Starting with the tool instead of the problem.    
The rapid “AI-ification” of the business landscape has created a paradox. While approximately 88% of companies report* regular AI use, many organisations are discovering that AI alone does not guarantee results.
The root of this frustration often lies in a “solution-first” approach.There is a telling phrase that has become increasingly common in leadership conversations about AI: “We need to be using AI — we just need to figure out what we’re using it for.” In these situations, the solution is defined first, and only afterwards do organisations attempt to identify problems that might justify its use.   However, at its core, AI for growth must start with a business problem, not a technology.

Strategy before software

Being “AI-first” does not mean solving every problem with artificial intelligence. It means conducting rigorous operational design first, identifying where the true bottlenecks exist, and then finding the tools that amplify a process that already works.   AI is not a business strategy in itself; it is a catalyst for executing an existing business strategy faster and more effectively. When organizations shop for solutions before diagnosing the problem, they risk “doing things at scale” that don’t actually solve fundamental business constraints.   To determine if an AI solution is truly aligned with growth, leaders should be able to articulate:  
  • The problem: The specific bottleneck inhibiting the brand from reaching its goals.
  • The SMART outcome: The measurable result expected once the constraint is removed.
  • The solution criteria: How the specific outputs of an AI tool will resolve the problem, and the metrics used to judge success.
Successful AI adoption is fundamentally a leadership and change challenge. Technology alone does not drive adoption; people do. For AI to move to embedded capability it must be integrated into existing systems, data, and workflows in a way that teams find practical and usable.

Performance gap

A prime example of solving a specific business problem rather than just “buying a tool” can be found in how modern revenue teams approach skill gaps. To solve the problem of inconsistent sales execution and slow ramp-up times, companies are increasingly turning to specialized adaptive training platforms. We’ve recently been working with one called Deelan. They do it differently. By converting static playbooks into interactive, AI-powered learning paths, it enables reps to practice in an adaptive environment. It solves a specific operational problem – training at scale – ensuring that the technology serves the performance goal, rather than the other way around. As you may know, ‘scale’ is a real thing we’re passion about here at Meres Consult.

Designing for impact

The current winners in the AI race are not those with the most tools, but those with the clearest diagnosis of their own operational friction. By focusing on business fundamentals – designing the operation, finding the bottleneck, and solving the constraint – AI becomes a powerful lever for growth rather than a source of frustration.  

Your actionable advice

To achieve real return on investment, the goal must be to find the problem first, then choose the tool that amplifies the solution.  
* McKinsey - The State of AI