Q838

When AI becomes part of decision-making and operations, leaders often continue with the same habits — excessive reviews, manual overrides, intuition-first decisions, or micromanagement — even when those behaviors start reducing the value of AI.

Think of a specific leadership or managerial activity in your domain (approvals, reviews, performance checks, decision forums, etc.).
What is one thing leaders should consciously stop doing once AI is introduced into that process — and why does holding on to it limit AI’s effectiveness?

⚠️ Any answer that is generic or does not connect with a specific leadership activity or process will not be approved.

🏆 The best answer will be selected on the basis of:

• Relevance of the leadership behavior identified

• Depth of insight into why it becomes counterproductive

• Practicality of the behavioral shift suggested

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For state governments, AI is showing up in important spots that deal with the public a lot – like transport, health, schools, and courts. The biggest worry isn't using AI, but when leaders try to manage it with the same old government habits. AI changes how advice is given, decisions are reached, and services are delivered.

 

1.  Don't treat AI like it's just another IT project. Things often get handled like this:

a)      IT or a special department takes charge.

b)     It gets tested in a lab, but no one really owns it.

c)      This doesn't work with AI. AI changes:

a.      Who gets what benefits.

b.      How rules are enforced.

c.      How cases are handled.

d.      The advice given to officials.

        If departments aren't responsible, problems go straight to the top. You can't just push responsibility away.

2.  Don't wait for everyone to agree before starting. State governments usually want:

a)      Everyone on the same system.

b)     The same rules for everyone.

c)      Everyone to agree.

d)     But with AI, waiting means missing chances to help people. Don't wait until:

a.      Every department is ready.

b.      All the paperwork is the same.

c.      There's one system for everything.

        Instead, start small, with projects that:

a)      Are legal.

b)     Have someone in charge.

c)      Can help make better rules later.

3.  Don't just throw AI into tasks that affect citizens. Mistakes in state government have real consequences:

a)      Benefits can be messed up.

b)     People can get unfair fines.

c)      Access to health, housing, or schools can be blocked.

d)     Don't assume AI equals automation. The first wins come from:

 

e)      Helping caseworkers make choices.

f)        Deciding which inspections are most important.

g)      Spotting patterns in complaints.

        Having a human review thing isn't just a temporary thing, it's how things should be done when AI has a big impact.

4.  Pay attention to laws and rules. State agencies have to follow:

a)      Specific laws.

b)     Who has the authority to decide things.

c)      Fairness rules.

d)     Don't approve AI projects without figuring out:

a.      Who is legally responsible for decisions.

b.      How AI's suggestions are written down.

c.      How people can challenge the outcome.

        If you don't answer these questions, AI brings legal trouble, not progress.

5.  Don't expect AI to fix old, bad processes. State government processes are often based on:

a)      Old decisions.

b)     Budget problems.

c)      Old tech, not what people need.

        AI won't solve this – it makes it worse. Don't approve AI solutions if you have:

a)      Confusing rules.

b)     Data that doesn't match up.

c)      Services that are all over the place.

        Fix the process first, then add AI.

6.  Don't expect definite answers when AI gives probabilities. State government advice usually wants:

a)      Clear answers.

b)     Confidence for officials.

c)      Simple choices.

        AI doesn't work like that. If you demand certainty from AI, you might:

a)      Rely too much on the models.

b)     Not write down assumptions.

c)      Not be transparent when decisions are questioned.

        Instead, get used to:

 

a.      Possible answers.

b.      Knowing the limits.

c.      Watching and adjusting things.

        One Last Thought: In state government, AI is about more than just saving time, it’s about public trust. Leaders need to stop thinking of AI like:

 

a.      Another rollout of new software

b.      Just a policy

c.      Someone else’s problem

 

State governments that get get it right don’t rush; they actually move more carefully, because when they know who is responsible and trust goes up!

Domain: Aerospace subcontract precision machining shop

(Turnover:-  €85Million, employee Approx 420, make-to-print for Airbus, Boeing, Safran. Zero defects drive, tight margins).

Specific leadership activity: Manual final sign-off of AI-generated quotes and bid/no-bid decisions.

This represents the "last mile" in which the pricing lead / Commercial director manually evaluates all quotes with a Life time Value over €100k and decided to Approve, Adjust Margin, or Reject the bid. It’s the same way it’s been done for the past 20+ years: "I want to see it with my own eyes before we sign off on a 5-to-10 year deal."

The thing leaders should deliberately decide not to do once AI is involved in the quoting cycle:

Leaders should no longer manually review line by line and make adjustments on every quote from AI generated quote.

Why holding on to it, kills AI’s effectiveness

It effects velocity

The greatest benefit of the AI is the ability to make same-day decisions from quotes that used to tale 10-14 days. If the director wants the AI to change its margin, in this case, by 0.5% because it feels it’s risky, all the benefits will be lost. Customers will once again have to wait for 3-5 days, Customers notice and competitors can win our deals.

It erodes trust in the system

Because the AI system is seen as “just a suggestion” since the boss always overrides it, use is reduced – why put the time into perfecting input when the human input will just override it anyway? There is never actual learning feedback for AI model, because the overrides do not become “learning signals”.

It generates Inconsistent Decisions

One week the director is aggressive on margins (win at all costs), the next week the director is conservative on margins (protect cash flow). The AI system is consistent (historical data plus current capacity). Then there’s the unpredictable element of human whim, and this confuses the model and hurts margins.

It wastes expensive brainpower

The 180k€+/yr director spending 2-3 hrs/day on reviewing standard quotes has a cost of opportunity. This could be used for searching for new customers, negotiating contracts, or addressing actual bottlenecks.

What we actually did (and it worked)

We established a tough rule in Q3 2025 that:

·       Predictions that have risk score <30% & Margin is within target range (within ±2% of historical average) → auto-approved. There is no human intervention.

·       Outside this range → send into director’s office for review no longer than 15 minutes.

·       All overrides need to be documented with reason code, → helps to train the model every month.

Outcome after 6 months:

·       Director review time reduced by 85%

·       Quote Velocity up, win rate remains stable

·       Margin variance decreased (less human mood swings)

·       Team defends the AI system because “the boss believes it more than his own intuition about everyday matters.”

Bottom line from the commercial office

Once AI nets input on routine volume decisions, leaders must avoid fallaciously prefacing every decision as “my final say.”

Holding on to the old human approval ritual, will turns AI from a force multiplier into an expensive suggestion box.

The shift that we follow is not about giving up control, it’s about moving control to where it matters the most: exceptions, strategy and exceptions only.

When the boss stops micromanaging the middle 80% and starts owning the risky 20%, that’s when AI starts paying real dividends.

Evaluation Result – Q838

🏆 Best Answer: Adil Khan

A highly effective response anchored in a very specific leadership activity: manual final sign-off of AI-generated quotes. The answer clearly shows what leaders must stop doing (line-by-line overrides), why it destroys AI value (velocity loss, inconsistency, erosion of trust), and what to do instead (clear auto-approval thresholds + documented exceptions). Strong business logic, concrete outcomes, and a practical behavioral shift make this a textbook example of leadership change after AI adoption.

✅ Approved: Rakshika

Thoughtful and relevant to state government leadership contexts, with strong insight into governance, accountability, and public trust. However, the response addresses multiple leadership behaviors rather than clearly isolating one specific activity leaders should stop, which slightly dilutes focus against the question’s intent.