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CEOs Double Down on AI While Navigating Enterprise Hurdles

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study.

The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale.

Image
IBM

Image Source: IBM

According to the findings, 68% of surveyed CEOs identify integrated enterprise-wide data architecture as critical for cross-functional collaboration, and 72% view their organization's proprietary data as key to unlocking the value of generative AI. However, the research indicates organizations may be struggling to cultivate an effective data environment: half (50%) of respondents acknowledge that the pace of recent investments has left their organization with disconnected, piecemeal technology.

In the foreword of the study, IBM Vice Chairman Gary Cohn writes, "As AI adoption accelerates creating greater efficiency, and productivity gains, the ultimate pay-off will only come to CEOs with the courage to embrace risk as opportunity. Meaning, focusing on what you can control, especially when there is so much you can't. When the business environment is uncertain, using AI and your enterprise data to identify where you have leverage is a competitive advantage. At this point, leaders who aren't leveraging AI and their own data to move forward are making a conscious business decision not to compete."

"CEOs are balancing the pressures of short-term ROI and investing in long-term innovation when it comes to adopting AI," said Mohamad Ali, Senior Vice President and Head of IBM Consulting. "But we know that organizations that keep innovating, especially during periods of uncertainty, will emerge stronger and be better positioned to capitalize on new opportunities."

Other key findings include:

CEOs face competing pressures of short-term ROI and long-term innovation

  • Surveyed CEOs report that only 25% of AI initiatives have delivered expected ROI over the last few years, and only 16% have scaled enterprise wide. 
    To accelerate progress, two-thirds (65%) of CEO respondents say their organization is leaning into AI use cases based on ROI, with 68% reporting that their organization has clear metrics to measure innovation ROI effectively.
  • Just over half (52%) of CEO respondents say their organization is realizing value from generative AI investments beyond cost reduction.
  • 64% of CEOs surveyed acknowledge that the risk of falling behind drives investment in some technologies before they have a clear understanding of the value they bring to the organization, but only 37% say it's better to be "fast and wrong" than "right and slow" when it comes to technology adoption.
  • 59% of surveyed CEOs admit their organization struggles to balance funding for existing operations and investment in innovation when unexpected change occurs, as 67% say more budget flexibility is needed to capitalize on digital opportunities that drive long-term growth and innovation.
  • By 2027, 85% of surveyed CEOs expect their investments in scaled AI efficiency and cost savings to have returned a positive ROI, while 77% expect to see a positive return from their investments in scaled AI growth and expansion.

CEOs see strategic leadership and specialized talent as essential to unlocking AI value, amid expertise and skills gaps

  • 69% of CEO respondents say their organization's success is directly tied to maintaining a broad group of leaders with a deep understanding of strategy and the authority to make critical decisions.
  • 67% of CEOs surveyed say that differentiation depends on having the right expertise in the right positions with the right incentives.
  • CEOs cite lack of collaboration across organizational silos, aversion to risk and disruption, and lack of expertise and knowledge as top barriers to innovation in their organization.
  • Surveyed CEOs say roughly one-third (31%) of the workforce will require retraining and/or reskilling over the next three years, while 65% say their organization will use automation to address skill gaps.
  • 54% of CEO respondents say they are hiring for roles related to AI that did not exist a year ago.

Methodology: The IBM Institute for Business Value, in cooperation with Oxford Economics, surveyed 2,000 CEOs from 33 countries and 24 industries between February and April 2025.

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Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...

CEOs Double Down on AI While Navigating Enterprise Hurdles

CEOs are committed to advancing AI solutions across their organization even as they face challenges from accelerating technology adoption, according to the IBM CEO Study.

The survey revealed that executive respondents expect the growth rate of AI investments to more than double in the next two years, and 61% confirm they are actively adopting AI agents today and preparing to implement them at scale.

Image
IBM

Image Source: IBM

According to the findings, 68% of surveyed CEOs identify integrated enterprise-wide data architecture as critical for cross-functional collaboration, and 72% view their organization's proprietary data as key to unlocking the value of generative AI. However, the research indicates organizations may be struggling to cultivate an effective data environment: half (50%) of respondents acknowledge that the pace of recent investments has left their organization with disconnected, piecemeal technology.

In the foreword of the study, IBM Vice Chairman Gary Cohn writes, "As AI adoption accelerates creating greater efficiency, and productivity gains, the ultimate pay-off will only come to CEOs with the courage to embrace risk as opportunity. Meaning, focusing on what you can control, especially when there is so much you can't. When the business environment is uncertain, using AI and your enterprise data to identify where you have leverage is a competitive advantage. At this point, leaders who aren't leveraging AI and their own data to move forward are making a conscious business decision not to compete."

"CEOs are balancing the pressures of short-term ROI and investing in long-term innovation when it comes to adopting AI," said Mohamad Ali, Senior Vice President and Head of IBM Consulting. "But we know that organizations that keep innovating, especially during periods of uncertainty, will emerge stronger and be better positioned to capitalize on new opportunities."

Other key findings include:

CEOs face competing pressures of short-term ROI and long-term innovation

  • Surveyed CEOs report that only 25% of AI initiatives have delivered expected ROI over the last few years, and only 16% have scaled enterprise wide. 
    To accelerate progress, two-thirds (65%) of CEO respondents say their organization is leaning into AI use cases based on ROI, with 68% reporting that their organization has clear metrics to measure innovation ROI effectively.
  • Just over half (52%) of CEO respondents say their organization is realizing value from generative AI investments beyond cost reduction.
  • 64% of CEOs surveyed acknowledge that the risk of falling behind drives investment in some technologies before they have a clear understanding of the value they bring to the organization, but only 37% say it's better to be "fast and wrong" than "right and slow" when it comes to technology adoption.
  • 59% of surveyed CEOs admit their organization struggles to balance funding for existing operations and investment in innovation when unexpected change occurs, as 67% say more budget flexibility is needed to capitalize on digital opportunities that drive long-term growth and innovation.
  • By 2027, 85% of surveyed CEOs expect their investments in scaled AI efficiency and cost savings to have returned a positive ROI, while 77% expect to see a positive return from their investments in scaled AI growth and expansion.

CEOs see strategic leadership and specialized talent as essential to unlocking AI value, amid expertise and skills gaps

  • 69% of CEO respondents say their organization's success is directly tied to maintaining a broad group of leaders with a deep understanding of strategy and the authority to make critical decisions.
  • 67% of CEOs surveyed say that differentiation depends on having the right expertise in the right positions with the right incentives.
  • CEOs cite lack of collaboration across organizational silos, aversion to risk and disruption, and lack of expertise and knowledge as top barriers to innovation in their organization.
  • Surveyed CEOs say roughly one-third (31%) of the workforce will require retraining and/or reskilling over the next three years, while 65% say their organization will use automation to address skill gaps.
  • 54% of CEO respondents say they are hiring for roles related to AI that did not exist a year ago.

Methodology: The IBM Institute for Business Value, in cooperation with Oxford Economics, surveyed 2,000 CEOs from 33 countries and 24 industries between February and April 2025.

Hot Topics

The Latest

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

AI workloads require an enormous amount of computing power ... What's also becoming abundantly clear is just how quickly AI's computing needs are leading to enterprise systems failure. According to Cockroach Labs' State of AI Infrastructure 2026 report, enterprise systems are much closer to failure than their organizations realize. The report ... suggests AI scale could cause widespread failures in as little as one year — making it a clear risk for business performance and reliability.

The quietest week your engineering team has ever had might also be its best. No alarms going off. No escalations. No frantic Teams or Slack threads at 2 a.m. Everything humming along exactly as it should. And somewhere in a leadership meeting, someone looks at the metrics dashboard, sees a flat line of incidents and says: "Seems like things are pretty calm over there. Do we really need all those people?" ... I've spent many years in engineering, and this pattern keeps repeating ...