Integrated Data & AI Product Management with AI Agent

Learn how to turn business problems into measurable objectives before selecting AI solutions. This lesson introduces the relationship between business objectives, key performance indicators (KPIs), baselines, targets, and business ownership.

Using a customer risk assessment case from the banking sector, you will see how to define an objective to reduce assessment time by 40% while maintaining regulatory compliance and decision quality.

You will also learn to distinguish business outcome KPIs from technical AI performance metrics and document the results in a structured

Learn how to identify and prioritize AI use cases based on real business workflows and measurable objectives. This lesson explains how to examine business activities, identify opportunities for AI assistance, and distinguish AI use cases from improvements better addressed through conventional automation or data integration.

Continuing the banking customer risk assessment case, you will identify opportunities involving customer information summarization, transaction risk analysis, compliance evidence review, and explainable risk recommendations.

You will learn a practical prioritization approach based on four dimensions: business value, technical feasibility, data readiness, and risk. The lesson demonstrates how to compare candidate use cases and select an initial opportunity for further validation.

By the end of this lesson, you will understand how to select AI use cases that support business objectives while accounting for implementation constraints, governance, and human oversight.

Connect use-case performance to measurable business outcomes. Distinguish operational and AI performance indicators from business KPIs, and define how to assess whether an AI use case contributes to the intended improvement.

Move from an AI use case to a managed AI product. Define its purpose, users, capabilities, boundaries, responsibilities, and success measures before selecting technologies or implementation tools.

Identify the reusable data products required to support AI capabilities and business workflows. Learn to define their responsibilities, consumers, information boundaries, and requirements for quality, access, and reuse.

Establish accountability for individual AI and data products while coordinating their shared dependencies. Learn how the Value Stream Lead aligns delivery roadmaps, resolves cross-product conflicts, and maintains focus on the business objective.

Meet Kentaro, the open-source AI Business Design Companion used through Codex. Understand how it supports business objectives, use cases, product definitions, dependencies, and visual portfolio review through guided conversations.

Install the standard Kentaro package, create a local Codex workspace, and let Codex perform the setup and health checks. Verify the installed skills and prepare a fresh conversation for the practical exercises.

Begin a real Kentaro conversation using the customer risk assessment scenario. See how a natural business description becomes the starting point for clarifying objectives, scope, and missing information.

Continue the banking conversation by exploring use cases, AI products, reusable data products, and ownership responsibilities. Observe how Kentaro proposes design elements and connects them to the original business objective.

Inspect the DesignPortfolio and visual canvas produced by the Kentaro conversation. Examine the relationships between objectives, use cases, products, ownership, and dependencies, including proposed elements and unresolved questions.

Use Kentaro to critically evaluate the proposed design, identify weak assumptions, and prioritize missing decisions. Learn to refine the portfolio while keeping unverified information and genuine uncertainties visible.

Evaluate whether the DesignPortfolio is sufficiently defined for stakeholder review. Explore readiness findings, remaining risks, and decisions requiring validation, while distinguishing review readiness from implementation readiness.

Examine the delivery package generated from the Kentaro banking case. Review the DesignPortfolio, canvas, executive summary, readiness assessment, product definitions, and machine-readable ODPS family specifications, including validation results and outstanding decisions.

Compare the human-designed banking portfolio from the earlier activity with the Kentaro-assisted result. Examine differences in product boundaries, ownership, dependencies, evidence, and specifications, and learn why human judgment remains essential.

Evaluate proposed products against business value, feasibility, risks, dependencies, and investment requirements. Learn when to advance, validate, defer, redesign, or stop an initiative, and how to document accountable decisions.

Translate approved product designs into delivery priorities, acceptance criteria, and coordinated roadmaps. Understand how machine-readable specifications support engineering teams while product owners retain accountability for their products.

Learn to measure business outcomes alongside AI performance, data product reliability, adoption, and operating costs. Use operational evidence and user feedback to guide product improvements throughout the lifecycle.

Explore how reusable AI and data products support multiple business use cases. Learn to manage different product lifecycles, coordinate shared dependencies, rebalance investment priorities, and retire products that no longer deliver value.