The adoption of AI in business: How can initiatives be encouraged?

The adoption of AI: A transformation promoted, but incomplete

The adoption of AI in business is no longer a futuristic promise. By 2025, nearly 92% of large companies plan to increase their investment in artificial intelligence. However, only 21% of them have significantly rethought their processes to capture this value. According to McKinsey, generative AI could add between $2.6 trillion and $4.4 trillion to the global economy each year. Yet the majority of companies have only executed ad hoc projects, often disconnected from an overall vision.

Why this stagnation, despite massive investments? The problem is not technical, but organizational. The sustainable adoption of AI requires a overhaul of structures, mindsets, and modes of governance.

Three organizational obstacles to overcome

1. A defensive culture and mistrust of AI

A recent study by KPMG shows that more than half of employees use AI without informing their organization. The reasons for this include a lack of training, the absence of an ethical framework, and fear of judgment or replacement. This mistrust hinders the smooth integration of AI into decision-making processes.

2. Poor data quality and lack of skills

Organizations still underestimate the importance of reliable, structured, and unbiased data. Without this fundamental asset, algorithms generate little value and may even introduce bias. At the same time, competition for AI talent is intensifying, and internal skills are evolving slowly.

3. Poor governance and organizational silos

Only 28% of companies have a clear AI governance model led by the executive committee. And in many cases, units operate in silos, hindering the collaboration needed to deploy cross-functional use cases. These organizational barriers to AI severely limit the scope of projects.

Rethinking business for the deployment of artificial intelligence

Companies that successfully integrate AI undergo three major cultural transformations.

From working in silos to interdisciplinary collaboration

AI projects create more value when they are led by multidisciplinary teams. This means that IT leaders, business experts, data scientists, and designers collaborate at every stage. This type of collaboration allows AI solutions to be better aligned with the real challenges faced by business units.

From top-down hierarchy to augmented decision-making

For employees to integrate AI into their daily work, they must be able to rely on its recommendations without needing constant approval from management. This requires trust, training, and profound cultural changes.

From planned rigor to adaptive agility

Agile organizations accept mistakes as a source of learning. They operate in short cycles, test minimum viable products (MVPs), and iterate based on market feedback. This approach is essential for effectively deploying AI.

Preparing leaders and teams for success

AI transformation cannot be driven without the right leadership. Three actions are key :

1. Explain the vision : Demonstrate how AI fits into the overall strategy and what each person’s role is in deploying artificial intelligence.
2. Involve users : From the outset, co-create use cases with those who will be using them.
3. Invest in adoption : Nearly 90% of companies that have successfully transitioned to AI have invested as much (if not more) in training, communication, and process redesign as they have in the technology itself.

Create an AI project portfolio

AI planning must combine quick wins with a long-term vision. It is recommended to: – Prioritize quick projects with visible ROI (e.g., fraud detection) – Conduct transformational initiatives over 12-36 months (e.g., personalization of the customer experience) – Combine the two in a coherent roadmap

Next step: structuring the organization around an AI hub

Once the foundations are in place, mature organizations structure their AI ecosystem around a central core (center of excellence) and local champions in each unit. This approach enables consistency, governance, and adaptability.

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