Plan AI-powered business solutions

A large manufacturing enterprise is beginning its agentic AI journey. Several business units have independently started experimenting with Copilot Studio agents, resulting in duplicated effort, inconsistent security reviews, and no shared way to measure business value. Leadership wants to establish an AI Center of Excellence (CoE). As the solutions architect, what should be the CoE's primary initial responsibility to address these problems?

A large manufacturing enterprise has three business units that have each independently built agents using Copilot Studio and Microsoft Foundry. Leadership is concerned about duplicated effort, inconsistent responsible-AI practices, and no shared repository of reusable prompts or approved models. They want a sustainable operating model that centralizes standards and reusable assets while still allowing business units to build their own solutions. Which action best addresses these concerns as part of the AI strategy?

A global logistics company wants to give warehouse floor supervisors an AI assistant that answers questions about inventory locations, safety procedures, and shift schedules. The supervisors already work all day inside Microsoft Teams and Outlook, rarely open other apps, and have single sign-on with Microsoft 365. Leadership's top priority for the pilot is maximizing adoption by meeting workers in the tools they already use every day, while keeping build effort low. As the Business Solutions Architect, which building strategy best fits this priority?

A retail analytics team wants an AI agent to autonomously scan weekly sales, inventory, and weather feeds, surface anomalies, and generate a natural-language summary of emerging demand trends that analysts can review each Monday. The data feeds are well-structured, refresh reliably each night, and the summaries are advisory only — analysts still make all merchandising decisions. As the solutions architect, how should you assess this agent's fit during requirements analysis?

A logistics company wants to reduce delays at its distribution centers. The operations leader describes three candidate initiatives: (1) automatically generating and sending standardized carrier booking confirmations, (2) recommending optimal load consolidation plans by weighing cost, delivery windows, and truck capacity, and (3) producing a weekly summary dashboard of on-time delivery percentages. As the solutions architect, you must identify which initiative is the strongest candidate for an agent that performs autonomous decision-making (as opposed to task automation or data analytics). Which initiative should you classify as the decision-making use case?

A logistics company processes thousands of inbound shipping notifications daily. Each notification arrives as a structured EDI message, is validated against a fixed set of rules, and either routed to the warehouse system or flagged for a clerk when a rule fails. The rules rarely change, there is no ambiguity in the inputs, and the volume is highly predictable. Leadership asks you to recommend whether an agentic AI solution is the right fit for automating this workflow. What should you advise?

A retail company wants to deploy a Copilot Studio agent that answers employee questions about product return policies by grounding on documents in a SharePoint library. During requirements analysis, the architect finds the library contains policy PDFs from 2018 to 2024, several duplicate versions with conflicting return windows, marketing brochures mixed among policies, and many scanned image-only files without extracted text. Which data-readiness issue should the architect prioritize remediating FIRST to reduce the risk of the agent returning contradictory answers?

A logistics company wants to build a Copilot Studio agent that answers customer shipment-status questions by grounding responses in an internal SQL database. During requirements analysis, the architect discovers the shipment table is updated only once per night via a batch job, contains many duplicate records from a legacy merge, and includes several free-text status fields with inconsistent terminology. The business insists customers must receive accurate, up-to-the-minute status. Which data-grounding concern should the architect flag as the PRIMARY blocker to meeting the stated business requirement?

A manufacturing company wants an AI solution to accelerate supplier onboarding. The process involves collecting supplier documents, validating them against internal compliance rules, and routing approvals. The business has these constraints: the workflow logic is highly specific to their internal ERP, a vendor offers a generic onboarding SaaS agent that covers ~60% of the steps, and the company already uses Microsoft 365 Copilot broadly. Leadership asks the architect for a build-vs-buy-vs-extend recommendation that minimizes total cost of ownership while meeting the specialized requirements. Which recommendation best fits?

Contoso's HR department wants an AI assistant that answers employee benefits questions grounded in SharePoint policy documents, surfaces results inside the Microsoft 365 chat experience employees already use daily, and requires minimal custom development and infrastructure. The IT architect must recommend an approach that maximizes reuse of existing Microsoft 365 investments while meeting these needs. Which approach should the architect recommend?

A retail organization is beginning its enterprise AI journey. Leadership wants a structured, Microsoft-recommended methodology to move from initial experimentation to fully governed production agents. They have already run a few Copilot Studio proofs of concept but have no formal responsible-AI policies, no defined workload prioritization, and no operational monitoring in place. As the Agentic AI Business Solutions Architect, you are asked to identify the FIRST phase of the Cloud Adoption Framework (CAF) for AI that the organization should formally complete before scaling agents into production. Which activity should you prioritize?

A logistics company already deploys Microsoft 365 Copilot to all employees. The operations team wants an AI assistant that surfaces internal shipment data, applies the company's proprietary routing-optimization logic, and lets warehouse staff query it in the flow of their daily Outlook and Teams work. The proprietary logic is stable, well-documented, and does not require a bespoke trained model. Leadership wants the fastest path to value with the least ongoing maintenance. As the solutions architect, what should you recommend?

A logistics company wants an agent to predict optimal delivery routes based on their unique fleet constraints, historical delivery patterns, and regional traffic behavior specific to their operations. As the solution architect, you are assessing whether their data is suitable for grounding before committing to a design. You discover that route history exists but is spread across three legacy systems, contains duplicate records, uses inconsistent address formats, and the most recent six months of data has not yet been ingested. Which data-readiness dimension represents the MOST critical gap to resolve before this predictive agent can produce reliable outputs?

A global retailer already has Microsoft 365 Copilot deployed to all 12,000 knowledge workers. The merchandising team wants an AI capability that lets employees ask questions about seasonal product performance directly while drafting emails, chatting in Teams, and working in Word—without switching to a separate tool. The data lives in a governed Dataverse table already surfaced to Copilot. As the solutions architect, which approach best meets the requirement while minimizing user friction and duplicate maintenance?

A retail company is building a custom agent in Microsoft Foundry that handles a mix of requests. Roughly 70% are simple FAQ lookups and short summaries, while 30% require complex multi-step reasoning over product catalogs and financial data. Leadership wants to minimize total cost of ownership without sacrificing quality on the complex requests. Which approach best meets these requirements?

A retail company is building a customer-facing product-recommendation agent in Microsoft Foundry. Traffic is highly variable: roughly 70% of incoming prompts are simple lookups (e.g., 'What is my order status?') that need fast, low-cost responses, while 30% are complex, multi-turn styling requests that require deep reasoning and higher-quality output. The architect wants to minimize cost without degrading the experience for complex requests, and wants the routing decision to be handled automatically rather than by hard-coded application logic. Which approach should the architect implement?

A global manufacturer wants a solution where employees ask questions in Microsoft Teams and receive answers drawn from SharePoint and Exchange, while a separately owned, code-first agent handles complex supply-chain optimization by calling proprietary Python models and orchestrating several specialized sub-agents. The Teams-facing experience must reuse existing Microsoft 365 grounding with minimal custom code, and the two agents must be able to invoke each other. As the solution architect, how should you allocate the workloads across Microsoft's agent platforms?

A global manufacturing company wants an agentic solution with three capabilities: (1) employees ask questions grounded in Microsoft 365 documents and Teams chats from within their normal productivity apps, (2) a customer-facing web and WhatsApp bot that answers warranty questions with low-code authoring by a business team, and (3) a high-throughput backend agent that orchestrates several specialized reasoning models against proprietary telemetry data, requiring custom code and fine-grained control. As the solutions architect, which mapping of capabilities to Microsoft platforms best fits the requirements?

A global manufacturer runs Dynamics 365 Sales in North America, Dynamics 365 Field Service in Europe, and Dynamics 365 Customer Service in Asia, each managed by separate regional teams with distinct data models. Leadership wants a unified agentic experience so sellers, technicians, and support reps can ask cross-region questions (for example, 'Are there open service cases affecting accounts in my sales pipeline?') without abandoning their existing apps. As the solutions architect, what is the most appropriate strategy to plan this multi-Dynamics 365 solution?

A logistics company built a custom shipment-tracking agent in Microsoft Foundry that resolves delivery exceptions. Leadership now wants the structured outputs this agent produces (exception categories, resolution actions, and confidence scores) to become reusable inputs for three other planned AI systems: a demand-forecasting model, a customer-sentiment analyzer, and a route-optimization agent. As the solutions architect, what is the most important design action to ensure the agent's data can effectively serve these downstream AI systems?