How Leaders Course-Correct AI and Technology Projects

A technology program can be on schedule and still be in trouble.

The team closes tickets. The vendor meets its milestones. The model answers test questions. Cloud resources are provisioned, dashboards are green, and the steering committee sees a reassuring percentage marked complete. Yet users keep working around the product, the expected business result has not appeared, or the original problem has changed while delivery continued.

This is strategic drift: movement without enough evidence that the destination still matters or that the chosen route can reach it.

AI makes drift easier to hide. A polished prototype can create the appearance of progress before the team has tested data quality, workflow adoption, operating cost, failure handling, or actual value. Agentic systems add another layer because completing more steps is not necessarily producing a better outcome. An agent can call tools successfully, generate fluent output, and still make the whole process slower or riskier.

Leaders therefore need more than status reporting. They need a disciplined way to ask for new information, challenge the route, and revise a decision without treating every change as failure. I call that a navigation review.

The navigation review in one page

The review starts with six fields. Each field should fit on one page for a single initiative. If the team needs fifty slides to explain them, the decision is probably not clear enough yet.

This is not another project dashboard. A dashboard describes a system; a navigation review forces a choice. It connects evidence to an explicit decision and makes uncertainty visible before more money and organizational commitment accumulate.

Define progress as a changed condition

Most weak reviews begin with the work completed: integrations built, people trained, documents indexed, prompts written, or features released. These facts matter, but they are inputs and outputs. They do not establish that the initiative has improved anything.

Suppose a company builds an internal support assistant. The delivery measures might include the number of policies indexed, weekly active users, answers generated, and releases shipped. The intended change may be shorter case resolution time without a rise in incorrect guidance. That requires a different set of evidence:

• median and tail resolution time compared with the baseline;
• the percentage of cases resolved without avoidable escalation;
• answer accuracy for high-impact case types;
• employee time spent checking or correcting output;
• cost per successfully resolved case;
• incidents caused or prevented by the new workflow.

The distinction is not that delivery metrics are useless. Leaders need them to understand execution. The problem begins when delivery becomes a substitute for value.

Deloitte’s 2026 Global Technology Leadership Study, based on more than 660 technology leaders, frames the modern technology mandate around enterprise value and measurable business outcomes rather than operational stability alone. That raises the standard for executive reviews. Uptime, velocity, and milestone completion remain necessary, but they cannot be the final answer to whether an investment is working.

An outcome should name a changed condition for a customer, employee, operation, or risk position. “Deploy an AI agent” is an implementation. “Reduce the time required to process routine claims while preserving review for ambiguous cases” is a direction that can be tested.

Write down the assumptions before they disappear

Projects often become harder to challenge after work begins. Early uncertainty gets translated into requirements, requirements become plans, and plans acquire budgets and owners. Six months later, a provisional belief looks like an established fact.

A useful navigation review restores those assumptions to view. For an AI workflow, they may include:

• the necessary data exists and can be used lawfully;
• users experience the problem often enough to change their behavior;
• model quality can meet the threshold for this consequence level;
• integration with the system of record is technically and commercially viable;
• human review is available where automation is not dependable;
• the expected saving remains after inference, monitoring, support, and correction costs;
• the process itself is stable enough to automate.

Every assumption should have an owner, a confidence level, and a test. “Users want it” is not a test. Observed use in a representative workflow, followed by interviews about abandoned or corrected cases, is much stronger. “The model is accurate” is also too broad. A versioned evaluation set covering common cases, edge cases, and high-impact failures gives the claim a boundary.

This is where leaders should be willing to seek expertise without outsourcing judgment. A security specialist may identify an access-control problem. Frontline staff may explain why the proposed handoff does not fit the work. Finance may uncover a cost model that changes at scale. An external adviser may recognize a failed pattern from another implementation. None of these voices should decide automatically, but refusing to ask protects the plan at the expense of the outcome.

Use metrics as a map, not a scoreboard

Metrics distort behavior when they reward a visible activity that is only loosely connected to the goal. More code, more releases, more model calls, more automated cases, and more AI-active employees can all rise while the business result gets worse.

The answer is not one perfect KPI. Complex initiatives need a small measurement chain:

1. Outcome: Did the customer, employee, or operation improve?
2. Adoption: Is the intended group using the changed workflow appropriately?
3. Quality: Does the result meet its functional and safety thresholds?
4. Flow: Where do delay, correction, escalation, or rework accumulate?
5. Economics: What does a successful outcome cost at realistic volume?
6. Exposure: What harm, compliance issue, dependency, or lock-in is increasing?

These measures should be read together. Faster answer generation means little if verification time rises. A lower cost per model call is not a win if weaker answers create more repeat contacts. Higher developer output can be misleading if review queues and production instability grow.

Google’s DORA research on AI-assisted software development describes AI as an amplifier of an organization’s existing strengths and weaknesses. That is a useful warning against interpreting a local speed improvement as system-wide progress. If requirements are unstable, feedback is slow, or quality controls are weak, generating work faster can send more flawed work downstream.

The related note on cross-wired feedback loops in AI teams goes deeper into that systems problem. For the navigation review, the practical rule is simple: pair every speed or volume measure with evidence of outcome, quality, and downstream effect.

Compare routes without defending sunk costs

Once a team has selected a platform or architecture, reviews often shrink to two choices: keep going or admit defeat. That is an unnecessarily poor decision frame.

There are usually several routes to the same business outcome. A customer-service team trying to reduce handling time might improve search, simplify policies, restructure forms, add deterministic workflow rules, use an LLM to draft responses, or automate only classification. A large autonomous agent may not be the best next step. The valuable unit of strategy is the business change, not loyalty to the first technical idea.

At each review, compare at least three credible options:

• continue the current route with a specific reason;
• narrow or redesign the intervention;
• meet the need through a non-AI process or conventional software change;
• pause until a dependency such as data quality is fixed;
• stop because the outcome no longer justifies the cost or exposure.

Evaluate the options against time to evidence, total operating cost, reversibility, user disruption, security, reliability, and opportunity cost. The last factor is often missing. Continuing a mediocre initiative consumes people who cannot work on a better one.

This is also why AI strategy must include choices about what not to build. Stopping is not automatically evidence of poor leadership. Continuing mainly because the organization has already spent money is often worse. A responsible stop preserves learning: document which assumptions failed, what reusable capability remains, and what evidence would justify revisiting the idea.

Set decision boundaries before the review becomes political

Evidence is easier to interpret before it threatens a favored project. Define decision boundaries while the initiative is still small and expectations are flexible.

For example, a team might agree that an AI assistant will not expand beyond a controlled group until it meets quality thresholds on representative cases, shows a reduction in total handling time, keeps correction effort below a limit, and passes security review. It might pause if high-impact errors exceed a threshold or if required data access cannot be controlled. It might stop if user adoption remains weak after the workflow has been redesigned and tested.

Thresholds do not eliminate judgment. Sample sizes can be small, benefits may take time, and some outcomes are hard to quantify. They do, however, prevent every disappointing result from being explained away after the fact.

Microsoft’s current cloud adoption strategy guidance recommends linking objectives to measurable key results, assigning accountability, reviewing KPIs periodically, and adjusting strategy and measurement as conditions change. Its AI planning guidance similarly treats a proof of concept as a way to validate technical feasibility and business value before wider commitment. The important word is validate. A pilot should resolve uncertainty, not merely produce a demonstration.

A decision boundary turns that principle into governance. It tells the team what the experiment is intended to teach and what leaders will do with the result.

Ask for dissent in a form the organization can use

Telling people to “speak up” is not enough. A junior analyst may see weak adoption data but hesitate to challenge an executive sponsor. A product owner may know the workflow has changed but fear being labeled resistant. A technical lead may recognize an architectural limit after committing publicly to the design.

The review should create structured permission for dissent. Ask each accountable group for three short statements:

• the strongest evidence that the current route is working;
• the strongest evidence that it is not;
• the next fact that could change their recommendation.

This format is better than asking whether everyone agrees. It separates evidence from status and gives disagreement a useful shape. Leaders can also invite the person closest to the work to speak before the most senior sponsor frames the answer.

Good advice still needs evaluation. Check whether the adviser understands the outcome, has access to relevant evidence, carries incentives that bias the recommendation, and can explain uncertainty. Asking widely does not mean averaging every opinion. It means improving the information available to the accountable decision maker.

For executive teams that struggle to connect technical evidence to choices, a shared language for technology decisions can make the review much more productive. Outcome, exposure, options, evidence, and ownership provide a compact vocabulary for debate without reducing the issue to red, amber, or green.

Keep the cadence proportional to uncertainty

An annual strategy review is too slow for a young AI initiative, while daily executive intervention will suffocate a stable platform program. Review cadence should follow uncertainty, irreversibility, and consequence.

A small, reversible prototype may need a short review every two weeks because its purpose is rapid learning. A mature service may use monthly operational reviews and quarterly outcome reviews. A high-impact system that can change customer eligibility, move money, expose sensitive data, or take external action needs more frequent control evidence and clear escalation paths.

The cadence should also change over time. Early reviews focus on assumptions and feasibility. Pre-launch reviews focus on quality, security, operating readiness, and human handoffs. Post-launch reviews focus on adoption, real-world failures, cost, and whether the business outcome is appearing. Scale reviews ask whether the controls and economics remain valid at higher volume.

This lifecycle view prevents a common error: using the same status template from idea through production. Different stages require different evidence and different decisions.

Leadership means making revision normal

Teams watch how leaders react when evidence contradicts a plan. If changing course is treated as embarrassment, people will hide ambiguity, soften bad news, and keep delivering against obsolete assumptions. If every setback triggers a strategic reset, they will stop trusting priorities. The aim is neither stubbornness nor constant motion. It is disciplined revision.

That culture begins with language. Say which assumptions have changed. Distinguish a delivery failure from a learning result. Explain why a decision boundary was crossed. Protect people who surfaced useful evidence. Record the decision so the same debate does not restart without new facts.

Shared direction still matters; the companion note on turning AI strategy into an operating signal explains how priorities travel into daily work. Course correction completes that loop. Direction tells teams where to concentrate. Navigation reviews tell leaders whether reality still supports that direction.

A fast-moving program is not necessarily a healthy one. The meaningful test is whether the organization can name the intended change, locate itself with honest evidence, expose its assumptions, compare real alternatives, and act when the facts no longer support the current route.

The strongest technology leaders are not those who never revise a plan. They are the ones who make it safe to discover that a plan needs revision—and who change it before motion becomes waste.