The answer isn’t more tools. It’s what people are enabled to do with them.

The friction we see is that many people are unsure how to use AI to their greatest benefit. Companies often struggle to measure the impact of their AI investments because they likely haven’t yet demonstrated return on investment for their employees.

Progress comes when employees actively adopt AI and see meaningful impact on their work—when they’re confident about questioning outputs, applying judgment, and integrating it into their real work.

But there’s another layer to that friction.

Alongside the industry’s excitement and expectations, there’s real hesitation. AI still feels uncertain: Where do I start? Am I already behind? What if I get this wrong?

That hesitation is a signal that access alone isn’t enough; people need to feel confident that AI will elevate their work, not detract from it, or worse, make them irrelevant.

You aren’t behind; you just need to get started. And you do that by learning one new skill at a time. Even skeptics can become strong advocates if they start by learning how to use AI to do the traditional task they dislike most. Once they feel the inevitable benefit, they’re highly likely to try the next task they don’t like doing. From there, we often see a path of continuous learning.

Here’s what too few people realize: technology alone isn’t going to elevate their performance. When everyone knows how to use the tools, the differentiator will be their uniquely human skills that no AI tool can replace.

Human skills aren’t “soft”—they’re foundational

In the New York Times bestselling book Open to Work: How to Get Ahead in the Age of AI, the authors describe five human capabilities that no machine can replace: curiosity, compassion, creativity, courage, and communication.

That same idea extends beyond the individual—organizations aren’t abstract systems; they’re made of people.

What we often call “organizational skills” are simply human skills, practiced consistently and scaled intentionally.

From human potential to organizational capability

A new IDC InfoBrief sponsored by Microsoft, Powering Up: Human Skills for the AI Era,¹ highlights a familiar gap: organizations are investing heavily in AI tools but far less in the capabilities needed to turn them into value.

These capabilities span cognitive, collaborative, leadership, ethical, and business domains.

How do these skills scale? They come together across three levels:

1. Individual. How people think, decide, take risks, and act—especially when working with AI.
2. Teams. How those capabilities show up in collaboration and workflows.
3. Organization. What leaders reinforce through culture, systems, and governance.

This is where personal capability becomes organizational advantage.

How human skills scale in the AI era

The human skills explored in Open to Work don’t disappear at the organizational level; they show up differently at scale.

1. Curiosity: Cognitive and collaborative capability

At the individual level, curiosity starts with a desire to explore and learn what’s possible. At scale, this shows up as:

• Asking better questions to challenge assumptions.
• Exploring different approaches beyond the first answer.
• Sharing learnings across teams.

2. Compassion: Ethical and leadership capability

Compassion is empathy and awareness of impact. At scale, this shows up as:

• Applying ethical judgment and accountability.
• Identifying and addressing bias.
• Practicing responsible data use.

3. Creativity: Cognitive and business capability

Creativity isn’t about aesthetics. It’s about imagining what doesn’t yet exist. At scale, this shows up as:

• Framing problems more effectively.
• Creating new sources of value.
• Driving innovation beyond efficiency.

AI can optimize what exists. Humans decide what’s worth building next.

4. Courage: Cognitive and leadership capability

Courage starts with acting even when outcomes aren’t certain. At scale, this shows up as:

• Applying critical thinking and judgment.
• Making decisions in complex environments.
• Leading change without guaranteed outcomes.

5. Communication: Leadership and business capability

Communication starts with clarity and listening. At scale, this shows up as:

• Setting a clear vision for AI transformation.
• Translating technical capability into business meaning.
• Aligning teams across functions.

What leaders should consider next

Taken together, these examples point to a clear pattern: personal strengths become organizational advantage when they’re built at scale.

If human skills are the differentiator, how do we design for them intentionally? Three mindset adjustments matter most—especially in a moment where excitement about AI is often matched by hesitation about where to begin:

1. Focus on the work, not just the training
   • Human skills develop through real decisions, real collaboration, and real accountability—not one-off courses.
2. Model the behaviors consistently
   • What leaders practice signals what’s safe. Judgment, curiosity, empathy, and learning must be seen, not just stated.
3. Measure what actually changes outcomes
   • Beyond adoption, organizations need to track decision quality, trust and confidence, and cross-functional outcomes.

The real opportunity of AI

AI won’t make organizations less human—but it will raise expectations for how people think, decide, and work.

The organizations that succeed won’t be the most automated. They’ll be the ones that invest in people as intentionally as they invest in technology.

That’s the opportunity—and the work—in front of us.

Continue learning at Microsoft AI Skills Fest

If you’re looking for a practical way to build AI and human skills, no matter your role, join us for Microsoft AI Skills Fest, June 8–12, 2026. It’s a week of free, guided, digital learning designed to make skilling more approachable and relevant, with options for leaders, business users, technical roles, and developers.

On the AI Skills Fest Mainstage, human skills will be a prominent theme. I’ll be hosting a conversation with Aneesh Raman, co-author of Open to Work, and Gina Smith, PhD, co-author of Powering Up: Human Skills for the AI Era. Together we’ll unpack what it takes to build human capability alongside AI—from individual habits to team practices to organization-wide norms.

To go deeper, we’ll also have a dedicated session with Dr. Michael Gervais, sport performance psychologist and founder of Finding Mastery, to help you develop the mindsets and human skills that will help you thrive as AI reshapes how we work.

We hope to see you there.

¹IDC InfoBrief, sponsored by Microsoft, Powering Up: Human Skills for the AI Era, Doc. US54451326-IB, May 2026.

The post AI needs more than intelligence—it needs humanity appeared first on The Microsoft Cloud Blog.

Financial service institutions have long been among the sectors requiring the greatest levels of security, compliance, and reliability. Today, in the age of AI, organizations in the financial sector are looking to apply AI to alleviate some of these burdens, while also unlocking meaningful competitive advantage through AI applications.

The good news: If you’re in this industry you will likely have decades of sensitive data you can use for learning and insights that can lead to real customer solutions.

The bad news: Yesterday’s data infrastructure might not be up to the task. Delivering the scale, speed, predictive maintenance, access, and performance that today’s financial data platforms need—along with the standard security and compliance—requires rethinking your database solution for the modern era.

The stakes are higher with sensitive data

Maintaining always-on services and meeting stringent regulatory requirements have been baseline expectations in finance for years. Now, with surging digital transactions and AI-powered projects, the pressure has only intensified. In some financial organizations, even a few minutes of downtime can be disastrous, given the reliance on every day availability. Aging, self-managed databases struggle to keep up with high-volume transactions and real-time analytics demands. The operational overhead of managing such systems (like patching, scaling hardware, and manual failovers) drains resources that could be better spent on innovation.

It’s telling that predictive maintenance and infrastructure automation have become focal points for banks to avoid costly outages. Yet, acting too fast also carries risk: one fintech leader recently cautioned that “in financial services, harm historically scales faster than benefit,” underscoring that if you scale up without a solid foundation, problems can amplify rapidly. In other words, bad data or brittle systems will only wreak havoc faster when accelerated by AI. This reality makes it clear that simply layering AI on top of old infrastructure isn’t viable—the core data platform needs modernization.

An investment in PostgreSQL on Microsoft Azure

Azure PostgreSQL managed services, including Microsoft Azure Database for PostgreSQL, address these modern challenges, combining PostgreSQL’s versatility with Azure’s cloud resilience and ecosystem. It’s a fully managed service, meaning Azure handles the heavy lifting of maintenance, updates, and scaling behind the scenes, so teams can focus on value-add work instead of upkeep. Crucially for financial institutions, Azure Database for PostgreSQL offers flexible performance scaling. You can start with a small instance and scale up to large multi-vCore servers or even scale out across elastic clusters to distribute heavy workloads across multiple nodes. This elasticity ensures that sudden surges in trading volume or customer usage won’t degrade application performance.

Enterprise-grade resilience and availability

Downtime isn’t an option for these critical applications, so continuous availability is baked into Azure PostgreSQL services. With a few clicks, you can enable zone-redundant high availability, deploying a fully synchronized standby server in a different Azure availability zone. In the event of an outage or even an entire datacenter zone failure, the service triggers an automatic failover to the standby typically within 60 to 120 seconds with zero data loss. This architecture delivers up to a 99.99% availability service level agreement (SLA) for mission-critical workloads, which is a key assurance for financial apps that cannot go down.

For read-intensive scenarios, Azure Database for PostgreSQL supports read replicas which asynchronously replicate data and allow you to offload analytics or reporting queries without impacting the primary database’s performance. These replicas can even be in different Azure regions, doubling as a disaster recovery option to keep services running through regional disruptions. The bottom line: whether it’s handling a hardware failure or scaling out reads, the service preserves uptime and consistency so your customers and applications see uninterrupted service.

Security, compliance, and an integrated ecosystem

Azure Database for PostgreSQL helps simplify compliance for sensitive and highly regulated data by providing layered security controls out of the box. All data is encrypted at rest by default, and you have the option to use customer-managed keys for encryption if you need full control over key rotation and access. Network isolation is straightforward: you can deploy your PostgreSQL server into an Azure Virtual Network with private endpoints, so that database access stays entirely on your private Azure network with no exposure to the public internet.

For identity and access management, Azure Database for PostgreSQL supports Microsoft Entra ID authentication, allowing you to manage database users and permissions through centralized Entra ID identities instead of static credentials. This means you can use existing corporate security policies and easily onboard and offboard users per compliance needs. Together, these features help meet strict standards like payment card industry data security standard (PCI DSS) and Security Operations Center (SOC) compliance by controlling who has access to what data and ensuring data is protected at rest and in motion.

Because it’s an Azure service, PostgreSQL integrates naturally with the broader Microsoft ecosystem. You can connect your data to analytics and AI services (such as Microsoft Fabric and Azure AI) without complex Extract, Transform, and Load (ETL), accelerating the development of AI-powered apps on top of your operational data.

In fact, after modernizing its platform, BNY Mellon reported that its teams could “innovate faster in areas such as data management, analytics, AI, and machine learning” once they were running PostgreSQL on Azure. Developers also retain the full power of PostgreSQL’s extensibility. Azure’s managed service supports a wide range of popular Postgres extensions (from PostGIS for geospatial analysis to pg_cron for scheduling), so developers can continue to use specialized plugins for financial calculations, time-series analysis, or even graph queries as needed.

A transformation with returns in nine months

To see these benefits in action, consider BNY Mellon, a global financial services company that modernized a critical data platform by migrating to Azure Database for PostgreSQL. BNY Mellon’s Data Vault system ingests and manages mission-critical, multitenant data for clients—it demanded high resilience, scalability, and agility that their legacy self-managed database couldn’t easily provide. Working closely with Microsoft, BNY Mellon moved this workload to Azure Database for PostgreSQL, completing the migration in just nine months.

By adopting Azure’s fully managed Postgres, the company achieved simplified data storage and analytics and built a “cohesive, customized solution” aligned with their microservices architecture. Resiliency improved immediately, with Azure’s high availability and backup capabilities, and BNY Mellon’s engineering teams gained more time for innovation now that routine database maintenance is offloaded to Azure. This new foundation is not only handling today’s needs but is flexible enough to evolve with future AI and analytics initiatives, exemplifying how a modern cloud database can empower a venerable financial institution to stay on the cutting edge.

A step toward readiness for the era of AI

Modern financial services requires a database platform that can scale effortlessly, stay secure and compliant by default, and free up your teams to innovate with data. Azure Database for PostgreSQL, with its combination of performance, high availability, advanced security, and rich PostgreSQL compatibility, rises to that challenge. It’s a solution that lets developers and Database Administrators (DBAs) spend less time wrestling with infrastructure limitations and more time building the next generation of financial applications.

Ready to take the next step? Explore our PostgreSQL for Financial Services solution guide for architectural best practices and implementation tips.

The post AI is requiring financial services to modernize their data platforms appeared first on The Microsoft Cloud Blog.

This is a defining moment for healthcare and the decisions made now will shape outcomes for years to come. The question is no longer whether to adopt AI—it’s how to alleviate these pressures with agency, security, and trust.

AI for Better Health is our ambition to enable every person on the planet to live healthier by transforming how care is discovered, delivered, and accessed at scale. We are redefining how AI works alongside people to transform health across three priorities: 

1. Accelerating lifesaving breakthroughs.
   
2. Transforming the healthcare experience.
   
3. Advancing global health equity.
   

Accelerating lifesaving breakthroughs

Across healthcare and life sciences, organizations are bringing human judgement together with AI to enable discovery—helping researchers identify patterns that may support earlier insights and accelerate innovation.

In practice

• Novo Nordisk aimed to scale a pipeline of drug discovery, development, and data science capabilities with AI and machine learning. The teams built a Novo Nordisk AI platform and amplified its culture of innovation across many use cases, including predictive AI models for advanced risk detection in cardiovascular diseases. The algorithm may be able to predict patients’ cardiovascular risk better than current clinical standards. 
  
• In clinical settings, AI is helping clinicians act sooner and with greater precision. For example, AI-supported tumor boards are helping organizations like Providence surface potential data insights for clinician review, such as identifying biomarkers or matching patients to clinical trials, to support decision making.
  

These innovations are helping advance research so it can reach more patients, more communities, and more health systems worldwide.

Transforming the healthcare experience

As new discoveries move into practice, the way care is delivered is beginning to change. Increasingly, AI is being embedded directly into workflows with tools designed to reduce friction, connect information, and provide insights in context. As routine work is streamlined, people gain the time and clarity to focus on deeper impact—strengthening care, discovery and operations in service of better health for all.

In practice

• At Piedmont Healthcare in Atlanta, for example, an OB‑GYN is using Microsoft Dragon Copilot to capture and structure clinical conversations in real time—freeing her to listen more deeply, build trust, and apply more informed judgment during sensitive moments like pregnancy and postpartum. This shift toward more empathetic, patient-centered care improves reproductive health outcomes for women of color at her clinic.
  
• These capabilities extend beyond documentation. AI is beginning to surface new signals within the flow of care. For example, identifying patterns in vocal characteristics may provide additional context for clinician evaluation, subject to appropriate validation and oversight. Baptist Health in Kentucky is beginning to apply this approach within an ambient workflow, adopting tools from Canary Speech to support earlier and more informed intervention.
  

What begins as support for individual interactions can scale across teams, specialties, and health systems. And as some of the constraints on their time and attention are lifted, providers can focus more fully on their patients—bringing the human element back to care.

Advancing global health equity

Ensuring that better health is accessible to everyone remains one of the most important and complex challenges in healthcare.

AI has the potential to extend the reach of clinicians and care teams—bringing health information to remote communities, supporting resource-constrained environments, and providing individuals with more direct access to trusted health information.

As these capabilities scale, they can help reduce barriers tied to geography, infrastructure, and access to specialized care—supporting more equitable access to care.

In practice

• Through innovations like Microsoft Copilot Health, AI-powered health companions are helping individuals make sense of complex health information. By bringing together clinical records, wearable data, and medical knowledge, supported by Microsoft security and privacy technologies that help protect data, individuals can better understand their health. This can help them feel more empowered in discussions with their care teams.
  
• Partnerships are helping redesign care models and address long-standing inequities. For example, Microsoft is collaborating with Kearney to mobilize a global community of innovators through the Women’s Health Tech Manifesto—using data and technology to help close gaps in women’s health.
  
• Through the Rural Health Transformation (RHT) Collaborative, a multi-sector public and private partnership, co-chaired by Microsoft and others, we are uniting technology providers, health systems, payers, and non-profits to help states deploy CMS’s rural health funding into ready-to-adopt AI-enabled care models. Together, we are supporting efforts to expand access to primary care, telehealth, and remote monitoring for rural communities at risk of losing critical health services, while strengthening the cybersecurity and interoperability foundations more than 700 rural hospitals already rely on.
  

Ultimately, advancing health equity will depend not only on leveraging AI, but also on the responsible design, deployment, and use of these technologies with appropriate human oversight, transparency, and accountability.

Building the future of health—together

The decisions being made today will shape how AI is used in healthcare for years to come.

Progress will not be uniform. Every worker, leader and organization is learning how to harness AI-enabled workflows that amplify what we as humans can do to make meaningful change. Each step forward unlocks new possibilities for organizations and communities.

Taken together, these outcomes move us toward a broader vision for the future where every person on the planet can live healthier. That’s the goal of AI for Better Health.

Our ambition of AI for Better Health is grounded in use cases that create impact today.

• Get our e-book, AI for Better Health: Enabling transformation in healthcare, to explore how organizations are applying this approach in practice.
  
• Explore Microsoft for Healthcare to see how to drive innovation and improve healthcare experiences with trusted, AI-powered solutions.

The post AI for Better Health: Enabling every person on the planet to live healthier appeared first on The Microsoft Cloud Blog.

That era is over. Brands and retailers have entered a new phase of AI adoption. Agentic AI is where systems don’t just analyze or recommend; they execute. And the conversation has finally moved from hype to economics. Technology budgets across retail and consumer goods are projected to hit $113B in 2026, but the mandate from the C‑suite is no longer “innovate.” It’s “prove the ROI and scale.”

The research is here. A recent Forrester Total Economic Impact™ study of Microsoft AI solutions for retail and consumer goods organizations projects 124% to 282% ROI over three years, with $7.7M to $17.6M in net present value for a composite $5B enterprise.¹ That’s not aspirational. That’s P&L math.

What matters even more than the topline number is where the value is showing up. Across brands and retailers, three lines of business—marketing, supply chain, and store operations—are delivering scaled, measurable impact through proven, named Agentic AI use cases. Not pilots. Not experiments. Production.

Marketing: AI shopping assistants and AI‑assisted campaign execution

Marketing is where many brands and retailers first touched AI, but now it’s where the economics are clearest.

The Forrester TEI highlights three core marketing use cases driving ROI today:

1. AI shopping assistants embedded in digital commerce.
   
2. AI‑assisted content creation and campaign execution.
   
3. AI‑driven marketing performance optimization.
   

AI shopping assistants, built to support product discovery, evaluation, and conversion, delivered up to a 4% improvement in conversion rate, generating $1.5M to $3.4M in incremental digital revenue over three years for the composite organization. Among brands and retailers deploying these assistants at scale, Forrester observed reductions in cart abandonment and increases in average order value that materially changed digital revenue trajectories.

Behind the scenes, AI‑assisted marketing execution is where productivity compounds. According to the study, the composite organization reclaimed 7–13 hours per month per person, shifting time away from mechanical production and toward creative judgment, experimentation, and optimization. By automating research synthesis, content drafting, summarization, and performance analysis, the composite organization is on track to realize $4.5M to $6.7M in labor productivity gains over three years.

Brands and retailers also reduced dependency on external agencies. Early‑stage creative development and campaign prep moved in‑house, cutting outsourced marketing spend by an expected $433K to $881K over three years, while preserving agencies for high‑value strategic work.

The 2026 Work Trend Index reinforces what we’re seeing operationally: 66% of AI users say using it allows them spend more time on high‑value work, and 58% say they’re producing work they couldn’t have created a year ago. This isn’t automation replacing marketers; it’s agentic AI upgrading the role from execution to orchestration.

Supply chain: AI demand forecasting, inventory optimization, and autonomous planning

If marketing proves AI can grow revenue, supply chain proves it can protect margin for brands and retailers that live or die by forecast accuracy.

The TEI study identifies three supply‑chain use cases driving the majority of value:

1. AI‑driven demand forecasting.
   
2. Inventory and allocation optimization.
   
3. Exception‑based planning with agentic execution.
   

AI‑driven demand forecasting and inventory optimization delivered $3M to $6.3M in three‑year benefits, driven by higher forecast accuracy, better buy decisions, and earlier detection of demand shifts. One consumer goods leader cited a 10‑point improvement in forecast accuracy versus traditional statistical models, which is enough to materially reduce both stockouts and excess inventory.

On the labor side, AI automated routine planning tasks like data pulls, reconciliation, and reporting, freeing 6–12 hours per month per planner across hundreds of planning FTEs. One retailer reduced its planning workforce from 50–60 planners to 40–50 while maintaining performance, as AI took over SKU‑store allocation and replenishment decisions.

The real inflection point is agentic execution. Instead of analysts identifying issues and manually implementing changes, planners now work in exception‑based workflows, where AI flags anomalies and agents execute adjustments via natural language commands. As one planning leader put it: planners focus on decisions, not spreadsheets.

For brands and retailers, this is the shift from AI as insight to AI as operator. And the economics follow.

Store operations: Digital shelf labels and frontline task automation

Store operations are where brands and retailers have historically struggled to unlock productivity. Agentic AI is changing that.

The Forrester TEI highlights two frontline use cases delivering immediate ROI:

1. AI‑powered digital shelf labels (DSLs).
   
2. Frontline task automation and employee copilots.
   

Digital shelf labels eliminated manual price changes, saving an estimated 200 labor hours per store per year. For large brands and retailers, that translates into thousands of hours redirected from label maintenance to customer engagement and execution.

Frontline task automation covering price updates, inventory checks, and information lookup delivered 9–15 hours of time savings per store per month. More importantly, it improved employee experience. By stripping out repetitive, low‑value work, retailers and brands can reduce burnout and turnover, and aim to drive $1M to $1.3M in reduced frontline attrition costs over three years.

This is where agentic AI, such as those utilizing Azure AI and Copilot Studio, quietly becomes a people strategy. When frontline roles become less tedious and more customer‑centric, retention improves along with execution.

From line‑of‑business wins to institutional advantage

The ROI is real. Across marketing, supply chain, and store operations, Forrester projects $14M to $23.9M in total three‑year benefits for brands and retailers that scale these use cases. But here’s the uncomfortable truth: Most organizations will capture the first wave of gains and then stall.

The 2026 Work Trend Index shows that organizational factors drive more than 2x the AI impact of individual capability. Agents can take on execution. Human agency expands. But only organizations that redesign how work gets done convert those gains into durable advantage.

Microsoft calls the leaders in this shift Frontier Firms. They’re organizations that move beyond deploying tools to rebuilding operating models around agents, workflows, incentives, and decision rights. These firms become learning systems, compounding insight from every transaction, every forecast, and every customer interaction.

Retailers and brands that treat agentic AI as “just another system” will see diminishing returns. Those that treat it as an operating‑model reset will build something harder to copy than any algorithm.

The leadership imperative is clear: ROI is no longer in question. Sustaining it requires redesigning incentives, workflows, and management systems so line‑of‑business gains become institutional advantage. The hype was fun. The economics are better.

For brands and retailers, agentic AI has moved from experimentation to execution; from insight to action; from promise to profit. The organizations that win from here won’t be the ones with the flashiest pilots or the longest vendor lists. They’ll be the ones that redesign incentives, rebuild workflows, and re‑architect management systems so agentic gains in marketing, supply chain, and store operations compound into institutional advantage.

For brands and retailers, the time is now to maximize agentic AI.

¹ New Technology: The Projected Total Economic Impact™ Of Microsoft AI Solutions For Retail And Consumer Goods Organizations is a Forrester Consulting New Technology Projected Total Economic Impact Study Commissioned by Microsoft, April 2026

To understand the benefits, costs, and risks associated with this investment, Forrester interviewed four decision-makers and surveyed 134 global respondents at the director level and above with experience using Microsoft AI solutions. For the purposes of this study, Forrester aggregated the results from these customers into a single composite organization.

The post Agentic AI is reshaping retail and consumer goods economics appeared first on The Microsoft Cloud Blog.

Property and casualty (P&C) insurers face growing challenges, including macro-economic factors and cyber-attacks, but none is bigger than climate risk. Catastrophic events are nothing new, of course. What has changed is the scale and frequency of weather-related losses and the operational strain that follows. Swiss Re estimates global insured losses from weather‑related natural catastrophes have exceeded $135 billion in 2024, marking the fifth consecutive year insured losses topped $100 billion, and underscoring a structural escalation in climate‑related risk.¹

In response, many insurers are rethinking how to best deliver customer value, profitability, and growth. Mutual and cooperative insurers are under sustained pressure to balance financial strength with their purpose of providing protection in an environment marked by increasingly severe risks and closer regulatory scrutiny. It is a challenge that AI is well suited to answer, helping to expand the role of insurers from risk transfer providers to proactive risk partners.

Insurers and AI: early adoption and opportunity

A 2024 survey by the International Cooperative and Mutual Insurance Federation (ICMIF) found that 62% of respondents were already using AI, with a further 19% planning adoption within the next year. In practice, however, most deployments were commonly concentrated in specific functional areas, such as supporting underwriting, claims processing, and customer interactions. About 67% of insurers expect AI to become more central to their operations, even as many cite data quality and talent gaps as key challenges.²

According to a recent BCG study, only about 7% of insurers have successfully scaled initiatives, with 67% engaged in pilots, fragmented across functions. The opportunity now is to move from isolated use cases to AI embedded across end‑to‑end processes, extending to more automated, interconnected workflows and setting the stage for a shift toward risk prevention.³

How AI helps improve efficiency, service, and relationship management

Prevention does not replace excellence in risk transfer. Forward-looking organizations pursue both. They modernize service and core operations across the customer engagement cycle, while investing in prediction and prevention-oriented capabilities that help reduce future risk and strengthen long-term resilience.

One area where AI delivers important benefits is in enabling faster, more consistent client service by helping representatives locate and validate policy information faster. At Unum Group, for example, a new AI-powered application lets representatives search across 1.3 terabytes of policy and related documents and receive highly relevant answers in four to five seconds, with reported accuracy of up to 95%. This reduces time spent on manual lookup and frees representatives to focus on higher-value client interactions.

Likewise, NFU Mutual uses Copilot for Sales with Microsoft Dynamics 365 to establish a centralized “single source of truth” for customer data and interactions. By capturing and summarizing communications in real time, employees can quickly understand customer needs and respond with greater precision, helping to reduce response times and deliver more informed, personalized engagement.

AI can also streamline First Notice of Loss by ingesting call transcriptions, images, and videos, and guiding representatives to capture the right information in the first conversation, helping accelerate remediation.

In claims review, AI can turn static documentation into insights that inform action. Gallagher, for example, built an internal AI platform that summarizes complex claims files in minutes rather than hours, helping adjusters move faster and apply those insights more effectively across claims and client workflows.

In cases of widespread impact, such as a storm that causes power outages that result in many food spoilage claims, AI can route low-complexity claims through specialized AI agents that can help validate coverage, correlate weather data, detect fraud, calculate payouts, and generate audit trails. This increases service representative capacity for higher-impact cases by addressing low-risk claims with autonomous AI.

These innovations use document processing, contextual summarization, natural language interface and workflow automation, all of which can be used to help improve other processes across core insurance capabilities, customer service, and relationship management.

How AI helps with prevention and protection

The impact of prevention‑led approaches, whether applied to customer risk or enterprise risk, is twofold: financial resilience and stronger trust. This positions insurers as partners that mitigate, not just transfer risk for their customers.

Prevention‑led use cases extend well beyond field‑level interventions, such as property risk scoring or event‑readiness outreach. Increasingly, they focus on identifying and reducing risks earlier, before disruptions, security incidents, or service failures occur.

This shift is visible in how organizations are applying AI to support faster, more informed decisions. At Aon, which has an enterprise grade platform that can operate across solution lines, teams use AI-enabled tools to better assess and respond to risk. To enhance decision quality while maintaining strong governance, they built an Azure-based AI platform called AonGPT that securely connects data and supports consistent, governed analysis, especially in fast-moving situations. During recent California wildfires, Aon’s teams combined near real-time satellite imagery with proprietary data to generate timely insights that helped clients assess damage and plan their response.

AI also enables a shift from paying claims to helping customers reduce exposure before losses occur. Zurich Insurance Group deployed more than 200 AI tools to interpret unstructured inputs in the form of images, reports, and emails in multiple languages, and translate them into clear, consistent risk signals for underwriters. This improves the accuracy and timeliness of risk assessments, helping customers anticipate and reduce potential exposures before losses occur, and supports better informed underwriting decisions.

Prevention can also take the form of making dormant risk visible early enough to act. For example, AI can analyze large volumes of historical risk engineering reports to identify patterns, such as construction materials or design features that are associated with higher structural risk. This can distinguish specific higher-risk properties for expert review—in weeks rather than months in some cases—letting insurers engage earlier, prioritize inspections, and reduce the likelihood of disruption.

Emerging external data sources help improve risk prevention

Many prevention types depend on spotting and interpreting early signals, often from outside of core insurance systems. Using generative AI and machine learning, insurers can integrate third-party signals with internal data to help create new ways to refine risk selection, pricing, event readiness, customer outreach, and more. Sources such as external research, disclosures, regulatory filings, sensor data, and geospatial imagery can have immense impact, provided they are reliably accessible.

Initiatives from Microsoft Research and AI for Good highlight advances in third-party data that can significantly enrich the power of predictive solutions:

• First, Aurora is a foundation model of the atmosphere that produces fast, high-resolution forecasts, especially during extreme and fast-moving conditions. For insurers and reinsurers, that means more timely environmental intelligence to support underwriting, catastrophe modeling, claims surge planning, and reinsurance response.
  
• Second, SPARROW uses solar-powered devices with cameras, microphones, and sensors to detect meaningful changes on the ground and send near real-time insights to the cloud. For insurers, it shows how AI and sensor data can enable earlier risk detection, faster intervention, and reduce loss severity.
  

Earlier, more precise forecasting can inform proactive risk alerts, giving customers and commercial clients time to take preventive actions (for example, securing property or adjusting operations) and support coordination among insurers, risk engineers, brokers, and public authorities. The objective is straightforward: Improve analysis, lead time, and decision quality to mitigate large losses.

Priorities for success with AI and risk prevention

For leaders, realizing measurable value from AI across the business, including enhancing prevention, can happen in a matter of months or quarters. Microsoft’s view of industry patterns indicates that successful approaches often prioritize the following:

• Define a clear strategy and start with a small number of high‑value, extendable use cases aligned to core business priorities.
  
• Build strong data foundations and effective governance.
  
• Balance innovation with credibility and responsible adoption.
  
• Pursue business-led process re-architecture, change management, and talent skilling.
  
• Commit to stretch goals with active leadership, resourcing, and accountability.
  

Insurers who employ this comprehensive approach and tailor AI to their unique business requirements can improve the most critical aspects of their operations. Critically, they can enhance prevention as an important part of their future growth strategies.

Learn more

• To explore how leading insurers are using agentic AI to transform claims, underwriting, and customer experience, read our ebook.
• To explore solutions and resources for insurers, visit Microsoft for Insurance.
• To learn how frontier firms in financial services are using AI to improve efficiency, innovation, and customer satisfaction, get the e-book.
  
• Visit our blog for stories of how Microsoft for Financial Services helps firms accelerate business value.
  

¹ Swiss Re, “Hurricanes, severe thunderstorms and floods drive insured losses above USD 100 billion for 5th consecutive year, says Swiss Re Institute,” December 2024

² International Cooperative and Mutual Insurance Federation, “Balancing AI innovation with member-driven values at mutual and cooperative insurers,” February 26, 2025

³ BCG, “Insurance Leads in AI Adoption. Now It’s Time to Scale.” September 04, 2025

The post From risk transfer to risk prevention: How AI supports long-term financial resilience in insurance appeared first on The Microsoft Cloud Blog.

Despite widespread adoption, many organizations remain early in their AI maturity, constrained by fragmented foundations, unclear governance, and limited organizational alignment. These gaps make it difficult to move from experimentation to repeatable, enterprise‑wide impact.

The difference is not access to technology, but how prepared organizations are to deploy AI at scale—securely, responsibly, and in direct support of business outcomes. New research from Microsoft reveals a clear pattern: AI readiness is the foundation of Frontier Transformation—the next phase of enterprise change, where organizations align AI and human ingenuity to achieve their most ambitious goals.

In this research, AI readiness refers to an organization’s ability to deploy and scale AI in a way that is technically robust and organizationally aligned. It encompasses not only the underlying technology—such as data, cloud platforms, security, and AI models—but also the strategic, cultural, and governance capabilities required to operationalize AI responsibly and at scale. Organizations with high AI readiness can move beyond experimentation, embedding AI into core business processes to drive measurable outcomes.

Frontier Transformation starts with readiness

Frontier Transformation describes how leading organizations are embedding AI across every layer of the business—from employee productivity and customer engagement to core operations and decision-making. These organizations are AI leaders, referred to in the research as Frontier Firms that have moved beyond pilots. AI is not a side initiative; it’s a strategic capability.

The AI Readiness Assessment Whitepaper is based on a global study of 1,000 organizations across 15 countries and eight industries. It connects AI capabilities directly to business performance—and the results are striking.

Organizations with high AI readiness report 47–64% stronger performance across key metrics, including operational efficiency, innovation speed, workforce productivity, customer experience, and revenue growth. Readiness doesn’t just enable progress—it compounds advantage.

The readiness gap is widening

Only 17.7% of organizations qualify as AI leaders, meeting the threshold for both technology and organizational readiness. These Frontier Firms realize 56% higher AI value than organizations earlier in their journey.

This gap matters. While many organizations are investing in AI tools, far fewer are building the foundational capabilities required to scale those tools across the enterprise. As a result, leaders continue to accelerate—while others remain stuck in perpetual experimentation.

Readiness must be balanced, not siloed

One of the clearest insights from the research is that AI readiness must be balanced across both technology and organization. Organizations that overindex on technology often struggle with adoption and trust, while those that focus only on governance lack the platforms needed to scale. Frontier Firms avoid this tradeoff by progressing both dimensions together.

Roughly 30% of organizations reach a strong level of technology readiness. A similar share reaches organizational readiness. But only those that achieve both consistently deliver business impact.

Frontier Firms take a unified approach—aligning strategy, governance, culture, and platforms rather than treating them as separate workstreams.

To make readiness measurable, the Microsoft’s AI Readiness Advisor framework evaluates 10 domains across two dimensions:

Technology readiness

• AI models and generative AI applications
  
• Data and integration
  
• Cloud and hosting
  
• Information security
  

Organizational readiness

• Business and AI strategy
  
• AI experience and skills
  
• Organization and culture
  
• Responsible AI and governance
  

This end‑to‑end view helps organizations understand not just where they’re investing, but where gaps may limit scale.

Four readiness profiles—one clear leader

The research identifies four AI readiness segments:

• Observers are early in their journey, focused on exploration and isolated pilots, with limited operational impact.
  
• Operators excel at execution and governance but lack the modern AI platforms needed to accelerate innovation.
  
• Innovators invest heavily in models and applications but struggle to drive consistent adoption and change at scale.
  
• Frontier Firms lead across both dimensions—enabling secure, scalable AI that is embedded into everyday business operations.
  

Frontier Firms have largely moved from experimentation to optimization. Their focus is on standardization, reuse, and managing AI as a portfolio tied to business KPIs.

Cloud maturity differentiates AI leaders

Cloud strategy is a defining characteristic of Frontier Firms.

Frontier Firms treat the cloud not simply as infrastructure, but as a control plane—where data, models, applications, security, and governance operate together. Approximately 60% of AI leaders run workloads on Azure, reflecting the importance of integrated governance, compliance, and data management for enterprise‑grade AI.

This approach allows AI leaders to standardize security, governance, and data access while enabling teams to innovate faster—without re‑creating foundational capabilities for each new use case.

Leaders also tend to invest platform‑first—building strong cloud, data, and model foundations before scaling applications. That sequencing enables faster innovation and more predictable outcomes over time.

Responsible AI accelerates adoption

Trust is not a barrier for Frontier Firms—it’s a capability.

AI leaders consistently score highest on responsible AI maturity, with formal frameworks, oversight, and monitoring in place. Rather than slowing progress, governance enables scale by building confidence among employees, customers, and regulators.

In Frontier organizations, responsibility and innovation move together—unlocking broader adoption and faster value realization.

AI leadership spans every industry

Frontier Firms appear across every industry studied, from financial services and healthcare to retail, manufacturing, and professional services.

What differs is not ambition—but execution. Leaders report improvements in productivity, accuracy, efficiency, and customer experience tailored to their sector. The takeaway is clear: Frontier Transformation is driven by capability, not industry position.

Turning insight into action

The data is clear: AI value is not unlocked by tools alone, but by readiness across technology, organization, and governance. Frontier Firms don’t wait for transformation—they prepare for it.

Importantly, readiness is not a binary state. Organizations progress through stages as they mature their platforms, operating models, and governance. Understanding where you are today is the first step toward making intentional, high‑impact investments that move the organization forward.

Is your organization ready for AI?

Read the AI Readiness Assessment Whitepaper to understand the research behind AI leadership, then take the AI Readiness Assessment to benchmark your organization and identify the most impactful next steps on your journey to Frontier Transformation.

Download the AI Readiness Whitepaper

Learn how to help your business assess and advance its AI readiness, and unlock Frontier Transformation.

Read the Whitepaper

The post From AI ambition to Frontier Transformation: Readiness defines the leaders appeared first on The Microsoft Cloud Blog.

• Author: You’re producing the work, calling on AI to help as needed — a line of code, a sentence, a chart.
• Editor: You set the intent and AI creates the first draft for you to edit and approve.
• Director: You create a spec and hand off entire tasks for AI to execute in the background.
• Orchestrator: You design a system where multiple agents run in parallel across a workflow, flagging exceptions and escalations to you.

Every business leader knows the world is changing, but far fewer have a clear picture of what to do about it. These four patterns are the place to start. The real work ahead for leaders is redesigning their firm’s operating model around the collaboration patterns.

As agent use increases, human involvement doesn’t disappear — it changes shape. What declines is the amount of tactical, step-by-step execution work humans do themselves. And what rises is the need for humans to set direction, define standards and evaluate outcomes.

Ultimately, the goal is not to move every task and business process to the fourth pattern. Instead, it’s up to leaders to help their organizations develop clarity around matching workstreams to the right collaboration pattern. That’s the shape of the Frontier Firm: defined by how deliberately leaders design work across functions, matching the level of human involvement to the outcome.

What the data shows

Our 2026 Work Trend Index research reinforces this shift across roles and industries. We analyzed trillions of anonymized Microsoft 365 productivity signals and surveyed 20,000 workers using AI across 10 countries. We also spoke with leading experts in AI, work and organizational psychology to help us unpack the insights from the data and understand where all this is going. The conclusion is consistent: the constraint is no longer what people can do, it is how work is structured around them.

• AI lifts individual potential. A privacy-preserving analysis of more than 100,000 chats in Microsoft 365 Copilot shows that 49% of all conversations support cognitive work — helping workers analyze information, solve problems, evaluate and think creatively. This shift is already visible in output, with 58% of AI users saying they’re producing work they couldn’t have a year ago, rising to 80% among Frontier Professionals, the most advanced AI users in our research. Additionally, when AI users were asked which human skills are most important as AI takes on more work, they said two topped the list: quality control of AI output (50%) and critical thinking — that is, analyzing information objectively and making a reasoned judgment (46%).
• The Transformation Paradox. We are seeing a pressure point emerge within the organization where the pull to perform collides with the push to transform. 65% of AI users surveyed fear falling behind if they don’t use AI to adapt quickly, yet 45% say it feels safer to focus on current goals than to redesign work with AI. And only 13% of workers say they’re rewarded for reinvention of work with AI even if results aren’t met. The same forces accelerating AI adoption are holding it back.
• Every organization is a learning system. Our results show that organizational factors like culture, manager support and talent practices account for more than 2X the AI impact of individual factors like mindset and behavior (67% vs. 32%). Specifically, the findings underscore the importance of an AI-ready environment: a culture that treats AI as a strategic advantage and encourages experimentation, managers who model and incentivize AI use and talent practices that build skills and create space to apply them. The real question isn’t whether people have the right skills, it’s whether the organization is built to unlock them.

The firms that build a new operating model today won’t just move faster in the short term. They’ll build something more durable, setting themselves up to create value in ways that we can’t yet conceive of: an organization that learns faster than its competitors, compounds its own intelligence and gets harder to catch with every cycle.

For deeper analysis, see the 2026 Work Trend Index Report.

Enabling the Frontier Firm with Copilot Cowork — now mobile, extensible and enterprise-ready

None of an organization’s system scales without infrastructure that brings people and agents into the same flow of work with connected data and the ability to manage and govern it all. Microsoft 365 Copilot is built for exactly that.

Today, we’re expanding Copilot Cowork with new capabilities for Frontier customers to help organizations move from isolated AI tasks to coordinated, multistep work. Cowork enables people to define outcomes and delegate work across apps, business systems and data, with execution that stays directed and controlled throughout.

This update introduces Copilot Cowork Mobile for iOS and Android, along with a growing plugin ecosystem for Cowork, bringing more of an organization’s tools and data into these experiences. This includes native plugins across Microsoft services like Dynamics 365 and Fabric, and partner integrations available in the coming weeks like LSEG (London Stock Exchange Group), Miro, monday.com, S&P Global Energy and more. Organizations can also build custom plugins to turn their own workflows and expertise into reusable, scalable processes. Additionally, a first wave of federated Copilot connectors in Researcher and Microsoft 365 Copilot Chat is generally available today from partners like HubSpot, LSEG (London Stock Exchange Group), Moody’s, Notion and more.

Together, these updates extend Copilot Cowork from a task-based assistant into an extensible platform that helps orchestrate work across Microsoft and third-party systems. With management and governance through Microsoft Agent 365, organizations can deploy and scale agents across core business functions like sales, service and operations.

For more on these product innovations: Microsoft 365 blog.

AI is no longer an experiment. It is an execution challenge. Employees are already working across all four patterns. The open question for every leadership team is whether they can catch up. Access to AI won’t be the advantage for much longer. How the work is designed around it will be.

Jared Spataro, CMO, AI at Work at Microsoft, shapes how every organization applies AI and agents to reduce costs, create new value and define the future of work. He leads research, strategy and product across Copilot, Copilot Studio, Microsoft 365, Dynamics 365 and Power Platform.

The post How Frontier Firms are rebuilding the operating model for the age of AI appeared first on The Microsoft Cloud Blog.

As Australia looked to extend its winning streak to four straight Ashes on home soil, it was clear that left-handed batter Travis Head was leading the way for Australia as the runs piled up. But just how good was his performance? Fans using the Cricket Australia Live app had an instant answer. 

Thanks to the app’s new AI Insights feature, which provides live insights on player milestones, records and key moments using OpenAI’s GPT-5 within Microsoft Foundry, cricket aficionados and newcomers can now access much-needed context to better engage with the game. They can also dig deeper by asking follow-up questions about the insights provided. It’s an exciting development for Cricket Australia, the governing body of the sport in the country. 

“The recent series where England were here in Australia had a couple of key moments where I saw the insights come to life in real-time,” says Cricket Australia CEO Todd Greenberg. “And you can see the engagement through the analytics and the tracking that when something is delivered in the right time frame, in the right format, into the right hands, it has a huge effect.” 

Indeed, AI Insights showed that Head’s 172 runs for the match were his fifth-highest aggregate total in a test. His only higher efforts were 220 runs against Sri Lanka, 213 against West Indies, 181 against India and 180 against England. Head’s big day earned him Player of the Match honors and helped Australia claim a five-wicket victory in the match and a 4-1 Ashes series victory against its archrivals. 

Going beyond the box score 

“Scores and highlights tell you what happened. But the context tells you why you should care about it,” says Balamurugan P M, chief technology and digital officer at Cricket Australia. 

“It comes down to the storytelling. From my perspective, I thought it was essential for fans to learn more about the story rather than just following the scores or watching highlights. So, we wanted to give a different experience.” 

Cricket Australia had a corker in its arsenal as AI Insights came into focus – an extensive archive of official scorecards that dates to 1886, providing a wealth of historical data that could bridge the gap between the past and present. Those scorecards were carefully integrated over a period of three months to ensure the information would pass muster among the serious cricket experts. 

“We had hundreds of years of data, and when it comes to fans, trust is non-negotiable,” Balamurugan says. “When you’re dealing with records and milestones, you can’t make mistakes. There are some hardcore fans who know these stats like the back of their hand. History is core to cricket’s identity. And instant context turns a scoreboard into a story. 

“Getting that volume of data, integrating it and surfacing greater context for live games required huge data alignment and validation. With our systems and with the skilled team that we’ve got, that was made possible.” 

Creating a solution fans can use in real time 

Cricket Australia joined forces with Microsoft, alongside technical partners Insight Enterprises, HCL Tech and Skewer, to create the new iteration of the app. With the important Ashes and T20 international tournaments on the horizon, time was of the essence to launch the app before the bats were raised on those key fixtures. 

The app is anchored by Microsoft Azure, the cloud foundation that Cricket Australia uses to run and scale its digital platforms and the app experience. AI Insights takes advantage of Azure OpenAI Service in Microsoft Foundry, which generates the real-time, match-aware insights that serve as a companion to what fans are seeing on the field. 

“What we’re talking about is a really good example of solving a fan-facing problem with deep technical capability and a shared vision on delivery,” Greenberg says. “Microsoft brought world-class cloud and AI foundations. Without them, we would not have been able to get as far as we have. And our partners have helped accelerate the build, the integration and, importantly, operational readiness.” 

One of the biggest challenges with AI Insights is ensuring that fans watching a match and using the app can get updates and context within the flow of the game, making it an additional resource for fans at the grounds or watching alongside with commentary. 

Azure Cosmos DB supports Cricket Australia’s ecosystem of apps – including Cricket Australia Live with AI Insights and PlayCricket, which hosts scores for up to 7,000 community matches a weekend. The technology provides a fast, scalable data layer that can update quickly during live play, always keeping fans aware of the latest scores. 

“All live sport has one thing in common. There are no pauses,” Greenberg says. “It’s not like reality television. So, the experience has to be fast, reliable and consistent, especially when it’s under peak demand and when you have millions of people enjoying it at the same time.” 

An experience for every type of fan 

While cricket has its ardent supporters, especially in Australia, it can also be difficult for newcomers to pick up. As Cricket Australia looks to cultivate the next generation of fans, Greenberg realizes that the app can prevent sticky wickets for the sport’s novices. 

“I mean, we play a crazy sport that goes over five days and sometimes at the end of the five days, you still don’t get a result,” Greenberg says. “We can’t expect people to be tuned in at every moment, but what we can do is we can hyper-personalize the way they would like to engage with the sport during the contest.” 

The Seddon Cricket Club in Melbourne has been in existence since the 1920s and is now home to several senior, junior and all abilities sides that compete in associations across Australia. It is also home to a loyal supporters group, featuring fans who love the game in all forms. For them, the AI Insights on the Cricket Live App has been a value add as they go deeper into the game. 

“It’s definitely made it more interesting to follow along and learn more about the players,” says Cassie Gray, a Seddon Club supporter and cricket fan. “You could follow a player, you could see what they’re known for, as well as figure out what’s their next step or what do they need to get an amazing moment next. 

“Cricket is a game of history. It’s been around for a really long time, and the players influence other players, and countries influence other countries. With the insights, it gives me an understanding of not just what’s happening today, but what’s led up to that in the game itself.” 

The next step for AI insights is to create greater personalization within its levels of information for different types of fans. A user can select “newcomer,” “history buff” or “stats guru” and receive insights tailored to their persona. 

“We want to understand every fan and cater to how they want to be served by the app,” Balamurugan says. “We have moved from scores to storytelling, but we want to move from storytelling to fans setting up the narrative themselves. Fans should hear the story how they want to hear it. That is one of our lodestars.” 

With the initial success of the AI Insights feature, Greenberg said other sports organizations have reached out to learn more about how it was developed and the impact on the fanbase. Most people working at Cricket Australia have a deep love of the sport, often having played for many years. Greenberg hopes the app’s success and further innovation can continue the sport’s momentum. 

“The thing we’ll never know until much later on is the impact that we’re having on young kids falling in love and choosing cricket as their preferred sport,” he says. “And if we help them love it, what we can create for a fan on their journey between the ages of 8 and 80 is astronomical for a sport like cricket. And so, we’re very mindful of ensuring kids get the opportunity to engage in cricket so we can form lifelong partnerships.” 

Top Image caption: Supporters at the Seddon Cricket Club in Melbourne love the game in all forms, and the Cricket Live App featuring AI Insights has allowed them to gain further insights into the sport, whether they are a novice fan or stats guru. Photo by Graham Denholm for Microsoft.  

Elliott Smith writes about AI and innovation at Microsoft, from how the Premier League is transforming its online presence to why AI may play a major role in saving the Amazon rainforest. Previously, Smith worked as a sports reporter in Washington, D.C., Washington state and Texas, covering high schools to the pros. You can contact him on LinkedIn. 

The post Cricket Australia uses AI Insights to bring fans closer to the action appeared first on The Microsoft Cloud Blog.

MWC26 Barcelona, the GSMA’s flagship connectivity event, brought the global ecosystem together at scale: GSMA reported over 105,000 attendees from 207 countries and territories. In that backdrop, one theme kept surfacing in nearly every discussion I had: telecoms have moved past debating whether AI creates value and into the harder question of how to scale it across the enterprise with the right security, governance, and operating model.

In other words, the industry is shifting from isolated pilots to enterprise execution, embedding AI into customer engagement, network operations, and day-to-day workflows. This recap shares what we heard, what we showed, and what it signals for the next phase of telecom transformation.

Ahead of the event, we shared our point of view on how telecoms can realize AI ROI with a unified, trusted AI platform in our industry blog: MWC 2026: Microsoft Helps Telecoms Realize AI ROI. We described how Microsoft helps telecoms achieve return on intelligence and trust by scaling AI through a single intelligence platform—Microsoft IQ—with built‑in, carrier‑grade trust and governance so operators can innovate with confidence. During the week, additional customer and partner momentum included:

• AT&T unveiled Connected AI for Manufacturing and Connected Spaces for Enterprise, powered by Azure, enabling real-time data integration across factories, supply chains, buildings, and campuses to improve efficiency, safety, and decision-making.
• Tech Mahindra launched an ontology-driven agentic AI platform built on Microsoft Fabric and Azure AI Foundry to help operators modernize complex data environments and move from AI experimentation to production across use cases such as network optimization, fraud detection, and revenue assurance.
• Orange introduced Sharlie, a next-generation voice assistant for Sosh built with Microsoft AI, expected to handle more than 3 million customer conversations annually at full rollout.

What changed at MWC this year

The most important shift I saw wasn’t a single product announcement, it was a change in posture. Telecom leaders are increasingly treating AI as a core capability to be industrialized, not a set of experiments to be evaluated. The questions sounded less like “What use cases should we try?” and more often pointed to a simple reality: Scaling AI is a systems challenge. It requires bringing data, security, governance, and operational processes together so insights consistently turn into action. That’s the idea behind Microsoft’s Return on Intelligence—measurable business outcomes created when intelligence is embedded end-to-end across the telecom value chain.

At MWC, our goal was to make this practical, showing how AI can be applied across customer experience, operations, and growth, with trust built in from the start. Three themes came up repeatedly in these conversations:

• Data readiness: Connected intelligence that brings network, customer, and operational data together so models and agents can act with context.
  
• Trust at scale: Security, privacy, compliance, and governance that are designed in, not bolted on after pilots.
  
• Operationalization: Integrating AI into workflows, tools, and KPIs so teams can adopt it and leaders can measure outcomes.

That’s why we focused on an end-to-end story: Not just what AI can do, but how it can be delivered responsibly and repeatedly across the business. The show floor is where those ideas get tested quickly, so we designed the booth experience to reflect the real priorities operators are working on now.

What we showed: Turning intelligence into action

In the Microsoft booth, we brought Return on Intelligence to life with hands-on experiences designed around real operator workflows. The intent was simple: show how AI moves from insight to execution when it’s connected to the data people rely on, the tools they already use, and the guardrails organizations need.

Across 14 interactive demo stations, we explored five priorities many operators are investing in right now. Each one reflects a different place AI can create value and a different set of operational requirements to get it into production.

1. Copilots and AI agents for employees to reduce toil and speed decisions across customer care, operations, and field teams.
   
2. Agentic customer experiences that resolve issues faster, personalize interactions, and escalate to humans when needed.
   
3. Intelligent business operations that streamline order-to-cash and service fulfillment with better orchestration.
   
4. Autonomous network operations to detect, predict, and remediate issues—moving from reactive to proactive operations.
   
5. AI-enabled growth and monetization that helps identify opportunities and launch new offers faster.

What connected these scenarios wasn’t a single model, it was the operational pattern behind them: Unified data, secured access, governed AI, and integration into the workflows where work actually happens. That’s what turns a compelling demo into something a team can deploy, adopt, and measure.

The level of engagement reinforced the momentum behind this shift. Over the course of the week, more than 12,000 customers and partners visited the Microsoft booth. More than 3,200 attendees took part in more than 30 demos across 14 stations, and 1,387 people joined more than 38 in-booth theatre sessions with Microsoft and partner speakers. We also held 396 executive meetings with priority customers and partners—many focused on what it will take to move from pilot success to enterprise-scale execution.

Beyond the booth: Keeping the momentum going

MWC is four days on the calendar, but it’s really a milestone in a longer journey. The weeks before and after the show are where teams align on priorities, validate approaches, and translate interest into concrete next steps.

Our announcement blog helped frame the week by sharing Microsoft’s approach to scaling agentic and autonomous AI on a unified, trusted platform—and we continued the dialogue through customer and partner communications, follow-ups with teams exploring next steps, and ongoing industry programs.

Four takeaways from the week:

1. AI is an operating layer, not an add-on. The most consistent message was that AI is being stitched into how telecoms run: across customer experiences, operations, and growth. That shift changes what leaders prioritize, from isolated tools to enterprise foundations.
   
2. The maturity journey is speeding up. Many conversations reflected the same evolution: From pilot projects to targeted productivity improvements, to enterprise-wide transformation and growth. The winners will be the teams that can standardize what works and scale it across functions.
   
3. Agentic experiences raise the bar on trust. As copilots and AI agents take on more autonomous work—from customer interactions to network operations—security, privacy, and governance can’t be optional. Operators want guardrails, monitoring, and controls that work in production, not just in proofs of concept.
   
4. Outcomes depend on integration. AI delivers ROI when it connects to real data, real processes, and real workflows, so it can move from insight to action repeatedly. That’s why unifying data and AI, embedding security, and governing end-to-end matters: It’s what makes execution scalable.

Together, these themes point to the same conclusion: Telecoms that operationalize AI, securely and at scale, will move faster and compete differently.

What comes next: Moving from momentum to measurable outcomes

The post-MWC opportunity is straightforward: take the excitement and turn it into a repeatable operating model. For most operators, that means industrializing AI as a trusted layer, grounded in enterprise data, secured by design, governed end-to-end, and integrated into the workflows where customer experience and operational performance are won.

MWC 2026 made one thing clear: The telecoms that lead in the next cycle won’t just deploy AI, they’ll operationalize it. The organizations that can reliably turn intelligence into action, measure impact, and scale what works will set the pace for the industry’s next wave of transformation.

Continue the conversation

• Consolidated list of MWC announcements and news: Return on Intelligence: Powering the AI-First Telco
• Fierce Network: Microsoft & Nokia; Microsoft & Lumen; Microsoft, PWC & Orange

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• Accelerating revenue in telecommunications through agentic sales processes

The post MWC 2026 recap: From AI pilots to enterprise execution in telecom appeared first on The Microsoft Cloud Blog.

Return on intelligence and trust

For media and entertainment organizations, unlocking value from AI at scale depends on two things: intelligence and trust.

Built on three complementary elements—Work IQ, Fabric IQ, and Foundry IQ—Microsoft IQ is the intelligence layer that connects AI, data, and context across the media value chain. It gives AI agents a deep understanding of how creative teams work, how content moves through production and distribution, and how business decisions are made. This shared intelligence accelerates content creation, personalizes audience engagement, streamlines operations, and opens new paths to monetization—all while keeping human intent and creativity at the center.

None of this works without trust. Media organizations operate under intense intellectual property (IP), regulatory, anti-piracy, and contractual constraints. Frontier transformation depends on intelligence that is secure, governed, and observable by design. Microsoft delivers this through an AI control plane, with Agent 365 providing unified governance, identity, and observability across agents—ensuring they are discoverable, auditable, and policy‑controlled as they operate across creative, operational, and business workflows. Combined with Microsoft’s end‑to‑end security and compliance stack—spanning Microsoft Entra, Microsoft Purview, Microsoft Defender, Fabric, and Foundry—media organizations can scale AI confidently while protecting creative IP on a global scale. 

At NAB Show 2026, Microsoft is showcasing how media companies can move beyond experimentation to real business impact with AI. Through a single, unified platform that brings together AI, data, intelligence, and governance, Microsoft enables connected, actionable insights that help media organizations use intelligent work, AI-powered creation, agentic operations, and new growth with AI.

Read more to see where frontier media organizations are already achieving this.

Intelligent work

Frontier media organizations start by transforming how people work. Instead of being spread across dashboards and systems, intelligence shows up directly in the flow of work through Copilot, agents, and Microsoft IQ. See how a few examples of frontier media organizations are embedding intelligence into everyday work:

• Publicis Groupe announced it is expanding its partnership with Microsoft to enable intelligent, agent‑driven work for its more than 110,000 employees while powering the future of agentic marketing for its customers worldwide. Publicis is rolling out Microsoft 365 Copilot across its workforce to embed AI into daily work. Additionally, Publicis Sapient’s Slingshot framework will use Microsoft’s cloud, while Sapient AI solutions will integrate Microsoft Copilot Studio, Microsoft Agent 365, and Microsoft IQ, enabling customers to embed AI directly into core business processes. Sapient’s Bodhi platform will then allow organizations to deploy and scale secure, enterprise-grade AI agents across the business. The partnership is anchored in Epsilon, Publicis’ IP intelligence layer. AI agents built on Microsoft Fabric and powered by Epsilon will be able to reason, decide, and act on trusted, real-world data, to deliver impact that extends beyond model performance to sustained business value.
• The New York Jets are using technology to turn one of the most high‑pressure moments in sports—the NFL Draft—into an example of intelligent work in action. Through their Microsoft powered Titan platform and Copilot enabled tools, coaches, scouts, and front office leaders bring together film, analytics, historical data, and real‑time insights to make faster, better‑informed decisions. By augmenting human expertise with AI and cloud intelligence, the Jets show how intelligent work helps teams operate with speed, alignment, and confidence when every decision matters.

Together, these organizations show how intelligent work starts by meeting people where they already work and embedding intelligence directly into daily media workflows.

AI-powered creation

For creators and content teams, intelligence must move as fast as the moment. Frontier media organizations connect content, audience signals, and creative context in real-time, so insight immediately translates into action. See how frontier media organizations are using AI-powered creativity to scale their content:

• Collective Artists Network is working with Microsoft to support creators with AI-native content systems that keep human storytelling at the center. By embedding intelligence into filmmaking workflows, the collaboration aims to help teams iterate faster while preserving director-led creative vision.

We’re using technology being developed here in India to take our culture and history to a global audience, at a scale that wasn’t possible earlier. For us, this is a long-term priority, building stories that are rooted in who we are, but can travel anywhere in the world.

—Vijay Subramaniam, Founder and Group CEO, Collective Artists Network

• The NBA uses Microsoft Azure AI to power dynamic highlights, real‑time stats, and in-game insights embedded directly into fan touchpoints like the NBA App—bringing fans closer to the action through personalized, data‑driven experiences.

Microsoft has also announced new Microsoft AI models in Microsoft Foundry and Microsoft AI Playground to help media organizations further accelerate this shift. MAI-Transcribe-1 delivers state-of-the-art speech-to-text transcription across the top 25 most-used languages.¹ MAI-Voice-1 generates natural, realistic speech, that preserves speaker identity even across long-form content. MAI-Image-2 was created with photographers, designers, and visual storytellers, delivering natural lighting, accurate skin tones and texture, and clear in-image text for diagrams, layouts, and graphics.

Empowering creators is not about adding AI features. It is about orchestrating intelligence across content, data, and delivery—so creativity becomes action in real time.

Agentic operations

The most profound transformation in media today is operational. Frontier organizations are embedding intelligence across the entire media supply chain—from production and post to rights, distribution, and monetization—using agentic systems to replace manual handoffs with coordinated, end-to-end workflows.

• Penguin Random House is using agentic AI to modernize accessibility at scale, embedding governance and human oversight into core publishing workflows to improve efficiency and compliance.

Penguin Random House leverages Azure AI to scale the creation of high‑quality, context‑aware Alt-Text content across our e-book catalog. This initiative advances our accessibility commitments while materially reducing manual effort, cost, and operational complexity. By embedding Azure OpenAI into our accessibility workflow with a human‑in‑the‑loop governance model, we can generate image descriptions at scale, strengthening regulatory compliance and enabling a more accessible and efficient publishing process. 

—Christopher Hart, CIO Penguin Random House 

• The International Tennis Federation (ITF) is using Microsoft Azure and AI orchestration to power a real‑time intelligence platform that unifies match telemetry and delivers instant, on‑court insights to coaches and players. By processing more than 700,000 data points per match and generating over 1,500 statistical combinations in real time, the ITF is enabling teams to make faster, data‑driven decisions during play through applications like Match Insights, helping standardize access to advanced analytics across more than 140 competing nations regardless of their resources.

• Kantar is using Microsoft Copilot Studio to deploy teams of AI agents that automate complex data preparation tasks across its global operations. By breaking down manual workflows such as translating documents, validating policies, and organizing HR content into smaller subtasks handled by specialized agents, Kantar enabled its People Team to clean, tag, and structure 4,000 artifacts into 400 policy documents in just six weeks, laying the operational foundation for scalable, agent‑driven workflows that support employee queries across 60 countries.

With Foundry IQ and Fabric IQ, agents now operate with shared context across data, workflows, and knowledge—allowing operations to scale without chaos and intelligence to move end-to-end.

Additional partner solutions continue to enable agentic operations:

swXtch.io will introduce swXtch.ai and the swXtch AI Router, a platform that integrates with Microsoft Fabric and NVIDIA AI to enable real-time AI in live media workflows through a simple chat-driven interface, reducing the need for custom pipelines or specialized expertise.

New growth with AI

The clearest signal of frontier leadership is how media organizations innovate. Instead of experimenting at the edges, leaders are building AI‑native platforms that unlock entirely new creative and commercial opportunities.

See how some of these frontier organizations have experienced new growth with AI:

• Microsoft recently announced a partnership with the Mercedes‑AMG PETRONAS Formula 1 Team to apply cloud and enterprise AI across race strategy, team operations and business intelligence, transforming massive volumes of telemetry into real‑time intelligence from the factory to the circuit. With each car generating more than a million data points per second, Microsoft technology helps turn complex race data into faster insights that power smarter decisions and more effective strategies in the moments that matter most. Together, the companies are harnessing data as intelligence to drive performance and strategy, enabling teams to move from raw information to sustained competitive advantage both on and off the track.
• Art Basel is using Microsoft Foundry to power the Art Basel Companion app, unlocking new digital pathways for audience growth and artist discovery across its global fairs. With AI‑powered features such as personalized recommendations and instant artwork recognition through the Art Basel Lens, the platform creates new opportunities for deeper visitor engagement—helping attract new audiences, increase return visits, and expand how collectors and fans interact with galleries through AI‑enabled discovery.
• The Premier League is using Azure AI and Foundry to unify decades of match statistics, editorial content, and video into real‑time, personalized digital experiences for its global fanbase. By enabling rapid innovation through agentic AI and real‑time personalization, the League has unlocked new forms of fan engagement across its owned platforms, driving a 20% year‑over‑year increase in engagement and activating more than 60 million users in the early months of rollout.

Additional partner solutions continue to unlock new growth with AI:

SymphonyAI’s Revedia is an AI‑first platform supporting over $40B in industry content revenue, rapidly ingesting and normalizing third‑party data to deliver accurate revenue and viewership insights at scale. Beyond data management, the Revedia Suite provides prescriptive intelligence—recommending actions and forecasting outcomes to maximize distribution performance and revenue. Revedia is trusted by a broad cross‑section of the media industry, including major studios, broadcasters, cable networks, and Direct-to-Consumer (D2C) platforms.

The Microsoft and MediaKind partnership continues to accelerate, with MK.IO emerging as the proven cloud-native streaming platform for live sports. Built on Azure, MK.IO supported DAZN’s delivery of the FIFA Club World Cup 2025, streaming 63 matches to audiences across over 200 markets with consistent, broadcast-quality performance. It reflects a broader industry shift toward platforms that combine reliability with the agility of API-driven services. A transformation MediaKind is showcasing at NAB 2026 through MK.IO’s self-serve platform and large language model (LLM)-optimized documentation, with live demonstrations in Microsoft’s booth highlighting AI-assisted workflows in action. This momentum continues to grow through MediaKind and Microsoft’s collaboration on some of the most prestigious sports ecosystems in the world, including ongoing work supporting top-tier football experiences such as the Premier League. 

Join us at NAB Show 2026

Frontier media organizations are already proving what is possible when intelligence, data, and trust come together on a single platform. Join Microsoft at NAB Show 2026 to see how Copilot, agents, Microsoft IQ, Foundry, and Fabric come to life through real deployments, live demos, and customer stories shaping the future of media.

¹ Top 25 languages by Microsoft product usage

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