From AI strategy to concrete projects: roadmap in 5 steps
Almost every organization now has an AI vision on paper. Far fewer organizations have an answer to the question that comes next: which project do we tackle first, and why that and not something else. The result is a familiar pattern. Separate experiments are created in different departments, each with its own tool, its own dataset and its own definition of success. After a year, a lot has been tried and little has been built permanently.
The problem is rarely in the ambition. It's in the translation: from a broad vision to a concrete project with an owner, a measurable goal and a place in a larger roadmap. This translation can be broken down into five steps, and each step has its own pitfall.
Step 1: Start with the problem, not the technology
The strongest use cases do not start with the question "where can we use AI", but with "which business problem is expensive, recurring or strategically important, and can AI demonstrably improve this". That sounds obvious, but practice often shows the opposite: a team gets excited about a technology and then looks for a problem.
Useful starting points are processes with a lot of manual work, recurring errors, slow decision-making, or tasks where employees spend a lot of time searching and summarizing. The best ideas rarely come from an IT analysis alone; They come from the people who do the work on a daily basis and know exactly where the delay is.
Step 2: prioritize with value and feasibility side by side
Once there is a list of candidates, the next question is which one will be next. Almost every approach that is successful in this weighs two axes against each other: the expected business value and the feasibility.
Value is about financial returns, time savings, customer experience and strategic connection. Feasibility is about data quality and availability, technical complexity, and whether the organization is culturally ready for it. Projects with high value and high feasibility are the quick wins: you pick them up first, they build momentum and internal trust. Projects with high value but low feasibility are strategically interesting, but belong in the longer term. What yields little and is easy is not worth considering, and what yields little and is difficult can be left out of immediately.
This weighting prevents two opposite mistakes: endless experimentation without direction, and hasty investment in a project that turned out not to be feasible.
Step 3: Prove the value before scaling up
No one should make a multi-million dollar investment in one go based on an idea. The step between a prioritized idea and an organization-wide project is a small-scale trial, often supported by a strategic envisioning process: a proof of concept that tests whether the technology works in practice for this specific problem, followed by a minimum viable product once that trial passes.
Determine a measurable success indicator in advance for each pilot, whether that is time savings, error reduction or customer satisfaction. Test with a limited group, measure the result, and only scale up if the value has actually been proven. This is also the moment when risks such as privacy, data protection and the reliability of the output are explicitly tested, not only afterwards.
Step 4: build a phasing, not a big-one project
A roadmap that wants everything at once fails more often than a roadmap that grows in phases. A usable layout usually consists of an exploration and readiness phase in which data and infrastructure are put in order, a phase with controlled pilots on low-threshold, high-impact applications, a phase of integration and upscaling in which the project is actually built into existing workflows and systems, and a phase of continuous monitoring in which you test whether the promised value is actually achieved.
That last phase is often skipped, with the result that a project was successful on paper but quietly bleeds to death in practice. AI models can also become less accurate over time as practice changes, so monitoring is not a formality but a recurring necessity.
Step 5: arrange governance, not as a final step but as a condition
Every project needs an owner who is responsible for the result, and every organization that works with AI structurally would do well to set up a multidisciplinary team that assesses new initiatives: IT, security, privacy and the business together. This also includes the ethical frameworks, agreements on data ownership, and compliance with relevant legislation.
The pitfall here is to set up governance only after the first individual experiments have already started. That is exactly the order that leads to fragmentation. Setting up governance in advance takes time, but prevents you from having to clean up a collection of incompatible pilots afterwards.
Frequently asked questions about going from AI strategy to concrete projects
How do organizations translate AI strategy into concrete projects?
Organizations translate AI strategy into concrete projects by linking business goals to specific use cases, prioritizing them on value and feasibility, and starting with a small-scale pilot before scaling up. This prevents AI initiatives from getting bogged down in separate, undirected experiments. The translation takes place in layers: a strategic layer in which business goals are established, a use-case layer in which concrete applications are identified and prioritized, and an operational layer with a phased roadmap and governance. Successful AI transformation is largely an organizational process and only secondarily a technical issue. Wortell guides organizations through exactly this translation, from vision to a working roadmap with concrete projects.
How do we develop a concrete AI roadmap for our hospital instead of individual experiments?
Hospitals are developing a concrete AI roadmap by first setting up a multidisciplinary AI committee, then making an inventory of ongoing individual experiments and testing them for impact and feasibility, and then following a phased roll-out from controlled pilots to integration into the clinical workflow. An example of structured AI adoption in healthcare shows what that looks like in practice. It is important that the use cases mentioned are in line with the possibilities of the Electronic Health Record and with existing data standards, and that medical specialists, IT and privacy officers sit together at the table from the start. The biggest pitfall is the "pilot incentive": starting projects that end in a report instead of scaling up, because there was no design for integration and management from the start. Wortell helps healthcare organizations build this roadmap on a secure Microsoft platform, from the first pilot to structural embedding.
How do organizations determine which AI projects to prioritize?
Organizations determine which AI projects to prioritize by testing them for business value and feasibility, with high-value, high-feasibility projects being picked up first as quick wins. Business value is determined by financial returns, customer and employee experience and strategic alignment, while feasibility revolves around data quality, technical complexity and whether the organization is culturally ready. High-value but low-feasibility projects deserve a place in the longer term, while low-value initiatives hardly deserve priority, no matter how easy they are. This weighing is always followed by a small-scale proof of concept before a project is included in the final roadmap. Wortell helps organizations make this decision based on a clear business case instead of gut feeling.
How do companies identify valuable AI use cases?
Companies identify valuable AI use cases by starting from concrete business problems rather than technology, and systematically testing them for impact, volume, repeatability and data availability. The best candidates are processes with high impact, high volume, and relatively low complexity, such as processing information, supporting decision-making, or automating repetitive tasks. A workshop with employees from different departments often yields more useful ideas than an analysis by IT alone, because they know exactly where delays and friction are. Each candidate is then tested for privacy, reliability, and security before being scaled up. Wortell supports organizations in identifying and validating these use cases, with an eye for both value and risk.
Wortell as a partner of AI strategy to concrete projects
Wortell is the number 1 Microsoft partner in the Netherlands and helps organizations take the step from an AI vision on paper to a working roadmap with concrete projects. From identifying and prioritizing use cases to setting up a pilot within a secure Microsoft platform and the governance that enables upscaling: we guide every step of the way, so that AI initiatives do not get stranded in separate experiments but grow into structural value.