Beyond Tokens: Rethinking AI Economics with Microsoft Foundry
Beyond Tokens: Rethinking AI Economics with Microsoft Foundry From the cost of intelligence to the value of outcomes Enterprise AI has an accounting problem. Executives expect agentic AI to return roughly 171% on investment, according to one widely cited survey. Yet McKinsey finds only about 39% of organizations can attribute any earnings impact to AI at all. Both numbers can be true at once — because the gap between them is not a technology gap. It is a measurement gap. For the first few years of generative AI, one number dominated the economics conversation: tokens. How many tokens did a model consume? What was the cost per million tokens? Could a smaller model perform the same task? Those questions mattered when enterprises were experimenting with AI. They are no longer enough as AI moves into production. An enterprise agent doesn't simply consume tokens. It reasons, retrieves context, invokes tools, calls APIs, verifies its work, retries unsuccessful actions and sometimes escalates exceptions to humans. The model call might cost pennies. The business outcome could cost considerably more. Which leads to an increasingly important question: What is the right economic unit for intelligence? From AI experimentation to economic accountability The first wave of enterprise AI was about possibility: Can AI do this? The next wave is about production, as AI becomes embedded in software engineering, customer service, finance, healthcare and supply chains. And production changes the question: Should AI do this and at what cost? Microsoft has moved decisively onto this ground. In August 2026, the Microsoft Foundry team launched its Economics of Agent Optimization series, arguing that "tokens have become the new unit of technology spend" and that AI should be run as a managed investment system. On the latest earnings call, Satya Nadella described Microsoft's objective as "advancing the frontier on the cost-to-outcome curve, ensuring every customer can turn tokens into business results." The discipline is going mainstream too: 98% of FinOps teams now manage AI spend, up from 31% two years ago. Microsoft's series is largely about the numerator of that curve - making every request, agent and dollar more efficient. This article is about the denominator: what an outcome is, what it truly costs, and what it is worth. The evolution of Microsoft Foundry reflects the same shift. At Build 2026, Microsoft expanded the conversation beyond building agents toward tracing behavior, evaluating quality, monitoring production performance, optimizing agents and connecting their operation to ROI. Think of the progression as: Trace → Evaluate → Monitor → Optimize → ROI This is more than a technology roadmap. It represents a shift from observing AI as technology to managing AI as an economic asset. Tokens became the unit of spend. They were never the unit of value. Consider two AI agents handling the same customer-service workflow. Agent A costs $0.08 per interaction. Agent B costs $0.20. Agent A appears cheaper. But suppose Agent A successfully resolves only 55% of cases, while Agent B resolves 90%. The remainder require retries, additional reasoning or human intervention. Which agent is actually cheaper? The inexpensive interaction may produce the expensive resolution. This illustrates a fundamental problem: We often measure AI where it is consumed rather than where value is created. Tokens are a unit of consumption. Businesses operate in outcomes. A customer-service leader cares about issues resolved. An engineering leader cares about high-quality software reaching production. A finance leader cares about reconciliations completed accurately. The economic denominator needs to move closer to the business. The AI Economic Ladder I think of this evolution as an AI Economic Ladder: Tokens → Interactions → Tasks → Outcomes → Value Each step moves measurement closer to what the enterprise actually cares about. At the token level: What intelligence did we consume? At the interaction level: What did each AI run cost? At the task level: What did it cost to complete the work? At the outcome level: What did a successful result cost? At the value level: Was the outcome worth creating? An AI system can become more efficient at every technical metric while creating little economic value. Conversely, an expensive AI workflow could be extraordinarily valuable if it prevents revenue leakage, reduces operational risk or accelerates a critical business process. The objective isn't cheaper AI. It is better economics. Not every completed task is a successful outcome There is another complication. If an agent completes a workflow, should we count it as a successful outcome? Not necessarily. A meaningful outcome needs three characteristics: Completed. Quality-gated. Attributable. It must reach its intended end state, meet an explicit standard for quality, accuracy, safety or business acceptability, and be attributable to the agent or workflow that produced it. That gives us a more meaningful measure: Cost per Successful Outcome = Fully Loaded AI Workflow Cost / Completed, Quality-Gated, Attributable Outcomes The denominator becomes real only when named in business language: cost per prior authorization resolved in healthcare, per pull request triaged and tested in engineering, per disputed invoice reconciled in finance operations. If you cannot name the outcome in a sentence the process owner recognizes, you are not ready to measure it. The quality gate matters. With AI, "the system ran successfully" and "the system produced a good outcome" are not the same thing. Microsoft Foundry's tracing and evaluation capabilities become economically important for precisely this reason. Evaluation isn't merely quality control. It helps determine what gets counted as value. What does an AI outcome really cost? The true economic footprint goes far beyond inference: Model + Reasoning + Grounding + Tools + Orchestration + Infrastructure + Retries + Evaluation + Governance + Human Intervention Human intervention is particularly easy to overlook. Every time someone must review, correct, approve or recover an AI-generated outcome, the economics change. The same applies to verification. An agent reaching an acceptable result in three steps has different economics from one requiring fifteen steps and multiple retries. And verification is not a rounding error — it is the bulk of the bill. McKinsey's 2026 analysis of production agentic workflows found roughly 60% of an agentic task's cost is tied to refining answers — checking, repairing, re-verifying — not generating the initial response. Most of what you pay for is not intelligence. It is assurance. This means quality and economics are connected. The quality bar you set influences the cost you pay. The challenge isn't simply minimizing consumption. It is finding the right balance between quality, cost, speed and risk. Cost per outcome is only half the equation Now imagine two agents. Both cost $5 per successful outcome. One saves an employee ten minutes of administrative work. The other prevents $500 in revenue leakage. Their cost efficiency is identical. Their economics clearly aren't. So we need to move another step up the ladder: from Cost per Outcome to Value per Outcome. The question isn't only how cheaply AI can complete the work. It is: How much economic value does this outcome create relative to the intelligence required to produce it? Now the CIO, CFO, CAIO and business leader have a common conversation. Give every outcome an Intelligence Budget Not every problem deserves the smartest model available. Classifying an email may require relatively little intelligence. Resolving a complicated customer complaint may justify more context and reasoning. Assessing the risks in a multimillion-dollar contract may justify sophisticated reasoning, multiple validations and human review. Every business outcome therefore has an economically rational amount of intelligence worth spending on it. Call it an Intelligence Budget. This changes the architecture question from which model should we standardize on, to: What combination of model, reasoning, context, tools and human judgment does this outcome deserve? This is where Microsoft Foundry's model router becomes interesting. Individual requests can be dynamically routed so simpler work doesn't consume the same model resources as complex reasoning. If the Intelligence Budget is the economic principle, intelligent routing is one way of operationalizing it. The future enterprise AI architecture won't be about one model doing everything. It will route intelligence according to the economics, quality and risk of the outcome. Making AI economics observable None of this works without visibility. An AI system can be technically healthy and economically unhealthy — responsive and error-free while repeatedly choosing inefficient reasoning paths, invoking unnecessary tools or producing outputs requiring expensive human correction. AI economics and AI observability are becoming inseparable. Microsoft Foundry increasingly connects these disciplines. Tracing shows what an agent did. Evaluation determines whether it met required criteria. Observability helps monitor production behavior. Agent optimizer can test improvements across prompts, skills and models. Microsoft's emerging ROI capabilities take the next step by connecting operating costs with measures such as task completion, time saved and cost efficiency. Attribution is the bridge to the finance conversation. Teams place Azure API Management in front of Foundry endpoints as an AI Gateway, stream token telemetry into Application Insights, and use Entra Agent ID to give every agent run a discrete identity that maps cost to its cost center. Microsoft Agent 365 extends the discipline tenant-wide — spending policies, budget caps and departmental chargeback across Microsoft and third-party agents. Together, they create something enterprises have historically lacked: A feedback loop between how intelligence is consumed and what that intelligence accomplishes. The paradox of cheaper intelligence There is another reason AI economics will become more important as models get cheaper. The Jevons paradox suggests that when technology makes a resource cheaper and more efficient, total consumption can actually increase. AI may experience the same effect. Cheaper intelligence enables more agents, more reasoning and more workflows that were previously uneconomic. So we could see cost per unit of intelligence fall while total intelligence consumed rises. Cheaper AI may therefore produce larger AI bills. That isn't necessarily bad — provided value grows faster than consumption. The objective isn't minimum AI consumption. It is maximum economic value from AI consumption. From workload economics to portfolio economics As AI scales, economics becomes a capital-allocation question. I see three levels. Workload Economics: Is this AI system running efficiently? Outcome Economics: Is it producing quality outcomes economically? Portfolio Economics: Where should we put our next AI dollar? That final question will become increasingly important. An enterprise with hundreds of AI initiatives shouldn't assume every one deserves continued investment. Some should scale. Some need optimization. Some should be redesigned or consolidated. And some should be stopped. The ability to experiment cheaply created the first explosion of enterprise AI. The discipline to allocate capital intelligently will determine what scales. Who owns AI economics? Once an agent becomes part of how work gets done, its economics cannot remain purely an IT metric. The business understands the value of the outcome. Technology understands the architecture and optimization levers. Finance brings economic discipline and comparability. That suggests a shared model: Business owns the outcome. Technology owns the optimization levers. Finance owns the economic discipline. AI economics ultimately isn't just a technology-cost conversation. It is a business-performance and capital-allocation conversation. From abundant intelligence to intelligent economics We are entering an era where intelligence is becoming an increasingly abundant, programmable and variable-cost resource. Microsoft Foundry and the broader Microsoft AI stack are making it easier to build, evaluate, observe, optimize and govern that intelligence. But abundant intelligence does not guarantee abundant value. Enterprises still need to decide where AI belongs, how much intelligence each problem deserves, what defines a successful outcome, when humans should remain involved and which AI investments deserve more capital. The winners won't necessarily use the cheapest models. They won't consume the fewest tokens. And they won't be the organizations that build the most agents. They will become exceptionally good at moving up the AI Economic Ladder: from consumption, to outcomes, to value. Because the next era of AI won't be won by organizations that buy intelligence most cheaply. It will be won by those that convert intelligence into value most efficiently. Where to start: the first 90 days Define the denominator for your top three agents — what counts as done, what quality gate applies, who signs off. Instrument attribution — Azure API Management as an AI Gateway, token telemetry to Application Insights, Entra Agent ID on every run. Wire evaluations into the cost pipeline so only quality-gated outcomes count. Set Intelligence Budgets — model router per request, agent optimizer against your evaluators, Agent 365 policies as circuit breakers. Stand up a joint monthly review — business, technology and finance on one dashboard: outcomes delivered, cost per outcome, value per outcome. Frequently asked questions What is Cost per Successful Outcome in enterprise AI? The fully loaded cost of an AI workload divided by outputs that were completed, quality-gated and attributable - for example, cost per prior authorization resolved or per pull request triaged. It turns token metrics into the unit economics of AI-performed work. What is an Intelligence Budget? The economically rational amount of intelligence - model capability, reasoning, context, tools and human review — worth spending on a given outcome, based on its value and risk. Model router in Microsoft Foundry is one way to operationalize it. Why do AI agents cost more than single model calls? One agent task can involve planning, tool calls, retries and verification - many model calls with compounding context. Research on production agentic workflows attributes roughly 60% of task cost to refining and verifying answers, not generating the first response. Will falling model prices make AI cost management unnecessary? No. By the Jevons paradox, cheaper intelligence expands consumption, so total AI spend typically rises as unit prices fall. The discipline that matters is maximizing value per unit of intelligence. Who should own AI economics? A shared model: the business owns the outcome and its value, technology owns the optimization levers, and finance owns the economic discipline and review cadence. #MicrosoftFoundry #Agent365 #AzureAI #FinOps #AgenticAI #AIAgents #Azure #MicrosoftCostManagement #AIEconomics #Tokens References Microsoft Azure Blog: "The Economics of Agent Optimization: From pilots to measurable returns" (August 12, 2026) Microsoft FY26 Q4 earnings call (Satya Nadella, July 2026) McKinsey — "Cost versus value: managing agentic AI system performance" (July 2026) FinOps Foundation — State of FinOps 2026; Microsoft Learn — Model router for Microsoft Foundry; Agent optimizer; Foundry Control Plane cost optimization
Prepare for the launch of growth margins with API readiness resources
Starting October 1, 2026, growth margins will provide eligible partners with incremental margin on qualifying Microsoft 365 growth opportunities. Built on partner feedback, growth margins give you more flexibility to structure deals, compete for new business, reward reseller performance, and reinvest in capabilities that support long-term growth. Review the available readiness resources now so you can maximize growth opportunities, maintain business continuity, and prepare for upcoming technical changes. Technical guidance is available to help you navigate API changes and implement best practices for data export and billing automation. Review the partner resource collection. Evaluate opportunities to incorporate growth margins into your Microsoft 365 sales and growth strategies. Eligible partners may open an advisory case with Microsoft Partner Technical Consulting for API transition guidance.From M365 MSP to M365 E7 MSP: Your Playbook is Here
Your Existing M365 Practice is Already the Foundation Microsoft 365 E7 services runs directly on top of your customer’s existing M365 configuration: identity, permissions, SharePoint governance, data hygiene. Every tenant you already manage has an AI readiness posture — and you’re the one who owns it. The playbook makes this explicit: oversharing remediation, MFA enforcement, sensitivity label rollout, and SharePoint permissions hygiene are not new work streams — they are the Copilot readiness checklist. Treating Copilot readiness as a data governance exercise, not just a licensing event, is a core principle throughout. Partners with existing security, compliance, and governance services have a structural advantage that resellers and consultants simply cannot replicate. A Repeatable Lifecycle — Built Into Your Existing Cadence The playbook structures the Copilot journey into five phases: Assess → Activate → Adopt → Optimise & Govern → Extend with Agents. Each phase maps to services you already deliver and slots into your existing monthly operational cadence and quarterly QBRs. Phase What You Deliver Recurring Service Opportunity Assess (Weeks 0–2) Readiness assessment, oversharing hotspots, security baseline, executive use-case prioritisation Copilot Readiness Subscription, data governance service Activate (Weeks 3–6) CSP provisioning, Copilot Control System setup, governance controls, first prompt clinic Copilot Admin & Governance line item, licence optimisation Adopt (Weeks 7–12) Role-based enablement, champions programme, adoption scorecards, oversharing remediation backlog Adoption-as-a-Service (monthly sprints), premium support tier Optimise & Govern (Ongoing) Monthly governance review, Purview controls, permissions hygiene, QBR value packs Copilot Managed Service — per-user/per-tenant recurring fee Extend with Agents (Selective) Agent strategy workshop, build & run Copilot Studio agents, agent lifecycle & cost governance AgentOps managed service (run/monitor/improve agents) One of the most actionable insights in the playbook: most Copilot deployments stall after the first month. The answer is analytics-driven Adoption-as-a-Service — structured 30/60/90-day sprints, role-based scenario coaching, champions communities, and prompt clinics that sustain usage growth. This is a recurring revenue stream your service desk team can own. Package It, Price It, Sell It — Good / Better / Best The playbook provides a ready-to-adapt commercial packaging model with three tiers, each anchored to a distinct buyer: Tier What’s Included Primary Buyer Commercial Model Good — Copilot Enablement One-time onboarding: readiness checklist, licence provisioning for 20–50 users, 2 enablement sessions, service desk how-to scripts and escalation path IT Manager / Ops Lead Fixed onboarding fee + optional monthly check-in per user Better — Copilot Managed Service Good, plus: monthly governance reviews (Copilot Control System + key tenant settings), permissions hygiene targets, Adoption-as-a-Service sprint, monthly adoption scorecard Head of IT / COO Per-user or per-tenant monthly managed fee + QBR pack included Best — Copilot + AgentOps Better, plus: agent strategy workshop, build & run Copilot Studio agents with security boundaries, agent registry + lifecycle management, Copilot Credits cost governance COO / Business Function Owner Monthly AgentOps fee per agent + per-tenant retainer + build sprint (one-time) Pricing levers are per-user (scales with Copilot seats) and per-tenant (covers governance overhead) — protecting margin through standardised onboarding checklists and productised monthly sprints. Two Microsoft partner incentive programmes are worth aligning your offers to: the Copilot + Power Envisioning & PoC programme (supporting readiness workshops and customer pilots) and the Copilot + Power Deployment Accelerator (rewarding partners who drive rapid deployment). Both are accessed through the Partner Incentives portal and can offset customer acquisition costs. For QBR retention and expansion, the playbook provides an ROI proof pack structure: an adoption scorecard (active users, app-level engagement trends), business KPI impact (1–3 customer-defined KPIs with before/after deltas tied to usage increases), and an intervention plan for low-adoption groups. This shifts the customer conversation from “is this working?” to “here’s the data — where do we expand next?” Governance and Security: Your MSP Moat This is where MSPs separate from the pack — and the playbook dedicates significant depth to it. The primary new risk Copilot introduces is oversharing amplification: if a tenant has broad SharePoint permissions, legacy shared folders, or poorly governed Teams/OneDrive sharing, Copilot will surface sensitive information that was technically accessible but previously hard to find. The playbook frames this not as a reason to avoid Copilot, but as the business case for MSP-led governance. The three-layer control framework the playbook recommends: Preventive (before rollout): Tighten SharePoint, Teams, and OneDrive permissions before broad rollout. Apply sensitivity labels to business-critical data so Copilot respects protection boundaries. Remove overly broad access on high-value sites. Detective (during operation): Use Microsoft Purview DSPM for AI to identify overshared sites and risky data exposure patterns. Monitor Copilot prompts and responses via the Unified Audit Log. Corrective (when issues arise): Remediate overshared content using SharePoint Advanced Management and Purview recommendations. Adjust DLP and label policies where Copilot interactions surface risk. MSPs who can walk into a customer conversation and say “here’s your current AI exposure, here’s how we remediate it, and here’s how we keep you compliant on an ongoing basis” are operating at a fundamentally different level than those simply reselling licences. AgentOps: The High-Margin Horizon Copilot assistance is the starting point. For customers ready to move beyond in-app AI help toward workflow automation, the playbook introduces an AgentOps managed service tier — the highest-margin, most differentiated position available to MSP partners. The playbook draws a clear line: knowledge-intensive tasks — drafting, summarising, analysing — can be served by M365 Copilot alone. Process-execution tasks, where work needs to move across systems, require agents. The right solution depends on the workflow. The playbook identifies the common execution workflow categories where agents add value: Ticketing and service desk routing — categorising and escalating support requests without manual triage. Approval workflows — routing documents, purchase orders, and HR requests through sign-off chains with audit trails. HR and Finance operations — moving data across systems for processes that require coordination beyond a single Microsoft 365 app. In each case, M365 Copilot handles the human-in-the-loop knowledge work; agents handle the execution steps. The playbook recommends MSPs assess each customer's workflows to determine the appropriate service tier — agents are introduced when execution complexity justifies it, not by default. What the playbook flags as a key risk — and a corresponding MSP opportunity — is uncontrolled agent sprawl: different teams independently building agents with no ownership, cost control, or lifecycle management. MSPs who establish an agent registry, define approval and retirement policies, manage Copilot Credits consumption, and monitor agent performance address this directly — and in doing so, create a differentiated managed service that elevates the relationship to the COO/Business Function Owner level. Your Next 30 Days The M365 Copilot MSP Playbook is built for partners who already know their customers’ tenants, manage their Microsoft 365 environment, and want a structured path to turn that trusted relationship into an AI services practice. Recommended next steps from the playbook: Enable Copilot internally for a pilot cohort — become Customer Zero, capture before/after stories, and develop standard operating procedures. Define your Copilot readiness checklist: identity baseline (MFA), data sharing hygiene, minimum Purview controls — to support repeatable onboarding. Define a simple Copilot service catalogue: Readiness, Activation, Adoption, Optimise/Govern, and (optional) AgentOps — with clear inclusions and SLAs. Target existing M365 managed customers: start with a 20–50 user pilot, then expand seat coverage based on adoption and outcomes. Operationalise measurement: use adoption and usage insights to support customer value conversations and prioritise next steps within existing QBRs. Download the M365 Copilot Managed Services Playbook and take your first step in the next 30 days.
From insight to action: how Adobe and Microsoft are helping marketers move faster with AI
Today’s marketing leaders are under pressure to do more than ever—deliver meaningful personalization, accelerate execution, and prove measurable business impact. At the same time, teams are navigating increasing complexity: fragmented data, disconnected tools, and insights that arrive too late to act on. AI can change this—but only when it’s embedded directly into how people already work. That’s why Microsoft and Adobe are deepening our partnership: bringing customer experience intelligence, AI-powered workflows, and enterprise-grade AI directly into Microsoft 365 Copilot—so teams can move from insight to alignment to execution in one continuous workflow. The result is faster decisions, more coordinated execution, and clearer business outcomes—without breaking flow or context. Bringing customer experience intelligence into the flow of work Marketing teams don’t struggle because they lack data. They struggle because insights live in one place, collaboration in another, and execution somewhere else entirely. That disconnect slows teams down and creates unnecessary friction between analysis and action. Together, Adobe and Microsoft are changing that dynamic by connecting Adobe’s customer experience capabilities with Microsoft 365 Copilot and Copilot Cowork—so insight, collaboration, and next-best action can happen where work already happens: in Copilot Chat and in everyday apps like Teams, Word, and PowerPoint. Marketers can ask questions, explore insights, align with teammates, and take action without jumping between tools—turning intelligence into impact at the moment it matters. Adobe Marketing Agent for Microsoft 365 Copilot: now generally available A major milestone in this journey is the general availability of the Adobe Marketing Agent for Microsoft 365 Copilot, now available via Microsoft Commercial Marketplace. The Adobe Marketing Agent brings Adobe customer experience intelligence directly into Copilot, enabling marketing teams to: Accelerate time from insight to decision Move seamlessly from analysis to execution Keep humans firmly in control, with AI supporting—not replacing—decision‑making Importantly, the agent is enterprise-ready by design. IT administrators can deploy and manage the experience through the Microsoft 365 admin center, ensuring security, governance, and compliance at scale. Expanding executive experiences with Copilot Cowork Looking ahead, Adobe skills designed for customer experience orchestration will be accessible in Copilot Cowork—in a future release. This upcoming experience will enable customer experience leaders to engage with customer experience insights in a more direct, conversational way, bringing strategic visibility into the same Copilot environments where decisions are made and actions are coordinated. Built on Azure to scale securely and responsibly The technology foundation of this innovation is Azure. Adobe Experience Platform, Adobe Experience Platform Agent Orchestrator, and Adobe AI Agents are built on Azure and leverage Azure AI models, providing the scalability, security, and reliability enterprises require. By running on Azure, these agentic experiences benefit from Microsoft’s global infrastructure, enterprise‑grade security, and responsible AI commitments—supporting customer trust as organizations scale AI across their business. Designed for interoperability across agent ecosystems Modern enterprises don’t operate in a single ecosystem—and their agents shouldn’t either. Adobe agents are built to interoperate with agents created using Microsoft Azure AI Foundry or Copilot Studio, enabling customers to orchestrate richer, cross‑functional workflows across marketing, sales, service, and operations. This architecture is designed to enable organizations to compose agentic solutions that reflect how work actually happens—across systems, teams, and business processes. Moving from experimentation to execution This partnership reflects a broader shift in how organizations adopt AI—moving from experimentation to embedded, enterprise‑ready execution. By bringing the full power of Adobe Experience Platform together with Microsoft’s AI platform, cloud infrastructure, and Copilot experiences, we’re helping teams move faster with clarity, confidence, and control. This is how AI becomes not just powerful—but practical. Learn more Adobe + Microsoft partnership page Adobe Marketing Agent for Microsoft Copilot pagePartner Blog | Partner-led momentum, broader availability for SMB: Microsoft 365 Business with Copilot
Small and medium-sized businesses (SMBs) are entering a new phase of AI adoption. It is no longer defined by experimentation. It is about moving from individual use cases to everyday, scalable impact as organizations move toward Frontier Transformation. Recent findings from the 2026 Work Trend Index Annual Report reinforce what many partners are already seeing with customers. 58% of AI users say they are already producing work they couldn’t a year ago, and 66% report spending more time on higher-value work as AI takes on execution. For partners serving SMB customers, this marks a clear inflection point. The opportunity is to move beyond access to AI and focus on empowering SMBs to scale and operationalize it by simplifying buying conversations, driving more predictable renewals, and building on existing services around AI-powered productivity and secure operations. These trends are already starting to be reflected across our partner ecosystem. In December, we introduced Microsoft 365 Copilot Business to streamline AI adoption for SMBs and support a clearer partner-led motion. Partners who were early adopters validated strong and growing demand. This momentum signals a broader shift in how work is evolving. AI delivers the most value when it is embedded in the everyday flow of work and surrounded with the right security, governance, and business context built in from day one. SMB customers want a straightforward buying experience, predictable renewals, and a consistent way to deploy across users and locations without adding complexity. Building on that momentum, we are evolving our approach to unlock more partner-led opportunities with new SKUs built for the way SMB customers buy and renew. They give partners an always-on offer that can be standardized across accounts and carried confidently into renewal conversations. What is launching on July 1 Starting July 1, 2026, we are transitioning the promotional bundles into two durable SKUs, available at the current promotional price point, giving partners a more consistent and scalable way to drive adoption and renewal opportunities with customers. Microsoft 365 Business Standard with Copilot: $23.50/user/month 1 (1–300 seats, annual subscription with annual billing) Microsoft 365 Business Premium with Copilot: $32/user/month (1–300 seats, annual subscription with annual billing) We are also launching a new 25% promotional offer on Microsoft 365 Business Basic plus Microsoft 365 Copilot Business ($21/user/month, 1–300 seats, for annual subscription with annual billing), available through December 31, 2026. This provides a clear entry point for Business Basic customers who would like to get started with Copilot. In addition, the current 15% promotional offer on Microsoft 365 Copilot Business ($18/user/month, 1–300 seats, for annual subscription with annual billing) has also been extended through December 31, 2026, providing partners with more opportunities to scale AI adoption and build pipeline. We encourage partners to lead with Microsoft 365 Business Standard with Copilot or Business Premium with Copilot as integrated offerings for productivity, AI, and security, designed to support partner-led SMB conversations. Microsoft 365 Business Basic, Standard, and Premium also remain available as standalone plans for customers who prefer to purchase Copilot separately. This gives you flexibility to meet customers where they are. Continue reading hereThree Tiers, One Platform: Building Agents Together with the Build-Along Series
The Agentic Opportunity Every partner has agent ambition, but a single workshop format cannot serve every builder. Business makers want a fast win without writing code. Citizen developers want to orchestrate real processes across enterprise systems. Pro developers want a code-first surface with evaluations, tracing, and managed identity. Run the wrong session for the wrong audience, and engagement collapses - too abstract for one group, too constrained for another. The Agent Build-Along Series solves that by meeting builders exactly where they are, across three tiers of the Microsoft platform stack. Behind the series sits a self-serve GitHub repository - a curated library of build-along sessions with easy-to-follow, step-by-step instructions, scoped to specific industry scenarios and business functions. Partners pick the scenario that fits the room, clone the assets, and run the session. No bespoke content build, no facilitator guesswork. A Self-Serve Repository for Build-Along Sessions Diagram illustrating three tiers of agent building The repo at Agent Build-Along Github is a self-serve catalog of ready-to-run workshops. Every session is curated for a specific industry and business function, with easy-to-follow instructions a facilitator can pick up and deliver. Pick the combination that matches the room and run the session - the content, scenario, and assets are already there. Sessions are organised across three axes so partners can find the right fit fast: Industry - Financial Services, Healthcare, Retail, Manufacturing, Public Sector, Energy, Professional Services Business function - Sales, Marketing, Finance, HR, Operations, IT, Customer Service Workload - Agent Builder, Copilot Studio and Azure AI Foundry Each session in the repo includes a real-world scenario, step-by-step build instructions, sample data, prompts and configuration, and a facilitator narrative. Clone, brief the room, build the agent - that is the loop. Three Tiers - Meet Builders Where They Are The pyramid is intentional. Builders start where their skills are today and move up as confidence grows. The tier defines platform, audience, duration, and approach - content stays consistent. Tier Platform Audience Duration Approach 1 - Foundation Microsoft 365 Copilot Agent Builder Business makers, end users, departmental teams 60 min No-code 2 - Extend Copilot Studio Citizen developers, power users, ops teams 90 min Low-code 3 - Pro-Code Azure AI Foundry Pro developers, architects, AI engineers 180+ min Code-first Tier 1 - M365 Copilot Agent Builder Author your first agent - no code required. Microsoft 365 Copilot Agent Builder virtual workshop overview, What your customers will build A custom agent scoped to a real workplace scenario Grounded with knowledge sources (SharePoint sites, files, web URLs) Configured with custom instructions and conversation starters Tested in the preview pane and shared with a team Session flow Define → Ground → Refine → Test & Share Prerequisites Microsoft 365 Copilot licence (or Copilot Chat free) Modern browser (Edge / Chrome) A workplace scenario in mind, or use our template Outcome Attendees leave with a working agent they can use immediately, plus a repeatable pattern for future use cases. This is the right entry point for departmental teams who want to operationalise Copilot without engineering involvement. Tier 2 - Copilot Studio Orchestrate multi-step agents across enterprise systems. Microsoft Copilot Studio virtual workshop overview What your customers will build A copilot with generative answers and authored topics Custom actions that call APIs and Power Platform connectors Knowledge sources spanning SharePoint, Dataverse, public web, and enterprise data Channel deployment to Microsoft Teams and a public web embed Session flow Design → Connect → Author → Publish Prerequisites M365 Copilot license with access to Copilot Studio Familiarity with Power Platform helpful, not required A multi-step workflow or process in mind Outcome Attendees leave with a published copilot running in Teams or on the web, plus a pattern for connecting copilots to enterprise systems. This is where citizen developers and ops teams turn manual processes into orchestrated, agent-driven workflows. Tier 3 - Azure AI Foundry Ship a code-first agent with evaluations and observability. What your customers will build A code-first agent with custom tools and function calling Grounding via Azure AI Search and the customer's own data Model selection from the Foundry catalog, with optional fine-tuning Evaluations, tracing, and managed-identity deployment Session flow Provision → Build → Evaluate → Deploy Prerequisites Azure subscription with Azure AI Foundry access VS Code, plus Python or .NET familiarity Sample data or a use case ready to ground on Outcome Attendees leave with a deployed agent - code, evaluations, and tracing in place - plus reusable SDK templates. This is the right tier for architects and AI engineers shipping agents into production with the governance bar enterprise customers expect. How the Repository Works The repo removes the heaviest lift in running these workshops: content creation. Every session is curated against the same three axes - industry, business function, tier - and shipped with step-by-step instructions a facilitator can follow with minimum prep. Industry/Business Function × Tier → Ready-to-run Build-Along Each session in the repo ships with: A real-world scenario grounded in the chosen industry and business function Step-by-step build instructions tuned to the selected tier Sample data the agent can ground against Prompts, conversation starters, and configuration snippets Facilitator narrative so anyone can run the session Need an enterprise agent that can handle integrate with CRM data? There is a curated Copilot Studio session with a deal-desk scenario and sample CRM data. Need a pro-code health care agent? Choose the Foundry session with a patient-flow scenario and a grounded dataset. Same repo, different curated paths - at the right depth for the room. What Attendees Walk Away With Agent-Builder - A working agent in Microsoft 365 Copilot, ready to share with colleagues. Copilot Studio - A published copilot running in Teams or on the web, connected to enterprise systems. Foundry - A deployed Foundry agent with evaluations, tracing, and SDK templates for the next build. In all cases attendees leave with assets - not slides - and the confidence to build the next one without facilitation. Next Steps - Host a Build-Along Pick your tier - Start where your skills are today. Move up the pyramid as confidence grows. Drive Registration – Socialise your Build-Along session to maximise attendance. Show up and build. Bring a real scenario. Customers leave with a working agent to share. Where to Next Browse the self-serve repository of Build-Along sessions - curated by industry and business function, with step-by-step instructions ready to run: Agent Build Along.Help Shape the Future of Microsoft Teams for Small and Medium Businesses
Have you ever wished Microsoft Teams worked just a little better for the way your SMB customers run their business? Maybe you've thought: “It would be great if Teams could do this…” “This workflow would be so much simpler if…” “Why isn’t there a feature for…?” Now’s your chance to directly influence what comes next. Microsoft is inviting Small and Medium Business (SMB) partners to join the Teams SMB Partner Advisory Council (PAC) — a simple, low-effort way to share real-world feedback with the product team and help shape the Teams roadmap for SMB customers. What is Teams SMB PAC? Our Teams SMB Partner Advisory Council (PAC) brings together a Microsoft partners who: Work with SMB customers implementing Teams Have insights into the challenges SMBs face when adopting collaboration tools Want to help improve the products they rely on Through monthly virtual sessions, participants get the opportunity to: ✅ Preview upcoming features and improvements ✅ Provide feedback during early stages of product development ✅ Share what’s working well — and what isn’t ✅ Highlight real-world business scenarios that should be better supported ✅ Influence priorities for SMB-focused innovation in Teams This isn’t a sales call or a support channel — it’s a direct line to the Teams product team. What’s the Commitment? We know you are busy. That’s why participation is designed to be: Flexible – Virtual meetings combined with async conversation Low effort – No prep required for most sessions Conversational – Small-group discussions, not presentations Most sessions are informal and focused on understanding how Microsoft can better support you and your SMB customers. Your input helps ensure we build features that reflect how SMBs actually work — not just how software assumes they do. Why Join? Participants often tell us that one of the biggest benefits of joining the PAC is the opportunity to: Get early visibility into what’s coming next Understand where Teams development is headed Provide feedback that directly reaches the team building the product Help shape experiences that impact SMB customers worldwide Your perspective matters — and it can directly shape the future of Teams for SMB. Trusted by partners across regions including EMEA, APAC, and North America. Interested in Participating? If you'd like to be considered for the Microsoft Teams SMB Partner Advisory Council, simply fill out this short interest form: 👉 Express your interest to join the Teams SMB Advisory Board Once submitted, our team will review your response and follow up with more information on next steps.Now generally available: Microsoft 365 E7 and Agent 365
We're excited to announce the general availability of Microsoft 365 E7 and Agent 365! Microsoft 365 E7 is the Frontier Suite powered by Work IQ, designed for a human-led, agent-operated enterprise—turning human intent into AI action that functions securely and at scale. Microsoft 365 E7 bundles Microsoft 365 E5, Copilot, Agent 365, and more into a single integrated offer built for customers’ Frontier Transformation. Designed for organizations to move from AI experimentation to scale, it includes Microsoft Entra Suite and advanced Defender, Intune, and Purview capabilities, delivering comprehensive security across agents and employees. Agent 365 is the control plane for customers to observe, govern, and secure both Microsoft and third-party AI agents, grounded in shared intelligence and built with trust to protect the organization. This marks a clear path for customers to upgrade to Microsoft 365 E7, unlocking access to Agent 365 for a setup built for secure agentic operations. Get more detailsPartner Blog | Introducing Microsoft 365 E7: The Frontier Suite
Frontier Firms—those who adopt an AI-first approach as a core part of their business strategy—are shaping the future of work, which is increasingly fueled by agentic AI. As customers move toward Frontier Transformation, you can help them seize the agentic moment with the premium Microsoft 365 E7 plan, generally available on May 1, 2026. Microsoft 365 E7 is the Frontier Suite, designed for a human-led, agent-operated enterprise, turning human intent into AI action that functions securely and at scale. To accomplish this, Microsoft 365 E7 includes access to Microsoft Agent 365—the control plane to govern and scale agents. Microsoft 365 E7 bundles Microsoft 365 E5, Microsoft 365 Copilot, Entra Suite, and Agent 365 to serve customers who are ready to scale AI with governance and security. This means your customers will have AI-powered productivity apps with advanced security and AI capabilities, grounded in intelligence and trust. You can help your customers make the most of the agentic AI opportunity and unlock new monetization streams with the premium Microsoft 365 E7 plan. Starting on April 1, 2026, to accelerate Microsoft 365 E5 to Microsoft 365 E7 upsell and Agent 365 adoption, Copilot + Power Accelerate will include Agent 365 across Immersion Briefings, Envisioning and proof of concept (PoC) engagements, and Deployment Accelerators. Microsoft 365 E7 and Agent 365 will also be included as eligible workloads for core tier one strategic product accelerators and growth levers in Cloud Solution Provider (CSP) incentives. The AI future is here. Encourage your customers to upgrade to Microsoft 365 E7 so they can move faster and deploy AI with confidence. Read more in our blog post
