What company size makes AI profitable?

From 5 to 10 active users on repetitive tasks (writing, summarisation, document search). Below, the return on investment stays positive on consumer tools (Copilot Pro at €22/month). Above 30 users, formal scoping and IT integration become unavoidable.

Which model to choose: Copilot, ChatGPT, Claude, Mistral?

Depends on context. Copilot M365 = Microsoft ecosystem, simplest deployment. Claude Enterprise = excellent at long reasoning and code. ChatGPT Enterprise = versatile, plugin ecosystem. Mistral Large = FR sovereignty, ideal public sector. Local models = absolute trade secrecy. We compare for you against your constraints.

How to handle data confidentiality?

Three levels: 1) cloud models with enterprise agreements (Copilot, Claude Enterprise) that don't use your data for training and host in the EU; 2) sovereign cloud models (Mistral) hosted exclusively in France; 3) on-premise local models (Ollama, vLLM) for the most sensitive cases. Choice depends on your risk map.

My employees already use personal ChatGPT at work. Is that a risk?

Yes, classic shadow IT: data leakage to OpenAI, no traceability, no legal framework, GDPR risk. The right answer isn't prohibition (impossible to enforce), but an equivalent official solution + clear usage policy + awareness. We support you on this.

How much does it cost to deploy Copilot for 20 people?

Copilot M365 licenses: ~€30/user/month (Microsoft 2026 pricing). For 20 people = €600/month in pure license. Our support (audit, training, governance setup, support) is billed separately. Typical ROI: if AI saves 30 min/day for 10 people, that's ~€5,000/month of freed time.

Do teams need training or do they learn on their own?

Without training: 30% adoption, basic usage. With structured training (2-4h initial + business workshops + internal champions): 70-80% adoption, advanced usage (well-built prompts, workflow integration). Training cost pays back in 2-3 months.

Can AI really replace a developer or accountant?

No. AI augments, it doesn't replace. A developer with Copilot codes 30-50% faster but must still architect, debug, validate. An accountant with an AI assistant handles more cases, but must still judge, validate, advise. The real question: who wants to lag 50% behind competitors who are adopting?

Which sectors gain most from AI?

All those where writing, documentation and analysis weigh: legal, HR, consulting, accounting, marketing, research, customer support, engineering. Industry too (predictive maintenance, documentation quality). Common criterion: volume of textual or tabular information to process.

Do you offer open-source / on-premise models?

Yes. Deployment of Llama 3.3, Mistral, Qwen, DeepSeek on your servers or our private cloud via Ollama, vLLM or TGI. Typical cases: lawyers, medical, defence, sensitive banking data. Performance close to cloud models in most cases, controlled usage cost after hardware amortisation.

Is it compliant with NIS2 and GDPR?

GDPR: yes, provided you choose a model whose data handling is framed (Copilot, Claude Enterprise, Mistral) or local. NIS2: AI falls within the cyber risk management scope — we integrate these aspects in our ANSSI / NIS2 audits (usage policy, logging, prompt injection, leak prevention).

How long to start an AI project?

Use-case audit: 1 to 2 days on site. Pilot: 4 to 8 weeks. Industrialisation: 2 to 4 months depending on scope. First measurable ROI typically within 3 months for a simple office use case (Copilot M365 + training).

— AI · Scoped · Sovereign · Measurable

Artificial intelligence — scoped, sovereign, measurable.

Generative AI deployment in the enterprise without hype: Copilot M365, ChatGPT Enterprise, Claude, Mistral, local models. Use-case audit, IT integration, training, GDPR and NIS2 compliance. Measured ROI, not announced.

— AI in numbers

Three figures to grasp the stakes.

70%

Of AI projects fail without use-case-first scoping (Gartner). Without use-case audit, AI ends up shelved within 18 months.

30 min/day

Average measured gain per Copilot M365 user on writing, summarisation and analysis tasks, after structured training.

100%

GDPR compliance depending on chosen model: sovereign cloud (Mistral, Claude EU), Copilot M365 EU Data Boundary, or on-premise local models.

01 · WHY BISPRO

Why deploy AI with BISPRO

No AI for AI's sake. We scope, we cost, we integrate, we measure. And we protect you from data leakage.

Use-case-first scoping, not hype-first
We don't deploy AI for the showcase effect. We start from your processes: where writing takes hours, where document searches drag on, where data analysis stalls. AI is only justified if it frees measurable time.
GDPR & data sovereignty
Model choice aligned with your sensitivity level: Copilot M365 (EU), Claude Enterprise (EU/US), Mistral (sovereign FR), or on-premise local models (Ollama, vLLM) for absolute trade secrecy. No leakage to public OpenAI when forbidden.
Integration into your existing IT
AI is only useful when integrated into your tools: M365, DMS, ERP, CRM, helpdesk. Native connectors, APIs, custom agents. No extra tab to open.
Change management
AI fails 70% of the time because of humans, not tech. Acculturation workshops, concrete business use cases, hands-on training, internal champions identified. Measured adoption.
Cyber-AI & shadow AI
Frame usage before it explodes into shadow IT. Usage policy, prompt logging, data leakage control, NIS2 and GDPR audit including AI.
Measurable ROI
Every deployment is instrumented: time saved per task, adoption rate, user satisfaction, usage cost. Quarterly priced report, continuous tuning.

02 · USE CASES

What we deploy concretely

Six broad usage families covering 90% of enterprise needs. Customisable by business line.

Augmented office — Copilot M365
Word, Excel, PowerPoint, Outlook, Teams: draft generation, mail summarisation, meeting recap, Excel analysis. The copilot integrated, without changing tool.
Tailored business assistant
AI agent specialised on your business: HR (job description writing, CV screening), legal (contract review, case law search), sales (prospect briefing, targeted email generation), support (level 1 qualification).
Intelligent knowledge base
Indexing and querying of your internal documentation via AI: procedures, FAQ, project archives, contracts. The AI answers citing its sources, without hallucination. RAG (Retrieval-Augmented Generation) technology.
Workflow automation
Make, n8n, Power Automate combined with LLM calls: data extraction from emails / PDF, classification, routing, notifications. 50 to 80% reduction in manual entry.
Business data analysis
LLM connection to your databases (SQL, Excel, BI) to query in natural language: 'What's our product X margin in Q3?', 'Top 10 churn-risk customers'. Copilot Studio or custom agents.
Content generation
Blog articles, LinkedIn posts, product sheets, multilingual translations, e-commerce descriptions. Respect for your editorial style guide via scoped prompts and style fine-tuning.

03 · OUR APPROACH

How we deploy — step by step

Proven method: start small, measure, expand. No AI big bang that ends up shelved.

01 · Use-case audit
1 to 2 day workshop on site: mapping of your processes, identification of high AI-potential tasks (volume, repetition, low value-add), feasibility evaluation, GDPR, usage cost.
02 · Model choice
Selection of the most suitable LLM: Copilot M365 for Microsoft ecosystem, Claude Enterprise for long analyses, Mistral Large for FR sovereignty, local models (Llama, Mistral) for trade secrecy. Comparative cost / performance / legal table provided.
03 · Quantified pilot
Deployment on a restricted scope (1 team, 5-10 users) for 4 to 8 weeks. KPIs measured: time saved, satisfaction, output quality. Go / no-go decision based on data, not enthusiasm.
04 · Industrialisation
If pilot validated: extended rollout, team training, integration with IT tools (SSO, AD, M365, ERP), prompt governance, compliance logging, usage supervision.
05 · Run & AI MCO
Monthly follow-up, prompt tuning, model updates, watch on new capabilities (Claude 4.x, GPT-5, Mistral Large 2). Continuous evolution, not a one-shot.

04 · SOVEREIGNTY

Sovereignty & confidentiality

AI is only useful if you retain control of your data. Three levels by sensitivity.

Sovereign cloud models
Mistral AI (FR) hosted in France, Claude Enterprise EU (Anthropic, EU datacenters), Copilot M365 EU Data Boundary. Native GDPR compliance, no transfer outside the EU.
On-premise local models
For defence secrecy, HDS health data, sensitive legal: deployment of open-source models (Llama 3.3 70B, Mistral, Qwen) on your servers or our private cloud. No data leaves your infrastructure.
AI usage policy
Internal framework: which models allowed, which data forbidden from prompts, logging, audit, NIS2 compliance and cyber-insurance clauses. Enforceable document, legally vetted.
Shadow AI audit
Detection of unframed AI usage in your company (personal ChatGPT used for work, etc.). Remediation plan: equivalent official tool, awareness, access control.

05 · FAQ

Frequently asked questions

Going further

Deploy AI at your place.

Free audit, identification of high-ROI use cases, model recommendation aligned with your data sensitivity level.

Request an AI audit See Cybersecurity

Financing up to 63 months available* — *subject to approval