BRAC IT

Product Discovery Framework

Choose the discovery path that matches the request.

Scan the approaches and open the one that fits best.

Discovery Type
Tactical Discovery

For tightly scoped change work where the problem is mostly understood, but the team still needs clarity on scope, rules, edge cases, and acceptance criteria.

Best for

Use when the request is bounded, delivery is near-term, and the main risk is misunderstanding the details rather than choosing the wrong strategy.

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Discovery Type
Operational Discovery

For internal workflow or program work where the challenge is understanding roles, approvals, handoffs, exceptions, process gaps, and rollout context.

Best for

Use when a workflow, department, branch network, or program needs a shared way of working and success depends on process clarity, ownership, training, and adoption.

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Discovery Type
Scaled Solution Discovery

For large enterprise products or reusable solutions that must work across multiple teams, units, clients, roles, or governed contexts. This may or may not become SaaS.

Best for

Use when the team must decide whether the solution should stay custom, become an internal platform, support multiple enterprise contexts, or evolve into a SaaS-friendly product.

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Discovery Type
Mass Product Discovery

For external or public-facing product work where many users are affected and the team must understand adoption, trust, usability, support impact, and behavior at scale.

Best for

Use when success depends on whether a large user base can understand, trust, adopt, and repeatedly use the product without creating service or support overload.

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Discovery Type
Integration Discovery

For system-dependent work where APIs, data flow, source-of-truth mapping, sync rules, reconciliation, dependencies, and failure handling are central.

Best for

Use when the hardest part is not the screen design, but how systems connect, exchange data, recover from errors, and stay consistent over time.

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Discovery Type
Analytics and AI Discovery

For reporting, scoring, recommendation, prediction, automation, or decision-support work where value depends on data quality, metric logic, model behavior, and user trust.

Best for

Use when the product outcome depends on insights, alerts, AI assistance, scoring, dashboards, or decisions that users must understand and act on confidently.

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