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The real shift starts when AI meets the way we make decisions
When ChatGPT launched in November 2022, it felt like the world had split overnight. Some people didn’t even notice. Others were terrified, sure that AI would take their jobs or hollow out entire industries. And a third group was exhilarated, convinced that a new industrial revolution had just begun.
Almost three years later, all three groups are still with us: the unaware, the fearful, and the optimistic. But the reality is more complicated. For every promising success story, there is a failed experiment, a half-baked pilot, or a shiny demo that never touched real business outcomes.
The hype was never about what AI could do. It was about what companies imagined it might do for them.
Most people’s first contact with AI is trivial. Drafting emails, summarizing a meeting, and generating a blog outline. It feels magical, but it is surface-level. It is AI as a personal productivity booster.
Companies that are slightly more advanced string these tools together into workflows. Automating customer support replies. Generating variations of ad copy. Building assistants for internal teams. These efforts save some time and make processes smoother. But they still hover at the edge.
The deeper shift does not start until AI meets the company’s own data.
Here is the uncomfortable truth: your competitors have access to the same models you do. What they do not have is your data.
Your own data, the messy, unstructured, scattered information sitting across spreadsheets, CRMs, finance systems, project tools, and Notion pages, is where the real moat lies. It is the difference between using AI as a gimmick and using AI as an engine for transformation.
And yet, most companies ignore this. They obsess over which model is cheaper, faster, or smarter, but neglect the fact that their teams do not even agree on the definition of “margin.”
Without trusted, reliable data, AI is theater.
At Appunite, we have seen this firsthand. We generate an enormous amount of information: client insights, meeting notes, delivery metrics, and financial reports. We are far above average when it comes to data collection. But when we need to connect the dots, extract the bigger picture, or align across functions, the system breaks down.
Leadership cannot rely on one view of the truth. Finance has one answer, Delivery has another, and Client Success tells a different story. Meetings turn into arguments, not about strategy, but about whose number is right.
This is not an AI problem. It is a management problem. And until the foundations are fixed, AI will not fix it for us.
The companies that win the next decade will not be the ones that used ChatGPT first. They will be the ones who treated AI not as a tool but as a catalyst, a reason to reorganize how decisions get made, how data is trusted, and how insights flow across the company.
It starts with clarity. Building reliable pipelines. Agreeing on definitions. Creating a single source of truth. Only then can AI become a multiplier rather than a distraction.
AI hype promised shortcuts. The real work is slower but far more powerful. It is not about shiny demos. It is about building a company that trusts its own data enough to let AI amplify it.
Three years on, the divide between hype and reality is clear. Most are still stuck at the edges. The real transformation begins inside, with your data, your processes, and your culture.
That is where AI stops being a headline and starts being an advantage.
