How do I know whether I am starting my artificial intelligence journey the right way? How do I know whether I am on the right path? And what should I expect to see as I move forward?
Today, many organizations begin by using generative AI to write, summarize, research, analyze documents, or create better prompts. All of that is AI, and it can deliver real productivity gains.
But there is an important difference between using AI tools and building an enterprise AI capability.
“The key question is not how much AI we are using. It is this: what critical business problems are we solving, and which performance indicators are improving as a result?”
The AI Friction Point Illusion
A 78-second cinematic perspective on where the real business value of AI begins.
A Restaurant Chain “Doing AI”
Consider a restaurant chain. Its name and country do not matter. There is tremendous enthusiasm around the latest trends. Teams use AI to create promotions, prepare content, summarize information, and develop increasingly sophisticated prompts.
Is it using AI? Absolutely.
But let us ask a more uncomfortable question: is it using AI to solve the problems that actually determine the profitability of a restaurant chain?
A chain can make more money by selling more, but it can also make much more by selling better. It can understand which products, combinations, time periods, channels, and customers generate higher margins; anticipate demand; identify commercial opportunities; and improve customer interactions.
It can also buy better: forecast needs by location, optimize purchase orders, compare suppliers, reduce stockouts, excess inventory, and waste. It can operate better: optimize staffing, reduce waiting times, detect deviations, and improve productivity.
Those are real friction points. And that is where AI begins to change the business.
From Enthusiasm to Value
The maturity journey can be viewed in stages.
1. Assisting People: First, AI helps people: it researches, summarizes, writes, and accelerates tasks.
2. Executing Processes: Then it begins to execute processes: it connects activities, prepares information, and automates repetitive work.
3. Business Understanding: At a more advanced stage, it begins to understand the business by using data on customers, products, sales, costs, inventory, and operations. It no longer answers only “What happened?” but also “What is likely to happen?”
4. Prescriptive Capability: The next leap is prescriptive: it not only predicts, it recommends. It can tell a salesperson which customer to contact, what product to offer, and why; or alert Procurement before an inventory problem emerges.
5. Specialized Agents: Then specialized agents emerge, capable of researching, analyzing data, financially evaluating opportunities, monitoring conditions, or preparing proposals while working within a coordinated ecosystem.
6. Ecosystem Integration: Eventually, that intelligence can be integrated with enterprise systems, equipment, and sensors, bringing AI, analytics, automation, and robotics together.
Friction Points and Hard Metrics
But none of these stages makes sense if we lose sight of the business.
AI should attack friction points: places where we lose time, sales, margin, productivity, capital, or quality; where we accumulate inventory, generate waste, make decisions too late, or manually perform work that could be executed better.
And every initiative should have a hard metric.
If we reduce waste, measure by how much. If we improve purchasing, track the cost impact. If we optimize inventory, measure working capital released, turnover, and stockouts. If we improve commercial performance, measure average ticket, margin, conversion, frequency, or retention.
Not perceptions. Real data.
In a chain with many locations, even a small improvement repeated thousands of times can produce a major economic effect. And when a meaningful improvement at a friction point is scaled across the entire ecosystem, the impact can be extraordinary.
Start Small, but Measure from the Beginning
There is no need to build the entire ecosystem at once.
Select a relevant friction point. Establish the baseline. Build a focused solution. Test it with real data. Compare before and after. Learn, adjust, and, if it creates value, scale it.
The architecture can be ambitious. Implementation should be progressive.
A company can have hundreds of people using artificial intelligence and still make essentially the same decisions it made before.
The next competitive advantage will not come from having access to AI. Almost everyone will. It will come from turning AI into measurable results: higher revenue, better margins, lower costs, less capital tied up, greater productivity, better decisions, or lower risk.
So before asking, “What other AI tool should we add?”, perhaps we should ask more difficult questions:
Where is our business destroying value today? Which friction points can we eliminate? And can we demonstrate the improvement with numbers?
Do not tell me how many AI tools your company uses. Show me which friction points you eliminated and which performance indicator changed.
That is where the real conversation about value begins.
And if those answers are still unclear, perhaps the next step is not to buy more technology, but to seek the expertise needed to identify where the value lies and design the path to capture it.