The Context Advantage
A strange thing happens when enterprises upgrade their AI models. The model gets smarter. The benchmarks improve. The demos become more impressive. And sometimes, the business outcome barely moves. The agent still asks questions the organization answered months ago. It recommends something policy doesn't allow. It treats a strategic customer like any other customer. It proposes an architecture your engineering team rejected six months ago. The problem isn't necessarily intelligence. It is ignorance of the business. That distinction matters because the world's most capable models are becoming available to almost everyone. Your competitors can increasingly access the same frontier intelligence you can. What they cannot access is your customer history, operating knowledge, organizational memory or understanding of how decisions actually get made. Models are becoming shared. Context remains proprietary. And that may be where the next enterprise AI advantage is built. Intelligence Without Context is Surprisingly Ordinary Consider a simple enterprise question: Can we offer this customer a larger discount? A powerful model can reason about discounts. But answering that question properly may require the customer's lifetime value, current contract, payment history, margins and approval thresholds and perhaps a conversation yesterday in which the account executive learned that a competitor had entered the deal. The model isn't missing intelligence. It is missing context. Humans rarely make important decisions from one piece of information. We combine data, experience, relationships, history and situational awareness almost unconsciously. Agents need a version of the same capability. That is why enterprise AI is moving from an era dominated by prompt engineering toward one increasingly shaped by context engineering. Satya Nadella recently described the emerging market simply as “a world of context engineering.” Prompt engineering asks: What should I tell the model? Context engineering asks the larger question: What does the model need to understand before it can act well? That difference becomes critical as AI moves from answering questions to performing work. The Enterprise Context Stack In discussions about enterprise agents, “context” can easily become shorthand for giving a model access to more data. That definition is too narrow. I think about enterprise context as a stack: DATA → SEMANTICS → KNOWLEDGE → WORK → MEMORY → JUDGMENT Data tells the agent what happened: orders, transactions, inventory, claims and customer records. Semantics tells it what that data means to the business: what constitutes a strategic customer, how margin is calculated, which assets belong to which production line. Knowledge tells it what the organization knows: policies, contracts, procedures, research and accumulated expertise. Work tells it what is happening now: who is involved, what has been discussed and which commitments already exist. Memory tells it what happened before: previous interactions, decisions, exceptions and outcomes. And judgment helps determine what should happen next. Memory deserves particular attention because it is context agents can accumulate through experience. Microsoft Foundry now distinguishes between session, user and procedural memory - moving agents beyond remembering a conversation toward remembering preferences and learning how work gets done across repeated interactions. Sometimes the cheapest way to make an agent meaningfully better may not be a bigger model. It may be a better memory. Not every agent needs every layer. But the more consequential its responsibility, the richer, and more carefully governed, its context usually needs to become. This is where context stops being a retrieval problem and starts becoming an enterprise architecture problem. Context Is Becoming Infrastructure This shift is now visible well beyond any one technology platform. Gartner increasingly describes a context layer as an architectural foundation for agentic AI - bringing together semantics, operational state, provenance and other enterprise signals so agents can act reliably. Anthropic, approaching the problem from AI engineering, argues that context should be treated as a finite resource that must be carefully curated rather than simply expanded. Microsoft's recent direction is particularly interesting through this lens. Work IQ, Fabric IQ, Foundry IQ and Web IQ increasingly form a shared intelligence layer across Microsoft's stack. Work IQ provides context about how work happens. Fabric IQ brings business semantics and relationships. Foundry IQ provides reusable enterprise knowledge and agentic retrieval. Web IQ extends that understanding with fresh external intelligence. The architectural implication matters more than the product names. Instead of every new agent rebuilding connectors, retrieval logic, semantic definitions and permissions, context becomes reusable infrastructure from which many agents can reason. Microsoft captured the shift particularly well: As agents become the new application model, context becomes critical infrastructure. Your Data Estate Is Not Automatically Your AI Moat For years, organizations have been told their data is a competitive advantage. Sometimes it is. But possessing data and making that data useful to an agent are very different things. Most enterprises don't suffer from a shortage of information. They suffer from an abundance of it -scattered across databases, documents, emails, meetings, applications and people's heads. The advantage doesn't come from connecting an agent to all of it. It comes from turning information into usable context: preserving business meaning, understanding relationships, distinguishing authoritative knowledge from convenient information, respecting permissions and retrieving what matters when a decision is being made. The enterprise with the most data may not build the best AI. The enterprise that makes its context most usable might. The Context Budget There is a trap on the other side too. If context improves AI, the natural instinct is to give the agent as much as possible. That is the wrong objective. Context consumes attention and compute. Irrelevant information creates noise. Stale information can mislead. Conflicting context can degrade reasoning rather than improve it. The objective should therefore not be maximum context. It should be minimum sufficient context the smallest trusted, relevant and permission-aware set of information required to produce the right outcome. In Beyond Tokens, I introduced the idea of an Intelligence Budget: How much intelligence does this outcome deserve? Enterprise agents increasingly need a companion concept. The Context Budget. How much context does this decision deserve? A scheduling agent may need calendars and preferences. A pricing agent may need customer economics, margins, commitments and approval policies. A clinical agent requires an entirely different level of completeness, provenance and governance. Every unnecessary token of context is cost. Every missing piece of necessary context is risk. Which gives us a useful way to think about enterprise agents: Right Intelligence × Right Context → Right Outcome Spend more intelligence where the outcome warrants it. Provide more context where the decision requires it. Do neither indiscriminately. What An Agent May Know Matters Too! There is another dimension to the Context Budget: permissions. Enterprise context belongs to someone. A system that retrieves the right answer but ignores whether the agent is entitled to see it isn't intelligent. It is a security problem. Enterprise context must therefore remain identity-aware and permission-aware as it moves from source systems into agent reasoning. This becomes especially important as agents gain persistent identities and greater autonomy. What an agent may know is as much a part of its job description as what it may do. Autonomy defines the boundary of action. Context defines the boundary of understanding. From RAG Projects To Context Architecture The first generation of enterprise GenAI often started with RAG: connect documents to a model and retrieve relevant passages. That was an important step. But an agentic enterprise cannot become a collection of hundreds of independent RAG pipelines, each separately rediscovering the organization's customers, products, policies, terminology and permissions. That risks creating another generation of technology fragmentation. The opportunity is to move from agent-specific retrieval toward reusable enterprise context. What knowledge should agents inherit? Which business entities should have common definitions? Which sources are authoritative? Which memories should persist—and which should be forgotten? And perhaps the hardest question: Who owns enterprise context? Today, responsibility is scattered across data teams, application teams, knowledge management, security, business functions and increasingly AI teams. In an agentic enterprise, context may need to be managed as a product, not everyone's side project. The Moat Is Not The Model Imagine two competing organizations using exactly the same frontier model. One connects it to fragmented documents and a handful of applications. The other has strong data foundations, shared business semantics, governed knowledge, connected workflows and organizational memory. They have access to the same AI. They do not have the same intelligence system. The second organization's agents understand customers more deeply, interpret signals more accurately and act with greater awareness of how the organization actually works. And that advantage compounds. Every workflow creates more context. Every interaction adds organizational memory. Every decision creates another signal from which future agents can reason. Models depreciate the moment a better one ships. Context appreciates every day you operate. That may be the most important strategic shift of all. Anyone can rent intelligence. No one can rent your context. Intelligence Is Becoming Abundant. Understanding Isn't. The first wave of enterprise AI taught us how powerful models could be. The next may teach us that models were only one part of the equation. An agent needs intelligence to reason. But it needs context to understand your business. As frontier intelligence becomes more accessible, enterprises may discover that their most defensible AI asset was never the model. It was everything the organization had learned before the model arrived: its data, semantics, knowledge, relationships, history, workflows and judgment. The organizations that turn those assets into trusted, reusable context will give every agent they build an advantage from day one. So perhaps the strategic question is no longer simply: Which model should we use? It is: What does our organization know that our competitors don't and can our AI actually use it? Because in a world where everyone can access intelligence, context may be what makes that intelligence uniquely yours. References Microsoft — FY2026 Q2 Earnings Conference Call Microsoft — FY2026 Q4 Earnings Conference Call Microsoft Learn — What is Foundry IQ? Microsoft Foundry — Foundry IQ: Build Smarter Agents Faster with Unified Knowledge and Serverless Retrieval Microsoft Foundry — What's New in Microsoft Foundry | Build 2026 Gartner — Context Engineering Is the New Prompt Engineering Gartner — Market Overview for AI Context Platforms Gartner — The 3 Core Components of the Context Layer for AI AgentsPartner Blog | Build. Monetize. Scale. Frontier Accelerate for Marketplace launches to power software companies on Microsoft Marketplace
For software development companies building the next generation of AI-powered apps and agents on the Microsoft Cloud, getting a solution ready for customers is only part of the commercial journey. Publishing, becoming transactable, reaching customers, aligning with Microsoft sellers, and building Marketplace momentum all require different resources as their business progresses. Today, we are announcing the availability of Frontier Accelerate for Marketplace, a new Microsoft AI Cloud Partner Program experience that brings those resources into one connected journey, while providing updated benefits and additional value. The experience follows three stages: Build & Publish, where partners prepare their solutions for Microsoft Marketplace and transactability; Grow, where they turn Marketplace presence into customer acquisition, transactions, and early commercial momentum; and Differentiate, where they build on established Marketplace performance through deeper go-to-market engagement, benefits, and opportunities to further differentiate their solution. As partners progress, a unified Partner Center experience connects guidance, skilling, Marketplace resources, and milestone-based benefits to the stage their business has reached. Eligible partners can choose the level of engagement that fits their strategy: The no-cost experience provides self-service tools, guidance, skilling, and milestone-based benefits that expand as partners progress through the Marketplace journey and meet applicable performance milestones. Frontier Accelerate for Marketplace Premium is available for a fee and adds deeper Microsoft engagement and investment across the journey. Eligible Premium partners can access up to $30,000 in Azure sponsorship for eligible build and test activities, along with technical consultations and a named engagement manager. 1 As they move into growth, Premium partners also receive customer propensity scoring, immediate access to Partner Marketing Center Pro, and additional go-to-market guidance and benefits. Both experiences follow the same Build & Publish, Grow, and Differentiate journey, giving partners the flexibility to choose the level of guidance and investment that fits their business. Continue reading hereWhen AI Stops Waiting for Instructions
Microsoft’s Autopilots mark a shift from AI that responds to prompts to persistent agents with identity, initiative and standing responsibilities. As AI begins to hold a “job,” the enterprise challenge moves beyond deploying agents to defining what they should own, how far their autonomy should extend, and where human accountability must remain.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 optimizationScaling Seller Impact with ASPX Insights: From Data Access to AI-Driven Execution
ASPX Insights in Partner Center Most sellers are not constrained by opportunity; they are constrained by time and signal. With up to 70% of effort spent on administrative work, portfolio coverage continues to expand while insight quality declines. Sellers are forced to manually interpret fragmented telemetry across multiple systems, slowing decision-making and reducing precision in where they focus. The result is missed revenue signals, delayed interventions, and inconsistent execution at scale. Productivity is not just about efficiency, it is about enabling every seller to consistently identify and act on the highest-value opportunity, in real time. To drive consistent, data-led growth across Security and AI Business Solutions, partners should connect to ASPX Insights within Partner Center. This provides direct access to adoption propensity models, customer telemetry, and actionable account-level insights across Microsoft 365 Copilot, E7, and Agent 365 scenarios. Access to ASPX Insights is only the starting point. The real value comes from how you operationalise that data and surface it directly to sellers in the flow of work. The most effective pattern is to build a lightweight “opportunity agent” that connects to ASPX data via API and translates telemetry into clear actions. This ensures your sales teams have the right conversation with the right customer at the right time - Conversations that are backed by intelligent insights to help guide your customer's on their AI and security journey. The Architecture ASPX Insights is powered by Partner Center data and exposes rich telemetry and propensity signals that can be accessed programmatically. This allows partners to move beyond dashboards and embed insights directly into seller workflows. As demonstrated in internal adoption patterns, partners can connect to ASPX via API and pull Customer-level adoption and usage telemetry Copilot, Security, and Agent usage signals Propensity scores for expansion, adoption, and conversion Licensing, whitespace, and engagement indicators The M365 Partner API – AI Business Solutions & Security Insights allows partners to ground an agent in real, actionable data rather than static pipeline assumptions. By pulling live telemetry and seat-level signals directly into the agent, the solution continuously reflects actual customer usage patterns - who is adopting, who is stalled, and where there is untapped potential. This means opportunity identification is no longer based on periodic reporting or manual interpretation, but on near real-time behavioural insight. As a result, account prioritisation, upsell motions, and intervention strategies are driven by evidence rather than instinct, enabling account teams to act with precision and focus on the highest-impact opportunities across their portfolio. The agent should act as a translation layer between raw telemetry and seller action. The goal is not to expose more data, but to remove ambiguity and tell the seller exactly where to focus. A simple architecture looks like this: Data layer Ingest ASPX Insights data via API on a daily or scheduled basis. Optionally store monthly snapshots to track trends and smooth out variability. This creates both real-time signal and historical context. With the data layer you have a couple of options. You can integrate directly into Partner Centre ASPX Insights by using the API. This is technically complex and may have a longer lead time to see results. Alternatively, to see results with less complexity, you can simply download snapshots of the data monthly from ASPX Insights in Partner Centre. This is manual but easy to achieve and offers a shorter time to value for your agent. Scoring layer Use ASPX propensity outputs directly or combine them with your own logic to rank accounts across a small set of opportunity lenses Expansion ready (Copilot scale) Conversion ready (free to paid) At risk (low adoption vs paid licenses) Transformation ready (Agent 365 and advanced AI scenarios) These align directly with the machine learning models and signals already surfaced in ASPX. Action layer Translate scores into next best actions Who to engage Why now What motion to run (sell, enable, expand, govern) This is where agents create value. Sellers should not see raw dashboards. They should see prioritised accounts and recommended actions. Internal Adoption Path The key design principle is simple: bring insight into the tools sellers already use. CRM integration Push ranked accounts and recommendations directly into CRM as opportunities, tasks, or account insights. This ensures data becomes part of pipeline management, not a separate activity. Copilot / Agent interface Expose the agent through Microsoft 365 Copilot or a custom chat interface where sellers can ask Which customers should I prioritise this week Which accounts are ready for Copilot or E7 expansion Where are my adoption risks The agent queries the ASPX-backed dataset and returns structured recommendations in seconds. Proactive notifications Trigger alerts based on signal changes Spikes in Copilot usage Drops in adoption for paid tenants New high-propensity accounts entering threshold This shifts sellers from reactive to proactive engagement. Portfolio dashboards (secondary) Maintain dashboards for leadership and planning, but not as the primary interaction model for sellers. Dashboards support strategy, agents drive execution. Extending into E7 and Agent 365 scenarios Once the agent is connected to ASPX, partners can extend the same model across broader solution plays E7 opportunities Use signals such as usage depth, licensing posture, and workload adoption to identify customers progressing toward advanced security and compliance requirements. The agent can flag these accounts as ready for E7-led conversations. Agent 365 opportunities Combine Copilot maturity with agent usage signals to identify customers moving beyond productivity into process automation. These are high-value transformation plays where partners should proactively engage. This ensures sellers are not operating in silos but are guided toward the next logical workload based on real behavior, not assumptions. Where to Next If you lead a partner sales team, connect to ASPX Insights and see how these insights can empower your sales teams to achieve more. Check out our ready made ASPX Insights agent in Github.Frontier Transformation campaign in a box now available in Partner Marketing Center
AI transformation is moving beyond isolated experimentation to organization-wide initiatives and customers are looking for partners who can help them scale. Organizations scaling AI are seeing measurable gains across productivity, efficiency, and customer engagement. To support this shift, the Frontier Transformation campaign in a box provides a ready-to-launch, co-branded marketing campaign aligned to Microsoft AI solutions. This campaign is aligned with the Frontier Success framework and engages customers as they advance in their AI maturity, with messaging that connects AI capabilities to real business context while reinforcing trust, governance, and security. Available in Partner Marketing Center Pro, the Frontier Transformation campaign in a box empowers you to go market faster, support lead generation, and build pipeline. Partner Marketing Center Pro is available to partners who have purchased a partner benefits package, attained a Solutions Partner designation or are enrolled in ISV Success. Explore Frontier Transformation campaign in a box.Differentiate yourself for the AI era—earn your Frontier Transformation Engineer badge
Each session of the Frontier Transformation Engineer Summit is designed for you to build the skills to advance your Frontier Engineering journey and accelerate progress toward earning your Frontier Transformation Engineer badge. With this badge, you can validate that you have the expertise to deliver agentic AI solutions at the hypervelocity that customers expect, with the breadth of the Microsoft technology stack to support you. Here’s how you can keep building your AI capability: View the summit sessions on demand. Work toward earning the Frontier Transformation Engineer badge. Join us at MCAPS Start for Partners on July 22 to refresh your go-to-market approach for the next year.
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 pageBuild AI skills at Microsoft AI Skills Fest
Register now for Microsoft AI Skills Fest, a no-cost digital skilling event taking place June 8–12. Designed so partners can stay ahead in the rapidly evolving AI landscape, attendees can build in-demand AI skills, gain practical insights, and discover ways to deliver innovative solutions, deepen customer relationships, and stay competitive in a fast-changing market. Get ready to lead Frontier Transformation If you’re a solution engineer or architect building AI agents across the Frontier stack—including Microsoft 365 Copilot, Microsoft Foundry, Copilot Studio, GitHub Copilot, Fabric, and Agent 365—you can validate your expertise with the Frontier Transformation Engineer badge. Continue reading this blog on our newSkilling discussion board
