are numerous obstacles that likely pose a challenge to market growth. Data security and privacy concerns are major challenges faced in Research And Development (R&D) Analytics Market.

Another challenge was with nomenclature followed by different companies in their data as several standards are designed by themselves internal of the organization or by respective government regulations. However, with the increasing usage of analytics in industries such as automobile, aerospace, and clinical research, demand in the Research And Development (R&D) Analytics Market is poised to grow exponentially during the assessment period.

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Why is North America Emerging as an Opportunistic Research And Development (R&D) Analytics Market?

Presence of a Leading Market Provider to Boost the Market Growth in North America

North America dominated the global Research And Development (R&D) Analytics Market and accounted for 34.4% market share and the global Research And Development (R&D) Analytics Market is anticipated to spur over the forthcoming years in this region. North America is projected to lead the global market during the forecast period with augmenting developments in the IT sector and the presence of large enterprises.

Also, the growing demand for innovative business intelligence products in the North American region has assisted the growth of the Research And Development (R&D) Analytics Market in this region.

Cloud computing, the Internet of Things (IoT), blockchain, and artificial intelligence are expanding the applications of Research and Development analytics and, as a result, driving the market. Due to the existence of prominent service suppliers, such as Microsoft, Oracle, and IBM corporation. A plethora of technological breakthroughs has been tested in the USA.

How is Europe Contributing to the Growth of the Research And Development (R&D) Analytics Market?

Increasing Adoption of Analytical Tools in Many Organizations to Drive The Market Growth

According to Future Market Insights, Europe is expected to provide immense growth opportunities for the Research And Development (R&D) Analytics Market, due to the technological development in the region. Europe’s Research And Development (R&D) Analytics Market accounts for a 24.7% share of the total global market. The European Market is expected to exhibit growth at a swift pace owing to the large-scale adoption of Research and Development analytics supporting tools in industries across the region.

Furthermore, the growing adoption of connected and IoT-enabled devices has driven the demand for innovative solutions based on the latest technologies, as well as the continued rollout of analytics. This is likely to expand the global Research And Development (R&D) Analytics Market size. The technological advancement in various industries, such as BFSI, IT and telecom, technology, automotive, and healthcare, in the region provides opportunities for the growth of the market.

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Will the Asia Pacific emerge as an Attractive Research And Development (R&D) Analytics Market?

The Asia-Pacific is expected to register the fastest growth in revenue generation for Research And Development (R&D) Analytics Market due to the large population in countries such as China, and India.

The growth of the regional market is driven by the widespread adoption of big data analytics tools and solutions in the region. The enterprises in the region are investing heavily in customer analytics to improve business efficiency and productivity.

Moreover, the expansion of the IT sector in countries such as China, India, and Japan is anticipated to elevate the market demand in the near future. The presence of leading Research and Development analytics providers in the region boosts the growth of the market in the Asia Pacific.

Start-up Scenario

The new entrants in the Research And Development (R&D) Analytics Market are continually indulging in several collaborations and highly investing in research and development activities to provide more convenient solutions to industry verticals. Some of the major start-ups that are leading the development of the market are- Fractal Analytics, Mu Sigma, Latent View

• In January 2022 - Fractal , a global provider of artificial intelligence and advanced analytics announced the acquisition of Neal Analytics, a cloud, data, engineering, and AI Microsoft Gold consulting partner.
• In November 2021- LatentView Analytics , announced the launch of its Growth Accelerator to support the evolving needs of enterprises to win and retain customers, open new revenue streams, and compete in the new digital economy. The Growth Accelerator is tailored to large organizations that face scale and innovation challenges tied to digital transformation.

Market Competition

Some of the key participants present in the global Research And Development (R&D) Analytics Market include Teradata, Oracle Corporation, IBM Corporation, SAS Institute Inc., Tableau Software Inc., Microsoft Corporation, Sisense Inc., SAP SE, and TARGIT among others. Major players in the Research And Development (R&D) Analytics Market follow the strategy of partnership or acquisition of various local players to gain a competitive edge in the market. Some of the developments are listed below

• In October 2020 - Microsoft announced the general availability of the web analytics tool, known as Clarity. Microsoft Clarity is a free-to-use analytics product built to help website managers improve their website experiences by a better understanding of site visitor behavior.
• In July 2022 - Oracle Corp and Microsoft Corp announced the general availability of Oracle Database Service for Microsoft Azure. With this new offering, Microsoft Azure customers can easily provision, access, and monitor enterprise-grade Oracle Database services in Oracle Cloud Infrastructure (OCI) with a familiar experience. Users can migrate or build new applications on Azure and then connect to high-performance and high-availability managed Oracle Database services such as Autonomous Database running on OCI.
• In April 2022: Bharat Petroleum Corporation Ltd. (BPCL) and Microsoft announced a strategic cloud partnership aimed at accelerating the firm’s digital transformation and shaping the future of innovation in the oil and gas industry. The collaboration seeks to unlock the opportunities that Microsoft’s cloud provides to address the unique challenges of the oil and gas sector, enabling BPCL to accelerate the modernization of its tech architecture to enhance customer experience.

Report Scope

Key Segments Profiled in the Research and Development Analytics Industry Survey

Research and development (r&d) analytics market by end use:.

• Research and Development Analytics in Pharmaceuticals
• Research and Development Analytics in Life sciences
• Research and Development Analytics in Clinical Research
• Research and Development Analytics in Automobile
• Research and Development Analytics in Aerospace
• Research and Development Analytics in Defense
• Research and Development Analytics in Other End Uses

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Scientific Research and Development Services Global Market Report 2024

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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What Is Early Childhood Development? A Guide to the Science (ECD 1.0)

Healthy development in the early years (particularly birth to three) provides the building blocks for educational achievement, economic productivity, responsible citizenship, lifelong health, strong communities, and successful parenting of the next generation. What can we do during this incredibly important period to ensure that children have a strong foundation for future development? The Center on the Developing Child created this Guide to Early Childhood Development (ECD) to help parents, caregivers, practitioners, and policymakers understand the importance of early childhood development and learn how to support children and families during this critical stage.

Visit “ Introducing ECD 2.0 ” for new resources that build on the knowledge presented below.

Step 1: Why Is Early Childhood Important?

This section introduces you to the science that connects early experiences from birth (and even before birth) to future learning capacity, behaviors, and physical and mental health.

This 3-minute video portrays how actions taken by parents, teachers, policymakers, and others can affect life outcomes for both the child and the surrounding community.

InBrief: The Science of Early Childhood Development

This video from the InBrief series addresses basic concepts of early childhood development, established over decades of neuroscience and behavioral research.

This brief explains how the science of early brain development can inform investments in early childhood, and helps to illustrate why child development—particularly from birth to five years—is a foundation for a prosperous and sustainable society.

Step 2: How Does Early Child Development Happen?

Now that you understand the importance of ECD, this section digs deeper into the science, including how early experiences and relationships impact and shape the circuitry of the brain, and how exposure to toxic stress can weaken the architecture of the developing brain.

Three Core Concepts in Early Development

Advances in neuroscience, molecular biology, and genomics now give us a much better understanding of how early experiences are built into our bodies and brains, for better or worse. This three-part video series explains.

8 Things to Remember about Child Development

In this important list, featured in the From Best Practices to Breakthrough Impacts report, the Center on the Developing Child sets the record straight about some aspects of early child development.

InBrief: The Science of Resilience

This brief summarizes the science of  resilience and explains why understanding it will help us design policies and programs that enable more children to reach their full potential.

Step 3: What Can We Do to Support Child Development?

Understanding how important early experiences and relationships are to lifelong development is one step in supporting children and families. The next step is to apply that knowledge to current practices and policies. This section provides practical ways that practitioners and policymakers can support ECD and improve outcomes for children and families.

From Best Practices to Breakthrough Impacts

This report synthesizes 15 years of dramatic advances in the science of early childhood and early brain development and presents a framework for driving science-based innovation in early childhood policy and practice.

Three Principles to Improve Outcomes for Children and Families

Understanding how the experiences children have starting at birth, even prenatally, affect lifelong outcomes—as well as the core capabilities adults need to thrive—provides a strong foundation upon which policymakers and civic leaders can design a shared and more effective agenda.

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• 04 June 2024

Superfast Microsoft AI is first to predict air pollution for the whole world

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Weather forecasting is benefitting from the boom in artificial intelligence. Credit: NESDIS/STAR/NOAA/Alamy

An artificial intelligence (AI) model developed by Microsoft can accurately forecast weather and air pollution for the whole world — and it does it in less than a minute.

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doi: https://doi.org/10.1038/d41586-024-01677-2

Bodnar, C. et al. Preprint at arXiv https://doi.org/10.48550/arXiv.2405.13063 (2024).

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Prestigious Funding - HORIZON ERC Advanced and Proof of Concept Grants Open Now

The prestigious HORIZON ERC Advanced and Proof of Concept Grants are now open for submissions.

Elevate your research with the prestigious European Research Council (ERC) Grants! Secure funding for your groundbreaking ideas through the ERC Advanced and Proof of Concept programs.

Two Prestigious ERC Grant Opportunities Are Now Open for Submission:

• ERC ADVANCED GRANTS (ERC-2024-ADG)
• ERC PROOF OF CONCEPT GRANTS (ERC-2024-POC)

Calls Description: 

1.  Call for Proposals for ERC Advanced Grant (ERC-2024-ADG)

Deadline date: 29 August 2024 17:00:00 Brussels time

ERC Advanced Grants are designed to support excellent Principal Investigators at the career stage at which they are already established research leaders with a recognised track record of research achievements. Principal Investigators must demonstrate the ground-breaking nature, ambition and feasibility of their research proposal.

Size of ERC Advanced Grants

Advanced Grants may be awarded up to a maximum of  EUR 2 500 000  for a period of  5 years  (the maximum amount of the grants is reduced pro rata temporis for projects of a shorter duration).

However, up to an  additional   EUR 1 000 000  can be requested in the proposal to cover (a) eligible "start-up" costs for Principal Investigators moving to the EU or an Associated Country from elsewhere as a consequence of receiving the ERC grant, and/or (b) the purchase of major equipment, and/or (c) access to large facilities, and/or (d) other major experimental and field work costs, excluding personnel costs. (As any additional funding is to cover major one-off costs it is not subject to pro-rata temporis reduction for projects of shorter duration. All funding requested is assessed during evaluation).

The Advanced Grant will be awarded as a single lump sum contribution for the entirety of the project. The lump sum will cover the beneficiaries' estimated costs for the project.

Profile of the ERC Advanced Grant Principal Investigator

ERC Advanced Grant Principal Investigators are expected to be active researchers and to have a track record of significant research achievements.

For further information, please see the ERC Work Programme 2024.

2.  Call for proposals for ERC Proof of Concept Grant (ERC-2024-POC)

Deadline dates: 14 March 2024 17:00:00 Brussels time

                           17 September 2024 17:00:00 Brussels time

Objectives The ERC Proof of Concept Grants aim at facilitating exploration of the commercial and social innovation potential of ERC funded research and are therefore available only to Principal Investigators whose proposals draw substantially on their ERC funded research.

Size of ERC Proof of Concept Grants The financial contribution will be awarded as a lump sum of  EUR 150 000  for a period of  18 months . The ERC expects that normally proof of concept activities should be completed within 12 months. However, to allow for those projects that require more preparation time, the grant agreements will be signed for 18 months. Extensions of the duration of proof of concept projects may be granted only exceptionally.

The lump sum will cover the beneficiaries' direct and indirect eligible costs for the project: if the project is implemented properly the amounts will be paid regardless of the costs actually incurred. The lump sum has been designed to cover the beneficiaries’ personnel costs, subcontracting, purchase costs, other cost categories and indirect costs.

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All Principal Investigators in one of the main grants are eligible to participate and apply for an ERC Proof of Concept Grant. Principal Investigators in an ongoing main grant or in a main grant that has ended less than 12 months before 1 January 2024 are eligible to apply. For further information please see the ERC Work Programme 2024.

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Introducing Apple’s On-Device and Server Foundation Models

At the 2024 Worldwide Developers Conference , we introduced Apple Intelligence, a personal intelligence system integrated deeply into iOS 18, iPadOS 18, and macOS Sequoia.

Apple Intelligence is comprised of multiple highly-capable generative models that are specialized for our users’ everyday tasks, and can adapt on the fly for their current activity. The foundation models built into Apple Intelligence have been fine-tuned for user experiences such as writing and refining text, prioritizing and summarizing notifications, creating playful images for conversations with family and friends, and taking in-app actions to simplify interactions across apps.

In the following overview, we will detail how two of these models — a ~3 billion parameter on-device language model, and a larger server-based language model available with Private Cloud Compute and running on Apple silicon servers — have been built and adapted to perform specialized tasks efficiently, accurately, and responsibly. These two foundation models are part of a larger family of generative models created by Apple to support users and developers; this includes a coding model to build intelligence into Xcode, as well as a diffusion model to help users express themselves visually, for example, in the Messages app. We look forward to sharing more information soon on this broader set of models.

Our Focus on Responsible AI Development

Apple Intelligence is designed with our core values at every step and built on a foundation of groundbreaking privacy innovations.

Additionally, we have created a set of Responsible AI principles to guide how we develop AI tools, as well as the models that underpin them:

• Empower users with intelligent tools : We identify areas where AI can be used responsibly to create tools for addressing specific user needs. We respect how our users choose to use these tools to accomplish their goals.
• Represent our users : We build deeply personal products with the goal of representing users around the globe authentically. We work continuously to avoid perpetuating stereotypes and systemic biases across our AI tools and models.
• Design with care : We take precautions at every stage of our process, including design, model training, feature development, and quality evaluation to identify how our AI tools may be misused or lead to potential harm. We will continuously and proactively improve our AI tools with the help of user feedback.
• Protect privacy : We protect our users' privacy with powerful on-device processing and groundbreaking infrastructure like Private Cloud Compute. We do not use our users' private personal data or user interactions when training our foundation models.

These principles are reflected throughout the architecture that enables Apple Intelligence, connects features and tools with specialized models, and scans inputs and outputs to provide each feature with the information needed to function responsibly.

In the remainder of this overview, we provide details on decisions such as: how we develop models that are highly capable, fast, and power-efficient; how we approach training these models; how our adapters are fine-tuned for specific user needs; and how we evaluate model performance for both helpfulness and unintended harm.

Pre-Training

Our foundation models are trained on Apple's AXLearn framework , an open-source project we released in 2023. It builds on top of JAX and XLA, and allows us to train the models with high efficiency and scalability on various training hardware and cloud platforms, including TPUs and both cloud and on-premise GPUs. We used a combination of data parallelism, tensor parallelism, sequence parallelism, and Fully Sharded Data Parallel (FSDP) to scale training along multiple dimensions such as data, model, and sequence length.

We train our foundation models on licensed data, including data selected to enhance specific features, as well as publicly available data collected by our web-crawler, AppleBot. Web publishers have the option to opt out of the use of their web content for Apple Intelligence training with a data usage control.

We never use our users’ private personal data or user interactions when training our foundation models, and we apply filters to remove personally identifiable information like social security and credit card numbers that are publicly available on the Internet. We also filter profanity and other low-quality content to prevent its inclusion in the training corpus. In addition to filtering, we perform data extraction, deduplication, and the application of a model-based classifier to identify high quality documents.

Post-Training

We find that data quality is essential to model success, so we utilize a hybrid data strategy in our training pipeline, incorporating both human-annotated and synthetic data, and conduct thorough data curation and filtering procedures. We have developed two novel algorithms in post-training: (1) a rejection sampling fine-tuning algorithm with teacher committee, and (2) a reinforcement learning from human feedback (RLHF) algorithm with mirror descent policy optimization and a leave-one-out advantage estimator. We find that these two algorithms lead to significant improvement in the model’s instruction-following quality.

Optimization

In addition to ensuring our generative models are highly capable, we have used a range of innovative techniques to optimize them on-device and on our private cloud for speed and efficiency. We have applied an extensive set of optimizations for both first token and extended token inference performance.

Both the on-device and server models use grouped-query-attention. We use shared input and output vocab embedding tables to reduce memory requirements and inference cost. These shared embedding tensors are mapped without duplications. The on-device model uses a vocab size of 49K, while the server model uses a vocab size of 100K, which includes additional language and technical tokens.

For on-device inference, we use low-bit palletization, a critical optimization technique that achieves the necessary memory, power, and performance requirements. To maintain model quality, we developed a new framework using LoRA adapters that incorporates a mixed 2-bit and 4-bit configuration strategy — averaging 3.5 bits-per-weight — to achieve the same accuracy as the uncompressed models.

Additionally, we use an interactive model latency and power analysis tool, Talaria , to better guide the bit rate selection for each operation. We also utilize activation quantization and embedding quantization, and have developed an approach to enable efficient Key-Value (KV) cache update on our neural engines.

With this set of optimizations, on iPhone 15 Pro we are able to reach time-to-first-token latency of about 0.6 millisecond per prompt token, and a generation rate of 30 tokens per second. Notably, this performance is attained before employing token speculation techniques, from which we see further enhancement on the token generation rate.

Model Adaptation

Our foundation models are fine-tuned for users’ everyday activities, and can dynamically specialize themselves on-the-fly for the task at hand. We utilize adapters, small neural network modules that can be plugged into various layers of the pre-trained model, to fine-tune our models for specific tasks. For our models we adapt the attention matrices, the attention projection matrix, and the fully connected layers in the point-wise feedforward networks for a suitable set of the decoding layers of the transformer architecture.

By fine-tuning only the adapter layers, the original parameters of the base pre-trained model remain unchanged, preserving the general knowledge of the model while tailoring the adapter layers to support specific tasks.

We represent the values of the adapter parameters using 16 bits, and for the ~3 billion parameter on-device model, the parameters for a rank 16 adapter typically require 10s of megabytes. The adapter models can be dynamically loaded, temporarily cached in memory, and swapped — giving our foundation model the ability to specialize itself on the fly for the task at hand while efficiently managing memory and guaranteeing the operating system's responsiveness.

To facilitate the training of the adapters, we created an efficient infrastructure that allows us to rapidly retrain, test, and deploy adapters when either the base model or the training data gets updated. The adapter parameters are initialized using the accuracy-recovery adapter introduced in the Optimization section.

Performance and Evaluation

Our focus is on delivering generative models that can enable users to communicate, work, express themselves, and get things done across their Apple products. When benchmarking our models, we focus on human evaluation as we find that these results are highly correlated to user experience in our products. We conducted performance evaluations on both feature-specific adapters and the foundation models.

To illustrate our approach, we look at how we evaluated our adapter for summarization. As product requirements for summaries of emails and notifications differ in subtle but important ways, we fine-tune accuracy-recovery low-rank (LoRA) adapters on top of the palletized model to meet these specific requirements. Our training data is based on synthetic summaries generated from bigger server models, filtered by a rejection sampling strategy that keeps only the high quality summaries.

To evaluate the product-specific summarization, we use a set of 750 responses carefully sampled for each use case. These evaluation datasets emphasize a diverse set of inputs that our product features are likely to face in production, and include a stratified mixture of single and stacked documents of varying content types and lengths. As product features, it was important to evaluate performance against datasets that are representative of real use cases. We find that our models with adapters generate better summaries than a comparable model.

As part of responsible development, we identified and evaluated specific risks inherent to summarization. For example, summaries occasionally remove important nuance or other details in ways that are undesirable. However, we found that the summarization adapter did not amplify sensitive content in over 99% of targeted adversarial examples. We continue to adversarially probe to identify unknown harms and expand our evaluations to help guide further improvements.

In addition to evaluating feature specific performance powered by foundation models and adapters, we evaluate both the on-device and server-based models’ general capabilities. We utilize a comprehensive evaluation set of real-world prompts to test the general model capabilities. These prompts are diverse across different difficulty levels and cover major categories such as brainstorming, classification, closed question answering, coding, extraction, mathematical reasoning, open question answering, rewriting, safety, summarization, and writing.

We compare our models with both open-source models (Phi-3, Gemma, Mistral, DBRX) and commercial models of comparable size (GPT-3.5-Turbo, GPT-4-Turbo) 1 . We find that our models are preferred by human graders over most comparable competitor models. On this benchmark, our on-device model, with ~3B parameters, outperforms larger models including Phi-3-mini, Mistral-7B, and Gemma-7B. Our server model compares favorably to DBRX-Instruct, Mixtral-8x22B, and GPT-3.5-Turbo while being highly efficient.

We use a set of diverse adversarial prompts to test the model performance on harmful content, sensitive topics, and factuality. We measure the violation rates of each model as evaluated by human graders on this evaluation set, with a lower number being desirable. Both the on-device and server models are robust when faced with adversarial prompts, achieving violation rates lower than open-source and commercial models.

Our models are preferred by human graders as safe and helpful over competitor models for these prompts. However, considering the broad capabilities of large language models, we understand the limitation of our safety benchmark. We are actively conducting both manual and automatic red-teaming with internal and external teams to continue evaluating our models' safety.

To further evaluate our models, we use the Instruction-Following Eval (IFEval) benchmark to compare their instruction-following capabilities with models of comparable size. The results suggest that both our on-device and server model follow detailed instructions better than the open-source and commercial models of comparable size.

We evaluate our models’ writing ability on our internal summarization and composition benchmarks, consisting of a variety of writing instructions. These results do not refer to our feature-specific adapter for summarization (seen in Figure 3 ), nor do we have an adapter focused on composition.

The Apple foundation models and adapters introduced at WWDC24 underlie Apple Intelligence, the new personal intelligence system that is integrated deeply into iPhone, iPad, and Mac, and enables powerful capabilities across language, images, actions, and personal context. Our models have been created with the purpose of helping users do everyday activities across their Apple products, and developed responsibly at every stage and guided by Apple’s core values. We look forward to sharing more information soon on our broader family of generative models, including language, diffusion, and coding models.

[1] We compared against the following model versions: gpt-3.5-turbo-0125, gpt-4-0125-preview, Phi-3-mini-4k-instruct, Mistral-7B-Instruct-v0.2, Mixtral-8x22B-Instruct-v0.1, Gemma-1.1-2B, and Gemma-1.1-7B. The open-source and Apple models are evaluated in bfloat16 precision.

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Apple Natural Language Understanding Workshop 2023

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SCIENCE & ENGINEERING INDICATORS

Research and development: u.s. trends and international comparisons.

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U.S. Business R&D

U.S. business R&D expenditures are measured as current costs, which include labor costs; materials and supplies; expensed equipment (not capitalized); leased facilities and equipment; and expenses for depreciation and amortization on property, plant, and equipment. These expenditures are dominated by labor costs, in comparison with current costs associated with facilities or equipment such as rental expenses or expensed equipment (Moris and Shackelford 2023b). ​ Separately, businesses also have R&D capital expenditures—payments for long-lived assets to support R&D activities. Businesses that performed or funded U.S. R&D in 2020 had $32.5 billion in R&D capital expenditures (Moris and Shackelford 2023a).

Of the $608.6 billion of U.S. business R&D performed in 2021, $602.5 billion was performed by companies with 10 or more domestic employees, and $6.1 billion was performed by businesses with 9 or fewer domestic employees (or microbusinesses) (Kindlon 2023; Britt 2023). ​ For foreign R&D by multinational enterprises, see Bureau of Economic Analysis (2022) and Moris (2021). Statistics are from NCSES’s Annual Business Survey (ABS) for microbusinesses and the Business Enterprise Research and Development (BERD) Survey for the larger companies. https://ncses.nsf.gov/surveys/business-enterprise-research-development/2020#survey-info for the BERD Survey and https://ncses.nsf.gov/surveys/annual-business-survey/2021#survey-info for the ABS. Microbusinesses are a small but important segment of business R&D and innovation. See Anderson and Kindlon (2019) and Knott and Vieregger (2020)." data-bs-content="For more information, see https://ncses.nsf.gov/surveys/business-enterprise-research-development/2020#survey-info for the BERD Survey and https://ncses.nsf.gov/surveys/annual-business-survey/2021#survey-info for the ABS. Microbusinesses are a small but important segment of business R&D and innovation. See Anderson and Kindlon (2019) and Knott and Vieregger (2020)." data-endnote-uuid="da90176a-8060-4802-a63f-779952de099a">​ For more information, see https://ncses.nsf.gov/surveys/business-enterprise-research-development/2020#survey-info for the BERD Survey and https://ncses.nsf.gov/surveys/annual-business-survey/2021#survey-info for the ABS. Microbusinesses are a small but important segment of business R&D and innovation. See Anderson and Kindlon (2019) and Knott and Vieregger (2020).

The largest proportion of R&D by businesses with 10 or more domestic employees is performed by the manufacturing sector (54% in 2021) ( Table RD-6 ), https://ncses.nsf.gov/surveys/business-enterprise-research-development/2021#data ." data-bs-content="At the same time, the U.S. R&D manufacturing share has declined over the years. See BERD Survey Table 59, Domestic R&D paid for by the company and others and performed by the company, by industry and company size: 2008–21, available at https://ncses.nsf.gov/surveys/business-enterprise-research-development/2021#data ." data-endnote-uuid="41c65a7c-efbd-4177-b4cb-c80823ebc426">​ At the same time, the U.S. R&D manufacturing share has declined over the years. See BERD Survey Table 59, Domestic R&D paid for by the company and others and performed by the company, by industry and company size: 2008–21, available at https://ncses.nsf.gov/surveys/business-enterprise-research-development/2021#data . whereas 88% of microbusiness R&D is performed by the nonmanufacturing sector (Kindlon 2023, Table 4). Figure RD-11 shows the distribution of domestic R&D for the top 5 R&D-performing industries (based on North American Industry Classification System [NAICS] codes) for these two broad size categories. The dominance of nonmanufacturing for microbusinesses is largely driven by the 73% share of R&D by firms classified in professional, scientific, and R&D services (NAICS 54), whereas the share of information (NAICS 51) was 12% for microbusinesses compared with 25% for larger companies. (See Table SRD-3 and Table SRD-4 for detailed company size R&D distribution from these sources.)

Domestic net sales, R&D, and R&D-to-sales ratio for companies that performed or funded U.S. business R&D, by selected industry: 2021

i = more than 50% of the estimate is a combination of imputation and reweighting to account for nonresponse.

NAICS = 2017 North American Industry Classification System.

a Dollar values are for goods sold or services rendered by R&D-performing or R&D-funding companies located in the United States to customers outside of the company, including the U.S. federal government, foreign customers, and the company's foreign subsidiaries. Included are revenues from a company’s foreign operations and subsidiaries and from discontinued operations. If a respondent company is owned by a foreign parent company, sales to the parent company and to affiliates not owned by the respondent company are included. Excluded are intracompany transfers; returns; allowances; freight charges; and excise, sales, and other revenue-based taxes.

b Domestic R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company.

Data are for companies with 10 or more domestic employees. Detail may not add to total because of rounding. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development (BERD) Survey, 2021.

Science and Engineering Indicators

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U.S. business and microbusiness R&D distribution, by top industries: 2021

Details may not add to total because of rounding. NAICS industry classification is based on the dominant business code for domestic R&D performance. Statistics are representative of companies located in the United States that performed or funded R&D.

National Center for Science and Engineering Statistics and Census Bureau, 2022 Annual Business Survey (ABS): Data Year 2021, and 2021 Business Enterprise Research and Development (BERD) Survey.

Industries That Perform the Most U.S. Business R&D

The rest of this section focuses on R&D activities by businesses with 10 or more domestic employees from the NCSES BERD Survey. Five industries accounted for 79% of the $602.5 billion of U.S. business R&D performed by these companies in 2021: information (including software publishing) at 25%; chemicals manufacturing (including pharmaceuticals and medicines) at 18%; computer and electronic products manufacturing (including semiconductors) at 17%; professional, scientific, and technical services (including R&D services) at 11%; and transportation equipment manufacturing (including motor vehicles and aerospace products and parts) at 8% ( Figure RD-12 ; Table RD-6 ). ​ Motor vehicle statistics include but do not separate out electric vehicles. Machinery manufacturing companies performed another 3%. The latter six NAICS industries are major R&D-intensive or knowledge- and technology-intensive industries covered in the Indicators 2024 report “ Production and Trade of Knowledge- and Technology-Intensive Industries ” with analysis of output, trade, and GVCs. Indeed, these six industries are among the largest R&D intensive as measured by domestic R&D-to-sales ratio ( Table RD-6 ). At the four-digit NAICS level, the industries with the largest R&D intensities were scientific R&D services (41%), semiconductor and other electronic components manufacturing (20%), pharmaceuticals and medicines manufacturing (16%), and software publishers (13%).

Industry share of U.S. business R&D, by top R&D-performing industries: 2010–21

Industry classification is based on the dominant business code for domestic R&D performance, when available. For companies that did not report business codes, the classification used for sampling was assigned. Beginning in survey year 2018, statistics are representative of companies located in the United States that performed or funded $50,000 or more of R&D. The 2010–16 data come from the Business R&D and Innovation Survey and do not include companies with fewer than five domestic employees. Data for 2017–18 come from the Business Research and Development Survey, whereas data for 2019–21 come from the Business Enterprise Research and Development Survey; both surveys do not include companies with fewer than 10 domestic employees.

National Center for Science and Engineering Statistics and Census Bureau, Business R&D and Innovation Survey (BRDIS), Business Research and Development Survey (BRDS), and Business Enterprise Research and Development (BERD) Survey.

Across industries, close to 90% of U.S. business R&D is funded by the performing company. In the information industry, this share is 99% ( Table RD-7 ). At the other extreme, only 18% of R&D performed by the scientific R&D services industry is funded internally , reflecting contract R&D for other companies, domestic and foreign, and on behalf of the federal government. Domestic company customers funded 54% of the U.S. R&D of this industry, and the federal government funded another 12%. In the manufacturing sector, aerospace products and parts had one of the lowest shares of R&D funded internally (46%). For this industry, the federal government funded 49% of its domestic R&D.

U.S. business R&D performance, by source of funds: 2021

i = more than 50% of the estimate or its component(s) is a combination of imputation and reweighting to account for nonresponse.

NAICS = 2017 North American Industry Classification System; nec = not elsewhere classified.

a All R&D is the cost of R&D paid for and performed by the respondent company and paid for by others outside of the company and performed by the respondent company.

Data are for companies with 10 or more domestic employees. Detail may not add to total because of rounding. Beginning in survey year 2018, companies that performed or funded less than $50,000 of R&D were excluded from tabulation. These companies in aggregate represented a very small share of total R&D expenditures in prior years. Had the companies under this threshold been included in the 2018 estimates, they would have contributed approximately $90 million to overall R&D expenditures. Industry classification was based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Excludes data for federally funded research and development centers.

Geographical locations of the performance of U.S. business R&D are not evenly distributed among the states. Of the $602.5 billion of business R&D performed by businesses with 10 or more domestic employees in 2021, California accounted for $211.6 billion, or 35%, in 2021 ( Table SRD-5 ). Science and Engineering Indicators State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-bs-content="Statistics on U.S. state trends in R&D, S&E education, workforce, patents and publications, and knowledge-intensive industries are also available in the Science and Engineering Indicators State Indicators data tool at https://ncses.nsf.gov/indicators/states ." data-endnote-uuid="f7bc14ca-3d14-4b8e-a868-616386a73b27">​ Statistics on U.S. state trends in R&D, S&E education, workforce, patents and publications, and knowledge-intensive industries are also available in the Science and Engineering Indicators State Indicators data tool at https://ncses.nsf.gov/indicators/states . The next-largest shares in 2021 were for Washington (8%); Massachusetts (7%); Texas (5%); and New York, New Jersey, and Michigan (4% each). https://www.bea.gov/data/special-topics , and for more on R&D investment in U.S. GDP statistics, see Moris (2019) and Moylan and Okubo (2020)." data-bs-content="Selected below state–level statistics are also available from the NCSES BERD Survey (Shackelford and Wolfe 2019). For upcoming statistics on regional R&D within GDP accounts, see https://www.bea.gov/data/special-topics , and for more on R&D investment in U.S. GDP statistics, see Moris (2019) and Moylan and Okubo (2020)." data-endnote-uuid="fb6da630-019e-425d-862c-af10520b85f7">​ Selected below state–level statistics are also available from the NCSES BERD Survey (Shackelford and Wolfe 2019). For upcoming statistics on regional R&D within GDP accounts, see https://www.bea.gov/data/special-topics , and for more on R&D investment in U.S. GDP statistics, see Moris (2019) and Moylan and Okubo (2020).

U.S. Business R&D in Selected Critical and Emerging Technologies

R&D in critical and emerging technologies, such as semiconductors, artificial intelligence (AI), synthetic biology, biomanufacturing, and other advanced manufacturing processes, contribute to economic competitiveness and national security (DOD/DSB 2022; NSTC 2022). ​ R&D-intensive manufacturing industries may engage in advanced manufacturing and intelligent manufacturing. Examples include additive or nano-based manufacturing and biotechnology and biomanufacturing. For additional information, see Brocal, Sebastián, and González (2019) and President’s Council of Advisors on Science and Technology (2020). This section covers U.S. business R&D by the semiconductor manufacturing industry, followed by analysis of software, AI, nanotechnology, and biotechnology R&D across industries. ​ Companies could report expenditure on the same R&D project in one, more than one, or no technology category. (Federal R&D funding initiatives in some of these areas are covered in the next section.)

Semiconductors or computer chips are critical components for applications in AI, quantum computing, autonomous or electric vehicles, and 5G communications (CRS 2020b, 2023c). Semiconductor production occurs along GVCs comprising R&D, engineering, and design; fabrication; and assembly, testing, and packing stages (CRS 2023c). Modular production and cost advantages in Asia facilitated the separation of design and production starting in the late 1970s and early 1980s with the emergence of chip foundries in Taiwan and other Southeast Asian locations performing contract manufacturing for design-only or fabless companies in the United States and other countries (Kuan and West 2023).

In the United States, semiconductor and other electronic components manufacturing is one of the most R&D-intensive industries, as highlighted earlier. In 2021, semiconductor business R&D increased 9.8% in current U.S. dollars to $47.4 billion after increasing 22.8% in 2020 ( Table RD-8 ). The share of semiconductor manufacturing within overall U.S. computer manufacturing R&D was 47% in 2021 after fluctuating around 40% since 2008.

U.S. R&D performed, by semiconductor manufacturing and other selected industries: 2008–21

Data are for companies with 10 or more domestic employees. Detail may not add to total because of rounding. Industry classification is based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Statistics are representative of companies located in the United States that performed or funded $50,000 or more of R&D and are not comparable with estimates published for years prior to 2018. For survey year 2008, industry classification was based on the 2002 NAICS. For survey years 2009–13, industry classification was based on the 2007 NAICS. For survey years 2014–19, industry classification was based on the 2012 NAICS. For survey years beginning in 2020, classification was based on the 2017 NAICS. Most statistics for years prior to 2020 have been revised since original publication. Revised statistics include adjustments based on information obtained after the original statistics were prepared. An estimate range may be displayed in place of a single estimate to avoid disclosing operations of individual companies.

National Center for Science and Engineering Statistics and Census Bureau, Business Enterprise Research and Development (BERD) Survey.

U.S. business R&D performance focuses on key areas of interest across a wide variety of industries ( Table RD-9 ; Table SRD-6 ). Software R&D, over half of which is performed in the information services industry, is an increasingly large technology area of U.S. business R&D expenditures. In 2021, software R&D accounted for $257.0 billion, or 43% of $602.5 billion. ​ This share was 32% in 2016 and 20% in 2006 (Moris 2019). In 2021, a separate 5% ($28.9 billion) was classified by businesses as R&D specifically devoted to AI applications. The professional, scientific, and technical services industry, which includes scientific R&D services, performed 19% of U.S. business R&D in AI in 2021. Biotechnology R&D accounted for 17% of total U.S. business R&D in 2021. Within R&D performed by pharmaceuticals and medicine manufacturing, 79% was classified as biotechnology. For its part, nanotechnology R&D accounted for 5% of total U.S. business R&D. Within semiconductor manufacturing R&D and semiconductor machinery manufacturing R&D, however, nanotechnology focus accounted for 50% and 43%, respectively.

U.S. business R&D performed, by industry and select technology focus: 2021

Data are for companies with 10 or more domestic employees. Detail may not add to total because of rounding. Industry classification is based on the dominant business code for domestic R&D performance, where available. For companies that did not report business codes, the classification used for sampling was assigned. Companies could report R&D in one, more than one, or no application area.

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USDA Rural Development Administrator Tours Landus Co-op Green Ammonia Facility in Boone and BioCentury Research Farm in Ames to Highlight Bioeconomy Innovations in Iowa

Landus Co-op in Boone County received a $4.8 million USDA grant in 2023

BOONE, Iowa, June 13, 2024 – U.S. Department of Agriculture (USDA) Rural Development Administrator for the Rural Business-Cooperative Service Betsy Dirksen Londrigan today toured a new facility at Landus Cooperative in Boone and participated in a ribbon-cutting ceremony to highlight the Agency’s investments in the bioeconomy. Landus Cooperative is the largest agricultural cooperative in Iowa, providing products and services to 7,000 farmer owners.

“The innovative technologies we have seen here today can help strengthen our nation’s food supply chain, create jobs, and foster new market opportunities,” said Administrator Dirksen Londrigan. “The Biden-Harris Administration is committed to increasing the supply of American-made fertilizer to our ag producers. Under the leadership of Secretary Vilsack, USDA is partnering with member-owned cooperatives to improve the landscape of options for farmers and ranchers who want to participate in climate-smart agriculture.”

In June of 2023, Landus Cooperative received a grant for $4,885,988 from USDA to offset the costs associated with building the greenfield fertilizer manufacturing and repackaging facility in Boone County. The facility will manufacture a foliar, slow-release nitrogen product to decrease in-ground nitrogen application rates and increase overall nitrogen efficiency in growing corn.

Administrator Dirksen Londrigan also toured the BioCentury Research Farm at Iowa State University in Ames. Accompanied by Theresa Greenfield , USDA Rural Development State Director in Iowa, she welcomed the media to a roundtable discussion with industry leaders to amplify the challenges and successes of bioeconomy innovations in Iowa.

USDA Fertilizer Production and Expansion Program

The USDA grant to Landus in 2023 was made through the Fertilizer Production Expansion Program ( FPEP ). This program provides grants to independent business owners to help them modernize equipment, adopt new technologies, build production plants, and more.

President Biden and USDA created FPEP to combat issues facing American farmers due to rising fertilizer prices, which more than doubled between 2021 and 2022 due to a variety of factors such as war in Ukraine and a lack of competition in the fertilizer industry. The Administration committed up to $900 million through the Commodity Credit Corporation for FPEP. Funding supports long-term investments that will strengthen supply chains, create new economic opportunities for American businesses, and support climate-smart innovation. FPEP is part of a broader effort to help producers boost production and address global food insecurity . It is also one of many ways the Administration is promoting fair competition, innovation and resiliency across food and agriculture while combating the climate crisis.

Contact USDA Rural Development

USDA Rural Development has 11 offices across the state to serve the 1.3 million Iowans living in rural communities and areas. Office locations include a state office in Des Moines, along with area offices in Albia, Atlantic, Humboldt, Indianola, Iowa Falls, Le Mars, Mount Pleasant, Storm Lake, Tipton and Waverly.

To learn more about investment resources for rural areas in Iowa, call (515) 284-4663 or visit www.rd.usda.gov/ia . If you’d like to subscribe to USDA Rural Development updates, visit our  GovDelivery subscriber page .

Under the Biden-Harris Administration, Rural Development provides loans and grants to help expand economic opportunities, create jobs, and improve the quality of life for millions of Americans in rural areas. This assistance supports infrastructure improvements; business development; housing; community facilities such as schools, public safety, and health care; and high-speed internet access in rural, Tribal, and high-poverty areas.

USDA is an equal opportunity provider, employer and lender.