The digital transition is not just about stacking SaaS tools. It requires precise sequencing, appropriate governance, and a nuanced understanding of increasingly stringent regulatory constraints, particularly with the gradual implementation of the European AI Act. Here are 12 operational tips to structure this project without being overwhelmed.
1. Map technical debt before any deployment
We observe that the majority of digitization projects fail not due to lack of budget, but because the existing technical debt has never been documented. Aging servers, non-standardized databases, internal APIs without versioning: each unlisted layer becomes a friction point when integrating new tools.
Before selecting an ERP or CRM, conduct a comprehensive audit of your infrastructure. List the dependencies between systems, identify software components nearing end-of-life, and estimate the cost of upgrades. This work determines the actual feasibility of your digital roadmap.
2. Appoint a data steward at the project’s launch
Data governance can no longer be treated as a secondary topic. With the European AI Act coming into effect on August 1, 2024, and its progressive obligations, any company developing, integrating, or deploying AI systems must document the quality and traceability of its datasets.
A structured support accelerates this compliance, as proposed by https://www.been-online.net/ for companies engaged in their digital transformation. Appointing a data owner for each business unit ensures that each department remains accountable for the quality of its data, without dilution of responsibility.
3. Prioritize a modular ERP over a monolithic deployment
The reflex to deploy a complete ERP in one go generates massive internal resistance and additional costs. We recommend starting with a critical business module (procurement management, invoicing, logistics) and gradually expanding the scope.
This modular approach reduces initial integration costs and allows for validation of business processes before industrializing them. It also facilitates team training, which absorbs change in stages.
4. Integrate a customer process-oriented CRM, not just an address book
A poorly configured CRM becomes a glorified Excel file. The tool must reflect your actual sales cycle: qualification stages, lead scoring, triggers for automated actions. Each custom field must meet an identified management need.
Before choosing the solution, model the customer journey from end to end. Identify friction points where data is lost between sales, marketing, and support teams. The CRM resolves these breaks, provided the process is clear before configuration.
5. Subject each tool to a GDPR and AI Act compliance audit
The regulatory scope of digitization now extends to the governance of AI usage. The CNIL reminds us that prohibitions on systems with unacceptable risk have been in effect since February 2, 2025, and that the transparency obligations of the AI Act apply from August 2, 2026.
Specifically, if your company uses chatbots or disseminates AI-generated content, you must inform the user of this interaction. Integrate this verification into your tool selection process, not after deployment.
6. Build a training plan by role, not by tool
Training “everyone on everything” dilutes learning. An accountant does not need to master the marketing automation module, and a salesperson will never use financial reporting functions. Structure training around business roles:
- Sales teams: CRM, sales pipeline, lead scoring, and automated follow-ups
- Support functions: ERP, ticket management, cost tracking dashboards
- Management: reading KPIs, budget arbitration on consolidated data, AI governance
This segmentation reduces overall training time and improves actual adoption rates.
7. Measure the hidden costs of digitization before budgeting
Software licenses represent only a fraction of the actual budget. Often underestimated items include the migration of historical data, customization of workflows, post-deployment corrective maintenance, and productive time lost during the adaptation phase.
Allocate a contingency budget of at least a quarter of the initial budget to absorb these unforeseen expenses. Without this margin, the project risks partial freezing at the first overspend.
8. Automate repetitive processes before investing in generative AI
Too many companies want to deploy generative AI models while their basic processes remain manual. Start by automating invoice entry, customer follow-ups, and periodic report generation. These quick wins free up time and produce structured data, which will then feed into more advanced tools.
Generative AI without solid data foundations produces inconsistent results. Automating business processes provides the necessary groundwork.
9. Implement step-by-step management with quarterly milestones
A digital roadmap without intermediate milestones becomes a document of intent. Break the project down into quarterly sprints with measurable deliverables: number of active users on the new tool, data completion rate in the CRM, reduction in processing time for a target process.
Each milestone should include a go/no-go decision point that allows for project reorientation without waiting for the end of the complete cycle.
10. Secure cybersecurity as a prerequisite, not as a parallel project
Cybersecurity and data governance have become a major bottleneck for digitization projects integrating AI. Each new connected tool expands the attack surface. Multi-factor authentication, data encryption at rest and in transit, network segmentation: these measures must be operational before going live.
Do not treat security as a one-off audit. Integrate it into every deployment sprint, with regular penetration testing.
11. Involve business units in drafting specifications
A specification document written exclusively by the IT department produces a tool that is technically compliant but functionally unsuitable. End users must co-write functional specifications, validate interface mockups, and test each iteration.
- Appoint a business representative for each department, responsible for functional validation
- Organize co-design workshops before each development phase
- Document real use cases, not theoretical scenarios
12. Anticipate AI compliance from the supplier selection phase
A company is affected by the AI Act not only by its size but by its role in the AI chain: development, integration, deployment, or professional use. Since August 2, 2025, rules on general-purpose AI models already apply.
Require your suppliers to provide clear documentation on the risk classification of their systems, the training datasets used, and the planned human oversight mechanisms. Integrate these criteria into your calls for tenders alongside price and delivery timelines. A supplier unable to answer these questions poses a direct regulatory risk to your company.