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

Learn more about IBM

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

Learn more about IBM

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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Signs It May Be Time to Reassess Your IT Infrastructure Strategy

Many organizations assumed their infrastructure strategy was settled. It had been implemented, optimized and built into long-term plans. Recent changes in technology and vendor consolidation are forcing a second look. Cloud outages and licensing changes have exposed how much dependency exists on a small number of platforms. As a result, organizations are reevaluating whether those decisions still hold up under current conditions ...

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

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Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

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