The adoption of AI cannot be decreed: it must be built with employees.
Companies have invested heavily in AI, but many employees remain hesitant. Concerns about job security or the place of AI in their daily work persist — even as Gartner predicts that AI will ultimately create more jobs than it destroys.
According to Ivanti, more than a third of employees (36%) keep their use of AI to themselves in order to maintain a perceived advantage, while 30% remain discreet for fear of putting their jobs at risk. This hesitation limits productivity gains and slows down business transformation.
Leaders must remove structural barriers, including model bias, and establish cultural standards that make AI safe, visible and useful. Return on investment should not only be measured in speed, but also in collaboration, quality and analytical capacity.
Create a safe space for experimentation and hands-on training
Advocating for the adoption of AI starts by meeting employees in an open setting, then by setting an example. Across industries, interest in AI is strong: nearly 90% of companies surveyed in the State of AI in the Enterprise report plan to increase their AI budget this year, and most anticipate a measurable transformation within two years. But the budget alone does not create value.
To realize this potential, companies must adopt an employee-centric approach. They must define a clear strategy at team level, enable secure experimentation, offer practical training and make tools for creating AI agents accessible. Leaders must show use cases, share their successes and failures, and provide clear pathways to develop AI skills. Providing attractive upskilling content is essential to encourage buy-in: mandatory fundamental training on AI, technical explanatory series, concise practical modules… all formats that help reduce reluctance, build confidence and encourage adoption.
Initiate change: for employee buy-in and increased productivity
AI adoption is not a race to volume. It’s about identifying where AI brings the most operational impact, driving those high-potential workflows, measuring the results, and then scaling what works. A good motto: take risks, fail quickly, move forward.
Through this process of experimentation, organizations can guide employees toward mutual benefits. Employees get used to the new tools, increase their market value, their productivity and their job satisfaction. Teams gain speed and decision-making quality; businesses gain resilience, competitiveness and sustainable growth. The role of leadership is crucial — not only in setting direction, but also in embodying responsible and effective use of AI.
Addressing the question of ROI
In many sectors, the question remains: is AI really creating value today? According to MIT’s State of AI in Business study, only about 5% of integration pilots produce significant value. This figure is not an indictment of AI, but rather an illustration of failing strategies. We see that AI can make work more efficient: improve onboarding, assist software development, automate business administrative tasks, process invoices, forecast demand or optimize inventory. Leading companies using AI to automate their processes report productivity gains of around 37%, far higher than their peers.
The problem with ROI lies not in the ability of AI to deliver results, but in the strategy. Where strategy fails, AI fails too. Organizations need AI advocates — advisors, architects — who can catalyze its value. The real value will not come from isolated pilots, but from integrating AI into the heart of business processes. Successful companies start with practical, high-impact improvements and then move toward broader transformation, built on trust, transparency, and a human-centered approach.
Building systems based on scalable trust
If you want to build a solid foundation for success with AI, being able to quickly deploy your AI tools and workflows is essential. But where are AI budgets really invested? It all comes back to having a sound investment strategy from the start. Once this foundation is in place, the benefits increase over time.
There’s no denying that implementing AI involves a profound process transformation and a review of how you organize your workflows around trust. This constitutes the cornerstone of any successful AI strategy and requires buy-in from customers, partners and employees. Trust at scale requires enterprise-grade security, clear data compliance, and transparent model behavior, so everyone can be confident that innovation doesn’t come at the expense of privacy or governance. That’s why governance and visibility must be built into every aspect of AI workflows, with controls to manage access, audit decisions, and maintain compliance as the business grows.
Advocate for the right kind of change
As we take a closer look at how AI creates value, it becomes clear that the human-machine relationship is a critical component of the ROI equation. Organizations need changemakers who can champion AI — effectively and ethically. AI is a technology that does not work without piloting. From biases to hallucinations to model failures, it is exposed to risk, and this risk must be strictly regulated by human supervision.
This is why it is essential to promote AI tools based on trust and training. When employees understand how AI achieves its results, they are more likely to use it with confidence and creativity. Over time, this trust strengthens: teams share their ideas more freely, innovation accelerates and the company becomes more agile.
