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Fueling the AI transformation:

Four key actions powering widespread value from AI, right now

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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.

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94%
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.

29%
more respondents
surveyed classify as
underachievers this year1
50%
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.

Four actions:
Scaling outcomes
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.

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Culture
Operations
Technology
Industry

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.

say AI increases job satisfaction and enhances performance say AI creates fear or concern 82% 47% Cultural optimism Cultural optimism 82% say AI is a concern say AI increases job satisfaction and enhances performance 47%

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.

high-outcome organizations high-outcome organizations low-outcome organizations low-outcome organizations Tracking ROI of applications Documenting AI model life cycle 86% 84% 71% 66% Tracking ROI of applications 86% low outcome organizations high outcome organizations 71% low outcome organizations high outcome organizations Documenting AI model life cycle 84% 66%

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.

build AI solutions in-house buy AI as a product, service or packaged solution 65% 35% Buy vs. build build AI solutions in-house buy AI as a product, service or packaged solution 65% 35% Buy vs. build

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.

44% Cloud pricing optimization 41% Voice assistants, chatbots, and conversational AI 41% Predictive maintenance Top uses across industries Cloud pricing optimization 44% 41% Voice assistants, chatbots, and conversational AI 41% Predictive maintenance Top uses across industries

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.

Pathseekers

Low deployed /
High achieving

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.

Transformers

High deployed /
High achieving

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.

Starters

Low deployed /
Low achieving

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.

Underachievers

High deployed /
Low achieving

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.

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State of AI in the Enterprise, 5th edition

Learn more about the top use cases and outcomes in your industry.

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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:

graph showing deployment outcome percentages

Transformers (27%)
Transforming but not fully transformed, this group has identified and largely adopted leading practices associated with the strongest AI outcomes.

Pathseekers (24%)
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.

Underachievers (22%)
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.

Starters (28%)
Getting a late start in building AI capabilities seems to characterize this group; they are the least likely to demonstrate leading practice behaviors.2

Endnotes

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