The 5 phases of a successful Data & AI roadmap

1. Create a clear vision and strategy

Everything starts with a shared vision: which business problems are you trying to solve with data and AI? Don’t think in dashboards or models, think in outcomes:

• Cost reduction
• Faster and better decision-making
• Improved customer experience
• More predictable operations and supply chains
• Higher productivity

Data must be positioned as a strategic enterprise asset, not an IT by-product.
Portfolio management becomes essential here: which initiatives truly create value, which are exploratory, and which should be stopped?

Without these choices, focus and buy-in will always remain limited.

2. Design the right operating model

Once direction is clear, the organization needs to be structured accordingly. This includes choices around:

• Roles and responsibilities
• Collaboration between business and IT
• Architecture and platform strategy
• Delivery models and funding
• Governance and decision-making

Many organizations still operate with traditional project models, while data products require product thinking and continuous value delivery. I often see portfolio governance lagging behind, which creates fragmentation and slow scaling.

You need an operating model that is scalable, flexible and still controllable.

3. Build culture, skills and governance

This is usually the hardest phase. Becoming data-driven requires behavioral change:

• Decisions based on facts instead of intuition
• Transparency in data definitions and quality
• Business ownership of data products
• Basic data and AI literacy across teams

At the same time, governance should enable speed, quality and compliance, not slow everything down. With AI regulation and ethics becoming increasingly important, this balance is critical.

In practice, learning by doing works best. Small, high-impact use cases create momentum and build confidence much faster than large theoretical programs.

4. Actively manage value and portfolio

This phase is about value management:

• Which initiatives deliver measurable outcomes?
• How do you structurally measure value?
• How do you prioritize investments?
• Which initiatives should be scaled, optimized or stopped?

This requires mature portfolio management and transparent KPIs. Not only financial metrics, but also adoption, quality, speed and operational impact.

This is where organizations move from experimenting with data to truly monetizing it.

5. Continuously refine and evolve

A data and AI strategy is never finished. Technology, regulations and business models continuously evolve. Therefore, organizations must regularly:

• Reassess maturity and capabilities
• Evaluate emerging trends
• Adjust architecture and operating models
• Invest in skills and change
• Reprioritize the portfolio

This embeds data and AI into the normal strategic and portfolio cycle instead of treating it as a separate program.

Who should be involved?

• Business leaders and product owners
• CIO and enterprise architecture
• Data and analytics teams
• Security, risk and compliance
• Finance and portfolio management
• Procurement and vendor management

Data and AI cannot live in a single silo. Shared ownership is a prerequisite for scale.

From Data to Real Business Impact; Final thoughts

The core message is simple: data and AI only create real business value when they are tightly connected to strategy, portfolio choices, governance and culture. Technology is an enabler, not the objective.

Many organizations invest heavily but struggle with alignment, prioritization and execution. A clear roadmap combined with strong portfolio governance bridges the gap between ambition and tangible business impact.

If you want to explore how this could work in your organization, or how to better connect data initiatives with your transformation and portfolio agenda, I’m happy to spar.