Too Many AI Tools, No Priority: How SMEs Find Focus

In many companies, AI has long been in use. Marketing writes texts with ChatGPT. Development uses Copilot. Someone in sales tests a tool for offers that no one has approved. Each department has its own access, its own budget, and its own idea of what AI should achieve.

The result is dispersion. Many AI tools in SMEs, but no common priority. Those who want to maintain an overview don't ask about the next tool first, but about the order.

This article shows why AI tools grow without a plan, what this proliferation specifically costs, and how to set priorities that stick. You'll get three critical questions, a cleanup procedure, and a list of common mistakes.

Why AI Tools Proliferate Without a Plan

The barrier to entry is low. A tool costs 20 euros a month, is ready to go in five minutes, and immediately solves a specific problem for a team. This is precisely what makes proliferation so easy.

Three drivers work together:

1. Self-service. Anyone can subscribe to an AI tool with a credit card, without IT, without approval. Traditional procurement is bypassed.
2. Silo mentality. Each team optimizes for itself. This makes sense on a small scale but creates isolated solutions that no one integrates.
3. Pressure for speed. "We need to do something with AI" leads to many small starts instead of a decision on where to begin.

What's missing is a single point of responsibility for the overall picture. No one sees where tools overlap, where data is flowing out, and where two teams are solving the same problem twice. After a few months, the company has a dozen isolated solutions whose benefits no one can articulate.

What Tool Proliferation Specifically Costs

The damage remains invisible for a long time because the individual amounts are small. In total, four cost blocks arise.

Money. Many small subscriptions add up. Plus duplications: two departments pay for tools that can do the same thing. Without an overview, the company pays for functions multiple times.

Data Risk and Shadow AI. If customer data or internal documents are fed into unapproved tools, a data protection problem arises, often only noticed when an incident occurs. "Shadow AI" means AI usage that management is unaware of.

Compliance. The EU AI Act sets requirements depending on a system's intended purpose. Those who don't know which AI applications are running in-house cannot fulfill their obligations. An inventory is a prerequisite for any assessment.

Lack of Impact. Without a goal and without a responsible party, no one measures the contribution. To the question of what AI delivered in the last quarter, there is then no reliable answer, only activity.

What's Missing Is an Operating Logic for AI

Tool chaos is rarely a tool problem. What's missing is the framework within which tools can be classified at all.

AI transformation needs this framework. At 6Rocks, it's described by six foundational pillars that support every AI strategy: Vision, Governance, Organization, Data, Technology, and Iteration. First come the goal, responsibility, and data foundation. Tool selection belongs to the Technology pillar and comes thereafter.

This order sounds unspectacular. However, it decides whether AI contributes to a business goal or dissipates as a collection of individual attempts. A tool without responsibility and without a clear goal produces activity, not a measurable contribution.

Three Questions Before Every New AI Tool

Before another tool enters the company, answer these three questions.

1. Which business problem does it solve? Not "it saves time," but specifically: which process, which department, what measurable effect. If the answer remains vague, the use case is missing.

2. Who is responsible for its use and data? A named person. They decide on approval, check data flows, and are the point of contact for questions. Without a name, there is no responsibility.

3. Does it fit the strategy and existing inventory? Is there already a tool for this purpose? Does it overlap with one used by another team? Does it contribute to a goal that matters this year?

Anyone who cannot clearly answer these three questions doesn't have a use case yet, but an idea. That's fine, but it belongs on a list, not immediately in the budget.

A Practical Example

This is the typical scenario we repeatedly encounter in discussions. An SME with around 120 employees discovers: seven different AI tools are running in three departments, no one has an overview, and management cannot quantify the benefit.

The first step is an inventory. The list shows: two tools do practically the same thing, a third processes customer data without approval. Afterward, the applications are sorted by impact and effort. Two clearly contribute to a business goal, such as faster proposals. They get a responsible party and a budget. The rest are consolidated or terminated.

The result is not a larger tool park, but a smaller one with clear responsibilities. The impact becomes measurable, data risk decreases, and new tools now go through an approval gate.

How to Consolidate Existing Initiatives

If the tools are already in place, start with an inventory.

List all AI applications: tool, department, purpose, cost, and what data flows into them. A simple table is sufficient. This step alone reveals duplications and open data risks.

Then, prioritize by impact and effort. Which two or three applications contribute most strongly to a business goal? These receive responsibility, budget, and attention. The rest are consolidated, paused, or consciously terminated.

Finally, establish an approval gate: New tools only enter the budget once the three critical questions have been answered. A named person makes the decision. This keeps the list short and manageable.

The result is a clear order, not a catalog of everything at once. Focus means consciously doing some things later.

Common Mistakes

• Tool first, goal later. The tool is purchased before it's clear what problem it solves.
• No ownership. No one is named as responsible, so no one checks benefits and data flows.
• Customer data in unapproved tools. The biggest and most common data risk.
• Everything at once. Ten initiatives in parallel, none with full attention.
• Impact never measured. Without a goal, the contribution cannot be proven, and the budget remains vulnerable.

What You Should Do Specifically

1. This week: Create a list of all AI tools in use, including department, purpose, and costs.
2. Next week: For each tool, mark which business problem it solves and who is responsible for it. What remains open is a candidate for removal.
3. Afterward: Select the two to three applications with the greatest contribution and give them clear ownership.
4. Ongoing: Implement an approval gate. New tools only enter the budget after the three questions have been answered.

Guiding questions for the round with your team: Where does our data flow? What goal are we pursuing with AI this year? And which three initiatives bring us closest to that?