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AI Appreciation Day Feature: Businesses View AI Agents as Essential, Not Just Experimental

A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation.

The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation.

As the pace of digital transformation accelerates, enterprises can turn to AI agents as the next evolution of intelligent automation. 83% of respondents say they expect AI agents to improve process efficiency and output by 2026, and 71% believe agents will autonomously adapt to changing workflows.

"We see more clients looking at agentic AI as the key to help them move past incremental productivity gains and actually gain business value from AI, especially when applied in their core processes like supply chain and HR," said Francesco Brenna, VP & Senior Partner, AI Integration Services, IBM Consulting. "This isn't about plugging an agent into an existing process and hoping for the best. It means re-architecting how the process is executed, redesigning the user experience, orchestrating agents end-to-end, and integrating the right data to provide context, memory, and intelligence throughout."

These are the top five benefits of agentic AI systems that are driving adoption across industries, according to the report:

1. Over two-thirds (69%) of executives surveyed say "improved decision-making" is the number one benefit of agentic AI systems.

2. 67% of those surveyed cite "cost reduction through automation" as a benefit.

3. Almost half (47%) of leaders surveyed indicate "competitive advantage" as a top benefit of agentic AI systems.

4. "Scaled employee experience" is cited as a top benefit, according to 44% of executives surveyed.

5. 42% of those surveyed indicate that "improved talent retention" is another major benefit to implementing agentic AI systems.

Though benefits were cited among those surveyed, concerns with agentic AI adoption are still present among leaders. Those surveyed indicate that concerns around data (49%), trust issues (46%) and skills shortages (42%) remain barriers to adoption for their organizations.

Other key findings include:

  • Executives surveyed indicate that AI investment has grown to about 12% of IT spend in 2024. By 2026, the participants indicated they expect this number is to increase by over two-thirds to account for 20% of IT spend.
  • Respondents say that 64% of AI budgets are now spent on core business functions, underscoring AI's move from an experimentation phase to an important part of business strategy.
  • The proportion of surveyed organizations relying on an ad hoc approach to AI has declined from a reported 19% last year to just 6% today, indicating increased confidence and commitment to AI-driven transformation.
  • One in four companies surveyed are already using an "AI-first" approach. Among those with an AI-first approach, respondents say they attribute more than half of their revenue growth (52%) and operating margin improvements (54%) to AI initiatives in the last 12 months

Methodology: The IBM Institute for Business Value study, in partnership with Oxford Economics, draws insights from two 2025 surveys: the "AI at the core survey" (2,500 executives across 18 industries and 19 regions) and the "Agentic AI pulse survey" (400 C-suite executives across 15 roles, 11 industries, and 6 countries). Survey questions explored AI's current role in business operations, including its impact on revenue, profitability, productivity, and resource efficiency. Further analysis was conducted to identify key relationships, trends and associations within the dataset.

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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 ...

AI Appreciation Day Feature: Businesses View AI Agents as Essential, Not Just Experimental

A new study by the IBM Institute for Business Value reveals that enterprises are expected to significantly scale AI-enabled workflows, many driven by agentic AI, relying on them for improved decision making and automation.

The AI Projects to Profits study revealed that respondents expect AI-enabled workflows to grow from 3% today to 25% by the end of 2025. With 70% of surveyed executives indicating that agentic AI is important to their organization's future, the research suggests that many organizations are actively encouraging experimentation.

As the pace of digital transformation accelerates, enterprises can turn to AI agents as the next evolution of intelligent automation. 83% of respondents say they expect AI agents to improve process efficiency and output by 2026, and 71% believe agents will autonomously adapt to changing workflows.

"We see more clients looking at agentic AI as the key to help them move past incremental productivity gains and actually gain business value from AI, especially when applied in their core processes like supply chain and HR," said Francesco Brenna, VP & Senior Partner, AI Integration Services, IBM Consulting. "This isn't about plugging an agent into an existing process and hoping for the best. It means re-architecting how the process is executed, redesigning the user experience, orchestrating agents end-to-end, and integrating the right data to provide context, memory, and intelligence throughout."

These are the top five benefits of agentic AI systems that are driving adoption across industries, according to the report:

1. Over two-thirds (69%) of executives surveyed say "improved decision-making" is the number one benefit of agentic AI systems.

2. 67% of those surveyed cite "cost reduction through automation" as a benefit.

3. Almost half (47%) of leaders surveyed indicate "competitive advantage" as a top benefit of agentic AI systems.

4. "Scaled employee experience" is cited as a top benefit, according to 44% of executives surveyed.

5. 42% of those surveyed indicate that "improved talent retention" is another major benefit to implementing agentic AI systems.

Though benefits were cited among those surveyed, concerns with agentic AI adoption are still present among leaders. Those surveyed indicate that concerns around data (49%), trust issues (46%) and skills shortages (42%) remain barriers to adoption for their organizations.

Other key findings include:

  • Executives surveyed indicate that AI investment has grown to about 12% of IT spend in 2024. By 2026, the participants indicated they expect this number is to increase by over two-thirds to account for 20% of IT spend.
  • Respondents say that 64% of AI budgets are now spent on core business functions, underscoring AI's move from an experimentation phase to an important part of business strategy.
  • The proportion of surveyed organizations relying on an ad hoc approach to AI has declined from a reported 19% last year to just 6% today, indicating increased confidence and commitment to AI-driven transformation.
  • One in four companies surveyed are already using an "AI-first" approach. Among those with an AI-first approach, respondents say they attribute more than half of their revenue growth (52%) and operating margin improvements (54%) to AI initiatives in the last 12 months

Methodology: The IBM Institute for Business Value study, in partnership with Oxford Economics, draws insights from two 2025 surveys: the "AI at the core survey" (2,500 executives across 18 industries and 19 regions) and the "Agentic AI pulse survey" (400 C-suite executives across 15 roles, 11 industries, and 6 countries). Survey questions explored AI's current role in business operations, including its impact on revenue, profitability, productivity, and resource efficiency. Further analysis was conducted to identify key relationships, trends and associations within the dataset.

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 ...