AI Project Kickoff Checklist

AI projects rarely fail in the final sprint. They fail in the first two weeks, when the outcome is left vague, access to data never arrives, nobody with authority is accountable, and evaluation is deferred until it becomes an argument. This checklist covers what must be true at kickoff. It draws on FISTA's scoping practice described in how we scope ai projects and how to start an ai project, and it is the first artifact in every forward deployed engineer mission.

Who should use this checklist?

Business owners sponsoring an AI initiative, engineering leads starting delivery, and project or program managers responsible for the conditions of success.

Is the outcome defined?

1. An outcome statement exists: what will be true for the business when this succeeds.
2. Success measures are named with the metric, the unit, and the measurement method.
3. A baseline exists or a plan to establish one during discovery is agreed.
4. The autonomy level the case assumes is stated (suggest, act with approval, act with sampling).
5. Out of scope is written down.

Reference: how to write acceptance criteria for ai and the AI ROI measurement framework whitepaper.

Is there a business owner?

1. A named business owner with authority over the workflow is accountable for the outcome.
2. The owner has committed time for discovery, reviews, and decisions.
3. The owner will staff the exception queue or review process the system requires.
4. An executive sponsor with budget authority is identified.

Reference: the enterprise AI adoption roadmap whitepaper.

Are data and systems identified and accessible?

Item	Status required at kickoff
Data sources needed for the first increment	Identified with owners
Access to those sources for the team	Requested with dates, or granted
System integrations needed	Identified with API or interface owners
Data classification and sensitivity	Known
Historical data for evaluation	Located
Environments for development and testing	Provisioned or scheduled

Reference: ai data readiness checklist and the data readiness for generative AI whitepaper.

Are security and compliance constraints known?

1. Data-handling rules for the classes involved are documented.
2. Provider constraints (which model providers may receive which data) are known.
3. Regulatory context (industry rules, jurisdictions, consequence classification) is documented.
4. Security onboarding for the team is scheduled: identities, least-privilege access, device policy.
5. A security and compliance contact is named.

Reference: enterprise ai security and the agentic AI governance whitepaper.

Is the evaluation approach agreed?

1. The team agrees that a specification and golden dataset precede tuning.
2. Domain experts who will label data and review outputs are named with time committed.
3. Quality thresholds will be set from consequence, with the business owner's sign-off.
4. The evaluation harness will run in CI as a gate.
5. Shadow or assist mode is the planned launch posture.

Reference: the AI evaluation and testing whitepaper and the spec-driven development for AI whitepaper.

Is the team right?

1. An engineering lead owns delivery.
2. Engineers have production AI experience matched to the problem type.
3. Domain experts are allocated.
4. Platform components (gateway, retrieval, evaluation, observability) are identified as reuse or build.
5. Gaps are filled through staff augmentation or an embedded forward deployed engineer where needed.

Reference: ai team structure.

Is the first increment scoped?

1. A first increment is chosen to produce evidence: bounded, measurable, integrable.
2. Exit criteria for the increment are written.
3. The increment includes baseline measurement, specification, and evaluation, not only a build.
4. A date for the increment review is set.

Reference: how to run ai discovery and ai poc vs mvp.

Is the decision cadence set?

1. A weekly review with the business owner is scheduled.
2. Scope and priority decisions have a named decider and a turnaround expectation.
3. An escalation path for access and blocker issues exists.
4. Stakeholder communication is planned, including what changes for whom.

Reference: the AI change management whitepaper.

Are commercial and governance items settled?

1. Budget covers discovery, build, evaluation, and the first operating period.
2. For external partners: contract, IP assignment, data terms, and asset ownership are signed.
3. The project is registered in the AI portfolio or governance register.
4. Risk tier is assigned, with the review requirements it implies.

Reference: how to negotiate an ai development contract and ai portfolio management.

How should missing items be handled?

Missing outcome, owner, or access items should delay kickoff; starting without them wastes the team's first weeks. Missing evaluation agreement is resolved in the first session with the business owner. Missing security or compliance clarity is escalated on day one. Record open items with owners and dates and review them at the first weekly meeting.

How FISTA Solutions runs kickoff

FISTA Solutions runs kickoff against this checklist before any engineer writes code: outcome and baseline plan, business owner and sponsor, access arranged, constraints documented, evaluation approach agreed, team confirmed, first increment scoped, and cadence set. Forward deployed engineers lead the process inside your organization, AI enablement supplies reusable platform components, and AI agents are scoped as outcomes from day one. The record behind the approach is 150+ projects with 47% average efficiency gains.

To kick off an AI project on these terms, message FISTA on WhatsApp, or read common ai project mistakes for the failures this checklist prevents.