Discover and Prove # Create direction and working evidence at the same time. Identify the AI opportunities worth pursuing, select a focused use case, and test it through a working Proof of Impact. Make the production decision using evidence from execution instead of assumptions alone. [Discuss an AI opportunity ](https://www.aixccelerate.com/talk-to-us)[See how we work ](https://www.aixccelerate.com/how-we-work) One engagementTwo connected workstreams Discovery Decide what is worth proving. Priority and workflow Opportunity selection Success measures Implementation Learn by building the proof. System behavior Operating fit Evaluation evidence A better-informed production decision One engagement, four connected outcomes ## Understand. Prioritize. Prove. Recommend. Each stage reduces a different source of uncertainty and creates the conditions for the next decision. - 01 ### Understand Clarify the priority, workflow, stakeholders, users, systems, data, constraints, risk, and measures. - 02 ### Prioritize Compare candidate opportunities and select one that is meaningful, measurable, and feasible to test. - 03 ### Prove Design, build, and evaluate a working system around the selected use case and operating context. - 04 ### Recommend Use the evidence to define what should stop, change, move toward production, or expand. Select for learning and value ## The best first use case is meaningful, visible, and feasible. It should matter enough to earn attention without depending on every part of the enterprise changing at once. Opportunity selection lens 01Business valueWould improving this work produce an outcome the organization cares about? 02Workflow clarityIs the process understood well enough to define the system’s responsibility? 03FeasibilityCan the required technology, data, tools, and stakeholder access support a focused scope? 04MeasurabilityCan the organization define evidence that would support a decision? 05Sponsorship and adoptionIs there an accountable owner and a real user group prepared to participate? 06Production relevanceWill the proof answer questions that matter for eventual deployment? Discovery grounded in execution ## Understand the work while building the system that tests it. Forward-deployed specialists maintain continuity between leadership intent, the real operating process, and what the system must do. ### What we explore - 01Business priority and success - 02Current workflow and exceptions - 03Users and operating responsibilities - 04Data, knowledge, and tools - 05Technology and integration boundaries - 06Risk, governance, and adoption ### What the proof tests - 01A defined AI worker or agentic workflow - 02Approved knowledge, data, or tool access - 03Human review, approval, or escalation - 04Evaluation criteria and observed performance - 05A representative user workflow - 06Architecture and integration assumptions A working system designed to answer a business decision Working deliverables ## More than a roadmap. More than a demo. The engagement creates an evidence package for the next decision. Exact artifacts and responsibilities are confirmed in the approved scope. 01 ### Opportunity view A structured assessment of relevant opportunities and the reasoning behind the recommended priority. 02 ### Use-case definition The intended outcome, workflow, users, system responsibility, constraints, and success measures. 03 ### Working Proof of Impact A functioning implementation of the agreed use case for evaluation within the approved scope. 04 ### Evaluation findings Evidence, observations, limitations, risks, and unanswered questions found through implementation. 05 ### Architecture and governance direction Initial direction for integration, deployment, evaluation, oversight, review, and operating responsibility. 06 ### Production recommendation A decision-oriented recommendation and the major work required for the next stage. Customer participation ### The quality of the evidence depends on access to the real context. - An accountable executive sponsor and process owner - Representative users and subject-matter experts - Relevant technology, data, security, and governance stakeholders - Appropriate access to agreed systems, data, documentation, or representative alternatives - Clear feedback, decision-making, and agreed evaluation scenarios Access, security review, data preparation, stakeholder availability, and internal approvals can affect scope and timing. From proof to decision ## Use evidence to stop, refine, deploy, or expand. The objective is not to force every proof into a larger program. It is to make the next investment decision more informed and defensible. 01 ### Stop The evidence does not justify further investment, or a critical dependency makes the use case unsuitable. 02 ### Refine The opportunity remains valuable, but the workflow, scope, data, architecture, or success criteria need adjustment. 03 ### Deploy The proof supports moving toward production with defined engineering, integration, governance, adoption, and operating work. 04 ### Expand The operating pattern is strong enough to consider additional responsibilities, users, processes, or related AI workers. A useful engagement makes the next decision clearer, including when the evidence shows that the use case should change or stop. Bring us the opportunity or the uncertainty ## What does your organization need to learn before it can move AI into production? Tell us about the business priority, candidate use case, or initiative that has stalled. We will begin with the decision you need evidence to make. 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