Artificial intelligence has already become routine at many companies, but one very common mistake still persists: measuring AI success by counting how many tasks were completed.
Sounds logical at first glance, right?
More emails answered, more reports generated, more tickets closed… but that math doesn’t add up when it comes to knowing whether AI is actually making the company run better. Activity volume and result quality are very different things, and confusing the two can make managers think they’re on the right track when, in practice, operations keep stumbling over the same old problems.
What should really be on managers’ radar goes far beyond activity numbers. When AI enters an operation just to speed up repetitive tasks without changing how people think, decide, and collaborate, it becomes an expensive shortcut that doesn’t truly transform anything. As Shafqat Islam, president of Optimizely, pointed out, counting completed tasks is just an activity metric. More output doesn’t tell you whether the team is working better — it just tells you everyone is working more.
The right question isn’t how much the team produced, but how the team is operating:
- Are priorities clearer for everyone?
- Are decisions being made faster?
- Are managers giving more useful feedback?
- Are problems being solved before they become headaches?
If the answer to these questions is still no, the AI might be generating more output without generating better results. And that difference changes everything. 🎯
Output is not the same as outcomes
When a company starts using artificial intelligence in its workflows, the first metric that pops up on dashboards is usually volume: how many interactions were automated, how many documents were processed, how much time was saved on manual tasks. This type of data is easy to pull, easy to present in a meeting, and honestly, very easy to misinterpret. The problem is that volume says nothing about impact. A team can triple the number of reports delivered per week and, at the same time, keep making bad decisions because nobody is reading those reports carefully or because the information in them isn’t reaching the people who need it.
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Islam argues that organizations should connect AI usage to business outcomes, instead of treating hours saved as an end in themselves. If an AI tool reduces administrative effort but has zero effect on revenue, service quality, customer retention, or any other relevant goal, that apparent efficiency gain may have pretty limited value. In other words: saving time is great, but it only makes sense if that time turns into something that actually matters for the business.
A team’s real productivity isn’t measured by how many things it delivers, but by the quality of choices it makes throughout the day. That includes knowing what to prioritize, understanding when to delegate, recognizing when to stop pouring energy into something going nowhere, and having enough clarity on company objectives to make aligned decisions without needing approval at every small step. When AI contributes to that kind of operational maturity, that’s when it’s truly generating value. Otherwise, it’s just speeding up a wheel that was already spinning crooked.
Team consistency also matters a lot here. When AI improves access to information and helps managers communicate priorities, teams should operate with fewer bottlenecks and less variation in how work gets done. Juan Jose Lopez Murphy, data science and AI leader at Globant, explains that speed and quality remain useful indicators, but the strongest signal of value may be increased autonomy within the team. According to him, the hallmark of that value is the level of proactiveness teams show — when they manage to stop merely reacting to shifting contexts and start actively seeking new possibilities for the business.
Measure how work moves
One of the biggest traps in adopting artificial intelligence within companies is treating automation and team performance as synonyms. Automating a task means a process that once depended on a person can now be executed by a machine, faster and with fewer operational errors. That has value, of course. But team performance is a much broader concept.
To evaluate AI honestly, organizations can look at the interactions behind the completed work. Some really useful signals include:
- The time needed to reach a decision;
- The number of clarification cycles required to align priorities;
- The consistency of feedback given by managers;
- The speed at which the team resolves problems.
These measures offer a much better view of organizational friction than raw task volume. A team that delivers the same amount of work but spends significantly less time searching for information, resolving misunderstandings, or waiting for approvals is probably operating more effectively. And it’s precisely this kind of improvement — invisible in traditional numbers — that makes a real difference day to day.
AI should also make managers more available for work that requires judgment and human interaction. If automation handles meeting summaries, status updates, and routine coordination, managers should be left with more time to guide their people, hold one-on-one conversations, and make tough decisions. The quality of those interactions doesn’t fit into a single dashboard number. That’s why companies need to combine quantitative measures, like decision time and resolution speed, with qualitative feedback from employees about clarity, support, and leadership availability.
Lopez Murphy raises an important warning: it’s way too easy to fall into the trap of measuring what can be measured, rather than what is important or intended, confusing proxy indicators with the real thing. This ends up creating metrics that push behaviors in perverse directions. Basically, if you measure the wrong thing, the team starts optimizing for the wrong thing. 💡
Establish a baseline before anything else
Even when team performance improves, companies shouldn’t automatically credit that improvement to AI. Team changes, workload variations, new leadership, and redesigned processes can all influence results. That’s why it’s critical to establish a baseline before introducing artificial intelligence — isolate a specific workflow or team and compare performance with a similar group, whenever possible. Then, just track the same measures after implementation, accounting for other changes happening in parallel.
Islam sums up this approach pretty directly: treat it like a lab. Set a clear performance baseline before introducing AI, so you don’t credit it with improvements that would have happened anyway. It’s simple advice, but a lot of companies skip it in the rush to show quick results.
This evaluation also needs to distinguish between an exceptional individual workflow and a repeatable organizational capability. A technically skilled employee might build an advanced AI process that produces impressive results, but that doesn’t mean other teams have the context, skills, or connected systems needed to replicate it. What works in the hands of a specialist doesn’t always scale to the rest of the organization.
It’s also worth noting that AI adoption can expose broken processes or unclear responsibilities. In these cases, the organizational changes made during implementation may deliver more value than the technology itself. That’s still a good outcome, but leaders need to identify the true source of the improvement. Lopez Murphy warns against the temptation to force a complex transformation into an oversimplified return-on-investment narrative. Teams need time to adapt, technologies keep evolving, and performance may even dip temporarily before it gets better. In his words, the pressure to make everything so simple that any board can understand it in five minutes is a recipe for corporate hallucination.
Watch out for false gains
Individual productivity can increase while team performance gets worse, and this is more common than it seems. Some warning signs worth paying attention to:
- More revisions and rework;
- Duplicated work across different people;
- Handoffs that fall through the cracks;
- Decisions made in isolation;
- Growing time spent verifying what the AI generated.
People’s behavior also reveals problems. Fewer check-ins with the manager, declining participation in team discussions, and more after-hours work can indicate that AI is accelerating the pace of work without improving coordination or well-being. Islam is clear on this point: if AI is making individuals more productive but creating more confusion for everyone else, that’s a red flag.
If there’s one thing artificial intelligence still can’t do well, it’s delivering quality managerial feedback. And it’s not for lack of data. The problem is that real feedback isn’t just about measurable performance. It’s about perception, context, relationships, and human development. That’s why managers need to be especially careful not to use AI as a substitute for conversations. Automatically generated feedback can be useful as preparation, but people development, conflict resolution, and sensitive performance conversations still depend on context and trust.
What AI can do — and do very well — is set the stage for that managerial feedback to be richer and more frequent. As Islam puts it, the whole point is to eliminate busywork so the conversations that matter happen more often, instead of getting pushed to the sidelines. Imagine a manager who, instead of spending hours trying to remember what each team member delivered last month, has access to a clear, contextualized summary of each person’s work. With that kind of support, they walk into the conversation much more prepared, able to be more specific and more empathetic. That’s the kind of artificial intelligence use that truly changes a team’s dynamic. 🚀
Start by piloting with leadership
Organizations should start with a well-defined managerial behavior or workflow, instead of spreading AI everywhere and then scrambling to figure out where the value is. A pilot can test, for example, whether AI helps managers give more consistent feedback, spot emerging problems, or resolve issues faster. The company measures performance before and after implementation and combines operational indicators with employee feedback.
Lopez Murphy notes that some companies may benefit from limiting the pilot by activity rather than user group — making a specific capability available across the entire organization so different teams can discover effective ways to use it. The ultimate goal is always the same: understanding whether managers are leading more effectively and whether teams are performing better because of it.
At the end of the day, the metric that matters is simple to state but tough to measure honestly: is the company doing better because of AI, or is it just busier? Real business outcomes show up in the quality of deliverables to customers, in how fast the company responds to market changes, and in the strategic clarity that reaches operational teams. It’s this combination of well-applied technology, present leadership, and consistent managerial feedback that transforms AI from a corporate buzzword into a real and sustainable competitive advantage.
Islam wraps it up with a line that says it all: if your only improvement is faster task completion, you measured efficiency. If managers make better decisions and their teams perform better, then you measured leadership. ✅
