Why traditional efficiency measurement logic does not work with AI
Traditional efficiency metrics measure time, costs and output. Traditional automation thinking often looks like this:
a process used to take 10 minutes
6 minutes after automation
→ Result: 40% faster execution
But this is only part of the truth - and with AI, it's often not even the most important part.
AI rarely replaces entire processes. Instead, it takes over individual activities within processes - especially those that are repetitive, information-intensive or purely preparatory. As a result, AI not only changes processes, but above all
Roles
responsibilities
decision paths
The effect of AI is therefore structural, not linear. And this is precisely why traditional efficiency metrics fail when it comes to evaluating the benefits of AI.
Increasing efficiency through AI: less speed, more clarity
The greatest effect of AI is not just the acceleration of tasks. The reality is more subtle.
Above all, AI reduces
lengthy research and searching for information
repeated queries between teams
Manual summaries and preparatory work
Coordination loops without a clear basis for decision-making
As a result, decisions are prepared more directly - not just processed more quickly. In practice, it has been shown time and again:
Efficiency gains through AI are not only achieved by automating individual steps, but above all by removing unnecessary detours in the flow of information.
👉 Conclusion: AI does not save time. It saves detours that previously delayed decisions.
AI productivity in the company: Value creation starts earlier
A pattern that can be seen across all industries:
Earlier:
Employees prepare
experienced people decide
Today:
AI takes over routine analysis and preparatory work
People can decide earlier
Organizations like Siemens therefore speak not of automation, but of augmentation: AI supplements human work instead of replacing it.
The productivity gain results from the fact that:
less time is needed for preparatory work
Decisions are made earlier in the process
Responsibility is assigned more quickly to where it belongs
This can hardly be measured in hours - but very clearly in the quality of results.
ROI of AI: why figures often send the wrong signal
Of course it is legitimate to talk about the ROI of AI projects - this is also shown by the demand for terms such as measuring AI efficiency.
But when ROI, time savings or percentage figures become the sole basis for decision-making, a problem arises:
Culture cannot be automated
Responsibility cannot be delegated
Decision quality cannot be accelerated without jeopardizing it
Many AI initiatives fail not because of the technology, but because of this reduced expectation. Those who see AI merely as an efficiency booster easily fall into the so-called efficiency trap: saving costs in the short term and losing competitiveness in the long term.
The truly relevant efficiency metric in the age of AI
If you had to name one key performance indicator for the benefits of AI, this would be it:
Time to viable decision.
Not:
Tickets processed per hour
Documents created per day
emails answered per employee
But rather:
How quickly do we understand a situation?
How quickly can we evaluate options?
How quickly can we make an informed decision?
This is where the efficiency gain of AI in the company lies - qualitatively, not quantitatively.
Conclusion: efficiency is the result - not the starting point
It is not wrong to ask about efficiency figures, productivity indicators or ROI. But these questions should not be the starting point.
Instead, the question should be:
Where are we losing time today due to uncertainty?
Where is a lack of access to knowledge blocking decisions?
Where are coordination processes delaying our ability to react?
AI makes organizations more efficient - but not through linear time savings or simple percentages. But through
better access to knowledge
a clearer basis for decision-making
less friction in the system
earlier assumption of responsibility
The greatest increase in efficiency through AI is structural, not statistical - and that is precisely why it is often underestimated.