Article by Nadine Soyez

“If AI strategy lives only in PowerPoint, it is not an AI strategy.”

In 2026, AI strategy no longer fails because of missing tools or budgets. It fails because organisations are not AI-literate enough to use AI consistently, responsibly, and at scale. Many companies are surrounded by AI, but very few have turned it into a real organisational capability. AI literacy, combined with clear and usable governance, has become the real strategic differentiator.

AI literacy in 2026 is not about prompts or model knowledge. People should understand how AI changes work, decisions, processes and accountability. It means they can use AI confidently in daily tasks, judge the quality of outputs, understand risks, and take responsibility for results. Without this foundation, even the most ambitious AI strategy remains theoretical.

 

 

Define what AI literacy actually means in your organisation

 

One of the biggest gaps in many organisations is that AI literacy is discussed but never defined. In 2026, companies must be explicit about what “AI-literate” means at different levels.

• Basic AI literacy: understanding what AI can and cannot do and using it safely for everyday tasks.
• Applied AI literacy: integrating AI reliably into workflows and decisions.
• Strategic AI literacy: shaping processes, priorities, and value creation with AI in mind.

Without this clarity, organisations cannot assess where they are today or define the next realistic step.

 

 

Anchor AI in real work instead of abstract learning

 

AI literacy and confidence does not emerge from generic training sessions. It develops when people work with AI on real tasks that matter in their roles. In 2026, organisations must deliberately connect AI learning to concrete workflows such as preparing analyses, drafting content, supporting decisions, or coordinating projects. When people see exactly where AI helps, where it does not, and what remains their responsibility, AI literacy becomes practical instead of theoretical.

At the same time, organisations must acknowledge a hard constraint that is often ignored: time. AI fails when it is treated as additional work on top of already full schedules. It succeeds only when AI replaces existing effort, removes friction, or shortens cycles. If AI adoption increases workload, it will stall. If it reduces effort and complexity, it will scale. This is one of the clearest signals that AI literacy is not a cultural initiative, but an operational one.

 

 

Build governance that enables use instead of blocking it

 

Governance is essential, but it must be usable. People need clear, practical guidance on which tools are approved, what data may be used, when human review is required, and where accountability always remains with them. When it is written in legal language or buried in policies, then it creates fear, hesitation, and avoidance – the contrary we want to have. Governance explained in simple terms and embedded into daily work creates confidence and consistency.

A common mistake is treating AI governance as something that must be perfect from the start. In reality, governance in 2026 must evolve with usage, maturity, and risk. Early governance should be simple and focused on the most critical rules – gbing just enough oversight without creating bureaucracy. As AI use deepens, governance must be reviewed, refined, and expanded. Organisations that over-engineer governance too early slow adoption. Organisations that ignore governance altogether create long-term risk. The balance lies in continuous adjustment.

 

 

Make decision ownership explicit at every level

 

AI changes how decisions are prepared, but it does not change who is responsible. Organisations must clearly state that AI never owns decisions. Every AI-supported outcome requires a human owner who understands the result and stands behind it. This clarity is essential for trust, quality, and accountability. When ownership is unclear, people either over-trust AI or disengage from it entirely.

This is where many organisations quietly struggle, especially. Managers often block AI unintentionally because they do not yet know how to redesign work, translate governance into overly cautious “better safe than sorry” rules, or avoid accountability for AI-supported decisions. Without targeted support for this layer, AI strategy stalls between leadership ambition and operational reality.

 

 

Train judgment as the core AI skill

 

The most dangerous form of AI is blind trust. People must learn how to critically evaluate AI output, recognise weak reasoning or hallucinations, and decide when results are good enough and when they are not. This judgment cannot be automated. Definine clearly and when verification is required.

AI literacy does not sustain itself. In 2026, organisations need clarity on who owns AI learning, who maintains governance, and how feedback from daily usage flows back into standards and rules. When AI literacy is “everyone’s responsibility,” it usually becomes no one’s job. A lightweight but explicit operating model ensures continuity, improvement, and alignment across teams.

 

 

Reduce tool chaos through intentional standardisation

 

AI literacy collapses when every team uses different tools and approaches. Organisations must be disciplined about tool selection and usage patterns. A small number of approved tools and shared AI practices create confidence, reduce risk, and make AI manageable. Standardisation does not limit innovation; it enables learning and scale.

 

 

Connect AI to business outcomes

 

AI is not a cultural initiative, but a performance driver. In 2026, organisations must explicitly link AI  to faster decision-making, higher quality outputs, reduced dependency on individuals, and more scalable expertise. When people see how AI improves outcomes, AI stops being a side topic and becomes part of how the organisation operates.

 

 

What companies can do now

 

Companies that want to be ready for 2026 should stop refining AI vision decks and start building AI literacy and governance together. Concretely, this means:

• Define AI literacy by level, making explicit what basic, applied, and strategic AI literacy mean in your organisation, so people know what is expected of them today and what comes next.
• Embed AI into a small number of real workflows, focusing on tasks where AI can immediately replace effort, reduce friction, or shorten cycles instead of adding additional work.
• Make time and capacity a design constraint, ensuring that AI adoption removes work before asking people to learn more, otherwise adoption will stall under daily pressure.
• Create clear, human-readable governance rules, covering approved tools, data usage, review requirements, and accountability in language people can actually apply in daily work.
• Treat governance as evolutionary, starting simple, reviewing it regularly, and expanding it as AI usage, maturity, and risk increase.
• Make decision ownership explicit, stating clearly that AI never owns decisions and that every AI-supported outcome has a named human owner.
• Support managers in redesigning work, helping them translate AI strategy and governance into new workflows instead of unintentionally blocking progress through over-caution or avoidance of responsibility.
• Train judgment as a core skill, teaching employees how to challenge AI output, recognise weak reasoning, and decide when verification is required.
• Establish a clear operating model, defining who owns AI literacy, who maintains governance, and how feedback from daily usage improves standards over time.
• Reduce tool chaos through intentional standardisation, limiting the number of approved tools and shared AI practices to enable learning, scale, and manageable risk.
• Tie AI literacy directly to business outcomes, measuring impact through faster decisions, higher-quality outputs, reduced dependency on individuals, and more scalable expertise.

 

When these actions are taken seriously, AI strategy moves out of PowerPoint and becomes a lived, scalable capability across the organisation.