How SYXPERIANE achieved 80% AI adoption across 200+ consultants in 3 months
- Industry
- Consulting
- Company Size
- 201-1000
- Department
- Company-Wide AI Adoption
Key Highlights
- 87% reduction in preparation time on client training documents (4 hours → 30 minutes)
- 80%+ adoption across 200+ employees within 3 months of company-wide deployment
- New AI implementation offering for clients launching Q2 2026, built from their internal success
💡 This story was written by Wesype, a Dust Platinum Partner with strong expertise in the IT services and ERP integration industry. It shows how SYXPERIANE transformed its internal operations with Dust: from a failed Copilot experiment to 80%+ company-wide adoption in three months. With Wesype's support, SYXPERIANE structured its rollout around purpose-built ERP expert agents and is now turning that proof of concept into a new AI service for its clients.
About SYXPERIANE
SYXPERIANE is a French IT services firm (ESN) that integrates complex enterprise software for large organizations: ERP solutions like Cegid XRP/Ultimate and Divalto, alongside BI, SIRH, and cybersecurity platforms. With 200+ employees distributed across specialized business units, the firm's competitive advantage rests on one thing: the depth of its functional expertise on systems that take years to fully master.
That expertise is hard-won. ERP implementations are long, technically complex, and context-dependent. A gap in a specification document or a misconfigured workflow can cost a client weeks of rework. SYXPERIANE's value is precisely that its consultants know these solutions - their edge cases, best practices, undocumented behaviors - better than almost anyone.
The challenge, according to Damien Laborie, Data & AI Strategy Director at SYXPERIANE was that this knowledge lived almost entirely in people's heads. And those people were always on the road.
Challenge: When expertise lives in silos and senior consultants are never available
The knowledge bottleneck problem
SYXPERIANE's consulting model creates a structural tension. Senior experts hold years of institutional knowledge. Junior consultants need months (sometimes years) to reach genuine autonomy. What made this structural was simple: senior consultants were never available to bridge the gap. Permanently deployed on client engagements, they could not simultaneously be the firm's frontline delivery capacity and its internal knowledge transfer engine.
Junior consultants waited. Answers arrived late. Documents were drafted, reviewed, and revised - several times over.
Low-value work consuming high-value time
Beyond onboarding, a daily productivity drain cut across all seniority levels: writing contextualized training materials for each client, searching configuration precedents scattered across SharePoint folders, verifying that specification documents met delivery standards. None of these tasks are strategic. All of them are necessary. For a firm that bills on time, every hour spent on production is an hour not spent on advisory work.
A prior attempt with Microsoft Copilot had gone nowhere. The company knew it needed to move on AI. It had not yet found the right approach.
Solution: Building a firm-wide AI capability on concrete use cases
Why Dust
When Damien Laborie joined as Director of Data and AI Strategy, the build vs. buy question took little time to resolve.
"The build approach — if you're not an AI company, it's not even a topic. It takes too long, the technology moves too fast, and access to the right experts is nearly impossible." — Damien Laborie, Director of Data & AI Strategy
Dust won on several grounds: no-code agent architecture, native SharePoint and OneDrive connectors that broke open the firm's document silos, European data hosting for compliance, and a B2B-first orientation that matched an integration firm's needs in a way consumer AI tools simply do not.
"Solutions like Dust are becoming essential. Without writing code, without solving existential questions about data architecture and agent orchestration, it's increasingly ready to use. There's no reason not to get started." — Damien Laborie, Director of Data & AI Strategy
A 3-month, 3-phase rollout
PHASE 1 — MONTH 1
BU Ultimate (largest BU, ~⅓ of company)
Purpose-built ERP expert agents deployed on Cegid XRP/Ultimate. From day one, agents addressed real daily needs: functional guidance, SFD drafting, document compliance checking. No theoretical demonstrations.
PHASE 2 — MONTH 2
BU Divalto
Learnings from Phase 1 fed directly into the Divalto deployment. Additional agents built. Approach refined based on field feedback.
PHASE 3 — MONTH 3
Company-wide (200+ employees)
Full organizational deployment. Three months from first agent to complete coverage - made possible by the phased approach and the managers-as-champions model.
Managers as the real scale lever
"We approached this top-down, but from field experience — not as a management directive saying 'use AI because it's the future.' It stayed grounded in concrete, operational reality." — Damien Laborie, Director of Data & AI Strategy
BU managers and identified champions were trained on both Dust's capabilities and the philosophy behind agent design. They identified use cases from field reality, built agents, and drove adoption within their teams. For non-adopters, the outreach was direct: give me 30 minutes, and I will save you 2 hours.
"The managers are the ones responsible for delivering results. Once they're convinced AI can help them, they become our strongest ambassadors." — Damien Laborie, Director of Data & AI Strategy
Results: 87% faster, 80% adopted, and a data quality transformation nobody expected
The numbers
The most impressive metric came from a task every consultant knows: preparing a contextualized training document for a client, tailored to their ERP configuration, processes, and team profile. Previously a 4-hour task. With Dust: 30 minutes.
- 87% reduction in client-facing document preparation time (4h → 30 min)"
- 80%+ company-wide adoption in 3 months
- 3 months to full company coverage
"If a collaborator doesn't save at least 2 hours a month, it means I've done my job poorly — because those who use it well save far more than that." — Damien Laborie, Director of Data & AI Strategy
AI as the catalyst for a data quality transformation
The deployment of Dust had an unexpected effect on data quality.. Getting agents working (even with imperfect data) demonstrated immediate value. That value created the motivation to finally clean, structure, and govern years of accumulated knowledge. A new function is now being formalized: data stewards, responsible for the quality of knowledge sources powering Dust agents.
"There is no quality AI without quality data. That conviction is now driving us to become an increasingly data-first company — and AI has proven concretely that investing in data has an immediate and significant ROI." — Damien Laborie, Director of Data & AI Strategy
Three use cases powering daily work
🔧 ERP expert agents
Daily guidance on Cegid XRP/Ultimate and Divalto: functional questions, SFD (Detailed Functional Specification) drafting, delivery quality control. Institutional knowledge, on demand.
📁 Project knowledge management
Unified view of all project info on large 12-24 month engagements. This accelerates mid-project onboarding for new team members.
💬 Meeting and commercial support
The sales team, with no technical background, became one of the strongest early adopters. Structured notes and follow-ups, faster.
What's next: deeper adoption internally, and a new offer for clients
Embedding AI into the organization's DNA
The roadmap focuses on two priorities: reaching deeper weekly usage across all BUs, and continuing the data quality cycle through a structured data stewards program. A strategic AI committee - parallel to the executive committee - is being established. AI objectives are being formally embedded in manager job descriptions as part of the Q2 2026 roadmap, with measurable targets on adoption rates and time savings.
From AI user to AI provider
SYXPERIANE is launching an AI integration service for its enterprise clients in Q2 2026. The logic is straightforward: years of deep knowledge of clients' business contexts from ERP integration work, combined with genuine AI implementation expertise from the internal deployment. Both packaged as a new service line.
"We've done it for ourselves. We can now do it for our clients." — Damien Laborie, Director of Data & AI Strategy
Key takeaways for IT services firms
Damien Laborie's advice to peers considering a similar journey:
- Start immediately: don't produce internal reports about what you're going to do. Get into the engine.
- Secure general management sponsorship first - this cannot be owned by IT alone.
- Engage managers earlier than you think necessary - they are the adoption make-or-break factor at scale.
- Deploy agents that solve real, current problems - abstract demonstrations don't create momentum.
- Don't build internally - unless you are an AI company, the no-code platforms available today make building unnecessary.
"Whatever your industry — go and involve the people responsible for delivering your product or service. Find the ones with the appetite for it. Do concrete things. There's no longer any reason not to get started." — Damien Laborie, Director of Data & AI Strategy
Interested in learning more about how Dust can help your team? Visit our solutions page or reach out to our sales team.
