AI makes unclear leadership impossible to hide
Every organization adopting AI says it wants speed. Faster product cycles. Faster engineering. Faster support. Faster analysis. Faster decisions. Faster everything. But AI is not only making work faster. It is making leadership more visible.
Before AI, unclear leadership had room to hide. A weak strategy could sit inside a long roadmap. A bad product instinct could be buried under delivery timelines, dependencies, planning rituals, and resourcing constraints. If something failed, it often took months to know whether the problem was the idea, the execution, the market, or the team.
AI removes a lot of that waiting.
And everyone's racing to ship faster. With AI, this is not a normal horse-race anymore. It is a top-tier F1. When the car gets faster, the driver does not suddenly have more room for vague direction. The opposite happens. Every small mistake matters more. Bad timing, weak coordination, poor judgment, and unclear strategy show up faster because the system is moving too quickly to absorb them.
That is what AI is doing to organizations.
What used to take weeks can now take days. What used to take days can now take hours. Teams can prototype faster, write faster, analyze faster, and ship more than they could before. Leaders look at that speed and expect 10x (even 100x) from their teams. But speed does not replace direction. In F1, you do not win by telling the driver to “go faster.” You win through strategy, timing, coordination, feedback, and knowing when not to push.
AI has handed companies a much faster car. For some, it has not handed them better leadership.
It's like being handed 100k to shop with. Suddenly, you can buy almost anything. But having the budget does not mean you have taste. It does not mean you know what fits, what lasts, what you actually need, or what you should leave on the shelf. When you can buy anything the questions comes, do you need everything?
Product teams are now facing that same problem. Building has become cheaper. The obvious feature requests can be prototyped or shipped faster. The backlog looks more possible than it used to. But that only makes the real leadership question harder: what is actually worth building?
AI can increase output. It can reduce friction. It can make capable teams move faster. But it cannot create leadership clarity. It cannot decide what matters. It cannot fix a culture where teams are expected to execute leadership’s ideas but are not encouraged to challenge them.
1/ Speed exposes unrealistic leadership expectations
AI multiplies output. If a team uses AI, they expect more code, more designs, more campaigns, more analysis, more tickets closed, and more features shipped. On the surface, that expectation makes sense. The tools are faster, so the team should be faster too.
But most organizational waste was never caused by slow typing, slow coding, or slow slide creation. It was caused by unclear priorities, weak decision-making, unresolved tradeoffs, and teams building what was asked instead of what was needed. AI can make execution faster, but faster execution does not fix bad direction (May be you can then use saved time to validate, but how often do organization spend time on validating leadership decisions?). It only gets you to the consequences sooner.
The problem shows up in the gap between ambition and readiness. That is not a tooling problem first. It is a leadership problem. Leaders are investing faster than their organizations can absorb, govern, measure, and trust the change.
2/ Measuring AI usage is a leadership shortcut
Once leaders expect AI to create speed, the next mistake is predictable: they start measuring whether people are using it.
This is understandable. Usage is visible. Adoption dashboards are easy to read. Licenses are easy to count. Lines of AI-generated code look concrete. A leader can point to those numbers and say the organization is changing. But AI usage is not the same as useful work.
You can use AI constantly and still solve the wrong problem. A team can generate more code and still create more rework. A product org can ship more features and still make the customer experience worse. Measuring AI activity is tempting because it gives leadership something simple to manage. It is also dangerous because it turns the tool into the goal.
3/ Layoffs are not an AI strategy
Another leadership mistake is using AI as a clean explanation for headcount cuts. When AI can do things quicker, why need these human employees who takes life-time to do what I wanted? I can just "prompt" it, thinks the most.
AI can absolutely reduce some kinds of work. It can automate repetitive tasks, speed up analysis, improve support workflows, and reduce manual coordination. But replacing people before the organization has proven the capability is not transformation. It is cost-cutting with a better story.
4/ More output is not the same as more impact
The deeper pattern underneath all of this is simple: leaders confuse output with progress. AI makes it easier to produce more. More documents. More prototypes. More code. More analyses. More campaigns. More summaries. More experiments. More everything.
The 100k shopping spree. When you suddenly have more buying power, the bad buyer does not become tasteful. They just buy more. They fill the cart because they can. They mistake abundance for judgment. When you can build more, does more matter?
AI creates the same risk for product and leadership teams. The fact that something can be built quickly makes it feel more reasonable to build. The fact that a feature can be generated, prototyped, tested, and shipped faster makes it easier to avoid asking whether it deserves to exist.
There is also a cost problem. AI is often sold inside companies as a path to efficiency, but the costs do not always disappear. Sometimes they move from salaries to vendors, compute, governance, security, integration, and rework. The shopping spree still has a receipt. The only difference is that the spending has moved to a different line on the budget.
5/ AI should inform leadership, not replace it
The quietest risk is also one of the most corrosive: leaders start outsourcing their own confidence to AI.
This does not usually happen all at once. It starts innocently. A leader asks AI for a second opinion. Then for a summary. Then for a recommendation. Then for a decision memo. Then for the decision itself. Eventually the leader is still sitting in the chair, but the hard part of leadership has moved somewhere else.
AI can absolutely improve decision-making. It can surface patterns, challenge assumptions, pressure-test ideas, summarize customer feedback, and expose blind spots. Used well, it can make leaders sharper. The problem is not asking AI for a read. The problem is treating the read as a verdict.
A leader who lacks clarity can now borrow confidence from a model. A product leader who does not want to sit with uncertainty can ask AI for the answer. A founder who is tired of debate can use AI to validate the thing they already wanted to do. A manager who does not want to push back can let the model become the invisible authority in the room.
And automation bias can be dangerous. People tend to trust automated outputs, especially when the output sounds confident and arrives quickly. In product and strategy work, that can quietly weaken the exact muscles leaders need most: listening, disagreement, synthesis, taste, accountability, and the willingness to make a call without pretending the call is risk-free.
What good AI leadership looks like
Good AI leadership is not anti-AI and my point with this piece isn't against the use of AI.
It starts with clarity. I don't mean to say leaders lack clarity (or else how would they be in that position in the first place?). As I mentioned in my other article, Your effort is the point, shortcutting the decision-making to AI can create problems and is surfacing the leadership issues like never before. Leaders need to be specific about what AI is supposed to improve. Not “productivity.” Not “innovation.” Not “efficiency” as a vague aspiration. The target should be concrete: reduce support resolution time without lowering quality, improve onboarding completion, shorten engineering cycle time while reducing defects, improve research synthesis, reduce manual handoffs, or increase the number of useful experiments that reach customers.
It also requires better measurement. Usage can be an input, but it cannot be the scoreboard. The scoreboard should include quality, workflow speed, customer impact, financial impact, risk, and compliance. Notion’s report shows that more mature organizations move in that direction. They integrate AI with existing systems, build governance and oversight, measure impact with metrics, connect AI to company knowledge, and support recurring workflows.
Good leadership also creates permission to challenge AI-shaped work. If AI generates the plan, someone should still be accountable for questioning it. If AI summarizes user feedback, someone should still check the raw signal. If AI recommends a roadmap decision, the team should still debate what customers actually need.
The point is not to slow everything down. The point is to slow down the decisions where being wrong is expensive. Leadership is the discipline of setting direction, creating focus, making tradeoffs, building trust, measuring what matters, and staying accountable when the tools get faster than the organization’s ability to think.
AI has made that discipline harder to fake.
Final Thoughts
AI is not exposing whether companies can move faster. Many can. It is exposing whether leaders know where they are going.
When execution was slower, unclear leadership had insulation. There was always another planning cycle, another dependency, another delivery timeline, another reason the consequences had not arrived yet. Now the waiting period is shorter. The team can build the thing. The campaign can launch. The feature can ship. The automation can go live. The decision can become real before anyone has fully tested whether it was the right one.
What was always missing is still missing: clarity about what is worth building, what is worth measuring, what is worth automating, and what should remain a human responsibility.
Modern problem requires modern solution, people say. But AI just makes the absence of clear leadership impossible to hide. And leaders are not owning to their bad decisions either. And in most cases, its not the leaders that take the blame for the product failures but the employees because to leaders, they are just as depensable as another tool.