AI Strategy 2026: From Experiment to Execution

The strategic question that every management must answer

78% of organizations need AI to fundamentally renew their operating model — not to optimize it, but to stay relevant. Nevertheless, the majority remains stuck in pilots that do not scale up, experiments without a roadmap, and separate tools without coherence.

2026 will be the year of truth. Organizations that currently do not have a clear AI strategy are losing ground to competitors that do have one. The question is no longer whether you use AI, but how you embed it strategically into your business model.

As co-author of the AI Strategy Handbook and advisor to 300+ organizations, I help directors and management teams answer that question. In this keynote, I'll share the framework that works.

Why Most AI Strategies Fail

The pilot graveyard

Recognisable? Dozens of AI experiments, enthusiastic project teams, great demos — but none of the pilots scale to production. McKinsey calls this the “pilot graveyard”: 90% of organizations have invested in AI, but less than 40% are seeing actual business results.

The three core issues:

1. No strategic prioritization — Everyone gets to experiment, no one decides what's really important
2. Technology without workflow redesign — AI is glued to existing processes instead of redesigning them
3. No measurable goals — “We're doing something with AI” is not a strategy

The 80/20 rule that no one follows

Technology only delivers 20% of the value of AI implementations. The other 80% comes from redesigning work, processes and organizational structures. Yet most organizations spend 80% of their budget on technology and 20% on change.

The reversal that is needed: Don't start with “what AI tool are we going to use?” but with “what would this process look like if we redesigned it today — with all the possibilities that AI offers?”

The AI Strategy Framework for 2026

Step 1: Choose strategic focus areas

Successful organizations don't try to use AI everywhere at the same time. They choose 2-3 high-impact workflows where AI makes a difference. The criteria:

• Volume — Is this task done often enough to justify automation?
• Complexity — Is it complex enough that AI adds value, but not so complex that it becomes uncontrollable?
• Strategic importance — Does it touch the core of your competitive position?
• Data availability — Do you have the data to power AI?

Step 2: Set up the AI Studio

PwC's research shows that leaders work from a central hub — an “AI Studio” that brings together:

COMPONENT FUNCTIONTechnical building blocksReusable components, APIs, model integrationsUse case frameworksStandardised way of assessing opportunitiesSandbox environmentSafe place to experiment without production riskDeployment protocolsHow do you take something from pilot to production?Talent hubTrained people who can supervise projects

This prevents each department from reinventing the wheel and ensures governance and compliance.

Step 3: Metrics that matter

Forget vanity metrics like “number of AI projects” or “adoption rate”. Measure what really matters:

Financial impact:

• ROI per use case
• Cost savings vs. investment
• Revenue growth through AI-driven innovation

Operational impact:

• Reduction of lead time
• Error reduction
• Capacity increase without FTE growth

Strategic impact:

• Market differentiation
• Customer Satisfaction
• Employee Satisfaction and Trust

Step 4: Governance and Risk Management

With the AI Act coming into full effect in 2026, governance is no longer a nice-to-have. A robust AI strategy includes:

• Risk classification — Which use cases are high-risk under the AI Act?
• Data governance — How do you ensure data quality and privacy?
• Human-in-the-loop — Where is human supervision mandatory?
• Audit trail — Can you explain how AI decisions are made?
• Vendor management — How do you prevent lock-in and ensure continuity?

Step 5: Change Management and Adoption

The best strategy fails without adoption. Research shows a gap: 83% of leaders thinks they communicate well about AI changes, but only 28% of employees experience it that way.

What works:

• AI Champions Program — Train ambassadors in each department
• Learning journeys — Structural structure of knowledge, not a one-off workshop
• Celebrate quick wins — Make successes visible to build momentum
• Psychological safety — Create space to experiment and fail

The three strategic horizons

A complete AI strategy addresses three time horizons at the same time:

Horizon 1: Optimization (now - 6 months)

Focus: Making existing processes more efficient with AI

• Copilots for knowledge work (writing, analysis, coding)
• Automation of repetitive tasks
• AI-assisted customer service
• Document processing and data extraction

Typical ROI: 20-40% efficiency improvement

Horizon 2: Transformation (6-18 months)

Focus: Redesigning processes with AI agents

• End-to-end workflow automation
• AI agents that perform tasks independently
• Predictive and prescriptive analytics
• Personalized customer interactions at scale

Typical ROI: 2-5x productivity increase in specific areas

Horizon 3: Innovation (18-36 months)

Focus: New business models and propositions

• AI-native products and services
• Entering new markets with AI capabilities
• Fundamentally different customer relationships
• Ecosystem strategies with AI partners

Typical ROI: New turnover flows, strengthening the market position

Case: How frontrunners are tackling it

The “Build a Startup” Method

An approach that we see more and more in progressive organizations:

1. Design a fictional startup that would disrupt your market
2. Use AI tools (vibecoding, agents) to build a prototype
3. No restrictions of existing processes, systems or business models
4. Analyze the difference between the startup and your current organization

This shows where the real opportunities lie — and where you run the risk of being disrupted.

Agentic Enterprise readiness

Forrester predicts that the top 5 HR platforms will offer “digital employee management” by 2026. Organizations that are now thinking about how AI agents function as virtual team members have an advantage.

Strategic questions:

• What tasks can agents perform autonomously?
• What does supervision and escalation look like?
• What does this mean for workforce planning?
• How do you integrate agents into existing systems?

European AI sovereignty as a strategic factor

With the Wennink Commission report and initiatives such as the AI Gigafactory, European AI sovereignty is becoming a strategic reality. This has implications for your AI strategy:

Considerations:

• Data sovereignty — Where is your data? Who has access?
• Vendor diversification — How dependent are you on US hyperscalers?
• Open source options — Can smaller, locally-hosted models control your use cases?
• Compliance — How do you prepare for the AI Act?

The shift to smaller, specialized models is making European alternatives increasingly viable.

The AI Strategy Handbook

This keynote is based on AI Strategy Handbook — the complete guide for organizations that want to strategically embed AI. The book offers:

• Assessment frameworks to determine your AI readiness
• Prioritization models for use case selection
• Governance templates for responsible AI
• Implementation roadmaps from pilot to production
• Change management playbooks for adoption

Keynote attendees can receive the book as a reference.

What you'll take away from this keynote

• Clear framework for AI strategy development
• Diagnosis from where your organization is (and where the gaps are)
• Prioritization model to choose where to start
• Governance checklist for AI Act compliance
• Roadmap template for the three horizons
• Business case building blocks to create internal support

Who is this keynote for?

• Directors and Supervisory Boards that have AI strategy on the agenda
• CDOs and CIOs who lead the digital transformation
• Strategy Directors that shape the long-term vision
• Program Managers that coordinate AI initiatives
• Management teams who want to transform their department

FAQs

What is an AI Strategy?
An AI strategy is a plan that describes how an organization uses artificial intelligence to achieve its business goals. It includes priorities, governance, talent, technology, and change management.

Why do we need an AI strategy?
Without a strategy, you're stuck in separate experiments that don't scale up. A strategy provides focus, alignment and measurable results. In 2026, it is also necessary for AI Act compliance.

How long does it take to develop an AI strategy?
A first version can be up in 4-8 weeks. But strategy is not a one-off document — it is a living process that you continuously adjust based on results and new developments.

What does an AI strategy process cost?
This varies greatly depending on scope and depth. Get in touch for a conversation about the options.

How does AI strategy relate to digital transformation?
AI strategy is part of broader digital transformation, but requires specific attention due to the speed of developments, the impact on work, and governance requirements.

The time for experimentation is over. 2026 calls for execution. Contact us for a free quote.

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