The same scene plays out in many companies at the end of every year. Someone on the board or in management asks what happened with artificial intelligence over the past twelve months, and the answer they get is a mix of scattered anecdotes. A team that tried Copilot. Another that built an agent to organize reports over a weekend. A pilot that stalled halfway through because the person running it changed roles. No one is lying, but there is no clear picture of where the company actually stands with this technology.
The problem isn't a lack of initiatives. Most companies had some kind of contact with AI this year. The problem is that these initiatives live scattered, with no one having looked at them together, which makes it hard to decide what to do next year based on real information rather than the feeling that "something was done."
Solving that doesn't require a weeks-long diagnostic or a hundred-page report. At fuubo we solve it with a structured conversation, around ninety minutes long, that covers a focused set of questions: where AI is already being used today, even informally; which processes actually changed and which only had a trial that went nowhere; what data and permissions are flowing through tools no one ever fully authorized; and which team, if any, ended up responsible for sustaining all of this going forward.
In the assessments we've run this year, most of the companies evaluated landed at initial maturity levels. What's surprising isn't that result, but where the real gap sits. The data, generally, is in fairly good shape. The biggest gap shows up in people's knowledge, in how much teams actually understand about how and when to use these tools in their daily work. That gap is exactly what makes an adoption service necessary, not to implement everything at once, but to start with small, defined processes and keep adding capabilities as the team gains confidence.
One of the clients that took this path went from 10% adoption of generative AI tools across their team to practically 100%, freeing up more than 300 operational hours in the process. None of these results show up by decree or overnight. They show up when a company first understands where it stands and then moves forward with a plan, not with loose enthusiasm.
This matters especially for whoever is putting together next year's plan. Setting budget, priorities, and ownership on top of an AI footprint no one ever mapped is betting blind. Starting the year with a reasonably clear picture of where the organization stands, instead, makes it possible to tell what's worth scaling from what should be quietly shut down, and what never should have started without a clear owner in the first place.
There's something freeing in this too. It isn't about measuring a company's AI maturity to compare it against the rest of the industry, or arriving at a score that ends up forgotten in a folder. It's about using the year-end, which is already a natural moment for review, to ask a simple and honest question: with everything tried this year, where does the company actually stand?
The difference between a company that starts the next year organized and one that goes back to improvising isn't how much AI it used, but whether it took the time to look at it with judgment before moving forward. Ninety minutes, well spent, is enough for that. What isn't enough is to keep piling on pilots without knowing, in the end, what's left of any of them.
If you want to start that conversation before the year ends, you can book a meeting directly with Fuuby, fuubo's assistant, right on our site, no forms and no waiting for an email back.
