Microsoft

Most business leaders I meet no longer need to be convinced that AI works. They’ve seen the pilots, the demos and the copilots. The question I hear in boardrooms now is different: How do we move from AI that impresses to AI that changes how the business actually runs?

That is the question Microsoft Frontier Company was built to answer, and to date, we have engaged on nearly 500 projects with over 200 customers across 28 countries.

Customers including 3M, Alaska Airlines, BMW, Core42, KT, Land O’Lakes, London Stock Exchange Group, Mastercard, McKesson, Northern Trust, Novo Nordisk, Publicis Groupe, Saint-Gobain, Siemens and Unilever are seeing real value from working side by side with our Forward Deployed Engineering teams.

More importantly, most customers where we have completed initial projects have returned for additional engagements, generating hundreds of follow-on projects. Customers don’t come back for engineers. They come back because the first engagement delivered something they could measure and they can see the next workflow worth redesigning.

The technology is ready. What slows AI down is everything around it. That means identifying where AI can deliver the greatest value, building in trust and governance, redesigning the operating models and helping people work in new ways. Technology and people have to move forward together.

Unilever is collaborating with Microsoft engineers to bring AI into its factories. On the factory floor, intelligent technology helps teams boost efficiency and deliver superior products to customers. In Pondicherry factory — Unilever’s largest Home Care Liquids factory in South Asia and a 2026 World Economic Forum Lighthouse site — a digital twin built on NVIDIA Omniverse and hosted on Azure simulates a real factory environment with integrated live data. This helps anticipate issues before they occur, monitors performance in real time and guides operators through the fixes and recommendations.

3M is working with our engineers to help its Global Business Services team automate customer order management. The companies are building an AI agent-driven workflow to assist with credit checks, delinquency assessments and system updates, with human-in-the-loop controls and a custom monitoring dashboard for real-time visibility and approvals. The solution is expected to significantly reduce manual effort, improve speed and consistency and accelerate cash flow — freeing staff for higher-value work while enabling scalable, auditable operations.

Publicis Groupe is co-building with our engineers to bring AI into the heart of how it works and delivers for clients. Microsoft engineers work side by side with Publicis teams to design and build agentic processes grounded in Microsoft IQ, so agents work with the business context, data and knowledge behind Publicis’ work. These processes use reinforcement learning, drawing on real-world outcomes and human feedback to keep improving how the agents perform over time. The approach combines very fast engineering with hands-on knowledge transfer, helping Publicis build lasting AI capability. The work builds on Publicis’ rollout of Microsoft 365 Copilot to all 114,000 of its employees.

Northern Trust needed to accelerate onboarding to its Asset Servicing Front Office Solutions platform, a process that took about two and a half weeks per client. Our engineers worked alongside Northern Trust’s experts to build an AI-powered onboarding agent within the Excel and SharePoint tools analysts already use. The result: data preparation time fell 83%, from two and a half weeks to three days, with 96.5% first-pass accuracy in testing. Analysts remain in control while gaining more time for higher-value client service.

KT, one of South Korea’s largest telecommunications providers, wanted to help network operators identify and respond to problems faster. Our engineers worked from KT’s offices to co-build AIO-Net, a continuously learning AI agent platform informed by KT’s operating procedures and veteran network experts. It now detects network issues in two to three minutes, down from more than ten, while keeping people in control of decisions and actions.

Saint-Gobain, the 360-year-old construction materials company, is using AI to help reduce the carbon footprint of cement. Working alongside its researchers, Microsoft engineers used Microsoft Discovery to gather 62,000 patents and 44,000 research abstracts into a knowledge base in hours, work Saint-Gobain estimates would have taken researchers a year. In about six weeks, the team identified new hypotheses for cement additives that scientists are now testing in the lab.

Across all of these customers, the pattern is the same:

1. Begin with the value, not the technology.
2. Redesign the process, not just the task.
3. Build in fast cycles with the customer’s teams.
4. Put security, governance and cost management in place from day one, so scaling doesn’t stall.

This is how organizations move from isolated AI projects to sustainable transformation: by focusing on measurable outcomes, redesigning work and building the capabilities to keep improving over time.

New research from IDC underscores why this approach matters. While 66% of organizations report substantial or transformative results from AI, only 48% are scaling or seeing consistent value across their organizations. At the same time, 71% expect AI spending to increase by at least 10% in the next year, putting even greater pressure on businesses to demonstrate measurable returns. The challenge is no longer simply adopting AI. It is reimagining how work gets done and turning that transformation into lasting business impact.

If we’re asking customers to redesign how they work around AI, we have to do the same. Microsoft Frontier Company was deliberately designed to be flat and fast, with one front door for customers, and as a reference for what an AI-native organization can look like. We are learning, adjusting and moving fast, the same way we ask our customers to.

The gap between AI that impresses and AI that delivers won’t close on its own. It closes workflow by workflow, with people who understand the business sitting next to people who can build.

Our success will be measured by more than what we deliver. The real test is what our customers can do after we’ve worked together: run, govern and improve their own AI systems, on their terms, with their intelligence protected. Their success is the clearest measure of ours.

Learn more at www.microsoft.com/en-us/frontier-company.

Rodrigo Kede Lima is the President of Microsoft Frontier Company and leads the operating business of 6,000 industry and engineering experts focused on delivering Frontier Transformation through AI for customers around the world.

NOTE

IDC Infographic, sponsored by Microsoft, The Real Business Value of AI, IDC #US54985026-IG, November 2026

The post From AI ambition to business impact appeared first on The Official Microsoft Blog.

Results are widespread. Value at scale is not, and that gap is what this year’s research from IDC, commissioned by Microsoft, set out to explain.

In our fourth year commissioning IDC, more than 5,200 senior IT and business leaders across 28 industries shared what it takes to maximize value as AI scales. The clearest lessons come from Frontier Firms, the group last year’s research introduced: the top 10% of organizations on IDC’s AI maturity scale.

• Frontier Firms lead on return. They report an average 4x return on AI versus 2.7x for slow adopters (the bottom 30% on IDC’s scale), and 39% see returns of 5x or more versus 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, versus 93% of Frontier Firms.
• Governance is in place, but confidence lags. 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, yet 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, IDC.

Organizations now use AI in 6.5 business areas on average, led by IT infrastructure, cybersecurity and customer service, with the fastest growth in industry-specific uses.

Employee experience, revenue growth, customer experience, innovation and risk reduction now rank nearly even as the places AI strengthens the business. Cost is an important consideration, but it is evaluated alongside accuracy, security, compliance and scalability.

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 is running well ahead of the plans to support it. The organizations pulling ahead are closing these gaps by design.

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 returns are leader-reported, and the research shows association rather than cause.

What they do differently comes down to five behaviors:

• They invest ahead of plan. 90% of Frontier Firms exceeded their planned AI spend.
• They deploy AI broadly, across most of the business, not just 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.

The lesson from Frontier Firms is one I believe every leader can act on: scaling AI value means managing models, agents, governance and measurement as one accountable system. Value and control aren’t a trade-off.

I see the same pattern with customers: the organizations scaling fastest treat AI as a change in how the business runs, not a technology rollout. “The skilling that a lot of companies miss is the business skilling,” says Eric Tabor, Chief Digital Officer, KOHLER. “We also focus on how to dissect processes and convert them into AI workflows.”

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

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%). Connect agents across processes, with human oversight where it matters.

Trust is building faster than confidence. Most leaders report high trust in their AI systems, yet fewer are highly confident 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, Conagra Brands. “We built a SteerCo to review risk factors, access and identity management.”

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. That’s where Frontier Firms are furthest ahead, and it’s where I’d urge every leader to start.

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, Black & Veatch.

IDC Executive Analyst Shari Lava will lead three sessions at Microsoft Ignite on Nov. 18, including The AI ROI Divide: Why Some Organizations Scale and Others Stall.

The gap that matters now isn’t between companies that use AI and those that don’t. It’s between those getting results in pockets and those turning AI into repeatable value across the business. There are three key ways leaders can step into this kind of transformation.

First, treat AI as business transformation, not a technology rollout. Frontier Firms connect AI to the outcomes that matter most, from growth to customer experience, and manage it as one accountable system. That is the heart of Frontier Transformation, and it’s the work we’re doing alongside our customers every day.

Second, start with your own function. In marketing, we’re applying these same disciplines to our own work: redesigning workflows around agents, setting clear guardrails and measuring what AI actually delivers. Every function can become its own proof point for the rest of the business.

Third, invest in people as much as in technology. Skills are the widest gap in this year’s research, and it’s the one I care about most. The organizations that pull ahead will be the ones where everyone, at every level, has the support to learn, experiment and grow with AI.

The next phase of AI won’t be defined by who uses it. It will be defined by who can scale its value and stay in control. This research gives every leader a benchmark for where they stand today and a clear view of what to do next.

Download the global infographic, The Real Business Value of AI, to see a summary of findings and benchmark your organization against Frontier Firms before your next AI investment review. Regional infographics are also available for Europe, Asia Pacific and the Americas. The full InfoBrief will follow.

Alysa Taylor is the Chief Marketing Officer for Commercial Cloud and AI at Microsoft, leading teams that enable digital and AI transformation for organizations of all sizes across the globe.

NOTE

IDC Infographic, sponsored by Microsoft, The Real Business Value of AI, IDC #US54985026-IG, November 2026

IDC Resource Map Document, Sponsored by Microsoft, Business Opportunity of AI Study, IDC Survey, July 2026, N=5204, Doc. #US54951426, November 2026

The post Scaling AI value without losing control: Lessons from Frontier Firms appeared first on The Official Microsoft Blog.

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