How to Run an AI Discovery Sprint in 10 Days

Why Speed Matters in AI Discovery

In fast-moving markets, the organisations that move from curiosity to clarity the quickest create defensible advantage. Long discovery cycles invite analysis paralysis, stakeholder fatigue, and lost momentum. Cellebris uses a 10-day AI discovery sprint to deliver executive-ready recommendations without sacrificing rigour. The sprint compresses stakeholder alignment, data exploration, concept ideation, and feasibility analysis into a tight, documented cadence that feeds directly into roadmaps and business cases.

Below is the step-by-step structure we deploy with enterprise clients, including artefacts, decision gates, and the team you need along the way.

Sprint Overview at a Glance

Day	Focus	Key Outputs
0	Kick-off & logistics	Sponsor alignment, success criteria, participant roster
1	Strategic framing	Opportunity map, board-level objectives, constraints
2	Stakeholder discovery	Interview notes, pain point inventory
3	Data deep dive	Source inventory, quality assessment, gaps
4	Use case ideation	Opportunity backlog, scoring criteria
5	Prioritisation	Ranked shortlist, value/feasibility matrix
6	Concept design	User journeys, system sketches, data flows
7	Feasibility & risk	Technical assessment, governance checklist
8	Value modelling	ROI scenarios, benefit hypotheses
9	Delivery blueprint	30-60-90 plan, resource needs, cost ranges
10	Executive playback	Board-ready narrative, decision requests

Assemble the Sprint Team

The sprint blends business, technical, and governance perspectives. At minimum include:

Executive sponsor (SVP/GM) to provide direction and unblock decisions.
Product or strategy lead to drive agenda and storytelling.
Data leader to evaluate sources, quality, and governance considerations.
Engineering/architecture lead to assess platforms, integrations, and constraints.
Risk/compliance representative to surface regulatory considerations early.
Change or enablement lead to consider adoption and training needs.
Cellebris facilitator(s) ensuring cadence, synthesis, and documentation.

Detailed Day-by-Day Playbook

Day 0: Kick-off & Logistics

Confirm sprint goals, decision criteria, and success metrics.
Align on scope (business units, geographies, customer segments).
Schedule interviews, workshops, and working sessions with stakeholders.
Set up collaboration spaces (Miro/FigJam, Confluence/Notion, shared drives).

Day 1: Strategic Framing

Review corporate and board priorities, aligning with themes like growth, efficiency, risk, and ESG.
Capture known constraints such as regulatory obligations, system outages, or budget windows.
Produce a one-page strategy canvas that guides downstream work.

Day 2: Stakeholder Discovery

Conduct structured interviews across business, operations, compliance, and technology.
Document pain points, KPIs, and existing initiatives; surface shadow AI usage.
Synthesise findings into opportunity statements with supporting quotes/data.

Day 3: Data Deep Dive

Inventory data sources, ownership, and access models.
Evaluate quality, freshness, sensitivity, and lineage challenges.
Identify critical gaps to address via Data Preparation for AI services.

Day 4: Use Case Ideation

Facilitate workshop(s) to brainstorm solutions translating pain points into agent-assisted workflows, knowledge retrieval, or automation.
Use structured prompts (customer journey, back-office, risk scenarios) to ensure coverage.
Capture dependencies, required integrations, and success indicators for each idea.

Day 5: Prioritisation

Score opportunities against value (impact, reach, urgency) and feasibility (data readiness, technical complexity, compliance).
Plot results on a value vs. feasibility matrix; highlight quick wins, strategic bets, and deferred items.
Align with executives to confirm prioritisation, noting any non-negotiable initiatives.

Day 6: Concept Design

Develop user journeys, service blueprints, and interface sketches for top concepts.
Define agent roles, orchestration flows, and handoffs between humans and AI.
Begin mapping to underlying platforms (LLMs, orchestrators, collaboration tools).

Day 7: Feasibility & Risk

Validate technical constraints with engineering: APIs, data latency, infrastructure readiness.
Work with risk/compliance to flag regulatory requirements, necessary approvals, and policy updates.
Create a preliminary risk register with mitigations leveraging Cellebris governance templates.

Day 8: Value Modelling

Quantify benefits using productivity hours, cost reductions, revenue influence, or risk avoidance.
Build conservative vs. ambitious scenarios mirroring the format the board expects.
Capture measurement strategies and instrumentation requirements.

Day 9: Delivery Blueprint

Outline a 30-60-90 day plan covering pilots, enabling workstreams (data prep, training, vendor selection), and success criteria.
Estimate resource needs (internal, Cellebris, partner vendors), cost ranges, and dependencies.
Define governance touchpoints, including councils and executive updates.

Day 10: Executive Playback

Present recommendations in a concise narrative: strategy linkage, value, cost, risk posture, and next steps.
Provide supporting artefacts: prioritised backlog, concept visuals, financial models, and roadmap.
Secure decisions (funding, resourcing, governance approvals) and assign owners for follow-up.

Templates and Artefacts to Prepare in Advance

Interview guides and note templates.
Opportunity backlog spreadsheet with scoring formulas.
Miro board (or equivalent) pre-populated with process outlines.
Value modelling workbook tied to finance metrics.
Presentation deck skeleton aligned to board format.
Risk register, governance framework, and compliance checklist drawn from Cellebris libraries.

Tips for a Successful Sprint

Time-box aggressively. Each session should produce a tangible artefact; avoid open-ended debates.
Document continuously. Assign a synthesis lead who turns raw notes into structured insights daily.
Keep executives close. Daily stand-ups with sponsors maintain trust and catch blockers early.
Balance ambition with realism. Be transparent about data or technology gaps and offer remediation plans.
Prime the next phase. End every day with “what needs to be true by tomorrow” to keep momentum.

How Cellebris Supports Discovery Sprints

Facilitation & synthesis. Experienced leads guide sessions, capture decisions, and craft executive-ready narratives.
Data readiness expertise. Our Data Preparation teams rapidly assess sources and create remediation plans.
Architecture insight. Engineers evaluate platform fit, integration feasibility, and infrastructure options (including Local AI Hardware Setup when required).
Value modelling. Strategy consultants translate opportunities into ROI scenarios and funding asks.
Governance and compliance. Specialists ensure proposals align with regulatory expectations using proven frameworks.

A tightly run 10-day sprint gives AI programs the credibility they need to secure investment, sequence initiatives, and move into delivery with confidence. With clear artefacts and stakeholder buy-in, you can transition directly into proposal development, business casing, or pilot execution—turning momentum into measurable value.