Deepfakes threaten trust in news, elections, brands and everyday interactions, leading us to question what’s real. Determining what’s authentic or manipulated is the subject of Microsoft’s “Media Integrity and Authentication: Status, Directions, and Futures” report, published today. The study evaluates today’s authentication methods to better understand their limitations, explore potential ways to strengthen them and help people make informed decisions about the online content they consume.

The authors conclude that no single solution can prevent digital deception on its own. Methods such as provenance, watermarking and digital fingerprinting can offer useful information like who created the content, what tools were used and whether it has been altered.

People can be deceived by media if they lack information like its origin and history, or if its information is low-quality or misleading. The goal of the report is to provide a roadmap to deliver more high-assurance provenance information the public can rely on, according to Jessica Young, director of science and technology policy in the Office of the Chief Scientific Officer at Microsoft.

Helping people recognize higher-quality content indicators is increasingly important as deepfakes become more disruptive and provenance legislation in various countries, including the U.S., introduce even more ways to help people authenticate content later this year.

Media provenance has been evolving for years, with Microsoft pioneering the technology in 2019 and cofounding the Coalition for Content Provenance and Authenticity (C2PA) in 2021 to standardize media authenticity.

Young, co-chair of the study, explains more about what it all means:

What prompted the study?

“The motivation was two-fold,” Young says. “The first is the recognition of the moment we’re in right now. We know generative AI capabilities are becoming increasingly powerful. It’s becoming more challenging to distinguish between authentic content — like content that was captured by a camera versus sophisticated deepfakes — and as a result, there’s a huge uptick right now in interests and requirements to use those technologies that exist to disclose and verify if content was generated or manipulated by AI.

“The moment has been building, and we have a desire to help ensure that these technologies ultimately drive more benefit than harm, based on how they’re used and understood.”

Young adds that the paper is meant to inform the greater media integrity and authentication ecosystem, including creators, technologists, policymakers and others to understand what is and isn’t possible currently and how we can build on it for the future.

What did the study accomplish, and what did you learn?

The report outlines a path to increase confidence in the authenticity of media. The authors propose a direction they refer to as “high-confidence authentication” to mitigate the weaknesses of various media integrity methods.

Linking C2PA provenance to an imperceptible watermark can bring relatively high confidence about media’s provenance, she says.

She notes the report has a lot of caveats too, such as how provenance from traditional offline devices like cameras, which often lack critical security features, can be less trustworthy because it’s easier to alter.

It isn’t possible to prevent every attack or stop certain platforms from stripping provenance signals, so the challenge, Young says, “is figuring out how to surface the most reliable indicators with strong security built in — and, when necessary, reinforce them with additional methods that allow recovery or support manual digital-forensics work.”

How is this study different from others?

Young says their study investigated two “underexplored” lines of thought for the three methods of verification. They define the first as sociotechnical attacks, where provenance information or the media itself could be manipulated to make authentic content appear synthetic or fake content seem real during the validation process.

“Imagine you see an authentic image of a global sporting event with 80% of the crowd cheering for the home team,” she says. “The away team engages in an online argument claiming, ‘Hey, no, that’s all a fake crowd.’ Someone could make one small, insignificant edit to a person in the corner of the picture and current methods would deem it AI generated — even if the crowd size was real. These methods that are supposed to support authenticity are now reinforcing a fake narrative, instead of the real one.

“So, knowing how different validators work, even through really subtle modifications, you could manipulate the results the public would see to try to deceive them about content,” she says. The second key topic builds on the C2PA’s work to make content credentials more durable, while also addressing reliability. This is where the research is especially novel, Young says. “We looked at how provenance information can be added and maintained across different environments — from high-security systems to less secure, offline devices — and what that means for reliability.”

Why is verifying digital media so difficult?

Authenticating media is complex because there’s not a one-size-fits-all solution, Young says.

“You have different formats that have different limitations or trade-offs for the signals they can contain,” she explains. “Whether it’s images, audio, video — not to mention text, which has a whole different array of challenges — and how strong the solutions can be applied there.”

Young says there are different requirements and opinions about what level of transparency is appropriate as well. In some cases, users might not want any of their personal information included in the digital provenance of a piece of media, while in others, creators or artists might want attribution and to opt-in for having their information included.

“So, you have different requirements or even considerations about what goes into that provenance information,” she says. “And then, similar to the field of security, no solution is foolproof. So, all the methods are complementary, but each has inherent limitations.”

Where do we go from here?

Young says that as AI-made or edited content becomes more commonplace, the use of secure provenance of authentic content is becoming increasingly important. Publishers, public figures, governments and businesses have good reason to certify the authenticity of the content they share. If a news outlet shoots photos of an event, for example, tying secure provenance information to those images can help show their audience the content is reliable.

“Government bodies also have an interest in the public knowing that their formal documents or media are reliable information about public interest matters,” Young says.

She adds that as AI modifications to media become “increasingly common” for legitimate purposes, secure provenance can provide important context to help prevent an average reader or viewer from simply dismissing that content as fake or deceptive.

“For the industry and for regulators, we note how important continued user research in this area is to drive towards more consistent and helpful display of this information to the public — to make sure it’s actually meaningful and useful in practice,” Young says.

“We have a limited set of technologies that can assist us, and we don’t want them to backfire from being misunderstood or improperly used.”

Learn more on the Microsoft Research Blog.

The post A new study explores how AI shapes what you can trust online appeared first on The Microsoft Cloud Blog.

Microsoft Global Skilling helps people and organizations build the skills they need to thrive in an AI‑powered world. Within Global Skilling, the Learning Lab is the innovation engine—a team focused on designing, testing, and evolving modern learning experiences to continuously improve how skills are developed, validated, and applied in the flow of work. 

AI is reshaping how organizations work. Teams aren’t just adopting new tools—they’re also figuring out how those tools fit into existing workflows, roles, and expectations, all while trying to keep pace with business demands in a rapidly changing landscape. It’s a heavy lift. As the leader of the Learning Lab team, I’m navigating these same pressures, along with my team members, as we balance day-to-day delivery with the need to evolve our processes in real time. That’s why we’re embedding AI assistants and agentic workflows into internal processes—using them not only to work differently but also to learn differently. Through experimentation, we’re uncovering new ways to streamline operations and improve the learner experience for our global audience.  

This blog highlights three of our team’s most impactful AI initiatives that could also benefit your organization. Inspired by these projects, we developed A Practical Guide for Bringing AI into Your Business Processes, featuring real-world examples and actionable ideas for integrating AI and human expertise across your organization. 

A Practical Guide for Bringing AI into Your Business Processes

Download the guide

3 impactful AI initiatives leading the way

1. Reducing time-intensive coordination to optimize research 

The challenge of coordinating teams for research  

Before any learning materials can be built, our team conducts extensive research to understand new technologies, identify required skills, and validate what learners need. This early-stage analysis requires input from multiple stakeholders and a deep review of internal documentation, product roadmaps, and existing training materials.  

How AI is helping accelerate our research tasks and optimize cross-team input 

One of the biggest bottlenecks for our research workflows has been the time it takes to synthesize information and align teams around what a course should achieve. To improve this, we began experimenting with Researcher in Microsoft 365 Copilot and persona-based agents to support our research and planning stages. Our new process looks like this: 

• Researcher synthesizes internal documentation, product roadmaps, and existing training materials to surface emerging themes and identify knowledge gaps. With the ability to process thousands of pages in minutes, it flags potential course objectives the team might have missed.
• In parallel, persona-based agents simulate the perspectives of stakeholders from varying teams to help validate ideas before bringing them to the key decision-makers.
• Throughout this process, our team members guide these AI tools through every step—providing the business context, analyzing AI outputs to identify gaps or inconsistencies, refining direction, and ensuring consideration of broader business objectives.  

In our experience with AI handling synthesis and early-stage validation, we’ve reduced the time required for core research processes from two weeks to just one day. This significant time savings extends to every course developed with this method, enabling us to redirect focus toward shaping stronger strategies, aligning content with business impact, and accelerating decision-making across teams.

Applying this approach in your organization 

AI-supported research and planning can help you make sense of complex information faster and build alignment earlier in your decision cycles. By using AI to synthesize documents, surface patterns, and validate assumptions, you can reduce the effort required to get teams on the same page. Your team members can then focus on refining strategy, confirming business priorities, and shaping higher-impact decisions. This combination improves speed and clarity throughout cross-functional work.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Drafting onboarding plans that human resources (HR) leaders can tailor to company culture.
• Developing quarterly sales plays informed by shifting buyer behavior and competitor activity.
• Creating campaign briefs rooted in audience insights, market trends, and performance data.
• Developing forecasting assumptions by synthesizing inputs from sales, operations, and historical data. 

2. Transitioning from manual maintenance to continuous quality improvements 

The challenge of shorter content lifecycles  

We maintain thousands of courses and lab environments as part of our skilling initiatives for Microsoft technologies. With the fast pace of product evolution, it can be challenging to keep learning content accurate and functional.  

How GitHub Copilot became the maintenance partner for the team 

We recognized that the demands for maintaining learning content were increasing beyond our capacity to manage effectively. So we integrated GitHub Copilot into the content maintenance workflow like this: 

• GitHub Copilot tools analyze content repositories—flagging inconsistencies, identifying outdated examples, and recommending updates based on current documentation.
• Throughout this process, our team reviews and refines the AI-generated recommendations. When GitHub Copilot flags an issue, we evaluate how those changes might apply to other training courses. We also ensure that all revisions align with learning objectives and verify that security and accessibility standards are met.
• Then GitHub Copilot helps implement some of the suggested updates, like generating new code samples or suggesting environmental configurations that align with the latest product releases. 

As a result, our team has reduced the time we spend on routine content maintenance by up to 25%. And with these time savings, team members can shift from reactive updates to proactive innovation—evaluating emerging skills, shaping next-generation modules, and exploring how agents, simulations, and personalized learning could improve outcomes. 

Applying this approach in your organization 

AI-assisted maintenance can help you keep large, fast-changing content ecosystems accurate and up to date without overwhelming your teams. By using AI to surface inconsistencies, flag outdated material, and recommend updates, you can dramatically reduce time spent on routine fixes. Your experts can then focus on reviewing changes for accuracy, regulatory needs, and strategic intent. This balance enables you to maintain quality at scale while freeing your teams to invest in higher-value innovation.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Maintaining and updating sales enablement content as product and service offerings evolve.
• Keeping product messaging frameworks and campaign assets consistent and up to date.
• Updating help center articles and support workflows after feature releases.
• Updating contract templates and clause libraries to align with new regulatory guidance.

3. Delivering inclusive learning at scale through diverse content formats 

The challenge of content relevance and engagement  

Our learners span every continent, speak dozens of languages, and have their own preferred learning methods. Creating multimodal, accessible, and inclusive learning experiences while managing constant content updates was stretching the team thin.  

How AI helps scale and translate content for global learners  

To support different learning styles and languages, we’re piloting how to create immersive, inclusive learning through two experiments with AI: 

1. We’re using AI tools to turn a single source of training content, like a session transcript or recording, into multiple formats, such as videos, podcasts, and recap summaries. This multimodal output lets us update learning materials at the pace required by our global audience and helps ensure that we’re reaching learners in their preferred formats.
2. We’re piloting an AI-powered tool that not only translates content but also generates avatars that deliver multilingual voiceovers with more natural lip-sync, eliminating one of the most distracting elements of dubbed content. 

Early results show that we can now recover up to 15 hours per course we develop—time our team can spend on more nuanced work that AI can’t do, like adapting cultural references, verifying that tone and pacing match learning objectives, and maintaining brand voice. 

Applying this approach in your organization 

AI-powered localization can help you deliver content that feels native to every audience you service, no matter the language or market. By pairing AI’s speed in translation, voiceover, and prompt generation with your team’s expertise in cultural nuance and brand standards, you can scale global engagement without diluting quality. This combination lets you reach more learners, customers, and employees while keeping your message consistent and relevant across regions.  

Explore A Practical Guide for Bringing AI into Your Business Processes to learn more about how you can apply this in processes like: 

• Localizing campaign assets for regional markets across languages and cultural norms.
• Tailoring pitch decks and demos for industry-specific or region-specific buyers.
• Creating multilingual chatbot responses and support scripts for global customers.
• Adapting standard operating procedure and process documentation for different facilities or regional regulations. 

Building skills and strengthening our AI strategy

As AI becomes an extension to the Learning Lab, we’ve discovered that it’s much more than just implementing new tools—it’s also a journey of building technical and human skills across the team. Our experiments require every team member to stretch into new capabilities, from process optimization and innovation to strengthening collaboration and creative problem-solving. As a result, we’ve been able to spend less time on repetitive tasks and to dedicate more energy to the kind of creative, relationship-driven work that leads to exceptional learning experiences. 

Looking to build skills for you and your teams? Explore AI Skills Navigator, the agentic learning space that brings together AI-powered skilling experiences and credentials that help individuals build career skills and organizations worldwide accelerate their business.

The post How to bring human expertise and AI together: 3 impactful initiatives appeared first on The Microsoft Cloud Blog.

Read the latest Cyber Pulse report

Agent building isn’t limited to technical roles; today, employees in various positions create and use agents in daily work. More than 80% of Fortune 500 companies today use AI active agents built with low-code/no-code tools.² AI is ubiquitous in many operations, and generative AI-powered agents are embedded in workflows across sales, finance, security, customer service, and product innovation. 

With agent use expanding and transformation opportunities multiplying, now is the time to get foundational controls in place. AI agents should be held to the same standards as employees or service accounts. That means applying long‑standing Zero Trust security principles consistently:

• Least privilege access: Give every user, AI agent, or system only what they need—no more.
• Explicit verification: Always confirm who or what is requesting access using identity, device health, location, risk level.
• Assume compromise can occur: Design systems expecting that cyberattackers will get inside.

These principles are not new, and many security teams have implemented Zero Trust principles in their organization. What’s new is their application to non‑human users operating at scale and speed. Organizations that embed these controls within their deployment of AI agents from the beginning will be able to move faster, building trust in AI.

The rise of human-led AI agents

The growth of AI agents expands across many regions around the world from the Americas to Europe, Middle East, and Africa (EMEA), and Asia.

According to Cyber Pulse, leading industries such as software and technology (16%), manufacturing (13%), financial institutions (11%), and retail (9%) are using agents to support increasingly complex tasks—drafting proposals, analyzing financial data, triaging security alerts, automating repetitive processes, and surfacing insights at machine speed.³ These agents can operate in assistive modes, responding to user prompts, or autonomously, executing tasks with minimal human intervention.

And unlike traditional software, agents are dynamic. They act. They decide. They access data. And increasingly, they interact with other agents.

That changes the risk profile fundamentally.

The blind spot: Agent growth without observability, governance, and security

Despite the rapid adoption of AI agents, many organizations struggle to answer some basic questions:

• How many agents are running across the enterprise?
• Who owns them?
• What data do they touch?
• Which agents are sanctioned—and which are not?

This is not a hypothetical concern. Shadow IT has existed for decades, but shadow AI introduces new dimensions of risk. Agents can inherit permissions, access sensitive information, and generate outputs at scale—sometimes outside the visibility of IT and security teams. Bad actors might exploit agents’ access and privileges, turning them into unintended double agents. Like human employees, an agent with too much access—or the wrong instructions—can become a vulnerability. When leaders lack observability in their AI ecosystem, risk accumulates silently.

According to the Cyber Pulse report, already 29% of employees have turned to unsanctioned AI agents for work tasks.⁴ This disparity is noteworthy, as it indicates that numerous organizations are deploying AI capabilities and agents prior to establishing appropriate controls for access management, data protection, compliance, and accountability. In regulated sectors such as financial services, healthcare, and the public sector, this gap can have particularly significant consequences.

Why observability comes first

You can’t protect what you can’t see, and you can’t manage what you don’t understand. Observability is having a control plane across all layers of the organization (IT, security, developers, and AI teams) to understand:  

• What agents exist 
• Who owns them 
• What systems and data they touch 
• How they behave 

In the Cyber Pulse report, we outline five core capabilities that organizations need to establish for true observability and governance of AI agents:

• Registry: A centralized registry acts as a single source of truth for all agents across the organization—sanctioned, third‑party, and emerging shadow agents. This inventory helps prevent agent sprawl, enables accountability, and supports discovery while allowing unsanctioned agents to be restricted or quarantined when necessary.
• Access control: Each agent is governed using the same identity‑ and policy‑driven access controls applied to human users and applications. Least‑privilege permissions, enforced consistently, help ensure agents can access only the data, systems, and workflows required to fulfill their purpose—no more, no less.
• Visualization: Real‑time dashboards and telemetry provide insight into how agents interact with people, data, and systems. Leaders can see where agents are operating, understanding dependencies, and monitoring behavior and impact—supporting faster detection of misuse, drift, or emerging risk.
• Interoperability: Agents operate across Microsoft platforms, open‑source frameworks, and third‑party ecosystems under a consistent governance model. This interoperability allows agents to collaborate with people and other agents across workflows while remaining managed within the same enterprise controls.
• Security: Built‑in protections safeguard agents from internal misuse and external cyberthreats. Security signals, policy enforcement, and integrated tooling help organizations detect compromised or misaligned agents early and respond quickly—before issues escalate into business, regulatory, or reputational harm.

Governance and security are not the same—and both matter

One important clarification emerging from Cyber Pulse is this: governance and security are related, but not interchangeable.

• Governance defines ownership, accountability, policy, and oversight.
• Security enforces controls, protects access, and detects cyberthreats.

Both are required. And neither can succeed in isolation.

AI governance cannot live solely within IT, and AI security cannot be delegated only to chief information security officers (CISOs). This is a cross functional responsibility, spanning legal, compliance, human resources, data science, business leadership, and the board.

When AI risk is treated as a core enterprise risk—alongside financial, operational, and regulatory risk—organizations are better positioned to move quickly and safely.

Strong security and governance do more than reduce risk—they enable transparency. And transparency is fast becoming a competitive advantage.

From risk management to competitive advantage

This is an exciting time for leading Frontier Firms. Many organizations are already using this moment to modernize governance, reduce overshared data, and establish security controls that allow safe use. They are proving that security and innovation are not opposing forces; they are reinforcing ones. Security is a catalyst for innovation.

According to the Cyber Pulse report, the leaders who act now will mitigate risk, unlock faster innovation, protect customer trust, and build resilience into the very fabric of their AI-powered enterprises. The future belongs to organizations that innovate at machine speed and observe, govern and secure with the same precision. If we get this right, and I know we will, AI becomes more than a breakthrough in technology—it becomes a breakthrough in human ambition.

Get the full Cyber Pulse report

To learn more about Microsoft Security solutions, visit our website. Bookmark the Security blog to keep up with our expert coverage on security matters. Also, follow us on LinkedIn (Microsoft Security) and X (@MSFTSecurity) for the latest news and updates on cybersecurity.

¹Microsoft Data Security Index 2026: Unifying Data Protection and AI Innovation, Microsoft Security, 2026.

²Based on Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the last 28 days of November 2025.

³Industry and Regional Agent Metrics were created using Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the last 28 days of November 2025.

⁴July 2025 multi-national survey of more than 1,700 data security professionals commissioned by Microsoft from Hypothesis Group.

Methodology:

Industry and Regional Agent Metrics were created using Microsoft first‑party telemetry measuring agents built with Microsoft Copilot Studio or Microsoft Agent Builder that were in use during the past 28 days of November 2025. 

2026 Data Security Index: 

A 25-minute multinational online survey was conducted from July 16 to August 11, 2025, among 1,725 data security leaders. 

Questions centered around the data security landscape, data security incidents, securing employee use of generative AI, and the use of generative AI in data security programs to highlight comparisons to 2024. 

One-hour in-depth interviews were conducted with 10 data security leaders in the United States and United Kingdom to garner stories about how they are approaching data security in their organizations. 

Definitions: 

Active Agents are 1) deployed to production and 2) have some “real activity” associated with them in the past 28 days.  

“Real activity” is defined as 1+ engagement with a user (assistive agents) OR 1+ autonomous runs (autonomous agents).  

The post 80% of Fortune 500 use active AI Agents: Observability, governance, and security shape the new frontier appeared first on The Microsoft Cloud Blog.

After decades of digitization—and years of rapid advances in AI—the industry now stands at the threshold of a far more profound shift: the rise of agentic AI. 

Unlike earlier forms of automation, agentic AI goes beyond task assistance. Intelligent AI agents can plan, reason, and act autonomously collaborating alongside clinicians, care teams, researchers, developers, and all workers from the back office to the front lines. When embedded into everyday workflows, agents transform intelligence from something accessed on demand into something continuously at work—embedding subject matter expertise with human ambition to achieve our highest aspirations.  

But if pervasive agentic intelligence is the destination, how far along is healthcare on the journey?

Measuring healthcare’s readiness for agentic AI 

To answer that question, Microsoft, in collaboration with The Health Management Academy, conducted original research published in the January 2026 issue of the New England Journal of Medicine. Based on surveys and in-depth interviews with senior healthcare executives across provider organizations in the United States, the research offers a grounded, reality-based view of how health systems are progressing along the agentic AI maturity curve—from early experimentation to enterprise level optimization. 

What the research reveals 

1. Agentic AI remains early—but strategic interest is rising
   While enthusiasm is growing, adoption remains nascent. 43% of respondents report piloting or testing agentic AI, yet only 3% have deployed agents in live workflows. At the same time, one-third of respondents indicate no plans to explore agentic AI within the next one to two years—highlighting the gap between experimentation and operational readiness. 
2. Confidence in long-term impact is strong
   Despite limited deployment today, belief in agentic AI’s future impact is clear. 60% of respondents agree or strongly agree that agentic AI will meaningfully improve or disrupt the provider–patient experience, with similar optimism around productivity gains (57%). Nearly half anticipate deeper human–AI collaboration within the next three to five years—reinforcing the view that agents will augment, not replace, clinical and operational roles. 
3. A catalyst for workforce, productivity, and experience transformation
   More than three quarters (77%) expect AI agents to improve backend productivity, while 60% believe they will fundamentally reshape the patient–provider experience. Yet this transformation will require change: 60% cite reskilling and upskilling as a top challenge as ecosystems of AI models and agents expand. 
4. A clear gap between belief and deployment
   Qualitative interviews reveal that leaders increasingly view agentic AI as a strategic end state—one that depends heavily on progress in workforce readiness, governance, and data infrastructure. Moving from promise to sustained value will require deliberate, coordinated investment across all three. 

Why this moment matters: A leadership imperative

The publication of this research marks a shift in the future of work. The question is no longer if agentic AI will reshape healthcare—but how intentionally health systems choose to shape that transformation. 

Healthcare has a rare window to define the role of agentic AI before patterns harden, and expectations are set. Success will be determined not by technology alone, but by how effectively organizations prepare their foundations and empower their people to work alongside digital colleagues in a hybrid workforce. 

Building strong governance frameworks, establishing a trusted data foundation, and developing an AI ready workforce are no longer optional—they are prerequisites for leadership in the organizations on the frontier of the next era of transformation. 

• Read the New England Journal of Medicine article.
• Read the full report: Agentic AI and the Next Frontier of Transformation.
• Explore how other healthcare organizations are already using AI to make tangible impacts.

The post Assessing healthcare’s agentic AI readiness: New research from Microsoft and The Health Management Academy appeared first on The Microsoft Cloud Blog.

Today’s business environment demands more with less. Organizations must deliver better personalization, higher volume, and increasingly complex insights while operating with greater efficiency. The gap between stakeholder expectations and what teams can realistically deliver continues to widen. 

Microsoft’s recent insights on Frontier Transformation address these challenges by embedding AI into the core of operations. Frontier Firms are organizations that treat AI as a foundational capability and are already transforming how they work. 

Frontier Firms don’t simply automate; they adapt. By adding adaptive intelligence to existing systems, they unlock three advantages: 

• Awareness: Systems perceive conditions in real time. 
• Reasoning: They prioritize tasks based on business needs. 
• Interaction: They communicate in natural, intuitive ways. 

Early adopters see small improvements compound quickly. These include faster service, more accurate recommendations, fewer equipment surprises, and clearer insights into peak times and bottlenecks. As agentic AI matures, companies can offer guidance and assistance that feels intuitive. Employees gain more time for high-value work, and leaders gain deeper visibility into operations. 

Frontier Transformation is more than a technology upgrade. It represents a shift in operating model. Organizations that treat AI as a foundation will lead the next wave of business innovation. 

Agentic AI is reshaping customer experience 

This shift is already visible in retail, where agentic robots are transforming customer experience and improving operational performance. Customers expect fast, personalized service, yet retailers often face staffing constraints, training gaps, and unpredictable demand. 

Industry studies show: 

• 75% of consumers are more likely to purchase when recommendations feel relevant. 
• Nearly 40% of in-store complaints relate to wait times. 
• Inventory inaccuracies account for 4–8% of lost sales. 

These challenges reflect a broader pattern across frontline-heavy industries. Customer expectations continue to rise, and employee workloads grow more complex. 

Microsoft’s Work Trend Index reinforces this dynamic. Frontline employees say AI tools that reduce repetitive tasks, surface real-time information, and streamline customer interactions have the biggest impact on satisfaction and performance. As organizations integrate adaptive intelligence into daily workflows, these benefits build on each other and help accelerate Frontier Transformation. 

Recent industry research shows that retail and consumer packaged goods organizations are generating significant business value from generative and agentic AI, with early deployments consistently delivering multi-times ROI and accelerating impact across frontline operations.

Agentic AI creates new possibilities for stores. Instead of relying on rigid automation, it blends environmental awareness, adaptive reasoning, and conversational interaction to help teams respond in real time. 

ADAM: From beverage service to customer care 

Richtech Robotics’ ADAM beverage robot illustrates how quickly agentic systems can enhance the customer experience. Richtech, based in Las Vegas, designs and commercializes autonomous robotic solutions for hospitality, retail, logistics, and manufacturing. Through a close, hands-on collaboration between Richtech’s engineering team and the Microsoft AI Co-Innovation Labs, the two companies jointly developed new adaptive intelligence for ADAM—transforming it into a conversational, context-aware assistant powered by Microsoft Azure AI. These enhancements enabled ADAM to move beyond routine beverage preparation and support richer customer interactions.

Today, ADAM: 

• Adjusts recommendations based on weather, time of day, and promotions. 
• Responds naturally to customer requests like “less sweet,” “extra ice,” or “what’s seasonal?” 
• Notifies staff about ingredient or equipment issues before problems occur. 
• Uses vision models to maintain speed and quality during busy periods. 

Retailers report smoother operations and better customer feedback. ADAM is context aware, conversational, and reliable—qualities customers consistently reward and areas where AI has historically struggled. 

While ADAM is a retail example, the pattern extends far beyond beverage automation. Across logistics, healthcare, hospitality, and manufacturing, Frontier Firms are adding ambient intelligence and agentic workflows to physical operations and seeing meaningful gains as a result. 

Unlocking retail transformation at scale 

Once retailers see how intelligence enhances a single customer interaction, the next question naturally follows: where else can this help? Building on the advancements made with ADAM, Richtech Robotics is extending these capabilities through its Agentic Store initiative. By applying vision, voice, and agentic reasoning to common in-store tasks, the initiative helps retailers address friction points that slow down the shopping experience. 

Examples under development include: 

• Robots that guide customers to products.
• Systems that detect empty shelves or misplaced items.
• Voice-enabled, in-aisle assistance.
• Real-time adjustments based on foot traffic or local events.

This approach does not require heavy hardware investments. These workflows are software-driven and build on existing store infrastructure. It reflects how Frontier Firms drive transformation by spreading intelligence across the ecosystem rather than upgrading a single process at a time. 

Retailers gain clearer visibility into peak demand, customer behavior, product movement, and service quality without increasing manual tracking. As one store manager described it, “it feels like having a second set of eyes that never gets tired.” 

Convenient, high-quality service becomes a blueprint for store-wide intelligence. In the coming years, a clear difference will emerge between retailers that treat AI as a tool and those that treat it as a foundation. The latter will set the pace for the industry. 

Steps toward Frontier Transformation 

Agentic AI gives retailers a practical and achievable path forward. It elevates customer experience, reduces operational strain, and creates the foundation for smarter and more adaptive stores. Organizations that embrace Frontier Transformation position themselves as Frontier Firms, ready to scale faster, work more intelligently, and unlock new value through the combination of human judgment and AI-driven insight. 

The journey begins with small, strategic steps and a bold vision for what is possible. To explore the broader business impact of AI across frontline and customer-facing roles, review Microsoft’s Work Trend Index: The year the Frontier Firm is born.

Explore how organizations are transforming with AI, and learn how you can build your own generative AI proof of concept with the Microsoft AI Co-Innovation Labs.

The post Frontier Transformation in retail: How agentic AI robots are redefining store experiences appeared first on The Microsoft Cloud Blog.

The question today isn’t whether AI can deliver value, but how quickly organizations can scale it across their business. Challenges such as aligning business and IT, ensuring data quality, navigating governance and regulatory considerations, and avoiding an overemphasis on experimentation can slow progress and widen the gap between leaders and slow adopters.

At Microsoft Ignite, the core message was clear: AI in the flow of work can unlock human ambition, and scaling with trust and observability is essential. Organizations that embed AI into everyday workflows empower people to achieve more, while trust and observability ensure innovation happens responsibly and at scale.

What sets a Frontier Firm apart

According to a recent study from IDC, 68% of organizations are using AI today, but the real difference lies in how they’re using it. Frontier Firms are realizing returns that are three times higher than slow adopters. What sets these leaders apart is the breadth and depth of their AI adoption. On average, Frontier Firms are using AI across seven business functions, with over 70% deploying AI in customer service, marketing, IT, product development, and cybersecurity.

A Frontier Firm is defined not by its size or industry, but by its mindset and execution. These organizations lead with AI-first differentiation, embedding intelligence across every layer of the business—from employee experiences to customer engagements to core processes. “Becoming Frontier” means moving beyond experimentation to enterprise-scale transformation, unlocking creativity, obsolescing the mundane, and driving competitive advantage in the agentic era. Leaders invest in upskilling, culture, and strong foundations—strategy, data, security, and compliance—to align technology with ambitious goals.

Successful Frontier Firms, as highlighted at Microsoft Ignite, share three common traits in their approach to AI. First, they integrate AI seamlessly into the flow of human ambition, amplifying creativity and accelerating decision-making through everyday workflows. Second, they foster ubiquitous innovation by democratizing AI creation—empowering everyone, from frontline employees to executives, to build agents and solutions that address real business challenges. Third, they prioritize observability at every layer, embedding governance, security, and compliance into all AI systems to ensure visibility, control, and trust as they scale. These traits collectively allow Frontier Firms to lead with agility, resilience, and measurable business impact.

Where AI is delivering business value today

The impact of AI is accelerating across industries. For example, Levi Strauss & Co. reduced project timelines from a year to a day with Microsoft Copilot and Copilot+ PCs. ABB is transforming industrial operations with Microsoft Azure and AI-powered insights, and Land O’Lakes is embedding AI into agricultural workflows to optimize supply chains and accelerate decision-making. These stories demonstrate how AI is driving measurable outcomes at scale.

This broad adoption is translating into measurable business impact. Compared to slow adopters, Frontier Firms report better outcomes at a rate that is four times higher across brand differentiation (87%), cost efficiency (86%), top-line growth (88%), and customer experience (85%). They’re not just automating tasks—they’re unlocking industry-specific value, with 67% monetizing AI use cases tailored to their sector and 58% already using custom AI solutions.¹

Partnering for Frontier transformation

As we’ve already alluded to, becoming a Frontier Firm isn’t just about technology—it’s about strategy, culture, and execution. Microsoft partners with organizations to accelerate transformation and measurable impact, combining deep expertise, integrated intelligence, and a global ecosystem. Trust is at the core of every AI experience, with enterprise-grade security, real-time observability, and automated governance built in.

Leading organizations are already putting these principles into practice—accelerating innovation, transforming customer experiences, and creating new sources of growth. These examples prove that becoming a Frontier Firm isn’t theoretical; it’s happening now.

The future of AI-powered business

The divide between AI leaders and others is growing, reshaping the competitive landscape. Frontier Firms are moving rapidly from pilots to widespread AI adoption, raising the bar for business impact. Microsoft Ignite underscored that the future belongs to organizations that embed AI at every level, empower their people, and lead with trust.

Becoming an AI-First Frontier Firm

Learn how bold organizations across industries are combining human expertise with AI agents to achieve faster growth, greater efficiency, and sustained innovation.

Get the e-book

For organizations ready to take the next step, the Becoming an AI-First Frontier Firm e-book offers practical insights and a roadmap for transformation.

¹ IDC InfoBrief: sponsored by Microsoft, What Every Company Can Learn From Frontier Firms Leading the AI Revolution, IDC # US53838325, November 2025

The post Becoming a Frontier Firm: Unlocking the business value of AI  appeared first on The Microsoft Cloud Blog.

1. Preparing for the integration of AI and agents.
2. Strengthening training so that everyone has the necessary skills. 
3. Fostering a culture that prioritizes cybersecurity. 

Preparing for the integration of AI and intelligent agents

Preparing for AI and agent integration calls for careful strategy, thoughtful business planning, and organization-wide adoption under solid governance, security, and management. Microsoft’s AI adoption model offers a step-by-step guide for businesses embarking on this journey and the guide offers actionable insights and solutions to manage AI risks.

Strengthening training so that everyone has the necessary skills

Technology alone isn’t enough. People are your strongest defense—and the foundation of trust. That’s why skilling emerged as a central theme throughout these past months and will continue beyond. Frontier Firms—those structured around on-demand intelligence and powered by “hybrid” teams of humans plus agents—lead by fostering a culture of continuous learning. Our blog “Building human-centric security skills for AI” offers insights and guidance you can apply in your organization.  

• Lean into your unique human strengths: Your team’s judgment, creativity, and experience are irreplaceable. Take time to invest in upskilling and reskilling them, so they can confidently guide and manage AI tools responsibly and securely. Explore Microsoft Learn for Organizations for resources to support your learning journey.
• Stay curious and agile through continuous learning: Building security resilience is an ongoing process. Regularly refresh your AI and security training, offer time and resources for employees to explore new skills, and create a supportive, engaging environment that motivates continuous growth. Find in AI Skills Navigator, our agentic learning space, AI and security training tailored to different roles.  

Investing in skilling doesn’t just reduce risk—it accelerates innovation by giving teams the confidence to explore new AI capabilities securely. 

Skilling is an ongoing practice that needs to constantly evolve alongside the business and technology landscape. Staying ahead requires an enterprise-wide strategy that aligns ever-changing business priorities with always-on skill-building. 

—Jeana Jorgensen, Corporate Vice President, Microsoft Learning

Fostering a culture that prioritizes security

As AI impacts everyone’s role, make security awareness and responsible AI practices shared priorities. Encourage your team to weave security thinking into their daily routines—creating a safer environment for all. As Vasu Jakkal, Corporate Vice President of Microsoft Security highlighted in her blog “Cybersecurity Awareness Month: Security starts with you,” it is critical that security become part of your organization’s culture and norms. 

Check out our new e-book, Skilling for Secure AI: How Frontier Firms Lead the Way for practical steps for leaders to upskill their workforce in identity management, data governance, and responsible AI practices.

From awareness to action

In the agentic AI era, people continue to be our most valuable resource. It’s essential to empower them with AI and equip them with the skills they need to use AI responsibly and securely. Cybersecurity awareness should go beyond designated months or campaigns; true awareness means taking meaningful action.   

Here are three actions you can take today to maximize your AI investments: 

1. Share the Be Cybersmart Kit with your employees. It includes tips for protecting yourself from fraud and deepfakes, guidance on safe AI usage, and key security best practices.
2. Invest in people: Focus on upskilling initiatives that support your AI transformation, cloud modernization, and security-first strategies.
3. Champion a security-first culture: Ensure cybersecurity is integral to every business discussion and woven into your overall strategy. 

Microsoft guide for securing the AI-powered enterprise

Download the guide

The post From awareness to action: Building a security-first culture for the agentic AI era appeared first on The Microsoft Cloud Blog.

After several years of experimentation, 2026 is shaping up to be the year AI evolves from instrument to partner, transforming how we work, create and solve problems. Across industries, AI is moving beyond answering questions to collaborating with people and amplifying their expertise.

This transformation is visible everywhere. In medicine, AI is helping close gaps in care. In software development, it’s learning not just code but the context behind it. In scientific research, it’s becoming a true lab assistant. In quantum computing, new hybrid approaches are heralding breakthroughs once thought impossible.

As AI agents become digital colleagues and take on specific tasks at human direction, organizations are strengthening security to keep pace with new risks. The infrastructure powering these advances is also maturing, with smarter, more efficient systems.

These seven trends to watch in 2026 show what’s possible when people join forces with AI.

AI will amplify what people can achieve together

Aparna Chennapragada, Microsoft’s chief product officer for AI experiences, sees 2026 as a new era for alliances between technology and people. If recent years were about AI answering questions and reasoning through problems, the next wave will be about true collaboration, Chennapragada says.

“The future isn’t about replacing humans,” she says. “It’s about amplifying them.”

AI agents are set to become digital coworkers, she says, helping individuals and small teams punch above their weight. Chennapragada envisions a workplace where a three-person team can launch a global campaign in days, with AI handling data crunching, content generation and personalization while humans steer strategy and creativity. She predicts organizations that design for people to learn and work with AI “will get the best of both worlds,” helping teams tackle bigger creative challenges and deliver results faster.

Her advice for professionals: Don’t compete with AI, but focus on learning how to work alongside it. The coming year, she says, “belongs to those who elevate the human role, not eliminate it.”

AI agents will get new safeguards as they join the workforce

AI agents will proliferate in 2026 and play a bigger role in daily work, acting more like teammates than tools, says Vasu Jakkal, corporate vice president of Microsoft Security. As organizations rely on these agents to help with tasks and decision-making, building trust in them will be essential, Jakkal says — starting with security.

“Every agent should have similar security protections as humans,” she says, “to ensure agents don’t turn into ‘double agents’ carrying unchecked risk.”

That means giving each agent a clear identity, limiting what information and systems it can access, managing the data it creates and protecting it from attackers and threats, Jakkal says. Security will become ambient, autonomous and built-in, she says, not something added on later. In addition, as attackers use AI in new ways, defenders will use security agents to spot those threats and respond faster, she says.

“Trust is the currency of innovation,” Jakkal says, making these shifts vital to helping organizations keep up with new risks as AI continues to become more central to how work gets done.

AI is poised to shrink the world’s health gap

AI in healthcare is marking a turning point, says Dr. Dominic King, vice president of health at Microsoft AI.

“We’ll see evidence of AI moving beyond expertise in diagnostics and extending into areas like symptom triage and treatment planning,” King says. “Importantly, progress will start to move from research settings into the real world, with new generative AI products and services available to millions of consumers and patients.”

That shift matters because access to care is a global crisis. The World Health Organization projects a shortage of 11 million health workers by 2030 — a gap that leaves 4.5 billion people without essential health services.

King points to achievements demonstrated in 2025 by Microsoft AI’s Diagnostic Orchestrator (MAI-DxO), which solved complex medical cases with 85.5% accuracy, far above the 20% average for experienced physicians. With Copilot and Bing already answering more than 50 million health questions daily, he sees advances in AI as a way to give people more influence and control over their own health and wellbeing.

AI will become central to the research process

 AI is already speeding up breakthroughs in fields like climate modeling, molecular dynamics and materials design, says Peter Lee, president of Microsoft Research. But the next leap is coming. In 2026, AI won’t just summarize papers, answer questions and write reports — it will actively join the process of discovery in physics, chemistry and biology.

“AI will generate hypotheses, use tools and apps that control scientific experiments, and collaborate with both human and AI research colleagues,” Lee says.

This shift is creating a world where every research scientist soon could have an AI lab assistant that can suggest new experiments and even run parts of them. That’s the logical next step, Lee says, building on how AI works alongside developers with “pair programming,” for example, and uses apps to automate everyday tasks like shopping and scheduling in other domains.

It’s a transformation that promises to accelerate research and change how scientific discoveries are made, he says.

AI infrastructure will get smarter and more efficient

 AI’s growth isn’t just about building more and bigger datacenters anymore, says Mark Russinovich, chief technology officer, deputy chief information security officer and technical fellow for Microsoft Azure. The next wave is about making every ounce of computing power count.

“The most effective AI infrastructure will pack computing power more densely across distributed networks,” Russinovich says. Next year will see the rise of flexible, global AI systems — a new generation of linked AI “superfactories” — that will drive down costs and improve efficiency, he says.

AI will be “measured by the quality of intelligence it produces, not just its sheer size,” he says.

Think of it like air traffic control for AI workloads: Computing power will be packed more densely and routed dynamically so nothing sits idle. If one job slows, another moves in instantly — ensuring every cycle and watt is put to work. This shift will translate into smarter, more sustainable and more adaptable infrastructure to power AI innovations on a global scale, Russinovich says.

AI is learning the language of code — and the context behind it

Software development is exploding, with activity on GitHub reaching new levels in 2025. Each month, developers merged 43 million pull requests — a 23% increase from the prior year in one of the main ways teams propose and review changes to their code. The annual number of commits pushed, which track those changes, jumped 25% year-over-year to 1 billion. The unprecedented pace signals a major shift in the industry as AI becomes increasingly central to how software is built and improved.

Mario Rodriguez, GitHub’s chief product officer, says that sheer volume is why 2026 will bring a new edge: “repository intelligence.” In plain terms, it means AI that understands not just lines of code but the relationships and history behind them.

By analyzing patterns in code repositories — the central hubs where teams store and organize everything they build — AI can figure out what changed, why and how pieces fit together. That context helps it make smarter suggestions, catch errors earlier and even automate routine fixes. The result will be higher quality software that helps developers move faster, Rodriguez says.

“It’s clear we’re at an inflection point,” he says. Repository intelligence “will become a competitive advantage by providing the structure and context for smarter, more reliable AI.”

The next leap in computing is closer than most people think

Quantum computing has long felt like science fiction. But researchers are entering a “years, not decades” era where quantum machines will start tackling problems classical computers can’t, says Jason Zander, executive vice president of Microsoft Discovery and Quantum. That looming breakthrough, called quantum advantage, could help solve society’s toughest challenges, Zander says.

What’s different now is the rise of hybrid computing, where quantum works alongside AI and supercomputers. AI finds patterns in data. Supercomputers run massive simulations. And quantum adds a new layer that will drive far greater accuracy for modeling molecules and materials, he says. This progress coincides with advances in logical qubits, which are physical quantum bits grouped together so they can detect and correct errors and compute — a critical step toward reliability.

Microsoft’s Majorana 1 marks a major development toward more robust quantum systems, Zander says. It’s the first quantum chip built using topological qubits, a design that inherently makes fragile qubits more stable and reliable. It’s also the only quantum solution engineered to catch and correct errors. That architecture paves the way for machines with millions of qubits on a single chip, providing the processing power needed for complex scientific and industrial problems.

“Quantum advantage will drive breakthroughs in materials, medicine and more,” Zander says. “The future of AI and science won’t just be faster, it will be fundamentally redefined.”

The post What’s next in AI: 7 trends to watch in 2026 appeared first on The Microsoft Cloud Blog.

Across industries, organizations are racing to harness the power of AI. The potential in intelligent automation alone motivated a $252.3 billion corporate AI investment in 2024,¹ and those investments have been evolving almost as quickly as this rapidly changing technology itself.

While longer-standing AI technologies like machine learning and chatbots continue to perform well, agentic AI has moved to the front of the pack, offering the kind of autonomous decision-making that companies crave. Early wins with generative AI—drafting emails, summarizing documents, automating routine tasks—have shown what’s possible when a single intelligent agent is put to work.

Microsoft defines agentic AI as the pairing of traditional software strengths—such as workflows, state, and tool use—with the adaptive reasoning capabilities of large language models (LLMs). This allows agents to understand intent, take action, and interact with other systems dynamically, moving beyond the limits of rule-based automation.

As organizations look to scale AI across more of their operations, many are finding that a single agent can’t always manage complex, multi-step tasks. This is where multi-agentic systems become valuable. 

Multi-agentic systems use a series of agents, with a single coordinating agent, to work as a sort of AI team. The coordinating agent works to understand complex queries and delegate workflows to other agents, making multi-step, multi-system queries possible. In collaboration with people who are essential for escalation, understanding significant ambiguity, and creative thinking, multi-agentic systems are becoming integral to digital-first workforces.

Because these AI teams often operate across different tools and systems, organizations need solutions that are secure and enterprise-ready. With Microsoft technologies, agentic systems are built with the security, compliance, and reliability businesses expect. For a deeper dive on multi-agentic AI, read “Designing Multi-Agent Intelligence” on Microsoft Dev Blogs.

Today, Microsoft customers are already seeing the impact of multi-agentic AI. Here are three stories out of the Microsoft AI Co-Innovation Lab in San Francisco that show how multi-agentic AI is transforming security, science, and retail.

Three real-world examples of multi-agentic AI transformation

1. Contraforce: Turning the tide in cybersecurity

In cybersecurity, every second matters. For managed service providers (MSPs), responding to threats quickly can mean the difference between business as usual and a major incident. Contraforce, a Microsoft partner, set out to change the game with a multi-agentic security delivery platform built on Microsoft Foundry.

The multi-agentic solution automates 90% of incident investigations and response tasks, working as an always-on security operations team to analyze security data, identify suspicious activities, and autonomously managing incidents. These autonomous AI agents don’t just automate tasks—they help create a new cyber defense workforce.

The results are striking:

• Incident response times plummeted from 30 minutes to just 30 seconds.
• The cost per incident dropped from $15 to less than $1.
• MSPs can now scale their services without scaling their teams.

Contraforce’s story is a testament to how agentic AI can transform security operations from reactive to proactive, delivering speed, scale, and cost-efficiency.

3. Stemtology: Accelerating discovery in health sciences

Medical innovations can move slowly, and sometimes for good reason. But in regenerative medicine, lengthy research cycles and complex data analysis can be optimized with AI intervention.

Regenerative medicine innovator Stemtology worked with the Microsoft AI Co-Innovation Lab to accelerate biomedical discovery using a multi-agentic platform.

By combining Azure Cognitive Search, GPT-based agents, and domain-specific knowledge graphs, Stemtology’s system allows agents to:

• Parse scientific literature
• Generate therapeutic hypotheses
• Design and evaluate experiments

The impact? Research timelines have been cut by up to 50% at Stemtology. Minimum viable products are delivered in weeks instead of months. And the path from idea to patient-ready therapy is shorter than ever. This has freed up researchers to focus on highly complex evaluation and design strategies for treatments, rather than spending hours on gathering and synthesizing research.

Stemtology’s journey shows how agentic AI can support critical human discovery and bring life-saving treatments closer to reality.

3. SolidCommerce: Personalizing customer engagement at scale

For retailers, delivering personalized experiences while managing vast product catalogs and backend operations is a constant challenge. SolidCommerce specializes in providing AI solutions that address these challenges in the retail industry.

Hoping to address time-consuming support processes, inconsistent customer communications, and operational inefficiencies handling customer support, they approached the Microsoft AI Co-Innovation Lab in San Francisco to create an AI agent that could automate accurate and brand-aligned responses to meet customer needs.

Their solution brings together multiple agents for customer triage, FAQ handling, account management, product recommendations, and compliance checks. Built on Microsoft’s Agentic AI framework and integrated with Microsoft Copilot Studio and Foundry Agent Service, the system is easy to deploy and scale.

The payoff:

• Richer, multimodal customer experiences
• Scalable automation across channels
• Real-time personalization with memory and context

SolidCommerce’s story demonstrates how multi-agentic AI can turn retail complexity into seamless, intelligent engagement, ensuring customer satisfaction to keep pace with technological change.

Learn more about the agentic advantage

Microsoft customers are realizing benefits every day across industries. As we’ve seen in the customer examples above, multi-agentic AI delivers speed and scale in operations, accelerates innovation in research and development, and enables personalized engagement at scale.

Microsoft AI Co-Innovation Labs

Accelerate your AI projects with personalized help from our Microsoft Technology Experts

Learn more

Multi-agentic AI isn’t just a technical upgrade—it’s a strategic shift. And companies that harness these systems to transform legacy processes can benefit not only from automation, but from truly intelligent optimization.

Learn how other customers are transforming with AI and explore creating your own generative AI proof of concept at Microsoft AI Co-Innovation Labs.

¹ The 2025 AI Index Report, Stanford HAI.

The post Multi-agentic AI: Unlocking the next wave of business transformation appeared first on The Microsoft Cloud Blog.

Leaders in the AI era are reimagining what AI can amplify: creativity, expertise, and the human ingenuity and leadership that drives progress. A recent IDC study commissioned by Microsoft shows that Frontier Firms are customizing AI for their unique workflows and seeing three times greater returns on their AI investments. Why? Because this approach keeps people at the center. They’re using AI to tackle big challenges, empower higher-value work, and help industries adapt quickly in a world where efficiency and resilience are non-negotiable.

Becoming Frontier isn’t reserved for tech disruptors or startups. Legacy brands across industries are bending the curve on AI innovation, pairing decades of expertise with AI-first differentiation to reinvent processes and accelerate growth. In a world where more than 99% of companies fail to reach 100 years in business,¹ centennial companies—brands that have been around for 100 years or more—are proving that reinvention is the key to longevity. Today, they’re using Microsoft AI to eliminate the mundane and unlock creativity to accelerate their journey to becoming Frontier.

Companies including The Kraft Heinz Company, Levi Strauss & Co., Wells Fargo, and Land O’Lakes have been serving customers for more than a century and remain leading household names because they never stood still. They’re positioning themselves at the forefront of AI innovation.

When you think about how quickly humans have grasped the concepts of AI, it’s influencing how they do everyday life right now. Compared to past technologies introduced into business or corporate settings, the learning curve and the adoption rate are not something that you have to worry about as much because people are actually craving it and they’re looking for it.

—Ken Meyer, Chief Information Officer for Enterprise Functions at Wells Fargo

These centennial brands show that reinvention isn’t a one-time event—it’s a mindset. By pairing their own expertise with a trusted partner like Microsoft, they’re transforming operations, accelerating innovation, and setting new benchmarks for what’s possible. Let’s look at how The Kraft Heinz Company, Levi Strauss & Co., Wells Fargo, and Land O’Lakes are leading the way.

Influencing the future with the wisdom of the past

In the consumer goods industry where companies are balancing shifting consumer preferences, supply chain complexity, and speed-to-market, AI-powered insights are especially crucial. The Kraft Heinz Company, one of the world’s leading food and beverage companies, is demonstrating how it is reaching for historical data as it prepares the organization to thrive in the future with the recent introduction of The Cookbook.

Built on Microsoft Azure OpenAI and trained on a proprietary central database, The Cookbook is a proprietary AI agent that puts decades of institutional wisdom around HEINZ Tomato Ketchup production processes at employees’ fingertips. With The Cookbook, users can ask questions on everything from the thickness and color of a batch of ketchup to insights about the efficiency of production processes, and more. Preserving and digitizing institutional knowledge and subject matter expertise in this way supports improvements in production consistency, quality, and efficiency—leaning into a legacy of innovation to maintain the quality that’s made HEINZ the world’s best-selling ketchup.

As part of our long-term strategy, we’re harnessing disruptive digital solutions to fuel growth across the organization. In doing so, we’re transforming the way we work, streamlining processes, enhancing decision making, and more—all of which enable us to continue delivering the great-tasting products consumers know and love as well as continue to innovate and address evolving preferences.

—Oliver Ganschar, Head of Digital Product Management and Innovation at The Kraft Heinz Company

The Cookbook joins a robust lineup of generative and agentic AI-powered projects already in use at The Kraft Heinz Company to optimize marketing, production, supply chain functions, and more. They have streamlined operations for Claussen pickles, cut manufacturing waste, and dramatically reduced timelines for brand asset creation across The Kraft Heinz Company’s portfolio. The digital-first solutions empower employees to focus on high-value tasks, make decisions rooted in data, and enhance engagement.

“When it comes to AI, we’re exploring integrated solutions that can drive scalability and connectivity across our organization end to end, rather than siloed deployment or disconnected applications,” said Ganschar. “We aim to create a connected ecosystem that enables our teams to work more efficiently and effectively, and this includes evaluating applications of generative and agentic AI in ways that we believe can unlock further value for our teams and the business.”

The Kraft Heinz Company and Microsoft have also collaborated on a Supply Chain Control Tower to preempt interruptions and develop digital twins of the company’s manufacturing facilities to virtually test and troubleshoot new processes. Together, these efforts hone The Kraft Heinz Company’s competitive edge, strengthening its ability to get products to market faster, better serve customers, and drive innovation.

Our collaboration with Microsoft has been an important part of our digital transformation, helping us drive innovation and efficiencies through machine learning and advanced analytics so we can get products into the market faster, better serve our customers and, ultimately, deliver on consumer demand.

—Oliver Ganschar, Head of Digital Product Management and Innovation at The Kraft Heinz Company

As The Kraft Heinz Company looks to continue leading the curve on AI innovation, it plans to scale The Cookbook beyond HEINZ Tomato Ketchup.

“We aim to use key learnings and insights from The Cookbook pilot phases to scale to other brands, products, and Kraft Heinz businesses, and we are currently in the process of exploring additional use cases for the technology,” said Ganschar.

Prioritizing data in decision-making at scale

Enthusiasm around AI is not just confined to the C-Suite—it is growing throughout entire organizations. Ken Meyer, Chief Information Officer for Enterprise Functions at Wells Fargo, says employees at every level are clamoring for AI products, with more than 30,000 using Microsoft 365 Copilot since it was rolled out in June 2025. The active usage rate for enabled employees is 92%, demonstrating the value the tool offers to the employees.

It’s really a proof point saying that not only did people want to use these products, but they were waiting for it and excited about it, and what’s really exciting is understanding the usage across the different ways in which they’ve engaged: creating content, doing summarization, and researching. That’s real time saved for our Microsoft 365 licensed users.

—Ken Meyer, Chief Information Officer for Enterprise Functions at Wells Fargo

This kind of data is the foundation of decision-making at the 173-year-old financial institution, particularly when it comes to choosing solutions to put in the hands of employees. Analytics drove Wells Fargo’s 2021 migration to Microsoft Azure as its primary public cloud provider and guided subsequent rollouts of Microsoft 365 and Microsoft SharePoint to enhance productivity and strengthen security. Now AI is increasing efficiency at Wells Fargo, with generative and agent capabilities in GitHub, Microsoft Copilot, and other Microsoft AI solutions equipping employees to more effectively support clients, each other, and the organization.

Organizations across the financial services industry are seeing the opportunities AI can create to unlock greater innovation and business value at an accelerated pace. It plays a critical role in streamlining operations and compliance management—making processes more efficient and secure.

Microsoft understands what it takes to be an enterprise business and do things at scale. When you think about being in a highly regulated industry, being a bank our size, and the commitments that we have to the number of clients that we serve, it’s important and it gives us a lot of confidence.

—Ken Meyer, Chief Information Officer for Enterprise Functions at Wells Fargo

Weaving innovation and intuition into all operations

In retail, as in finance, leaders must keep pace with their customers’ rapid adoption of emerging technologies. From evolving consumer expectations to the rise of omnichannel experiences, agility is key. Levi Strauss & Co., navigating new audiences and sales models, has partnered with Microsoft to stay resilient and innovative, using digital tools to streamline operations, personalize engagement, and scale sustainably in a fast-moving retail landscape.

Retail has always been a story of change. Microsoft is a big part of how we scale for the next 100 years.

—Jason Gowans, Chief Digital and Technology Officer at Levi Strauss & Co.

On the heels of a massive cloud migration to Azure, the 172-year-old company is prepared to lead in a new era of agentic AI. The first five years of a seven-year digital transformation at Levi Strauss & Co. saw streamlined workflows, improved analytics and data quality, and more robust security—enabling the company to scale AI-powered innovation across the organization. 

Now, Levi Strauss & Co. and Microsoft are collaborating on AI-powered solutions that enhance employee decision-making, efficiency, and creativity with seamless access to insights. The newest example of this is the development of a new “superagent,” which has the intelligence to intuitively understand which applications and subagents to activate based on a user’s prompt. 

The foundation of the superagent streamlines the process to develop and integrate future agents—creating substantial savings. With AI woven into every experience for employees and fans, by extension, Levi Strauss & Co. is supercharging its trajectory toward becoming a fan-obsessed, direct-to-consumer business. 

We believe in performance, but our core value is also integrity. Whatever we choose to do with AI, it’s going to be grounded in making sure that it’s the right decision for our people, for the company, and for the community. 

—Sheena Kunhiraman, Vice President of People Systems and Analytics at Levi Strauss & Co.

Modernizing a trusted resource to elevate human expertise

Land O’Lakes, one of America’s premier agribusiness and food companies, is a member-owned cooperative with industry-leading operations that span the spectrum from agricultural production to consumer foods. Behind the scenes, the company has executed a sweeping digital transformation: migrating more than two-thirds of its IT environment to Azure, driving widespread adoption of Microsoft Copilot, and fine-tuning its enterprise copilot.

We are not a tech company, but a tech-forward company. Having a true technology partner that helps our digital transformation was the foundation of our partnership with Microsoft. We wanted a bigger bat to swing. Microsoft gives us that.

—Teddy Bekele, Senior Vice President and Chief Technology Officer at Land O’Lakes

This modern infrastructure is the foundation for AI innovation and serves as the backbone for a new digital assistant called “Oz.” The assistant combines the power of Microsoft AI with Land O’Lakes’ deep agricultural expertise to help farmers make data-informed decisions to maximize yield potential and mitigate risk throughout the growing season.

Land O’Lakes is owned by highly knowledgeable agricultural retailers who act as trusted advisors to farmers. Historically, these retail agronomists have used the Land O’Lakes Crop Protection Guide, an 800-page agronomic resource built on 20 years of data and millions of agriculture-specific data points, to assist farmers. Oz allows retail agronomists to quickly surface critical agricultural information specific to a farm’s unique features and needs in a mobile-friendly format.

The idea has always been to make that agronomist the hero at the farm gate. Instead of flipping through a book, now agronomists can have this deep technical discussion with the AI. So, we go from a good recommendation to a highly customizable recommendation for that farmer.

—Teddy Bekele, Senior Vice President and Chief Technology Officer at Land O’Lakes

Oz is just the latest example of how Land O’Lakes’ AI transformation has enabled them to bring cutting-edge, AI-powered solutions to the farmers they serve.

Bringing AI into the next Frontier

By thoughtfully integrating AI into many levels and functions of their businesses, centennial companies are demonstrating the ingenuity and resilience that has allowed them to dynamically navigate past moments of disruption for more than 100 years. We’re proud to partner with these Frontier Firms and support their continued transformation.

Explore examples of AI in action from this year’s Microsoft Ignite 2025 conference to envision how Microsoft’s industry-specific solutions can augment your organization’s expertise and experiences with AI.

Use our resources to innovate with AI and start your journey to becoming a Frontier Firm.

¹ Building Indiana Business, The Centennial Secret: How Do Companies Last 100 Years?, October 23, 2020.

The post From legacy to Frontier: How 100-year brands are leading AI innovation appeared first on The Microsoft Cloud Blog.