Post by Nadine Soyez

Over the months, I spoke with dozens leaders from more than twenty organisations — from manufacturing and logistics to consulting and finance. Different industries, different levels of digital maturity, but all of them said the same thing:

“We’re experimenting with AI… but we don’t have a clear structure yet.”

That sentence perfectly captures where many organisations are right now. They have AI enthusiasm — but not yet AI direction. They run pilots, test tools, automate a few workflows, and invest in employee training. But when they look at the outcomes, they realise that AI hasn’t yet delivered the transformation they expected. It’s not a failure of technology. It’s a failure of structure. Disconnected initiatives, no clear goals, and a lack of alignment between leadership, teams, and data create chaos instead of progress.

2026 will be the year when that changes — the year when experimentation turns into execution. The year when organisations stop “trying AI” and start using AI strategically to achieve measurable business results. And the bridge between chaos and clarity? A simple, actionable AI roadmap that connects business goals, people, and technology.

Here’s how to design one.

 

 

Step 1: Understand where you stand

 

Before you can plan your route, you need to know your starting point. That’s why the first step in every AI journey should be an AI maturity assessment.

Think of it as your GPS for transformation. It shows you where your organisation currently stands, what capabilities are already strong, and where the gaps are that need attention before scaling AI. Without this clarity, every initiative risks becoming another isolated pilot that fades out after a few months.

A solid maturity assessment looks at five dimensions:

• strategy and leadership commitment
• people and skills
• data and infrastructure
• governance and compliance
• use case development.

It answers questions like:

• Is AI anchored in our business strategy?
• Do employees have the necessary literacy and confidence to use it responsibly?
• Is our data accessible, high-quality, and compliant with regulations?
• Are roles and responsibilities clear?
• Do we have a structured process for identifying and scaling AI initiatives?

Understanding where you stand doesn’t just highlight weaknesses — it provides direction. It helps you focus your energy and resources on what will actually move the organisation forward.

 

 

Step 2: Know what you want to achieve

 

Before defining an AI roadmap, every organisation must answer one fundamental question:

What are we trying to achieve?

An AI strategy can only be effective if it is in seamless alignment with the overall business strategy. Too often, companies skip this step. They get excited about new tools or pilots without connecting them to broader business goals. The result is a collection of disconnected experiments that don’t create measurable impact. Your AI initiatives should be designed to accelerate what the business already aims to achieve — whether that’s increasing efficiency, improving customer experience, driving innovation, or reducing operational costs.

Leaders need to be crystal clear about their forward-looking objectives before turning to AI to achieve them. Once this clarity exists, every initiative suddenly has context and purpose. AI stops being a playground for experimentation and becomes a lever for strategic execution. Leadership plays a crucial role here. AI is not an IT project. It is a leadership topic. Executives must define priorities, allocate resources, and communicate a clear vision for how AI supports growth and transformation.

 

 

Step 3: See through the hype

 

The AI landscape today is loud and crowded. Every week, a new product claims to revolutionise the workplace. But your organisation’s AI needs are unique. What works for one company might be completely irrelevant for another. Leaders — especially CIOs — must see past the hype. It’s easy to get distracted by what’s trending, but sustainable AI success comes from focusing on what drives your business outcomes.

Instead of chasing the newest tool or feature, ask whether a solution solves a real problem inside your organisation.

• Does it integrate into your existing workflows?
• Is it aligned with your compliance and data governance frameworks?
• Will it help you reach your defined business goals faster or more effectively?

It’s not about using AI everywhere; it’s about using AI where it matters most. The goal is not to follow hype — it’s to build an AI foundation that creates measurable value, supports your people, and aligns with your long-term strategy.

 

 

Step 4: Identify and prioritise high-value use cases

 

Once your goals are clear, it’s time to identify where AI can make the biggest difference. This is where clarity turns into action. AI success doesn’t start with technology; it starts with a problem worth solving. Look for pain points where teams spend significant time on repetitive tasks, manual data handling, or decision-making based on incomplete information. These are usually the processes where AI can create immediate and visible value.

When assessing potential use cases, consider both business value and feasibility. High business value means the outcome delivers measurable impact — for example, reducing process times, saving costs, or improving decision accuracy. Feasibility looks at whether you have the necessary data, infrastructure, and skills to make it work. Mapping these dimensions helps you prioritise what to start with, what to plan for later, and what to discard. And here’s an important lesson: Don’t be afraid to prioritise early wins.

Many organisations think AI should always begin with large, complex initiatives. But the truth is that small, high-impact projects build confidence faster. When teams see tangible results — like automating a reporting process or summarising customer feedback in seconds — enthusiasm spreads. These early successes generate momentum, trust, and credibility, which are the fuel for scaling AI later.

 

 

Step 5: Design your roadmap

 

Once your key use cases are identified, connect them into a single, coherent roadmap. A good AI roadmap doesn’t need to be complicated, but it must be actionable. It should show what will be done, why it matters, who is responsible, and when it will happen. The roadmap becomes the shared reference point that aligns leadership, IT, and operations around the same goals.

Group use cases by strategic themes such as “Operational Efficiency,” “Customer Experience,” or “Innovation.” Define clear timelines: what will be implemented in the next three months, what will be piloted next year, and what will be scaled in 2026. Assign ownership and ensure that every initiative has the right data, tools, and governance support.

When everyone in the organisation can see how individual projects contribute to overall business priorities, it creates alignment and focus. Decisions become faster. Teams stop guessing. Collaboration improves. A visible roadmap is not just a plan — it’s a leadership tool for clarity.

 

 

Step 6: Add governance and risk management early

 

Many organisations overlook governance until problems appear. But AI governance is not a blocker; it’s an accelerator. A well-defined governance structure provides the guardrails that allow innovation to happen safely. It ensures that data is used responsibly, that projects align with company policies, and that ethical and legal standards are met.

Setting up a lightweight AI governance board early — even with just a handful of people — helps maintain direction and consistency. This board typically includes representatives from business, IT, compliance, and HR. Together, they ensure that AI initiatives support business strategy, comply with regulatory requirements such as the upcoming EU AI Act, and manage risk effectively.

Governance also standardises how new ideas are evaluated, which data sources are used, and how success is measured. This consistency allows AI to scale across the organisation without losing control. Think of governance as your trust infrastructure. It gives leaders the confidence to move fast without breaking things.

 

 

Step 7: Build enablement and change management into your plan

 

Even the best roadmap will fail if people don’t understand or trust it. AI transformation is 20% about technology and 80% about people. That’s why enablement and communication must be built into your roadmap from the very beginning.

Start by raising AI literacy across all levels of the organisation. Employees don’t need to become data scientists, but they do need to understand what AI can and cannot do. Create role-specific training so that every team sees how AI can make their work easier and more effective. Encourage safe experimentation through internal AI hubs or communities of practice where teams can share experiences, prompts, and best practices. When employees feel included in the process, adoption grows naturally.

The goal is to transform AI from a top-down initiative into a company-wide capability. Once people experience real benefits in their daily work, AI becomes part of the culture rather than an external project.

 

 

Step 8: Track, measure, and adapt continuously

 

AI is not a one-time project. It’s an evolving capability that requires continuous learning and improvement. Define measurable success criteria from the start. Track how AI impacts efficiency, decision quality, costs, and customer satisfaction. Also measure adoption rates and employee feedback to ensure that people are not only using AI but benefiting from it.

Review progress quarterly and be willing to adapt. Some projects will exceed expectations, others will underdeliver — and that’s normal. The key is to learn fast, stop what doesn’t work, and scale what does. An adaptive roadmap keeps your AI strategy relevant in a landscape that changes almost daily.

 

 

What companies should do now

 

If your organisation is still in the experimentation phase, now is the moment to move forward with structure. Start by running a quick AI readiness check. Identify where you stand in terms of strategy, skills, data, and governance. From there, define a clear 2026 vision: what does AI success look like for your company? Is it improved efficiency, smarter decision-making, or entirely new products and services?

Next, select three high-value, feasible use cases that you can deliver within the next six to twelve months. Keep them small, visible, and measurable. This will help you demonstrate impact quickly and create momentum for scaling. Then, create a lightweight roadmap outlining your priorities, responsibilities, and milestones. Set up a small governance taskforce to provide oversight and ensure AI is implemented responsibly and consistently. Finally, bring your people on board. Communicate openly about what AI means for them, offer training opportunities, and encourage feedback. The earlier you involve employees, the faster adoption happens.

Companies that act now — with clarity, structure, and governance — will enter 2026 with a competitive advantage. They’ll move from experimentation to execution and from scattered projects to scalable impact.

 

 

The bottom line

 

The last two years were about experimentation. 2026 will be about execution, clarity, and measurable results. You don’t need a 200-page AI strategy. You need a simple roadmap that connects business goals, people, and technology — turning AI from confusion into confidence. That’s how organisations move from chaos to clarity. From pilots to performance. From potential to measurable impact.