Companies are handling artificial intelligence Like doctors of the Victorian era treated caterpillars: as a universal medicine to be applied liberally, regardless of the current problem. Board meetings across the country contain some changes to “We need a strategy” Without first asking “specific problem are we trying to solve?” The results are projected to be underestimated.

However, here we are with executives seeking solutions to him for problems that do not exist while ignoring the problems he can actually solve.

This is expensive in ways that rarely appear in quarterly reports. Companies pour millions In the initiatives of he This generates impressive demo and embarrassing results. They are writing checks that their data infrastructure cannot make money. And no one seems to notice the model.

Connected: How to avoid losing millions in it

First technology trap

Typical corporate journey he follows a predictable depressive path. First, an executive attends a conference where competitors boast their initiatives. Panic follows. One mandate comes down: “Apply it to all departments.” Teams try to find cases of use to justify the technology that has already been selected. Consultants arrive with sliding deck. The pilots have left. Demots are built. Press releases are designed. And a year later, when someone asks RoiThey all look closely at their shoes.

This backward approach to starting with the solution instead of the problem explains why so many projects it fail. Likes how to buy an expensive hammer and then wander to search for nails. Sometimes you find them! Most often, you discover your current problems require screwdrivers.

The thing is that the first technology strategies create great titles, but terrible business results. They mistake the movement for progress. They value innovation over services. And often, solutions are more difficult to build and use than they look.

Data fraud

There is a curious cognitive dissonance in the way organizations think their data. Ask any technical leader about the quality of their company data, and they will consciously crush. However, companies approve projects of one that assumes unmistakable, comprehensive data exist with magic somewhere in their systems.

Teaching There is no need for data. She needs significant models in good data. A learned learning algorithm does not become intelligent; It becomes extremely effective in the production of very safe waste.

This detachment between the reality of data and the ambitions of it leads to an endless cycle of disappointment. Projects begin with enthusiastic predictions about what he could accomplish with theoretical data. They end up with engineers explaining why current data cannot support those predictions. Next time will be different, everyone provides themselves. It is never.

Connected: No one wants another useless tool to him – here's what we have to build instead

The gap

The most sophisticated solution in the world is invalid if it is not integrated into the course of current work. However, companies routinely invest millions in algorithms while sharing approximately seventeen dollars and thirty cents to ensure that people actually use them.

They build solutions of what they seek perfect Participation by employees Who was not advised during development, does not understand the models and have not been trained to use the tools. This is approximately equal to installing a formula 1 engine in a car without modifying the transmission, then wonder why the vehicle continues to break.

Look, adopting technology is not a technical problem. Is a man. People are very resistant to changing the established behaviors, especially when the benefits are not immediately visible to them. A solution that requires significant changes in the course of work without giving apparent, immediate benefits is dead on arrival. No one wants to accept this, but it is true.

Return the strategy

What would a strategy look like and the reverse engineering? Start with identifying specific, measurable business problems, where current approaches are falling short. Evaluate these problems through rigorous, non -executive analysis. Evaluate if these problems actually require it or can be better solved through simpler solutions. Consider the organizational changes needed to implement any solution. Then, and only then, evaluate which data and technologies can address valuable problems.

A better implementation framework

Effective implementation of it requires reversing the typical approach:

1. Problems before solutions: Identify and authenticate specific business challenges with measurable impact

2. Data Reality Control: The existing audit Quality of data and collection processes before you assume the feasibility of it

3. Simplicity test: Determine if the simplest approaches, JO-I can solve the problem more effectively

4. Organizational readiness: Evaluate if the workflows and teams are ready to Integrate the solutions of it

5. Additional Implementation: Start with small -scale pilots centered on close, well -defined problems

Connected: When should you not invest in him?

Meta data training algorithms are like building a home in Quicksand. Architecture can be impeccable, but that will not matter when everything sinks. Companies proudly announce their initiatives to approximately the same level of strategic clarity as medieval alchemists had to transform superiority into gold. The main difference is that alchemists spent less money.

Probably the most valuable Implementation of it The strategy is simply changing the question. Instead of asking “how can he use it?” Try to ask “What specific problems are it worth solving, and can it be the right approach for some of them?” This recovery does not make for the impressive notes of the conference. It does not generate the same press coverage or slots to speak conference. But it tends to produce solutions that actually work, which seems like a reasonable goal for multi -millions technological investment.