AI Lab in SMEs: Launch and Scale in 90 Days

AI Lab in SMEs: Launch and Scale in 90 Days | Impulse Lab

In 2026, an SME that “tests AI” without a method quickly finds itself in one of two scenarios: a succession of POCs with no impact, or a rushed deployment that creates risks (data, compliance, adoption). The good news is that an SME AI lab can be launched and set in motion in 90 days, provided the lab is treated as a product (objectives, backlog, iterations), not as an innovation showcase.

This guide proposes a concrete plan to launch and scale an AI Lab in 3 months, with a cadence, deliverables, and decision criteria designed for structures that are starting to structure themselves and scale.

What we call an “AI lab” in an SME (and what it is not)

In an SME, an AI lab is generally neither an R&D department nor an isolated team of data scientists. It is a transverse capability to:

Identify AI opportunities linked to processes (sales, ops, finance, support, HR)
Prototype quickly, test in real conditions
Industrialize only what proves measurable value
Diffuse usage via training and standards (governance, security, quality)

An SME AI lab is therefore a delivery mechanism. The right indicator is not “how many POCs”, but “how many validated gains” (time, quality, revenue, risk).

To contextualize the stakes, McKinsey estimated that genAI could generate 2.6 to 4.4 trillion dollars of potential annual value (across all sectors), but real value depends mostly on execution and adoption, not technology alone (McKinsey, 2023).

The “minimum viable” prerequisites before Day 1

You don’t need a perfect data platform to start, but you need a clear foundation.

1) An explicit business target

Choose a maximum of 1 to 2 priority objectives for 90 days, for example:

Reduce client request processing time
Increase inbound conversion rate
Decrease administrative load (invoices, follow-ups, data entry)
Reduce data entry errors or non-compliance

Without a target, the AI lab becomes a “tool catalog”.

You need:

A sponsor (CEO, COO, CFO, Head of Sales) who protects the teams’ time
An arbiter who decides on priorities (often COO, or a Product/Operations duo)

3) A simple rule on data

Write down in black and white:

Which data is forbidden in external tools
Which data is authorized, and under what conditions
Who validates a new flow (IT, DPO, CISO depending on your context)

In France, rely on CNIL resources to frame GDPR and risks. And keep in mind that the European regulatory framework is evolving, notably with the EU AI Act (risk-based approach, reinforced requirements for certain systems).

The 90-day roadmap (with deliverables and passage criteria)

The goal is not to “do everything”, but to create a delivery machine: discovery, build, deployment, measurement, iteration.

Timeline diagram in 3 phases over 90 days for an SME AI Lab, with decision milestones at D15, D45, and D75, and visible deliverables at each phase.

Overview

Period	Objective	Concrete deliverables	Passage criterion
D1 to D15	Frame, prioritize, secure	Scored use case backlog, data/risk charter, experimentation plan	Top 2 to 4 cases validated, metrics defined
D16 to D45	Prototype and test	Functional prototypes, test scripts, initial measurements	At least 1 confirmed “quick win” or a solid pilot
D46 to D90	Pilot then industrialize	Pilot in limited production, integrations, monitoring, adoption plan	ROI or gain validated, scale plan, standards established

Phase 1 (D1 to D15): framing, backlog, risks, metrics

The number 1 trap in SMEs is starting with “which AI tool to choose”. Phase 1 must answer “where is the value, and how to prove it quickly”.

Build an impact-oriented use case backlog

A good AI backlog is described in business language. Example:

“Answer simple support emails in less than 2 minutes, with human validation”
“Prepare a structured sales meeting report in the CRM”
“Check supplier invoices and flag anomalies”

Each item must specify: users, frequency, pain point, available data, risks, and metric.

Simple, but non-negotiable scoring

To avoid endless debates, use a scoring grid (even out of 5 points) with:

Business value (time, euros, risk)
Feasibility (data, integrations, complexity)
Proof timeframe (in days)
Risk (sensitive data, compliance, possible errors)
Adoption (team ready, stable process)

Define KPIs before coding

If you don’t know how to measure, you won’t know how to scale.

Category	Useful KPIs in SMEs	Measurement example
Productivity	Time saved, tasks avoided	Minutes saved per file, per week
Quality	Error rate, rework	% of human corrections necessary
Customer experience	Response time, CSAT	Average delay, satisfaction score
Revenue	Conversion, pipeline	Qualified leads, meetings booked
Risk	Incidents, compliance	Number of critical errors avoided

Key deliverable: a common “test protocol”

Formalize an internal protocol inspired by experimentation approaches: hypothesis, test population, duration, metrics, decision. Impulse Lab also has a useful article if you want to structure this aspect: Enterprise AI test: simple protocol to validate your ideas.

Phase 2 (D16 to D45): prototypes in real conditions, not demos

Between D16 and D45, the goal is to obtain proofs, not promises.

Choose the right prototype formats

In SMEs, three formats work particularly well:

Internal Copilot: AI assists, human decides (very good for starting)
Automation with validation: AI proposes, a manager approves
Batch processing: AI prepares, the team checks at the end of the day

This avoids the “autopilot” risk too early, especially on client, financial, or legal topics.

Think integration from the start

A prototype “in a corner” doesn’t scale. Even in a quick version, plan for:

Where the AI runs (tool, application, API)
Where the results go (CRM, helpdesk, ERP, drive, internal database)
How we trace (logs, sources, validations)

If your use cases require properly connecting AI APIs, this guide can help your tech teams: AI API: clean and secure integration models.

RAG or no RAG, the practical question

Many teams want “a chatbot that knows everything”. In reality:

If the AI must answer based on your internal docs, you will often need a RAG (Retrieval-Augmented Generation) approach (documents, search, citations)
If the AI must mainly help write, rephrase, summarize, classify, a RAG is not necessarily required

To go further on production, you can read: Robust RAG in production: best practices, evaluation, and strategic choices.

Decision milestones at D45

At D45, you must be able to decide:

We stop (insufficient value, too risky)
We iterate (hypothesis to adjust)
We pilot (probable value, controlled risks)

This milestone prevents turning the lab into a “POC museum”.

Phase 3 (D46 to D90): pilot, light industrialization, adoption

This is the phase that distinguishes a useful AI lab from a cosmetic AI lab.

Pilot “limited production” with real measurement

The pilot must include:

A clear scope (team, client segment, request typology)
A degraded mode (if AI is unavailable)
Human validation when necessary
Metrics tracked every week

Standardize what will be reused

Even if you aren’t building a complete platform, you must standardize:

Prompt templates and quality criteria
Security and access rules
Log and validation formats
Update process (docs, knowledge base)

Train to avoid “phantom AI”

A solution can be excellent yet unused. Plan short training, oriented towards real tasks:

How to use the tool without data leaks
How to verify an answer
When to escalate to a human
What limits to accept

This step is also a cultural lever. On this subject, you can consult: AI Culture: the competitive asset for SMEs in 2026.

The “scale pack” to produce before D90

If you aim for scale, finish the 90 days with a usable pack:

Element	What it’s for	What it looks like
AI Lab Playbook	Repeat the method	Processes, milestones, templates
Backlog V2	Scale up	Prioritized and quantified use cases
Data and risk standards	Accelerate safely	Rules, validations, roles
KPI Dashboard	Prove value	5 to 10 metrics max
6-month plan	Budget and staff	Roadmap and capacity

The typical (lean) team to succeed in an SME

You don’t need 10 specialists. You need clear roles.

Role	Responsibility	Common profile in SME
Sponsor	Arbitration, time protection	CEO, COO, CFO
AI Lab Product Owner	Prioritization, metrics, adoption	Ops, Product, RevOps
Business Referent	Use cases, quality validation	Support, Sales, Finance
Tech Lead / Integration	Connections, security, deployment	Internal dev or partner
DPO/CISO (depending on context)	Compliance, risks	Internal or external

If you have a RevOps or GTM Engineering function, it can play a key role in integrations and automation (see the glossary: GTM Engineer and RevOps).

Mistakes that cause an AI lab to fail (and how to avoid them)

Confusing adoption and deployment

Installing a tool is not “adopted”. Add rituals: weekly review, sharing real cases, usage measurement.

Wanting the “autonomous agent” too early

AI agents can be powerful, but in SMEs, maturity (processes, data, controls) is often the limiting factor. Start with assisted flows, then increase autonomy. To clarify the concept, you can read: AI Agent (definition).

Underestimating integrations

ROI arrives when AI inserts itself into the existing workflow. Without integration (CRM, helpdesk, drive, ERP), the gain is lost in copy-pasting.

Measuring too late

If KPIs arrive at the end, you will have a debate of opinions. Measure from the first week of the pilot.

How to “scale” after 90 days (without creating an over-engineered system)

Once 1 to 2 use cases are proven, scaling consists of increasing delivery throughput and security.

Move from a lab to a portfolio

Set up an AI portfolio with:

A monthly review (value, risks, adoption)
A quarterly budget
“Gates” before industrialization

Implement proportionate governance

No need to import governance from a large corporation. An SME can succeed with:

An AI usage charter (data, confidentiality, responsibilities)
A validation process for new cases (30 minutes, not 3 committees)
A repository of approved models and tools

To structure the risk and opportunity aspect, you can rely on an audit-type approach: Strategic AI Audit: mapping risks and opportunities.

Tool the lab like a product team

Scaling becomes much simpler if you have:

A single and visible backlog
A documentation space
Traceability of decisions and tests

Workshop in a room in an SME, a multidisciplinary team around a whiteboard with an AI use case backlog, KPIs, and a value/risk matrix, scoping workshop atmosphere.

A pragmatic way to do it with an agency (if you want to go fast)

If you lack internal bandwidth, an agency can accelerate things provided it is structured around: audit, build, integrations, adoption.

Impulse Lab positions itself precisely as an execution-oriented AI agency (opportunity audit, automation, integration with existing tools, custom platforms, adoption training), with a weekly delivery logic and a dedicated client portal to maintain visibility.

If you want to quickly frame your SME AI lab (use cases, risks, priorities, 90-day plan), the most effective starting point is often a targeted audit, followed by a short delivery phase.

Discover the approach: Impulse Lab
Deepen the “lab” logic: AI Lab: transforming an idea into a profitable prototype

Conclusion

A successful SME AI lab in 90 days is not a question of the “best model” or “best tool”. It is a question of method: a value-oriented backlog, measured tests, daily integration, and managed adoption.

If, at D90, you have at least one use case in a real pilot, tracked KPIs, data standards, and a 6-month plan, you have already done the hardest part: transforming AI into an operational capability, and not a one-off initiative.