Summary In the days following the United Nations Micro-, Small, and Medium Enterprises (UNMSME) Day, we take a closer look at what the data shows, and why it matters for the 400 million businesses that power the global economy.

This year, I want to address something we’re watching happen in real time: small and medium businesses (SMBs), also referred to globally as Micro, Small, and Medium Enterprises (MSMEs), are stepping into AI leadership, moving quickly, and applying it directly into the work that drives their business forward.

We mark UNMSME Day to raise public awareness of their contribution to sustainable development and the global economy. The numbers deserve repeating every year: SMBs represent 90% of all businesses worldwide, 50% of global gross domestic product (GDP), and 70% of the world’s workforce.¹

SMBs are not a segment of the economy; they are a foundational part of it. They operate under real pressure. The median small business carries just 27 days of cash reserves.² There is no room to bet on the wrong transformation, they must pick the right direction and get it right the first time. That pressure is exactly why what is happening right now matters so much.

AI has moved from conversation to competitive advantage

The latest Microsoft Work Trend Index 2026 makes something clear: AI is no longer a productivity add-on. It is shifting what work is possible. 58% of AI users say they are already producing work they could not have done a year ago and 66% report spending more time on higher-value work as AI takes on execution.

In a small team, this shift affects capacity and growth. For a financial planning firm streamlining client reviews, a law firm cutting document preparation time, a title company accelerating closings, or a property management team eliminating administrative overhead, that is not a marginal improvement. It represents a structural advantage.

A different pattern is emerging among SMBs. Organizations that move beyond individual use and embed AI into how work happens across teams, workflows, and decisions are seeing step-change results. Not just better work, fundamentally different work. And because SMBs have leaner structures and shorter decision cycles, they can get there faster than large enterprise organizations ever could.

What Frontier Transformation looks like in practice

Frontier Transformation becomes clear when the SMB journey moves from simply experimenting with AI to achieving transformation at growth and scale. Frontier Firms are the ones making that shift real by embedding AI into productivity tools they already are using across their teams workflows and decision processes. The result is not just better output, but a different operating model and one that unlocks more creativity, innovation, and growth.

Here are three SMB’s doing exactly that

Turning bottlenecks into real-time workflows

At Dunaway, a multi-discipline design, planning, and engineering firm in Texas, regulatory research and compliance checks were once manual, time-consuming steps that slowed project delivery. By bringing AI agents into the workflow, engineers can access regulatory insights in real time, answer questions faster, and apply knowledge consistently across teams. The result: a 90% reduction in research time and roughly 10,000 hours saved annually. What once lived with a few experts now works for the whole team. That is what a Frontier Firm looks like in practice.

When I first saw that number, 10,000 hours, I sat with it for a moment. That is not an efficiency gain. That is an entire team’s year given back.

Scaling craft and personalization with trusted intelligence

Businesses built on craftsmanship, trust, and deeply personal service, where consistency is hard to standardize, must tackle a different kind of scale challenge. Chow Tai Fook, a 97-year-old global luxury jewelry brand based in Hong Kong with thousands of stores across Asia, integrated AI across its operations. The company moved from isolated digital efforts to a connected, real-time intelligence model—giving frontline associates the insights they need in the moment to better understand each customer and deliver more personal, relevant experiences at scale. The result is more than 70% efficiency gains across millions of monthly interactions, and a 97-year-old brand that feels personal at every counter.

For me, it always comes back to the customer. When sales associates are empowered with the right insight in the moment, we’re not just making the business more efficient, we’re creating richer, more personal experiences. That’s the power of AI when it’s done right, it doesn’t replace human expertise, it amplifies it.

Making security part of how the business runs

DT Swiss AG, a Swiss manufacturer of high-performance cycling components with teams across Europe, North America, and Asia, faced complexity from fragmented systems, manual compliance processes, and administrative overhead. By moving toward a unified security model, it made identity, access, and governance part of daily operations rather than separate layers of work. The result was a 60% reduction in administrative overhead and a stronger compliance posture. Security did not slow the business down. It made scale more practical.

This is the story I find myself telling most often right now. Security is not a tax on transformation. Done right, it is what makes transformation sustainable.

Across these examples, the pattern is consistent. The SMBs pulling ahead are not simply adopting AI earlier. They are applying it with more intention, moving from isolated use cases to integrated workflows, from individual productivity to team-wide execution, and from security as a separate control to security as the foundation for growth.

Trust is the precondition, not an afterthought

One thing these businesses share: AI adoption and security are unequivocally connected. A 2024 Microsoft Security study found one in three SMBs hit by a cyberattack in the past year, at an average cost of USD254,445. 94% consider cybersecurity critical. And 81% say AI increases the need for stronger controls.³

The businesses moving fastest are solving productivity, data protection, identity, governance, and compliance together. SMBs do not have the time or resources to make five separate technology decisions for one business outcome. Security by design is not a feature, but a foundation for lasting AI adoption.

The partner ecosystem is the multiplier

No SMB transforms alone. Across these customer stories, partners play a consistent role: they help leaders decide where to start, where to incorporate technology into real workflows, and how to support adoption after deployment.

The Microsoft Partner ecosystem brings AI, productivity, and security into one practical conversation. For many SMBs, that begins in Microsoft 365 Copilot supporting how teams create, communicate, and make decisions. Next, extend those workflows using Microsoft Copilot Studio, connect data, and add security tools like Microsoft Defender for Business and Microsoft Purview all working together on a foundation with built-in, secure AI. With more than 1,400 connectors to third-party business applications, these solutions integrate into how businesses already operate across a broader secure cloud foundation.

The Microsoft Partner blog post, “Partner-led momentum, broader availability for SMB: Microsoft 365 Business with Copilot,” has more information on our Microsoft Defender for Business bundles.

• If you are an SMB owner or leader: Start where the time cost is most visible. You do not need a grand transformation plan. You need a first process, a secure foundation, and the decision to act.
• If you are a partner: Almost every SMB conversation is now an AI conversation. Customers are ready. Many still need help knowing where to begin. The partners and Managed Service Providers (MSPs) who lead with outcomes, secure adoption, and real workflow change will be the ones SMBs trust to reach the frontier.

Recognizing UNMSME

I am grateful for the resilience and ambition of small business owners everywhere. I know firsthand, the challenges of operating a business are real. In addition to my role at Microsoft, my husband and I run a small design-build construction company. This experience shapes how I see Frontier Transformation. Together, we are proving that the AI era will not be defined by company size but by leadership. SMBs are leading this moment.

What is the one workflow your team has transformed with AI this year? Connect with me and look forward to the conversation.

¹ United Nations, Micro-, Small and Medium-sized Enterprises Day, June 27, 2026.

² JPMorgan Chase Institute, Cash is King: Flows, Balances, and Buffer Days.

³ Microsoft Security, New research: Small and medium business (SMB) cyberattacks are frequent and costly, 2024.

The post Small and medium businesses aren’t waiting for an AI invitation—they’re already leading appeared first on The Microsoft Cloud Blog.

With the global banking system intermediating hundreds of trillions of dollars in funds, corporate and commercial banking sits at the center of the global economy.¹ It spans geographies, regulatory regimes, client types, and balance sheet structures. And critically, it relies on decision‑making that unfolds across extended, non‑linear processes.

Where past transformation efforts often fell short in accelerating decision-making and streamlining cumbersome processes, the emergence of agentic AI is changing what is possible. Combined with enterprise‑grade platforms, new AI capabilities are being designed to help coordinate work securely, governably, and at scale.

This shift is best understood through four ways that AI is starting to reshape how corporate and commercial banking can operate.

1. Empowering relationship managers

Relationship managers have long been the lifeblood of positive client experiences, often serving as the trusted human face of the business. In this role, the ability to synthesize insights from complexity is a critical skill. Yet even the best managers often struggle to manage the growing sprawl of data and requirements.

In many cases, relationship managers function as human middleware—responsible for manually aligning information that the organization itself cannot easily connect as environments become more fragmented across treasury and credit/lending systems, client data, risk platforms, documents, and collaboration across channels.

Agentic AI addresses this by shifting from passive or responsive assistance to active work coordination. AI-powered agents can assist in monitoring where a client, deal, or request sits across multiple systems, identify what is missing, and surface the right context at the moment decisions are made.

Standard Chartered equipped more than 6,000 bankers with a unified platform spanning 53 markets, giving relationship managers real-time insights and more time to spend on client-centered engagement. Likewise, UBS deployed Microsoft Copilot across its employee base (including relationship managers), transforming legal research with an AI assistant that surfaces precise clauses across 26 million documents using natural language queries. The system eliminates manual search and accelerates information retrieval, freeing experts to focus on judgment-intensive work and giving relationship managers more time for client interactions.

The result is not “AI advice,” but improved decision readiness for teams. Relationship managers spend less time assembling inputs and more time applying expertise, consistently, audibly, and earlier in the process.

2. Improving the quality of client interactions

Clients do not experience the bank as a set of systems. They experience it as a series of moments across onboarding, credit, treasury, and servicing. Too often, these moments are disconnected, which creates friction and can erode confidence.

Agentic AI helps close these gaps not just by connecting interactions, but by making them more meaningful. AI agents enable financial professionals to understand the client in context by maintaining continuity across all channels, while also helping anticipate needs, surface relevant insights, and making sure requests move forward without repeated inputs or dropped handoffs.

Commerzbank, for example, built an AI agent that now handles more than 30,000 customer conversations per month, resolving approximately 75% of requests autonomously. By maintaining shared context across interactions and orchestrating workflows in real time, the bank reduces the need for customers to repeat information while ensuring requests move forward without disruption. The result is a more consistent, responsive experience at scale.

AI also helps bankers maintain continuity across interactions and generate consistent, empathetic responses by unifying previously siloed workflows in sales, service, and communications. First National Bank is using Copilot for Sales to reduce fragmented touchpoints and strengthen ongoing, relationship-driven engagement with commercial clients across channels and teams.

This extends across other complex and high-stakes moments in banking. In investment banking, for example, AI-assisted meeting preparation pulls together internal context and external market data, so bankers walk into conversations already aligned to the client’s situation. In corporate and commercial banking, onboarding and KYC agents can intelligently scan documents, cross-reference sanctions lists and adverse media, and surface key findings for relationship managers.

This can turn what has traditionally been a weeks-long, manually intensive process into a more efficient and consistent experience for both the bank and the client. 

The result is a shift from fragmented, episodic service to coordinated, proactive engagement, where interactions are more timely, more relevant, and more aligned to the client’s evolving objectives.

3. Modernizing risk and core systems

Banking operations have rarely struggled because of a lack of automation alone. They often struggle because complexity compounds.

Credit assessment depends on risk inputs, risk depends on documentation, documentation depends on counterparties, onboarding depends on compliance and core systems rely on all of the above. These processes span teams, systems, and timelines that were often not designed to operate in sync, and as a result work can slow down, fragment, and stall.

Agentic AI helps address these challenges by not only automating tasks but also coordinating workflows across stages. It can track progress across processes, resolve handoffs dynamically, and surface exceptions to the right experts when judgment is required, reducing friction by connecting workflows, aligning dependencies, and helping work move more smoothly across systems and teams.

Bank of Queensland, for example, used Microsoft Copilot to streamline complex, multi-step workflows, reducing risk analysis from weeks to a single day while improving quality by 22%. By automating document-intensive tasks and simplifying cross-functional coordination, teams accelerated decision-making and enabled more adaptive, efficient execution across core banking processes.

At an ecosystem level, Swift applied federated learning and confidential computing to detect financial crime across institutions without centralizing data, which enabled more coordinated risk modeling while preserving privacy and regulatory boundaries.

At Scotiabank, rather than replacing core systems, the bank embedded AI agents into its payment operations to coordinate data transformation, reconciliation, and exception handling across fragmented workflows. As a result, processes that once required weeks can now be executed in seconds, with clear auditability and control.²

When processes are streamlined and coordination costs fall, measurable outcomes can improve. Faster credit decisions, strengthened compliance, improved reconciliation and materially better banker and borrower experiences are enabled, without requiring wholesale system replacement.

4. Transforming trade finance

Innovation in corporate and commercial banking typically falls short because complexity has historically resisted scale.

In trade finance, structured lending, and cross-border treasury, work spans multiple stakeholders, documents, and exceptions across boundaries. These are not linear processes, but complex, judgment-driven environments where traditional automation can break down.

The shift enabled by agentic AI is reimagining how these processes operate. Instead of forcing complexity into fixed workflows, banks can execute work adaptively, coordinating across participants in real time and responding to exceptions as they arise.

Agentic systems open the door to different operating models. They can address deviations, help route actions to the right specialists, and capture how complex work is completed. Over time, these patterns can be encoded into reusable playbooks, so banks can scale expertise, deliver more consistent outcomes, and maintain continuity across complex, multi-party processes.

A glimpse of this future can be seen in a Microsoft-led trade finance proof of concept with ANZ, HSBC, and Lloyds. By embedding AI agents within ERP systems, the solution parses letters of credit, cross-checks them against invoice and shipping data, flags discrepancies, and securely transmits structured, standards-aligned information to bank platforms. Rather than simply accelerating existing steps, it demonstrates how trade workflows can evolve toward a more consistent, data-driven model, ultimately reducing fragmentation, minimizing manual rekeying, and improving traceability across trade finance processes.

What emerges here is not just more efficient execution, but a potentially fundamental new way of operating, one in which complex, exception-heavy processes can be coordinated, governed, and continuously improved at scale.

Realizing this shift depends on enterprise-grade AI platforms that combine advanced AI models, secure integration across systems and partners, and scalable data and analytics foundations.

Leading institutions are using GenAI to streamline trade finance and drive the shift from paper-based to digital platforms.

From experimentation to operating advantage

Experienced banking leaders are right to approach AI with caution. The industry has seen many moments where enthusiasm outpaced operational reality. What differentiates this phase is not model capability alone, but fit. Agentic AI aligns with how corporate and commercial banking functions. It streamlines key processes, respects variability, reacts to change, and reduces coordination costs rather than oversimplifying work.

Equally important, platforms matter. Without security, governance, and integration at enterprise scale, AI coordination becomes a risk rather than an advantage.

This is where disciplined adoption—not experimentation for its own sake—will separate leaders from laggards.

Learn more

• For a practical playbook to improve customer experiences, embed AI in high‑value workflows, and act on the shift to agentic AI, read The Microsoft Business Blueprint for Agentic Assistants.
• For a practical blueprint to scale AI, drive productivity, and deploy across banking, insurance, and capital markets, read Frontier Transformation: AI in Financial Services.
• To help unlock business value and deepen customer relationships in the era of AI, visit Microsoft for Financial Services.

¹ McKinsey & Company, “Global Banking Annual Review 2025,” October 23, 2025.

² Scotiabank, “How AI Agents are Transforming Scotiabank’s Payment Operations,” September 17, 2025.

The post How AI is reshaping corporate and commercial banking appeared first on The Microsoft Cloud Blog.

Across industries, CMOs are no longer asking whether AI will transform marketing but how fast they can move from experimentation to impact, and how to re‑architect work so AI shows up where decisions are actually made.

That question sat at the center of Microsoft’s CMO AI Innovation Forums, convened at CES and Cannes Lions, and designed for one purpose: helping marketing leaders navigate Frontier Transformation—the shift from tools and pilots to AI embedded in the flow of work, driving measurable business outcomes.

Frontier Transformation starts in the flow of work 

In the months between Cannes Lions last year and CES, it’s incredible to see how much things have changed. Six months ago, the question was “Where can we use AI?”Today, it’s “How do we make it deliver real business value—and prove it?” As we head toward Cannes again, the bar has moved even higher. The era of experimentation is over. Boards and CEOs are no longer interested in pilots—they’re expecting tangible outcomes: monetization, measurable growth, and a clear line from AI investment to business impact.

At the same time, most organizations aren’t set up to deliver that. At least not yet.

CMOs described teams juggling 25–30 disconnected applications, with AI pilots layered on top but rarely integrated end-to-end. The result is predictable: disconnected workflows, inconsistent insights, and limited scale. But the real challenge runs deeper than the tech.

What we’re hearing consistently from marketing leaders is this: AI initiatives fail when they are contained to a single function. You can succeed in marketing, but if your workflows aren’t connected to other functions in the enterprise, you will fail.

That’s why the next phase of transformation isn’t about deploying AI around the business it’s about embedding it through the business. Because ultimately, AI transformation is business transformation.

And let’s face it the stakes are rising fast:

• Monetization is mission-critical. AI investments must tie directly to revenue acceleration, margin expansion, or customer lifetime value not just productivity gains.

• Agentic commerce is reshaping the funnel. Discovery, consideration, and even purchase decisions are increasingly intermediated by AI agents disrupting traditional attribution models and forcing CMOs to rethink influence altogether.

• Trust is becoming a defining brand asset AND competitive advantage. As AI-generated interactions scale, consumer confidence in data usage, content authenticity, and brand integrity becomes a competitive differentiator.

• Measurement needs a reset. Legacy metrics can’t capture AI-driven, non-linear journeys. We need new protocols that reflect intent-based engagement, agent participation, and real-time orchestration.

CMO efforts are accelerating

So, as we think about how these shifts are impacting the role of CMOs, I wanted to bring you inside these CMO forums and share what leading CMOs are doing differently. These leaders aren’t hesitating. In fact, quite the opposite. They’re accelerating the integration and operationalization of AI in an effort to rewire processes and supercharge their people. Four patterns are emerging:

1. Measuring AI value is now non‑negotiable, but still unresolved

Efficiency and time savings are table stakes. CMOs are under pressure to tie AI directly to growth, effectiveness, and enterprise outcomes. To do this, they are moving beyond proxy metrics (time saved, content produced) toward value-based measurement frameworks, including:

• Linking AI-driven personalization to incremental revenue lift and conversion quality.
• Measuring speed-to-market as a competitive advantage, not just an operational KPI.
• Understanding how to measure attribution with agentic commerce increasingly mediating the buying journey.

CMOs are in agreement that measuring productivity and effectiveness end-to-end is a critical, unresolved issue.

2. Cross-functional workflows matter more than functional excellence

Marketing wins alone are no longer enough if sales, commerce, service, and supply chains are not connected. Leading organizations are:

• Embedding AI into end-to-end demand-to-fulfillment processes, not just campaign execution.
• Connecting marketing signals directly into sales prioritization, supply chain planning, and service resolution.
• Using AI to orchestrate real-time decisioning across functions, not just optimize within silos.

We have learned that you can knock it out of the park in marketing and still fail if the other organizations aren’t connected.

3. AI is changing who marketers serve—and how

It’s clear that we are no longer just marketing to consumers. This introduces a profound shift: 

• Brands must optimize not just for human attention, but for machine comprehension and recommendation.
• Content strategies must evolve toward structured, verifiable information that AI systems can trust.
• Influence changes as what the model believes about your brand becomes just as important as what the customer sees.

Customer and consumer engagement is not limited to human audiences, but LLMs and agents shaping discovery, consideration, and purchase in real time.

4. Agentic AI exposes operating model gaps

As teams experiment with agents, undocumented processes, tribal knowledge, and governance gaps surface immediately—forcing a rethinking of roles, incentives, and accountability. Leading companies are taking decisive action:

• Redesigning roles around human + agent collaboration, not task ownership
• Establishing clear governance models for AI decision-making and accountability.
• Creating shared data and process standards to enable agents to operate reliably.
• Investing in trust frameworks—including transparency, explainability, and responsible AI practices.

The fourth bullet is especially important, as this is where trust becomes critical not just externally with customers, but also internally. Can teams trust AI outputs enough to act at speed? And can leaders scale AI without introducing risk to their brand?

The takeaway

Across all of these conversations, one thing is clear: CMOs don’t just need more technology. They need clarity. They need connection. And they need confidence in how to scale. They’re looking for real patterns, proven approaches, and practical pathways from pilots to enterprise value. That’s because the next chapter isn’t about experimenting with AI. It’s about operationalizing it across the business to deliver real, measurable impact.

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