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SAS Study Examines Generative AI Adoption, Use, and Challenges

Leaders note lack of understanding, business strategy, sufficient data. and regulation preparedness as concerns; data privacy, security, and governance are primary challenges.

Note: TDWI's editors carefully choose vendor-issued press releases about new or interesting research and services. We have edited and/or condensed this release to highlight key study results or service features but make no claims as to the accuracy of the vendor's statements.

Generative AI is here to stay. Organizations around the world are enthusiastically using and investing in the technology. China is in the lead for generative AI use according to a recent global study SAS commissioned with Coleman Parkes Research Ltd. China business decision makers report that 83% of their organizations are using the technology. That’s more than in the United Kingdom (70%), the United States (65%), and Australia (63%). However, organizations in the U.S. are ahead in terms of maturity and having fully implemented generative AI technologies at 24% compared to China’s 19%, and the United Kingdom’s 11%.

What does this mean in terms of the global economic impact of AI and generative AI? In a 2023 report, McKinsey estimated generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across a variety of use cases. That’s comparable to the entire GDP of the United Kingdom in 2021. This impact would increase the overall influence of artificial intelligence by 15% to 40%.

Considering these economic implications, SAS and Coleman Parkes targeted 1,600 decision makers across key global markets. Respondents work in a range of industries, including banking, insurance, the public sector, life sciences, health care, telecommunications, manufacturing, retail, energy and utilities, and professional services. The smallest organizations surveyed employed a workforce of 500 - 999 people, and the largest employed more than 10,000.

“While China may lead in generative AI adoption rates, higher adoption doesn't necessarily equate to effective implementation or better returns,” said Stephen Saw, managing director at Coleman Parkes. “In fact, the U.S. nudges ahead in the race with 24% of organizations having fully implemented generative AI compared to 19% in China.”

Global Regions Charge Ahead with Generative AI

Highlights from the global survey results include indicators that signal different regions are on board and starting to adopt generative AI in meaningful ways but at different rates.

“With any new technology, organizations must navigate a discovery phase, separating hype from reality, to understand the complexity of real-world implementations in the enterprise. We have reached this moment with generative AI,” said Bryan Harris, executive vice president and CTO at SAS. “As we exit the hype cycle, it is now about purposefully implementing and delivering repeatable and trusted business results from generative AI.”

Where do regions rank in fully using and implementing generative AI into their organization’s processes?

  • North America: 20%
  • APAC: 10%
  • LATAM: 8%
  • Northern Europe: 7%
  • South West and Eastern Europe: 7%

Which regions have implemented generative AI use policies?

  • APAC: 71%
  • North America: 63%
  • South West and Eastern Europe: 60%
  • Northern Europe: 58%
  • LATAM: 52%

To what extent do those planning to invest in generative AI in the next financial year have a dedicated budget?

  • APAC: 94%
  • Northern Europe: 91%
  • South West and Eastern Europe: 91%
  • North America: 89%
  • LATAM: 84%

Note: North America comprise the United States and Canada; LATAM includes Brazil and Mexico; Northern Europe includes United Kingdom/Ireland, Sweden, Norway, Finland, Denmark; South West and Eastern Europe is France, Germany, Italy, Benelux, Spain, and Poland; and APAC encompasses Australia, China, Japan, and the United Arab Emirates/Saudi Arabia.

Industries and Functional Divisions Embrace Generative AI at Varying Rates

When split into industry segments, the data shows banking and insurance leading other industries in terms of incorporating generative AI AI into daily business operations across a variety of metrics. Highlights from those findings are below. 

How do specific industries rank in terms of fully implementing generative AI and fully implementing it into regular business processes?

  • Banking: 17%
  • Telco: 15%
  • Insurance: 11%
  • Life sciences: 11%
  • Professional services: 11%
  • Retail: 10%
  • Public sector: 9%
  • Health: 9%
  • Manufacturing: 7%
  • Energy and utilities: 6%

Which industries indicate they already use generative AI daily to some extent?

  • Telco: 29%
  • Retail: 27%
  • Banking: 23%
  • Professional services: 23%
  • Insurance: 22%
  • Life sciences: 19%
  • Health care: 17%
  • Energy and utilities: 17%
  • Manufacturing: 16%
  • Public sector: 13%

Which departments inside organizations are using or planning to use generative AI?

  • Sales: 86%
  • Marketing: 85%
  • IT: 81%
  • Finance: 75%
  • Production: 75%

Early Adopters Finding Plenty of Obstacles Using and Implementing Generative AI

At the top of the list of challenges organizations face in putting generative AI to routine use is the lack of a clear generative AI strategy.

Only 9% of leaders responding to the survey indicate they are extremely familiar with their organization’s adoption of generative AI. Of respondents whose organizations have fully implemented generative AI, only 25% say they are extremely familiar with their organization’s generative AI adoption strategy. Even those decision makers responsible for technology investment decisions aren’t familiar with AI, including those at organizations that are ahead of the adoption curve.

Nine out of 10 senior technology decision makers overall admit they don’t fully understand generative AI and its potential to affect business processes. At 45%, CIOs lead the way with executives who understand their organization’s AI adoption strategy. Only 36% of chief technology officers (CTOs) say they’re fully in the know.

Despite this understanding gap, most organizations (75%) say they have set aside budgets to invest in generative AI in the next financial year.

Other challenges organizations face include:

Data. As organizations adopt generative AI, they realize they have insufficient data to fine tune large language models (LLMs). They also realize - once they’re deep into deployment - they lack the appropriate tools to successfully implement AI. Organizations’ IT leaders are mostly concerned about data privacy (76%) and data security (75%).

Regulation. Only a tenth of organizations say they are fully prepared to comply with coming AI regulations. One-third of organizations that have fully implemented believe they can comply with regulations. Only 7% are providing a high level of training on generative AI governance, and only 5% have a reliable system to measure bias and privacy risks in LLMs.

Although there are obstacles, some early adopters have experienced meaningful benefits already: 89% report improved employee experience and satisfaction; 82% say they’re saving operational costs; and 82% state customer retention is higher.

Learn more in the full research report and an interactive data dashboard.

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