Explore key findings
State of AI in the Enterprise, 5th edition
If you’re like most of the 2,620 global business leaders we surveyed, you know AI is vital. In fact, most say it’s essential to driving outcomes, from cost reduction to entering new markets. But understanding AI’s value and achieving it are two different things. Our report takes a cross-industry look at AI deployments and outcomes achieved to reveal key actions every organization should be taking to gain widespread value from AI.
of business leaders surveyed say AI is critical to success
Business leaders believe AI is critical to success over the next five years.
The AI market continues to mature rapidly, and organizations are gaining competency. As a result, full-scale deployment is increasing across all AI technologies, with high-outcome organizations reporting—such as new market entry and product innovation—that go beyond cost reduction to significant revenue generation.
As companies move quickly to adopt AI, outcomes lag deployments
While AI deployments are up significantly this year—79% of respondents say they've fully deployed three or more types of AI compared to just 62% in 2021—many companies aren't achieving the value they anticipated as witnessed in the 29% increase in the share of respondents who identify as underachievers this year as compared to the last year.
more respondents surveyed classify as underachievers this year1
of surveyed leaders cite top AI scaling challenge as:
- Managing AI-related risk
- Lack of executive commitment
- Lack of maintenance and post-launch support
Challenges like managing risk and executive commitment remain widespread.
Challenges still exist, particularly to achieving enterprise value. Managing AI-related risks, lacking executive commitment, and maintaining or supporting initiatives after launch were cited by half of surveyed respondents as top challenges to scaling AI across departments or businesses within various companies.
and value from AI.
Regardless of sector, most high-outcome organizations focused on four key actions: building culture, reimagining operations, matching technology to their AI experience, and choosing use cases proven to accelerate value in their industries.Download full report
The workforce is increasingly optimistic about AI with most respondents indicating their employees believe AI will enhance their performance. High-outcome organizations fuel that optimism by promoting cross-organizational collaboration and establishing AI centers of excellence.
Organizations are redesigning operations to better enable AI. In fact, high-outcome organizations were more likely to have adopted operational leading practices—such as tracking application ROI and documenting AI model life cycle publication strategy—than low-outcome organizations.
Planning technology and talent investments in tandem could be key to achieving value early. Companies at all levels strike that balance by buying AI as a product or service rather than building in-house, giving them the talent and technology needed as they train people and advance their tech.
Every company’s unique journey is predicated on identifying key value drivers. Across industries, AI is proving valuable in a wide range of applications, from pricing in IT to improved customer service to predictive maintenance in supply and distribution.
"Culture is still a big barrier, and so, too, is the way that the organization used to operate.”
“To be competitive, you have to challenge operations and processes.”
— Group manager
Tech and talent
“Upskilling existing workforce, in my view, is critical.”
—AI/ML head of strategy and operations
Proven by industry: Make your case for speed to value
Whether you are starting out or highly successful in terms of deployments made and value gained, choosing the right use case can help create early wins, speed widespread adoption, and bolster support from leadership.
Select a maturity level to explore top use cases by industry.
Low deployed /
As a group, Pathseekers are achieving widespread value from relatively few deployments of AI, indicating they’re adopting what’s proven and aligning to leading practices. Common successes include cost reduction and efficiency. Next step? Generating revenue and driving innovation.
High deployed /
Transformers have largely adopted the practices associated with the strongest AI outcomes and are in the process of transforming and creating value across their enterprise. Many have moved beyond cost reduction and are now innovating products and generating revenue from AI.
Low deployed /
Starters may be behind in terms of deployments and outcomes, but each has an opportunity to rapidly advance their learning curve by adopting leading practices and leaning into packaged solutions proven in their industries and based on drivers of value in their companies.
High deployed /
These large companies are rapidly deploying AI; however, they haven't adopted the leading practices required to generate value and impact in all cases. Such companies should focus on what drives value for their businesses and look to packaged solutions and services to gain wins.
About this report
For the 2022 survey, we used the same foundational analysis model as for State of AI in the enterprise, 4th edition, with slight adjustments to reflect increasing AI activity in the market. The threshold at which firms are considered “Starters” consequently has increased, with the threshold shifting from zero deployed (beyond pilot) applications in the fourth edition survey to up to four deployed applications. Across the 2,620 respondents, the breakdown of performance was as follows:
Transforming but not fully transformed, this group has identified and largely adopted leading practices associated with the strongest AI outcomes.
This group has adopted capabilities and behaviors that are leading to success in fewer initiatives. In other words, they are making the right moves but have not scaled multiple forms of AI to the same degree as Transformers.
A significant amount of development and deployment activity characterizes this group; however, they haven't adopted enough leading practices to help them effectively achieve more meaningful outcomes.
Getting a late start in building AI capabilities seems to characterize this group; they are the least likely to demonstrate leading practice behaviors.2
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Few organizations are completely AI-fueled today, but a significant and growing percentage are displaying the behaviors that will get them there. What can we learn from the practices of leading-edge organizations?
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