AI is generating results across most organizations, but turning those results into value at scale remains uncommon. Closing that gap is what this year's research from IDC, commissioned by Microsoft — the fourth consecutive year of the study — set out to explain. More than 5,200 senior IT and business leaders across 28 industries described what it takes to maximize value as AI scales. The clearest lessons come from the Frontier Firms, the group introduced in last year's research: the top 10% of organizations on IDC's AI maturity scale.

What the research found

  • Frontier Firms lead on returns. They report an average 4x return on AI, versus 2.7x for slow adopters (the bottom 30% on IDC's scale), and 39% report returns of 5x or more, versus just 4% of slow adopters.
  • Measurement is the next test. Only 61% of organizations are highly confident they can measure AI's business value and ROI, compared with 93% of Frontier Firms.
  • Governance exists, but confidence trails. 65% of organizations have an established process to govern agents, yet only 60% have high confidence in addressing AI security and governance risks.
  • Workforce readiness is uneven. 67% say their workforce reports high trust in AI, while only 20% have a comprehensive, organization-wide skilling strategy.

"Organizations that scale AI value aren't simply spending more. They manage AI investments, models, agents, governance and skills as one discipline, and that is what turns early results into value they can repeat across the business," says Kathy Lange, Research Director, AI, Data and Automation Software at IDC.

AI value reaches beyond cost savings

On average, organizations now use AI across 6.5 business areas, led by IT infrastructure, cybersecurity and customer service, with industry-specific uses growing fastest. Employee experience, revenue growth, customer experience, innovation and risk reduction now rank nearly even as the places where AI strengthens the business. Cost remains an important consideration, but it is evaluated alongside accuracy, security, compliance and scalability.

Where scaling stalls

66% of leaders say AI has delivered substantial or transformative results, but only 48% are scaling that value across multiple business units. Agents follow the same pattern: 36% of organizations are still piloting them. The widest gap is people — workforce confidence in AI is running well ahead of the plans meant to support it. The organizations pulling ahead are closing those gaps by design.

Inside the Frontier Firm

IDC's maturity scale weighs spending discipline, breadth of use, agent adoption, skills, confidence in managing AI risk and outcomes. On agents specifically, Frontier Firms report a 3.8x return versus 3.0x for slow adopters. These figures are leader-reported, and the research shows association rather than causation.

Five behaviors set them apart:

  • They invest ahead of plan: 90% exceeded their planned AI spend.
  • They deploy AI broadly, across most of the business rather than a few teams.
  • They put agents to work: 96% are rolling out agents at scale, and 53% orchestrate them across workflows.
  • They govern so they can scale: 87% have a process to govern and oversee agents, versus 51% of slow adopters.
  • They build skills everywhere: 80% have an organization-wide AI skilling strategy, versus 3% of slow adopters.

At Bristol Myers Squibb, the RABIT agent "connects the dots across meetings, helping teams make better decisions while reducing administrative follow-up work," says Lakshman Yerramalla, Director of Portfolio Governance and Delivery for Global Manufacturing Operations. Project managers who use it save up to an estimated 20% of their time.

Four disciplines for scaling AI value

The lesson from Frontier Firms, the article argues, is that scaling AI value means managing models, agents, governance and measurement as one accountable system — value and control are not a trade-off. Eric Tabor, Chief Digital Officer at KOHLER, sees the same pattern: "The skilling that a lot of companies miss is the business skilling. We also focus on how to dissect processes and convert them into AI workflows."

Unlock model choice

Trust and security, leading-edge technology and model performance tie as the top criteria for choosing an AI platform, at 44% each. The guidance is to match the right model to each job, balancing cost with protection of intellectual property and data.

Redesign workflows around agents

Agents create the most value when work is redesigned around them. The bigger opportunity is what they free people to do more of: leaders point to creativity, innovation and business reinvention (41%), customer-facing work and relationships (39%), and analytical work and data interpretation (39%). Agents should be connected across processes, with human oversight where it matters.

Govern decisions and actions

Trust is building faster than confidence: most leaders report high trust in their AI systems, yet fewer are highly confident about addressing security and governance risks. As agents take on more of the work, trust has to be earned continuously, and governance has to reach beyond models to keep people in control of what agents decide and do.

Conagra Brands made governance its starting point. "Laying the foundation of proper governance, observability and responsible AI was our first step to engaging our employees with AI," says Tracy Schaefer, Chief Information Officer at Conagra Brands. "We built a SteerCo to review risk factors, access and identity management."

Measure and optimize value

The recommendation is to connect investment and performance data to business outcomes, compare how processes perform before and after AI, and direct spending where it delivers the most value. With 71% of organizations expecting AI budgets to rise 10% or more in the next 12 months, this discipline will separate leaders from the rest.

Underneath all four disciplines are people: none of it scales without an organization-wide plan to build AI skills, and that is where Frontier Firms are furthest ahead. Black & Veatch reached 98% adoption of Microsoft 365 Copilot through deliberate change management, including early-adopter cohorts that surfaced real business use cases. "Our goal is to be a Frontier Firm. The intention is not to stop at pure adoption," says Mike Adams, Chief Digital Technology Officer at Black & Veatch.

Microsoft Ignite

IDC Executive Analyst Shari Lava will lead three sessions at Microsoft Ignite on 18.11.2026, including "The AI ROI Divide: Why Some Organizations Scale and Others Stall."

What it means for business

According to Taylor, the gap that matters now is not between companies that use AI and those that do not, but between those getting results in pockets and those turning AI into repeatable value across the business. She outlines three steps: treat AI as business transformation rather than a technology rollout — the core of what Microsoft calls Frontier Transformation; start with your own function (in marketing, she says, her team is applying these same disciplines internally); and invest in people as much as in technology, since skills are the widest gap in this year's research.

The next phase of AI, the article concludes, will be defined not by who uses it, but by who can scale its value while staying in control.

Further resources

Microsoft is offering a downloadable global infographic, "The Real Business Value of AI," summarizing the findings; regional editions are available for Europe, Asia Pacific and the Americas, and the full InfoBrief is to follow.

The article was written by Alysa Taylor, Chief Marketing Officer for Commercial Cloud and AI at Microsoft. Cited sources are the Microsoft-sponsored IDC infographic (IDC #US54985026-IG, November 2026) and the Business Opportunity of AI Study survey (July 2026, N=5,204; Doc. #US54951426, November 2026).