Why the strategy of your company is probably back

Companies treat artificial intelligence, similar to the Victorian doctors treated leeches: as a universal medicine for free use regardless of the actual problem. Meetings of the management throughout the country contain a certain variety “We need AI strategy”, without asking “what specific problem we are trying to solve?” The results are predictably disappointing.

In any case, here we are with the management of AI solutions for problems that do not exist when ignoring the problems that AI can solve.

It is expensive in a way that rarely appears in quarterly reports. Companies pour hundreds of thousands of AI initiatives that generate impressive demos and gloomy results. They write checks that their data infrastructure cannot earn. And it appears that evidently no one notices the pattern.

Trap first technology

A typical corporate journey AI follows the depressing path. First of all, the executive participates in a conference at which competitors boast of AI initiatives. Panic is created. The mandate decreases: “Implementation of AI in all departments.” Teams try to search out cases of use to justify the technology that has already been chosen. Consultants come with slide decks. Pilots are launched. The data is built. Press editions are developed. And a 12 months later, when someone asks about Roi, everyone looks rigorously at the shoes.

This rearview approach starting with the solution as a substitute of the problem explains why so many AI projects fail. It’s like buying an expensive hammer, and then wandering around looking for nails. Sometimes you’ll find them! You discover more often that real problems require screwdrivers.

The point is that technological strategies are great headers, but terrible business results. They confuse traffic with progress. They value novelty over usability. And often solutions are tougher to build and use than they appear.

Data illusion

There is an interesting cognitive dissonance in how organizations think about their data. Ask any technical leader about the quality of their company’s data, and they may consciously break. However, corporations approve AI projects that assume flawless, comprehensive data sets magically exist somewhere in their systems.

Machine learning does not only require data. I want significant patterns in good data. The learning algorithm trained in the field of garbage does not grow to be intelligent; It becomes extremely efficient in producing very certain rubbish.

This disconnection of data between reality and AI ambitions results in an limitless disappointment cycle. Projects start with enthusiastic forecasts about what AI can achieve with theoretical data. The engineers explaining why real data cannot support these forecasts. Next time they will probably be different, everyone provides themselves. It is never.

Implementation gap

The most sophisticated AI in the world is worthless if it is not integrated with real work flow. However, corporations routinely invest hundreds of thousands in algorithms, while assigning about seventeen dollars and thirty cents to supply people with their use.

They build AI solutions that require excellent participation on the part of employees who weren’t consulted during development, do not understand models and have not been trained in the use of tools. This is more or less corresponding to the installation of the Formula 1 engine in the automotive without modifying the gearbox, and then wondering why the vehicle is still spreading.

Listen, accepting technology is not a technical problem. This is human. People are notoriously immune to changing behavior, especially when the advantages are not obvious to them. The AI ​​solution, which requires significant changes in work flow without providing obvious, direct advantages is dead after arriving. Nobody desires to admit it, but that is true.

Reversing the strategy

What would the AI ​​strategy seem like in reverse engineering? Start by identifying specific, measurable business problems in which current approaches are not short. Check these problems through strict evaluation, not executive intuition. Rate whether these problems actually require artificial intelligence or could be higher solved with simpler solutions. Consider organizational changes needed to implement any solution. Then and only then assess what data and technology can solve the approved problems.

Better implementation framework

Effective implementation of AI requires a typical approach:

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

2. Check the reality of the data: Audit existing quality processes and data collection before taking the enforceability of AI

3. Simplicity test: Determine if simpler, non -AI approaches can solve the problem more effectively

4. Organizational readiness: Rate whether work flows and teams are prepared to integrate AI solutions

5. Incremental implementation: Start with small pilots focused on narrow, well -defined problems

Training algorithms regarding defective data is like building a house on Quicksand. Architecture could be impeccable, but it won’t matter when all the pieces falls. Companies proudly announce their AI initiatives with the same level of strategic brightness as medieval alchemists with lead transformation into gold. The principal difference is that alchemists spent less money.

Perhaps the Most worthy AI implementation strategy is simply to reverse the query. Instead of asking “How can we use AI?” Try to ask “what specific problems are worth solutions and can AI be the right approach for some of them?” This reframing is not impressive conference channels. It does not generate the same press relationship or speaking nests. But it tends to create solutions that really work, which seems a reasonable goal for technological investments value many hundreds of thousands of dollars.