In 2026, an **AI consultant in Paris** doesn't sell "AI." They sell the ability to **transform a concrete use case** (support, sales, ops, product, finance) into a **measurable result**, with real IT integration, safeguards (GDPR, security, AI Act), and sustainable operation...
An AI consultant in Paris in 2026 doesn't sell "AI." They sell the ability to transform a concrete use case (support, sales, ops, product, finance) into a measurable result, with real IT integration, safeguards (GDPR, security, AI Act), and sustainable operation.
The problem is that the market still mixes up many profiles (freelance, firm, agency, one-off "prompt engineer"), so rates vary widely, and deliverables are sometimes vague.
This guide gives you 2026 rate ranges (indicative) and, more importantly, the expected deliverables to buy an AI mission without ending up with an unusable demo.
In practice, behind the query "AI consultant Paris," there are several realities.
Objective: decide what to do, in what order, with which KPIs, and with what acceptable risks.
Typically useful if you have many AI ideas and teams testing tools, but few proven results.
Objective: deliver a V1 that runs in your workflows.
This profile must understand architecture (APIs, integrations, security), evaluation (quality, costs, risks), and production deployment.
Objective: ensure AI is actually used, without tooling chaos or data leaks.
In 2026, value often comes from a combination: scoping (1) + delivery (2) + adoption (3).
The amounts below depend heavily on seniority, risk level (sensitive data, regulated decisions), integration needs, and the expected level of "production readiness."
Important: these ranges are indicative (B2B market in Paris in 2026), excluding potential API/model costs, hosting, and tooling.
Daily Rate (TJM): standard for freelancers and certain consulting missions.
Fixed Price per Phase: frequent for an audit, a pilot, or a scoped V1.
Retainer (monthly subscription): useful for continuous improvement, monitoring, and adoption.
Profile (simplified) | Expected on the mission | Indicative Daily Rate Range (excl. VAT) |
|---|---|---|
Junior AI Consultant (supervised execution) | POC, prompts, simple tests, light documentation | €600 to €900 |
Confirmed AI Consultant (delivery) | Instrumented MVP, simple integrations, iteration with business units | €900 to €1,300 |
Senior AI Consultant (lead) | scoping + architecture + risks + delivery management | €1,300 to €1,800 |
Niche Expert (LLM security, production RAG, compliance, data/ML) | complex subjects, technical audits, advanced patterns | €1,600 to €2,500 |
Useful reading: if you pay "little" but your context is complex (IT, GDPR, integrations), you will often pay later in rework, incidents, or refactoring.
Mission | Typical Duration | Indicative Budget (excl. VAT) | When it's Relevant |
|---|---|---|---|
Express AI Audit (quick wins) | 1 to 3 days | €1,500 to €6,000 | prioritize 3-7 use cases, decide quickly |
Strategic AI Audit | 2 to 4 weeks | €12,000 to €35,000 | align value, risks, data, roadmap |
Instrumented Prototype (MVP) | 2 to 4 weeks | €15,000 to €45,000 | prove impact with metrics, without overinvesting |
Integrated Pilot (Usable V1) | 4 to 8 weeks | €35,000 to €120,000 | connect to tools, secure, prepare for scale |
Industrialization (V2, operation) | 2 to 4 months | €80,000 to €250,000+ | reliability, monitoring, runbook, compliance, adoption |
Training + Team Rules | 1 to 3 days + follow-up | €2,000 to €15,000 | adoption, reduction of "shadow AI," best practices |
These discrepancies are mainly explained by:
The level of integration (CRM, helpdesk, ERP, IAM/SSO, data warehouse).
The quality/dispersion of knowledge (documents, tickets, procedures, versions).
Compliance and security (GDPR, logging, access control, prompt injection).
Evaluation requirements (test sets, acceptance criteria, monitoring).
A good AI consultant (or agency) must produce artefacts that make the project:
pilotable (KPIs, baselines, go/no-go criteria),
integrable (interfaces, flows, responsibilities),
governable (risks, traceability, usage rules),
maintainable (runbook, monitoring, ownership).
Phase | Expected Deliverables | Why it's Indispensable |
|---|---|---|
Scoping (opportunity) | use case sheet (objective, scope, constraints), KPI baseline, ROI hypothesis, major risks | avoids "gadget" POCs and aligns teams |
Audit (if needed) | process mapping, data status, tool mapping, impact/effort/risk heatmap, 30-90 day backlog | gives you an actionable roadmap |
Instrumented Prototype | representative scenarios, "golden set" (test set), quality metrics, estimated cost, demo connected to a real flow | proves value before adding complexity |
Integrated Pilot (V1) | minimal target architecture, connectors, logs/audit, security rules, human handoff, KPI dashboard | makes the solution useful and controllable |
Industrialization | runbook, monitoring, recurring tests, version management (prompts/models), adoption plan, internal SLA | makes the system sustainable and scalable |
If you want more formalized scoping, a good framework consists of separating: Business KPIs, AI Quality, Safeguards. Impulse Lab details a measurement approach in its content on KPIs and production deployment (see, for example, the logic exposed in the article on strategic AI audit and ROI-oriented approaches).
A serious AI audit is not just a list of ideas. In 2026, companies expect an audit that reduces risk and accelerates delivery.
Here is what it should contain.
Not "20 AI ideas," but a top 3 to top 7, with a clear rationale.
Without a baseline, your ROI becomes a matter of opinion. A typical example:
Average handling time (minutes)
Weekly/monthly volume
Error rate / returns / escalations
Customer response time
In France, you must think at least in terms of GDPR (personal data, minimization, sub-processing) and, depending on the case, requirements related to the AI Act.
Useful resources:
CNIL for GDPR benchmarks and recommendations.
NIST AI Risk Management Framework to structure AI risks.
The goal is to decide quickly: prototype, pilot, scale, or abandon.
Impulse Lab publishes an operational roadmap close to this format (see the 30-60-90 day AI plan).
This is where many missions fail: the solution "works in demo" but does not survive reality.
A serious pilot must include:
Which intents the assistant covers.
In which cases it escalates to a human.
Which sources it is allowed to use.
You must be able to answer: "Is it better than before?" and "Is it degrading?"
A minimum expectation:
a set of real scenarios (tickets, emails, internal requests)
acceptance criteria (quality, security, latency, cost)
a weekly review during the pilot
For an assistant or an agent, "the AI" is just one brick. The real system includes: orchestration, context (RAG), actions (tool calling), logs, secret management.
To delve deeper into clean integration models, you can consult this guide: AI API: Clean and Secure Integration Models.
Two quotes can show the same initial budget but have a very different Total Cost of Ownership (TCO).
Data Preparation: cleaning, deduplication, access rights, structuring.
Integrations: CRM/helpdesk connectors, webhooks, SSO, synchronizations.
Security: secret management, PII filtering, access control, audit logs.
Evaluation and Monitoring: dashboards, alerts, recurring tests.
Adoption: point-of-use training, playbooks, owners.
Run: knowledge base maintenance, prompt adjustments, tool evolution.
Without getting into complex models, you can ask the provider to estimate:
Item | Question to Ask | Expected Result |
|---|---|---|
Variable Model/API Cost | "What is the cost per 1,000 realistic requests?" | monthly envelope and alert threshold |
Hosting/Tooling | "Which services, which region, what fixed cost?" | fixed estimate + safety margin |
Monitoring/Observability | "What is logged, how much does it cost?" | cost and retention policy |
Knowledge Maintenance (RAG) | "Who updates it, how often?" | process and monthly load |
Support | "What internal SLA and which channel?" | clear responsibility |
If your project relies on model APIs, costs and quotas are a real subject in production. A good consultant must be comfortable with this topic, otherwise you risk surprises.
You don't need a "tech for the sake of tech" interrogation. You need questions that reveal if the person knows how to deliver.
What is the North Star KPI of the use case, and what is the plan to establish the baseline?
What decision will be taken at Day 30 or Day 45 (go/no-go), based on what criteria?
Which tools must be connected from V1 (CRM, helpdesk, ERP, drive, intranet)?
What is the authentication and access control plan (especially internal)?
How do you handle hallucination (sources, RAG, constraints, refusals)?
What anti-prompt injection and anti-data exfiltration measures do you put in place?
What is logged, for how long, and who has access to it?
Who owns the solution on the client side, and what recurring tasks are planned?
What is the runbook in case of an incident (cost, quality, potential leak, degradation)?
For a broader selection grid (product capacity, integration, compliance), you can also rely on due diligence criteria like in this guide: AI Agency: Essential Criteria for Choosing Well.
The intention behind "AI consultant Paris rates" is often: "how much does it cost for my case?". Here are three frequent cases.
Objective: reduce search time, standardize answers, decrease escalations.
Typical duration: 4 to 8 weeks to a useful pilot.
Indicative budget: €30,000 to €120,000 depending on integrations and security.
Key deliverables: indexing and source rules, golden set, quality metrics, logs, permissions by role, dashboard.
If you aim for a robust version, the RAG approach in production deserves real discipline (evaluation, monitoring, source updates). See: Robust RAG in Production.
Objective: reduce response time, increase self-service rate, better capture requests.
Typical duration: 3 to 6 weeks for a limited V1.
Indicative budget: €20,000 to €90,000.
Key deliverables: intent tree, human handoff, helpdesk integration, GDPR policies (consent, minimization), dashboard (containment rate, CSAT, escalation).
To scope the measurement, a good basis is to structure KPIs by layer (AI quality, operations, business result). Impulse Lab has a resource dedicated to Essential AI Chatbot KPIs.
Objective: reduce processing cost and errors on repetitive tasks.
Typical duration: 2 to 6 weeks depending on complexity.
Indicative budget: €15,000 to €80,000.
Key deliverables: routing rule specifications, safeguards (human validation if needed), integration (Make/n8n/ERP), audit logs, error rate tracking.
The cost difference doesn't just come from the margin. It comes mainly from the skill coverage and continuity.
A very senior freelancer can be excellent for scoping and launching a V1, but you will sometimes need to supplement (design, security, data, full-stack, run).
An agency can provide a team (product, dev, integration, UX, governance), often more suitable if you want to deliver fast and well, with less dependency on a single person.
If you are hesitating, this comparison helps decide based on your scenarios: AI Agency vs Freelance: What to Choose for Your SME?.
For a result-oriented "AI consultant Paris" mission, ask that the quote and proposal explicitly include:
Scope (use case, users, channels, limits).
KPIs (baseline, target, measurement method, review frequency).
Deliverables (concrete artefacts, not just "support").
Architecture (where it runs, which integrations, which permissions).
Security/GDPR (processed data, sub-processors, logs, retention).
Run Plan (maintenance, ownership, indicative monthly budget).
If your goal is to move quickly from "AI idea" to a useful V1, the best starting point is often a scoped opportunity audit, then an instrumented pilot.
Impulse Lab supports SMEs and scale-ups on this exact sequence (audit, training, custom development, integration, and automation). You can explore:
The strategic AI audit approach
The 30-60-90 day AI plan logic
Criteria for choosing a partner via AI Agency: Essential Criteria
To discuss your context (data, tools, constraints, KPIs) and get a realistic scope, you can contact the team via the Impulse Lab website.
Our team of experts will respond promptly to understand your needs and recommend the best solution.
Got questions? We've got answers.
Leonard
Co-founder
