The AI Productivity Paradox: How to Evaluate AI Initiatives

We’re seeing AI being adopted across companies of all sizes. Inside these companies, there is a growing sense that AI tools and agents are increasing internal productivity. Yet customers often see no difference. This is one of the paradoxes we are seeing today.

The request workflow looks great, answers reach the right people within minutes, and communication overhead has been reduced by streamlining roles. Every process has a dashboard with beautiful monitoring charts. Yet customer satisfaction and retention are not improving.

A good way to describe this activity might be: the team has become faster at treading water. The “AI Productivity Paradox” is being discussed at different levels.

What We Focus On Is What We Get

One possible reason for poor results is the business focus. For example, a company may believe that responding quickly to a user’s request is important and use AI for this purpose. But it could also focus on how many user problems are solved on the first try.

The situation is similar, but the focus is different. In the first case, we measure output — how fast the process runs. In the second, we measure outcome — whether the customer’s problem actually goes away. And to get stable results in the second case, simply adding AI is no longer enough.

“Speeding up responses to user requests” or “Improving the quality of responses to user requests.”

We can imagine another situation where a monitoring service is in place and AI helps resolve problems faster. But a more mature solution might be to predict these problems and prevent them before an incident happens.

“Accelerating incident resolution” or “Preventing incidents and resolving them proactively.”

A Framework for AI Initiatives

The result of a task often depends on how we approach it. So why not create a simple framework to help us decide which AI initiative to implement?

I suggest using a simple two-dimensional matrix. On one axis, we measure the value of the solution for the company’s operational efficiency. On the other, we measure the additional value it creates for customers.

On the company performance axis (X), we look at how much of the company’s costs the initiative affects and by how much. For example, if a task takes 5% of a project’s time, speeding it up several times still gives a low score on this axis.

Anchors for the company performance axis:

• 0–2. The effect is almost zero or even negative. Either the task is very small, or the initiative creates more work than it saves. For example, productivity monitoring or soft skills training.
• 3. There are some savings, but only in a small area. For one person or a small task. Wiki pages, offline A/B simulations.
• 4–5. Noticeable savings for one function. Routine tasks become easier, but some manual work is still needed.
• 6–7. A strong effect across a whole area of work. The task itself does not change; the way the team works changes.
• 8–9. The cost structure changes. For example, a large routine operation is automated with AI.
• 10. A whole class of costs disappears completely. It is hard to imagine what this could be.

On the customer value axis (Y), we look at changes that customers can notice: faster feature releases, better quality and stability, higher availability, lower prices, and so on. To do this, we need to define how the initiative creates value for customers.

Anchors for the customer value axis:

• 0–2. Customers see no difference. For example, timesheets, internal regulations, automatic file tagging.
• 3. The mechanism is in place, but the effect is almost invisible. Releases are slightly faster; documentation is slightly better.
• 4–5. Customers will notice, but not immediately and not always. Fewer release failures, faster recovery after an outage.
• 6–7. A noticeable improvement in a specific situation. The service remains available during a DoS attack, AI-powered accessibility audits, system localization into the user’s language.
• 8–9. The customer experience itself changes. Personalized recommendations, proactive service, finding a more affordable plan, and a concierge to help with complex decisions.
• 10. New features appear that would not exist without AI.

If we look at the resulting matrix (Fig. 1), we can identify four quadrants:

• Transformation. The impact is felt both inside the company and by the customer. Ideal, but difficult.
• Operational efficiency. There is a noticeable impact inside the company, but little impact for customers. This is where it is easiest to calculate the return on AI investment.
• Customer value. Customers benefit the most here. For companies, investments in these areas may look inefficient. But they can help retain customers.
• Low impact. These initiatives have little impact. Some may also look impressive in demos and presentations but have little effect even inside the company.

We can also identify specific zones within the quadrants.

Customer value at the company’s expense. Customers benefit significantly, while the company pays for it through lower operational efficiency or revenue. For example, if we create a support service, a guaranteed handoff to a human agent as soon as the customer asks will hurt many automation metrics. However, such initiatives may be justified by delayed effects, such as higher retention, or by regulatory requirements.

Value stays inside the company. These initiatives focus on operational efficiency. They are expected to create real and measurable savings, but customers simply will not notice them. These savings can become customer value if they reduce customer costs or if the freed-up resources are invested in creating additional value. The second option is preferable because it allows the company to increase productivity without reducing revenue.

Treading water. Here, the impact is small for everyone. Some initiatives may look effective for the company, but their actual impact on productivity is negligible.

I suspect that many corporate AI initiatives are linked to solutions that look impressive on the surface but are not very productive. At the same time, many small solutions and automations fall into this category only because their scope is limited. They can still provide local benefits.

Personal tools that people create to automate their own work can also fall into this category. For example, I created a Chrome extension for myself that uses AI to check spelling, and I use it regularly. However, the overall impact is very small because it affects only one person.

Ultimately, AI initiatives can be placed on the map. The process of thinking about where to place an initiative also helps us understand whether the chosen task is worth working on.

As an example, I’ve placed four ideas on this diagram. Because this framework involves judgment, people may disagree about where each initiative belongs. But even intuitively, it is clear that they cannot all be in the upper-right quadrant. Not all of them can even reach a significant level of customer benefit.

• Contracts and rates explained in plain language. A low X score because every rephrasing must be checked against the original legal wording. Otherwise, simplification may change the meaning of the obligation. A high Y score because it affects all customers, not just one segment, and addresses situations where a lack of understanding can cost money.
• AI analysis of customer feedback. X is high because manually reviewing feedback, inquiries, and interviews takes a lot of time, and the amount of material is almost unlimited. Y is high if the insights that are found are actually used.
• Document generation and summarization. X is high because this applies to almost all roles. Y is limited because the documents remain internal.
• AI scoring of the backlog using RICE and ICE. The scores are low on both axes. Prioritization takes little time, so there is almost nothing to save. The illusion of objectivity created by using AI in this case may be more dangerous than the time lost through manual prioritization.

This is only a model. It is a rough guide, but it can help identify which AI initiatives create opportunities for company growth and which are just box-ticking exercises. By creating new value for customers, we are also creating opportunities for growth.

I’ve posted an expanded example of this matrix on GitHub at https://podluzny.github.io/ai-initiatives-map/?lang=en, where you can also find the repository.

In Conclusion

In my view, the challenges of implementing AI effectively in companies are largely caused by a lack of focus on meaningful goals. For most companies, these goals will be connected to creating additional value for customers.

When AI can create this value while also improving internal efficiency, this is where the point of transformation appears. It can create a growing return on AI investment. Otherwise, a company may spend a long time treading water, creating only the illusion of moving forward.

The AI Productivity Paradox: How to Evaluate AI Initiatives was originally published in Bootcamp on Medium, where people are continuing the conversation by highlighting and responding to this story.