In this period of massive experimentation, some companies are not just adding AI to their usual practices. They use it to solve problems that already exist.
They achieve results not because they adopted AI, but because they understood why they were doing it.
A technology that reverses the rules of the game
In previous technology revolutions, from the rise of the Internet to the advent of the cloud, large companies had the advantage: They had the capital, infrastructure and IT teams to adopt early, experiment widely and scale quickly. This time, the IA curve is different. AI is the first big wave where size can actually be a liability. AI reverses traditional power dynamics in business communication. For the first time, SMEs can access capabilities worthy of large companies, without their burden. With ready-to-use models and tools, and open APIs that facilitate integration, the AI race is led not only by those with the deepest resources, but especially by those who are fast and whose objectives are clear. While large companies still have the advantage of size, they no longer hold a monopoly on power.
Agentic AI to strengthen humans and increase teams
Well-used AI solutions behave like true autonomous digital teammates; not because of fashion, but to solve the right problems, in the hands of the right customers. For example, in health, certain AI makes it possible to smooth out the three daily peaks in appointment requests. They manage reservations and routine requests 24 hours a day, freeing up staff who may be taking time off to focus on critical cases. It is therefore an automation which supports humans, and not which replaces them. Because this approach is highly personalized, each customer must identify operational pain points and then design systems that precisely address those issues. In fact, the concern isn’t that leaders are expecting too much from AI, it’s that they’re expecting the wrong things: speed, instead of smarter operations. The brands that are making progress are those that use AI to do more, not to eliminate humans, but to augment them. This is where the real growth lies.
Build a concrete and measurable roadmap
To move from vague excitement to strategic implementation, companies need a better framework, one that starts with good questions: What specific task or problem do we want to solve? What data do we already have? What result can we measure? Where should the human stay in the loop? Start with the most accessible solutions. Simple use cases that quickly prove ROI, then expand. For some clients, this means using a widget to turn a website into a functional FAQ assistant. For others, it’s complete automation of scheduling and triage. You don’t need to solve everything at once. But you have to start with something real. If AI does not demonstrate ROI, it is not ready.
From search engines to AI portals: keeping control of customer relations
As customer behavior shifts from search engines to AI interfaces, brands face a new challenge: how to stay visible when users stop “Googling” and start querying ChatGPT? We are entering a world where AI becomes the first point of contact. This means that brands must build AI-enabled customer portals today, or risk losing any direct relationships. The biggest challenge is to maintain control of this relationship. Brands don’t want OpenAI to own this interaction. Businesses therefore have every interest in investing in portals that offer rapid, AI-driven support while keeping the customer within their own ecosystem.
Think about your strategy before deploying AI
We come back to the central theme: not AI for AI’s sake, but AI as infrastructure. Not automation to reduce activity, but to expand the capabilities of the business. AI tools will continue to progress. The real question is: will your organization’s thinking also progress? Don’t wonder what you can automate. Ask yourself what you can make possible with this. The winning companies won’t be those who jumped in first, but those who took the time to ask better questions and made AI a truly useful tool. It all starts with the ability to clearly formulate the problem to be solved.
In many industries, leadership teams often rush to adopt AI with an urgency that seems more reactive than truly based on reason and conviction. But the real risk is not bad AI. This is lazy thinking. If you can’t explain what problem you’re trying to solve, no tool will save you. You’ll get results not because you adopted AI, but because you understood why you’re using it. So, ask yourself the right questions!
