Think Ahead// AI_TRANSFORMATION
AI-Transformation Roadmap
Become AI-ready in 6–12 months
Sometimes the most honest outcome of an AI scan is: "Not quite yet."
Not because AI doesn't offer opportunities, but because you first need to arrange some basics: data, processes, ownership, technology.
This roadmap helps you build a foundation in 6–12 months, so AI pilots can be successful afterwards.
// OVERVIEW
Four phases to AI-readiness
Direction & ambition
0–2 months
Data & processes in order
2–6 months
Organization & skills
4–8 months
Prepare first experiments
6–12 months
Note: Don't see this roadmap as a strict project plan, but as a sequence of focus areas. You can do some steps in parallel, as long as the basics remain logical: foundation first, then experiment.
PHASE 1 • 0–2 MONTHS
Direction & ambition
1. Determine why you want to work with AI
- •Formulate 2–3 sentences about what AI should deliver for you (e.g., less manual work, better customer service, faster insights).
- •Link this to existing goals (e.g., strategy, annual plan, digitalization agenda).
2. Choose an owner and a small core team
- •Appoint one person as the driver (not necessarily an AI expert, but someone who gets things done).
- •Form a compact team (3–5 people) with someone from: business, IT/BI, and possibly operations/customer service.
3. Make an initial inventory of opportunities and concerns
- •Where do people in the organization see opportunities?
- •What are they afraid of (jobs, loss of control, errors)?
- •Document this – it becomes input for your later communication and pilots.
DELIVERABLE PHASE 1
- ✓1 page with your AI ambition (why, what roughly, for whom)
- ✓Named owner and core team
- ✓Overview of top 5 opportunities + top 5 concerns
PHASE 2 • 2–6 MONTHS
Data & processes in order
1. Map your core processes and data sources
Choose 2–3 core processes (e.g., sales, customer service, project management). Note per process:
- •which systems are used (CRM, ERP, ticketing, Excel, etc.)
- •which data sources play a role
- •where most manual work or hassle is
2. Determine ownership per data source
Who is responsible for customer data quality? Who for product data, contracts, documents? Document this explicitly – no owner means no serious AI.
3. Improve basic data quality
- •Clean up the most annoying things: duplicate records, missing fields, outdated lists.
- •Make simple agreements about input (e.g., required fields, naming conventions).
4. Document privacy & security basic agreements
Check with your DPO/CISO/IT what is and isn't allowed. Create a simple framework:
- •which data you absolutely cannot send to external AI services
- •for which data there are possibilities under conditions
DELIVERABLE PHASE 2
- ✓Process overview + data landscape for 2–3 core processes
- ✓Overview of data owners
- ✓Basic set of privacy/security guidelines for future pilots
PHASE 3 • 4–8 MONTHS
Organization & skills
1. Make AI a theme, not a hobby
- •Discuss AI regularly in MT/teams: not just "scary or cool", but linked to concrete goals.
- •Plan 1–2 internal sessions showing what's possible with your type of data.
2. Build basic knowledge with key people
- •Give the core team access to some good AI tools (ChatGPT, Claude, etc.) and let them spend time on it.
- •Let them experiment with their own documents (but within the privacy framework from Phase 2).
3. Establish light governance
Agree on:
- •who may experiment and with which tools
- •how to prevent everyone from going off with their own AI solutions (shadow IT)
- •when something becomes an "official" experiment
4. Sketch criteria for good pilots
- •Clear business question (not "we want something with AI")
- •Measurable effect (time, quality, costs, satisfaction)
- •Owner, users and data known
DELIVERABLE PHASE 3
- ✓Internal slide or intranet page "How we view AI"
- ✓Agreements on who may experiment and how
- ✓Checklist for selecting good pilot ideas
PHASE 4 • 6–12 MONTHS
Prepare first experiments
1. Collect and prioritize pilot ideas
Ask teams to submit concrete pain points (short form, max 1 page):
- •problem, current approach, involved systems, estimated impact
Prioritize together with MT and core team on: feasibility, impact, support.
2. Choose 1 promising pilot at a time
Don't start with 5 things at once. Choose one pilot with a clear business case and a process you already have reasonable grip on.
3. Work out the pilot on main lines
- •Goal and scope (what's in, what's out)
- •Involved systems and data
- •Success criteria (how do we measure if it works?)
- •Timeline (e.g., 4–6 weeks) and people involved
4. Find the right partner(s)
Determine what you do yourself and where you need external help (AI expertise, development, change). Make sure your partner can work with your tech stack and data – no black box.
DELIVERABLE PHASE 4
- ✓1 detailed pilot description that's "MT-ready"
- ✓Decision: are we doing this, with whom and when?
// SUMMARY
10 concrete actions
Formulate your AI ambition in 2–3 sentences.
Appoint an owner and assemble a small core team.
Map 2–3 core processes and associated data sources.
Document ownership per data source.
Clean up the biggest data quality problems.
Make basic agreements on privacy, security and use of AI tools.
Ensure a few key people get time to experiment with AI.
Document how you choose and evaluate pilots.
Collect and prioritize pilot ideas from the organization.
Work out one pilot idea into a concrete proposal and decide whether to proceed.
Once you've checked off these ten points, your organization is ready to start a focused AI pilot project.
Ready to start?
Want to discuss in 6 months whether you're ready for that first pilot? Or align on your roadmap now? Get in touch.
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