Across industries, we are witnessing a fundamental shift. Organizations are moving past AI experimentation and laying the foundations for Frontier Transformation—using AI to drive innovation and growth. This evolution depends on both intelligence and trust. Intelligence is the unique human and organizational data, context, and expertise that makes AI relevant and grounded in the realities of work. Trust ensures that AI can scale securely and responsibly. As organizations strengthen these foundations, a new question comes into focus: what does ‘return’ look like when AI is moving from experimentation into the fabric of daily work?
Traditionally, return on investment is a financial measure—growth weighed against cost. With AI, returns still include financial impact, but they increasingly span a broader set of outcomes. As organizations internalize this shift, they see a return on intelligence: compounding gains across cost savings, risk mitigation, performance, growth, and innovation. Momentum often begins with efficiency improvements, then builds into innovation and growth outcomes such as more personalized experiences, faster cycles, better decisions, and new products and services.
At Microsoft, we’re seeing this shift play out firsthand as we work with thousands of organizations across various industries. This post is the first in a series on industry Frontier Transformation. In the articles that follow, I’ll further explore the key dimensions of this shift—how AI is driving growth and reshaping innovation across industries.
According to an IDC study, 68% of organizations are already using AI. Many report measurable gains, with an average 2.3x return on investment. The most advanced among them, Frontier Firms, take AI further, embedding it across functions, roles, and processes to solve high-impact, industry-specific challenges.
Industry context makes AI investment outcomes visible and measurable. Many organizations are already seeing early wins from AI in their industry, such as inventory optimization in retail, fewer safety incidents in manufacturing, and less documentation burden in healthcare. But industry leaders are pushing further, turning those early gains into frontier outcomes across growth and innovation. Examples include personalized engagement and product discovery in retail, higher order-fulfillment rates in manufacturing, and new care pathways or improved patient retention in healthcare. Frontier leaders are proving that when AI is grounded in industry realities, early wins turn into compounding impact across the value chain.
In my role at Microsoft, I get to see this shift up close as we work with thousands of organizations across industries. What I’m seeing is that Frontier Transformation is already taking shape in the flow of work: processes are redesigned, decisions accelerate, and teams move with greater clarity and speed. From there, that momentum scales into new sources of growth and innovation across the business. Below are some great examples of what I see as customers driving true frontier outcomes.
UBS’s in-house legal team must find very specific information, like a clause or regulation, across a library of 26 million legal documents in multiple languages.
Finding specific knowledge in this vast repository was like finding a particular grain of sand on the beach.
Vlad Stoian, Product Owner for the Legal AI Assistant at UBS
Working with Microsoft Azure, UBS refined its standard process and demonstrated innovation by launching the Legal AI Assistant (LAIA) to help employees pinpoint phrases, clauses, and paragraphs using natural language and semantic similarity, rather than keyword matching. UBS employees can now locate information much more quickly and easily than they could with prior search tools.
Makers of Italian chocolate and gelato since 1878, Venchi shares Italian allegria (joy) worldwide. Venchi built a loyalty program to gather data on customers by working with Dynamics 365. From this foundation, Venchi is introducing AI-powered personalization through Copilot capabilities in the Store Commerce app.
In the future, imagine our sales assistant can see on the app, from Customer Insights, that the customer shopped one year ago for his wife’s birthday, and we know she is allergic to dairy. With Copilot, we can easily access data for all 350 chocolate recipes and figure out which are safe options for the customer in just a few seconds.
Fabio Tormen, Chief Information Officer at Venchi
Venchi saves 1,500 hours annually by automating fulfillment. More accurate accounting and inventory management decreased the cost of goods sold by 2% year over year. And easy sign-up added 800,000 customers to the loyalty program in its first year.
To optimize the performance of its vehicles in development, BMW engineers must access and analyze massive amounts of telemetry data from test vehicles, but only BMW IT specialists have been able to run queries, slowing test cycles and innovation. With Azure and Foundry Agent Service, BMW delivers insights 12 times faster and empowers its engineers to analyze telemetry directly. It also embeds AI-powered workflows into daily R&D, speeding design cycles, and reducing late-stage fixes.
When an engineer asks a question—“How many braking maneuvers were performed by the development vehicles in the last two days?”—the system responds within minutes, complete with charts and written explanations.
“With multi-agent AI, engineers don’t just get data, they get insights they can act on immediately,” says Christof Gebhart, Manager of Advanced Vehicle Measurement Technology at BMW. “Ultimately, the steps of data extraction and pattern recognition can be performed directly in a single step, and in natural language.”
Clinicians at Cooper University Health Care were experiencing significant burnout due to after-hours documentation. The leadership team sought a solution to reduce administrative burden and restore joy in practice. Cooper implemented Microsoft Dragon Copilot, an AI assistant for clinical workflow and integrated with their Epic EHR, that streamlines documentation, automates tasks, and surfaces information—boosting efficiency, satisfaction, and patient care.
Clinicians at Cooper report saving more than four minutes on documentation time per patient, experiencing less burnout, and engaging more meaningfully with patients. Their notes are more comprehensive, communication is improved, and patient satisfaction is rising. Through ambient capture of the patient visits, clinicians can maintain eye contact and engage more meaningfully with patients.
Patients are instantly noticing their clinicians are looking at them again, making that eye contact. We’ve had several patients actually remark that, basically, hey, wow, you’re not typing today. That’s the power of AI. It gives eye contact back to medicine the way it was supposed to be practiced.
Snehal Gandhi, MD, VP and Chief Medical Information Officer (CMIO) at Cooper
Aon’s engineering and data teams set out to build a secure, enterprise-grade AI platform that could operate across its solution lines. The result was AonGPT, a generative AI assistant developed entirely on Microsoft Azure.
Over 62,000 users now have access to AonGPT. About 31,000 of those are monthly active users, with more than 6.4 million messages exchanged so far.
Amit Gawali, Head of Engineering at Aon
During the California wildfires, Aon’s catastrophe modeling team partnered with a satellite imagery provider to receive multiple visual updates each day. Using AonGPT, the team wrote code to connect those images to Aon’s proprietary data, producing near real-time insights that helped clients assess damage and plan responses.
Frontier Transformation is already taking shape across industries. The question now isn’t whether AI delivers impact—but where to start.
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