An **AI project** rarely fails because “the model isn't good enough”. It fails much more often because the scoping mixes several objectives, underestimates data constraints, or forgets integration into the real workflow. Classic result in SMEs and scale-ups: an impressive POC...
An AI project rarely fails because “the model isn't good enough”. It fails much more often because the scoping mixes several objectives, underestimates data constraints, or forgets integration into the real workflow. A classic result in SMEs and scale-ups: an impressive POC, then… nothing in production.
This scoping checklist helps you lock down key decisions before developing, to deliver a useful, measurable, and industrializable V1.
Use it if you are an executive, Head of Ops, PM, RevOps, or CTO and you have:
an “AI assistant”, “agent”, “chatbot”, “RAG”, or “automation” idea
a motivated sponsor, but fuzziness regarding value, scope, data, or the run phase
the will to deliver in short cycles, without debt and without unnecessary legal risk
If you are still at the stage of “we want to do AI, but we don't know where”, start instead with a quick identification of quick wins (see the Impulse Lab resource: AI Audit: express checklist for quick wins).
The checklist below is organized as production-oriented scoping. You don't need to have all the perfect answers, but you must know who decides, on what basis, and at what moment.
Start by formulating the problem without AI.
Who does the task today?
In which tool (CRM, helpdesk, ERP, Google Workspace, Notion, etc.)?
Where are the frictions (time, errors, escalations, rework)?
Expected output: 1 sentence like “Reduce X (task) for Y (team), in Z (tool), without degrading W (quality/risk)”.
An AI project without KPIs becomes a “demo” project. Before developing, define:
1 “North Star” KPI (main gain)
2 to 4 support KPIs (productivity, quality, delay)
1 to 2 guardrails (e.g., errors not to exceed, escalations, compliance)
Indispensable: a baseline (the measurement before AI). Without a baseline, you won't be able to conclude.
To go further on measurement, you can rely on a structured approach like “AI KPIs” (Impulse Lab resource in English: AI KPIs: Measuring the Impact on Your Business).
Explicitly define:
primary users (those who use it)
impacted users (those who receive the result)
usage context (real-time, asynchronous, mobile, under pressure)
what the system is allowed to do, and what it is not allowed to do
Simple contract example: “The assistant proposes, cites its sources, and triggers no action without confirmation.”
The right scope describes the flow:
trigger (when the need appears)
input (available data)
processing (reasoning, search, rules)
output (response, document, action)
feedback loop (correction, validation, learning)
Tip: if your flow isn't clear on 1 page, it is often too big for a V1.
In 80% of cases, perceived performance is capped by data.
Minimum scoping:
which sources are “source of truth” (wiki, article base, tickets, CRM, drive)
who is the owner (business, IT)
sensitivity level (PII, finance, HR, legal)
quality (obsolescence, duplicates, formats)
access constraints (RBAC, SSO, segmentation)
If your data includes personal info, you must integrate GDPR and minimization. The CNIL publishes useful benchmarks on processing and compliance.
In 2026, the right question isn't “does this concern us?”, but “what is our risk level and what proofs must we produce?”
To scope:
criticality level (internal vs client, decision vs recommendation)
hallucination risks and associated impacts
data leak risks (prompt, logs, connectors)
traceability requirements (logs, citations, versioning)
need for DPIA or not (depending on context)
For a regulatory overview, the European Commission maintains a reference page on the AI Act.
Decide on the right delivery mode before starting development:
Buy: market tool if the need is standard and low differentiation.
Assemble: SaaS bricks + integrations if value lies in the workflow.
Build (custom): if deep integration, proprietary data, or strong traceability requirements.
If you hesitate, the Impulse Lab resource can help: Custom AI development: criteria for a rational choice.
Don't choose “an agent” because it's trendy. Choose a pattern adapted to the risk and need:
Encapsulated AI API: a stable capability (e.g., classification, extraction).
RAG: when you must answer from a verifiable document base.
Agent: when steps need to be chained and tools interacted with, with guardrails.
A good scope describes the pattern and progression (often: API, then RAG, then guarded agent). See: AI integration in enterprise: API, RAG, and agent patterns.
Write down in black and white:
cases where AI must refuse
cases where AI must ask for confirmation
escalation thresholds (to a human, to a ticket)
security rules (PII filtering, blocking certain actions)
For agents, this is even more important (agent contract, authorized actions, idempotency). Impulse Lab resource: Autonomous agents in enterprise: guardrails and validation.
The scoping must include a test protocol, otherwise you will “test in prod”.
To prepare:
a pack of representative scenarios (20 to 100 cases)
success criteria per scenario
a non-regression strategy (reference cases)
an observability strategy (logs, quality, costs)
If you work on RAG, robustness depends heavily on evaluation (reference datasets, drift monitoring). See: Robust RAG in production: best practices, evaluation, and strategic choices.
An AI project has a recurring cost, sometimes very variable.
Minimum scoping:
usage estimation (volumes, peaks)
target monthly budget and alert threshold
cost reduction strategy (caching, model routing, context limitation)
operations responsibilities (who watches dashboards, who responds to incidents)
To anticipate bad surprises: AI APIs: guide to pricing, quotas, and hidden costs.
Even a good system fails if no one uses it correctly.
Scoping must include:
a simple usage rule (what to put in, what not to)
a targeted mini-training (by role)
a feedback channel (bugs, improvements, “missed” cases)
a ritual (weekly review: KPI, quality, costs)
On structuring rules: Artificial intelligence discussion: tools and team rules.
Use this table as a scoping scorecard. The goal is to reach “OK” on blocking items before any development.
Domain | Scoping Questions | “OK to develop” Criteria |
|---|---|---|
Problem | What process, what pain, what V1 scope? | 1 clear job-to-be-done, bounded V1 |
KPI | What main KPI, what baseline, what guardrails? | Measured baseline + defined KPIs |
Users | Who uses it, in what tool, at what moment? | Described journey + usage contract |
Data | What sources, what owner, what sensitivity? | Listed sources + validated access |
Risks | Hallucination, leak, compliance, reputation | Noted risks + planned mitigations |
Architecture | API, RAG, agent, or hybrid? | Chosen pattern + justification |
Tests | What scenarios, what success criteria? | Test pack ready |
Run | Costs, monitoring, incidents, maintenance | Budget + run owner + dashboards |
Integrations | What actions in what tools? | Priority integrations defined |
A solid scope produces concrete artifacts. Here is a minimal pack that avoids “endless meetings”:
a use case sheet (problem, scope, users)
a KPI framework (North Star, baseline, guardrails)
a data mapping and sensitivity levels
a Build vs Buy vs Assemble decision + hypotheses
a V1 architecture diagram (pattern and integrations)
a test protocol (scenarios, criteria, scorecard)
a pilot plan (duration, population, go/no-go)
a run plan (monitoring, costs, responsibilities)
If the AI doesn't know how to act in your tools, it becomes just another chatbot. Scope actions and confirmations from the start.
If your documents are obsolete or contradictory, the system will be incoherent. Scope the owner and updates.
“Number of messages” is not a business KPI. Keep usage metrics, but steer based on business gain.
Without monitoring, you discover drifts and costs too late. Scope observability and alert thresholds.
If you want to scope fast, without “theory”, Impulse Lab typically intervenes via:
Strategic AI Audit to qualify value, data, risks, and priorities (see: Strategic AI Audit: mapping risks and opportunities)
Custom development and integrations when value depends on the workflow and existing tools
Adoption training to align rules, usage, and security
How long does it take to scope an AI project correctly? An effective “V1” scoping is often done in a few workshops and actionable deliverables over 1 to 2 weeks, if business, data, and IT owners are available.
What is the minimum to have before developing? A bounded use case, a KPI baseline, access to data (and their owners), a chosen architecture pattern, and a representative test pack.
Is a RAG absolutely necessary for an AI project? No. RAG is useful when you must answer from a verifiable document source. For extraction, scoring, or an encapsulated capability, an API approach may suffice.
How to avoid hallucinations in production? By combining sources of truth (often via RAG), guardrails (refusals, confirmations), tests on real scenarios, and monitoring (quality, escalations, errors).
Who should be responsible for an AI project in an SME or scale-up? Ideally a pair: a business owner (value, adoption) and a product/tech owner (integration, run), with security and legal involved depending on sensitivity.
If you have an AI project to scope, the goal isn't to produce a perfect file, but a decision pack that allows delivering a measurable V1 in short cycles.
Impulse Lab can accompany you from scoping to V1 (audit, integration, custom development, and training), with a results-oriented approach and weekly deliveries.
Discover the agency: Impulse Lab
Or launch a structured scoping: Strategic AI Audit
Notre équipe d'experts vous répond rapidement pour comprendre vos besoins et vous proposer la meilleure solution.
Vous avez des questions ? On a les réponses.
Leonard
Co-fondateur
Adoption
Training, rules, feedback, ritual |
Simple adoption plan validated |
Continuez votre lecture avec ces articles
Déployer de l’IA en entreprise n’est plus un sujet “innovation”, c’est un sujet **production**. Et en production, les risques deviennent concrets : fuite de données, décisions erronées, dérives de coûts, non-conformité, attaques spécifiques aux LLM, ou tout simplement une adoption qui stagne.
