AI is transforming everything… unless you forget about strategy!

However, the majority of companies are still feeling their way forward. They experiment with a tool here, test a pilot there… but without any real consistency or overall vision. The result is fragmented initiatives that are difficult to make profitable and rarely capable of transforming the organization in any meaningful way. In fact, a recent MIT report, The GenAI Divide: State of AI in Business, paints a stark picture: only 5% of AI pilot projects succeed in generating rapid revenue growth, while 95% struggle to deliver a measurable impact. The problem does not lie with the models themselves… but with a lack of strategic integration. 

While companies are multiplying tools, tests, and investments, few have really structured their approach. The report points to an organizational “learning gap”: a lack of vision, governance, cross-functional coordination, and the ability to transform experimentation into sustainable value. The result? AI projects that remain at the technical demonstration stage, with no real impact on performance or profitability. 

So where do you start to successfully implement AI and avoid pitfalls? How can you make AI a real driver of organizational transformation? The answer lies in five words: strategic planning for artificial intelligence. A structured approach, aligned with your business objectives, that allows you to integrate AI in a cross-functional, responsible, ethical, and effective manner. And that is precisely where any ambitious AI transformation should begin. 

Why Link Strategic Planning with Artificial Intelligence?  

Because AI cannot be improvised… and, above all, it is not the sole responsibility of the CTO or IT department. Artificial intelligence does not work in a vacuum: it depends heavily on your data infrastructure and processes, collaboration between teams, and alignment with your business priorities. 

It requires reliable, well-structured, accessible data. It relies on your operations to generate relevant recommendations. It depends on the commitment of your teams to be used, understood, and improved. It impacts the way you work, your decisions, and even your corporate culture. 

In other words, AI can only deliver its full value if the organization is ready to embrace it on both a human and technological level. 

Strategic planning for artificial intelligence = direction + consistency + sustainable and measurable value creation 

It is the compass that guides your technology investments, aligns your teams around a common vision, and transforms your AI initiatives into real performance drivers, rather than isolated experiments. 

1. The importance of vision: before you run, know where you’re going 

In many organizations, AI is being introduced in small steps: a chatbot for customer service, a tool for automating administrative tasks, a predictive dashboard for sales, etc. These isolated initiatives sometimes yield one-off results but rarely lead to large-scale transformation. 

A clear strategic vision for AI changes the game. It allows you to: 

• Prioritize projects that generate a real competitive advantage rather than giving in to hype or pressure to adopt AI 

• Avoid spreading budgets and resources across experiments that are not linked to the overall strategy 

• Mobilize and align teams around a common goal, strengthening consistency and adoption of AI at all levels of the organization 

In other words, the vision acts as a magnetic north: it guides every investment, every project, and every technology choice, ensuring that AI becomes a strategic pillar rather than just another technological tool. 

2. Alignment with business challenges 

Good strategic planning in artificial intelligence starts with a simple but powerful question: What business problem are we trying to solve? 

Reducing costs, increasing productivity, optimizing the workforce, improving the customer experience, reducing errors in critical processes… AI can help solve all these issues, provided it is integrated into a measurable value creation strategy. 

However, what we often see is a disconnect between management and frontline teams. Senior management wants to do ” AI” at all costs to stay in the game, while operational units, closer to the data and processes, are looking for concrete solutions to their day-to-day problems, sometimes unrelated to the strategic vision. 

The result: poorly prioritized endeavours or projects that are difficult to scale. As leaders, you need to draw a direct link between AI use cases and your strategic objectives: growth, profitability, customer experience, agility, compliance, etc. 

Set SMART goals

AI is not an end in itself. It is a lever that serves the company’s mission.  Your AI strategy must therefore flow from your business strategy, not the other way around. To ensure alignment between efforts and business objectives, an AI strategy and in must be based on well-defined objectives. And like any initiative with a significant organizational impact, these objectives must be SMART: specific, measurable, achievable, realistic, and time-bound.

For example:
“We want to use AI to optimize our resources” to ” reduce overtime costs by 20% within 18 months using a predictive workforce allocation tool. ” 

The difference? One is a vague intention. The other is a strategic commitment that can be monitored, managed, and adjusted. What cannot be measured cannot be improved. And what is not clearly defined is unlikely to mobilize the right teams, at the right time, with the right resources. 

Setting clear objectives also sends a strong signal to the organization: AI is not an abstract testing ground, it is an accelerator for our business strategy. 

3. Choose the right use cases 

Strategic AI planning also means knowing where to invest your initial energy. A common mistake is to launch a multitude of pilot projects to test AI. The result is scattered initiatives that are difficult to measure and rarely transformative. 

It’s better to have three targeted AI projects aligned with your critical issues than a catalog of unstructured experiments. 

Examples of high-potential use cases:

• Forecasting periods of high demand: anticipate demand and peaks such as Black Friday in logistics to adjust staffing levels and reduce emergency costs. 
• Intelligent automation of email or customer invoice processing: reduce response times and free up your teams for higher value-added tasks. 
• Dynamic workforce allocation: adjust your workforce in real time according to volumes and operational priorities. 
• Predictive maintenance: in manufacturing or fleet management, anticipate breakdowns to minimize downtime and optimize investments. 

Every industry has its own priorities, but the rule remains the same: start with the business problem, not the technology. Ask yourself: How can we quantify the AI value creation?  Which AI project, if successful, would create the most value for our customers, our teams, and our shareholders? Then focus your efforts where the return on investment is fast and measurable. 

Establishing concrete indicators allows you to track the effectiveness of AI initiatives and make informed decisions: 

• Productivity gains per project: evaluate the hours saved, the reduction in manual tasks, or the increase in operational capacity. 

• AI tool adoption rate: measure actual usage by teams and identify barriers to adoption.

• Changes in internal and customer satisfaction: analyze the impact on the employee experience and the quality of service delivered. 

• Return on investment: track savings or revenue generated at 6, 12, and 18 months to adjust budgets and priorities. 

The key: establish a cycle of continuous improvement. Each quarter, analyze results, share learnings, optimize models, abandon underperforming projects, and reinforce those that create value. 

By treating AI as a strategic, data-driven asset, you avoid the “gadget” effect and transform your initiatives into a sustainable competitive advantage. 

5. Governance, ethics, and compliance: building trust and securing AI transformation 

The integration of artificial intelligence is not limited to technological choices. It raises major strategic and societal issues: algorithmic bias, data confidentiality, responsibility for automated decisions, regulatory compliance, etc. Ignoring these dimensions exposes the company to financial, reputational, and legal risks. 

Robust strategic AI planning incorporates solid governance and clear ethical principles from the outset: 

• Regulatory framework: ensure compliance with current legislation (GDPR, Law 25, AI Act) and anticipate future standards. 

• Validation process: establish evaluation committees to monitor the quality, transparency, and security of AI models. 

• Audit and traceability: document algorithmic decisions so they can be explained and justified in the event of a dispute or investigation. 

• Fairness and inclusivity: ensure that models do not reinforce discriminatory biases and that they serve all stakeholders. 

• Enhanced cybersecurity: protect sensitive data and anticipate cyberattacks targeting AI systems. 

Without governance and ethics, even the best AI project can face massive resistance from employees, customers, and regulators. With them, you build a foundation of credibility and sustainability that promotes widespread adoption. 

AI must not only be effective, it must also be responsible. It is this combination that secures your transformation and preserves your competitive advantage.