You've likely seen it: someone posts a "miracle" prompt on Slack, another tests a free tool "just to see," and two weeks later, you don't know who is using what, with which data, or if the results are reliable.
You have probably already experienced the scene: someone posts a "miracle" prompt on Slack, another tests a free tool "just to see," and two weeks later, you don't know who is using what, with which data, or if the results are reliable.
In an SME or scale-up, the discussion on artificial intelligence is not a philosophical debate. It is a team discipline. It serves to align usage with measurable objectives, reduce risks (confidentiality, errors, compliance), and avoid tool chaos.
This guide gives you concrete tools and simple rules to organize these discussions, decide quickly, and deploy AI without losing control.
A good AI discussion is judged by its deliverables, not the number of ideas.
Here are the minimum decisions to obtain, even if you start small.
Subject to decide | Question to ask | Simple deliverable (1 page) |
|---|---|---|
Objective | Which KPI do we want to move, on which process? | Use case sheet (objective, scope, baseline KPI) |
Authorized tools | Which tools and accounts are "OK"? | "Approved AI tools" list + access rules |
Data | What are we allowed to send to a model? | Data policy (green/orange/red) |
Quality | How to avoid false and silent answers? | Guardrails (sources, human validation, tests) |
Traceability | Can we audit and explain what happened? | Logging rules (logs, prompts, versions) |
Adoption | Who trains whom, when, how? | Micro-training plan + referents |
If you don't have these elements, the AI discussion quickly turns into a "permanent POC".
The goal is not to add yet another tool. It is to use your existing tools with a clear structure, then add one or two bricks if necessary.
Choose a "source of truth" (Notion, Confluence, Google Docs, SharePoint). It must contain:
The AI usage charter (short)
The list of authorized tools and accounts (enterprise vs personal)
A register of use cases (idea, owner, status, KPI)
A library of validated prompts, with context and examples
If your teams already have a product/tech culture, you can also manage part of it in "version control" (Git) for critical prompts, especially when they power automations.
To structure the "prompts" part, you can rely on prompt engineering principles (clarity, context, iteration), but the challenge in a company is primarily reproducibility.
Create a Slack/Teams channel like #ai-ops or #ai-usage. To avoid noise, impose a mini-template for every "new usage" message:
Objective (1 sentence)
Data used (green/orange/red)
Tool (and account used)
Observed result (examples)
Identified risk (if applicable)
Next step (test, doc, stop)
This format transforms a vague discussion into an actionable signal.
A simple table (Notion/Airtable/Sheet) is enough to start.
Useful fields: use case, owner, team, frequency (daily/weekly), data, tool, risk level, KPI, status (idea/test/pilot/prod), review date.
It is also a good entry point for an opportunity audit, as you quickly visualize high-leverage uses and those that should be stopped.
For uses that touch clients or important internal decisions, the discussion must be fueled by evidence.
At a minimum:
A set of real examples (10 to 30 cases) to test
Quality criteria (accuracy, tone, completeness, source citation)
A human validation protocol
On LLM topics, you can draw inspiration from risk frameworks and best practices like the NIST AI RMF (AI risk management) and the OWASP Top 10 for LLM Applications (LLM security risks). These are not "standards to apply to the letter" in an SME, but good reading grids.
Most issues come from two things: data and the illusion of reliability. Here are simple rules, applicable quickly.
The AI discussion becomes much simpler when everyone speaks the same language regarding sensitivity.
Level | Examples | Usage Rule |
|---|---|---|
Green (non-sensitive) | public content, generic internal templates, non-confidential procedures | Usage possible in an authorized tool |
Orange (sensitive) | non-public client info, internal documents, pricing, roadmaps, incidents | Usage possible only with framed tools (contract, settings, access), minimization, no mass deposits |
Red (critical) | personally identifiable information, secrets, regulated data, credentials, unpublished contracts | Forbidden in non-validated tools, prioritize controlled solution (e.g., usage via framed API, secure environment) |
In France, the CNIL regularly reminds us of the importance of data minimization, purpose, and personal data protection. Even without a "big compliance program," this classification avoids costly mistakes.
The team discussion must clarify an essential distinction:
Writing: AI is very effective for rephrasing, structuring, proposing variants. The main risk is confidentiality.
Truth (facts, figures, legal, procedure): The main risk is error. Here, you need sources, guardrails, and often access to verified internal documents.
When you mix the two, you get deliverables that are "well-written but false."
For any high-impact usage (client, finance, HR, legal, production), ask for an "auditable" output. For example:
Source citations (links, excerpts)
Explicit hypotheses ("I don't know," "missing data")
Constrained format (table, fields, checklist)
A human validation step before action
In practice, this often pushes to connect AI to an internal document base (RAG approach) rather than "discussing in a vacuum." If you want to dig deeper, Impulse Lab has a sheet on RAG.
It is a sensitive point, but essential.
Explicitly decide:
authorized tools and enterprise accounts
prohibitions (orange/red data in non-validated tools)
storage rules (copy-paste, attachments, exports)
If you have a "testing" phase, formalize it: duration, scope, and authorized data. Without this, tests become invisible production.
The AI discussion must translate into management.
An owner (business) responsible for the KPI
A referent (tech/data/sec) to validate constraints
A review every 2 to 4 weeks: continue, correct, stop
This is also an excellent way to reduce costs, because "infrequent" or "no KPI" uses disappear naturally.
For an SME, the best format is often a light, but regular ritual.
Recommended format (30 minutes, 6 to 8 people max):
5 min: quick metrics (KPI, volumes, incidents)
15 min: 2 use cases reviewed (one that works, one that has issues)
10 min: decisions and actions (who does what, by when)
This ritual works very well if you use your AI register as support.
In AI discussions, three traps often recur.
In a non-technical team, it is tempting to compare tools and models. Bring the discussion back to:
the actual task (frequency, variability)
the available data
the acceptable risk
the integration into the workflow
This aligns with a central idea: AI creates value when it is integrated. If you want a broader vision, you can read on Impulse Lab how to transform AI into concrete gains, in artificial intelligence advantages: concrete gains.
"We use ChatGPT" is not a KPI. In the discussion, demand a baseline:
time spent before / after
error rate / returns
processing speed
resolution rate (support)
Even a simple measurement, over 2 weeks, is enough to decide.
In 2026, the AI Act and compliance become an operational subject, not a "later" subject. The right compromise in an SME is proportionate governance:
clear data rules
minimal traceability
validation for sensitive cases
If you need to structure roles and responsibilities, reading AI Organization: roles, governance, and responsibilities can help set a framework without bureaucracy.
If you want to launch an AI discussion dynamic without spending a quarter on it, aim for this minimal pack.
An AI usage charter (1 page)
A green/orange/red data classification
A Slack/Teams channel with a mandatory template
An AI register (table) with owner and status
A weekly "AI review" ritual of 30 minutes
Only then do you invest in integrations or custom work.
If your discussions are going in circles, here are concrete signals:
lots of ideas, few deployments, no KPI
use of unmastered tools with sensitive data
first incidents (false answers sent, leaks, rising costs)
need to integrate AI into a CRM, helpdesk, ERP, internal tool
In these cases, an agency can accelerate by framing, training "at the point of use," and building an integrated V1.
Impulse Lab supports SMEs and scale-ups via AI opportunity audits, adoption training, and custom development with integration and automation. If you want to transform your discussions into an execution plan (prioritized cases, rules, measured prototype), you can start with a conversation on impulselab.ai.
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
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