Which questions should executives ask about an AI opportunity

When deciding which questions should executives ask when evaluating an AI opportunity, I recommend starting with the business decision, not the technology. An AI initiative is worth pursuing only when it improves a measurable outcome, fits the organization’s operating reality, and can be controlled when it fails. A polished demonstration is not enough.

The strongest evaluation process tests five things at once: strategic value, workflow fit, data and evaluation readiness, failure economics, and the organization’s ability to adopt and govern the system over time. That approach helps leaders distinguish useful AI from expensive experimentation.

1. Define The Decision And Business Case
2. Test Data, Workflow, And Operating Readiness
3. Evaluate Risk, Control, And Long Term Economics
4. Key Takeaways
5. Frequently Asked Questions
6. Sources And References

Define The Decision And Business Case

Ask What Specific Decision Or Workflow Will Improve

The first executive question is simple: What exact decision, action, or workflow will this AI system improve?

“Improve customer service” is not a decision-specific hypothesis. “Help claims specialists identify missing documentation before a claim enters manual review” is. It identifies the user, the intervention, the workflow point, and the expected operational effect.

I recommend requiring every proposal to state five elements:

• The user who will rely on the system

• The specific decision, recommendation, or action the system affects

• The current process and its constraints

• The expected behavioral change

• The measurable business outcome

For example, an AI assistant that summarizes customer calls may reduce note-taking time. That is automation value. A system that flags likely churn risks for account managers may improve prioritization. That is augmentation value. A pricing engine that improves margin decisions may create decision-quality value.

These are not interchangeable. Labor savings, capacity creation, revenue growth, risk reduction, and faster decisions each require different proof. A system can be technically accurate but commercially weak if it addresses a low-value decision.

Tandem Coach’s executive evaluation framework similarly emphasizes identifying the exact workflow or decision that improves, its current economics, and the reason non-AI approaches have not already solved the problem.

Ask Whether AI Is Actually Necessary

Not every problem with data is an AI problem. Before approving an AI solution, ask: Could process redesign, better data entry, standard software, or rules-based automation solve most of the issue?

This question protects the organization from deploying probabilistic technology where deterministic logic is more reliable and cheaper to maintain. If a compliance workflow has fixed thresholds, clear exceptions, and stable decision rules, conventional automation may be the better investment.

AI becomes more compelling when the workflow involves ambiguity, high volumes of unstructured content, changing patterns, prediction, classification, or natural-language interaction. Consider an anti-money-laundering review process. Fixed rules may detect known patterns, while AI may help investigators prioritize unusual behavior across complex transaction histories. Even then, the AI should complement clear controls rather than replace them blindly. Organizations assessing this type of regulated use case should examine AI-powered AML compliance tools through the same lens: what is automated, what remains a human judgment, and what evidence supports each escalation.

A useful comparison is below.

Option	Best Fit	Primary Strength	Primary Limitation
Process redesign	Steps are redundant or approvals are unclear	Reduces complexity at the source	May not solve judgment-intensive work
Rules-based automation	Logic is stable and explicit	Predictable and auditable	Struggles with ambiguity and exceptions
AI augmentation	People need faster analysis or recommendations	Improves capacity and consistency	Requires evaluation, monitoring, and oversight
Autonomous AI action	Decisions are repeatable and reversible	Can reduce cycle time substantially	Raises the highest governance and failure risk

DECODE’s opportunity assessment guidance supports this comparison: leaders should quantify current process cost, review data and systems, test rules-based alternatives, examine error handling, and define pilot stop conditions.

Ask How Value Will Be Measured

A business case needs a baseline before it needs an ROI percentage. Ask: What does the current process cost in time, money, errors, delay, lost revenue, or exposure? Then ask who owns that measurement and how frequently it will be updated.

Use operational measures, not vague promises. Depending on the use case, relevant measures may include:

• Cost per transaction, case, ticket, or review

• Average handling time and queue age

• Error, rework, escalation, or exception rates

• Conversion, retention, margin, or fraud-loss outcomes

• Compliance review time and audit findings

• Service-level performance and customer abandonment

The causal path matters. If an AI copilot saves analysts ten minutes per case but analysts cannot process additional cases because approvals remain the bottleneck, the projected labor benefit may not materialize. The value may instead be better documentation quality, shorter cycle time, or reduced employee strain. Those benefits can be real, but they should be measured honestly.

Test Data, Workflow, And Operating Readiness

Ask Whether The Organization Can Build An Evaluation Set

Data availability is not the same as evaluation readiness. An organization may have millions of documents, calls, transactions, or tickets but still lack a reliable way to determine whether AI outputs are correct.

Ask: Can we create a representative evaluation set before deployment? An evaluation set is a curated group of real examples with expected outcomes, expert judgments, or defined quality criteria. It allows the organization to test performance under its own operating conditions rather than relying only on vendor demonstrations.

For a document-extraction system, the set might include clean PDFs, scanned forms, handwritten notes, multilingual records, edge cases, and low-quality images. For a security triage assistant, it should include routine alerts, known benign events, high-severity incidents, and ambiguous cases.

The test set should reflect the cases that matter most, not merely the cases that are easiest to score. If 95 percent of requests are simple but the remaining 5 percent carry legal, financial, or safety consequences, an average accuracy figure can conceal unacceptable performance.

There is no universal accuracy threshold for AI. A marketing-content assistant may be useful with broad human editing. A system that recommends account freezes or payment actions demands a much tighter error tolerance. Before relying on consequential output, TechTarget’s guidance on verifying AI value recommends validating comparable outcomes, baseline definitions, measurement periods, lifecycle costs, data sources, and controls to audit, restrict, or disable AI-generated actions.

Ask Whether The Whole Workflow Can Support The Model

An AI model is only one component of the operating system. Executives should ask: What has to change before this output can become a reliable business action?

The answer often includes integration, identity management, permissions, user-interface design, exception routing, support ownership, records retention, training, escalation, and monitoring. These requirements can be more difficult than model selection.

Consider an AI system that identifies potentially fraudulent transactions. It may perform well in a test environment but fail to create business value if investigators receive alerts in the wrong system, cannot see the supporting evidence, or lack authority to take action quickly. The model did its job. The operating system did not.

A practical operating-readiness review should identify:

• Systems the AI must read from and write to

• Data provenance, permissions, retention rules, and ownership

• User roles and decision rights

• Exception queues and escalation paths

• Support, monitoring, and maintenance responsibilities

• Model drift indicators and review cadence

This is especially relevant where AI recommendations influence financial or blockchain activity. Audit trail creation for AI systems should be treated as a core operating requirement, not a reporting afterthought. If a recommendation cannot be reconstructed later, governance teams may be unable to determine what data, policy, or approval led to an action.

Ask Whether People Will Use It Under Real Conditions

Projected value should be tested against realistic adoption, not perfect adoption. Ask: What happens if only half of intended users adopt the system, if they bypass it during busy periods, or if review work offsets the time saved?

This is where many business cases become fragile. A helpful AI tool that adds one more screen, one more approval, or one more inconsistent recommendation may quietly lose adoption after launch.

I recommend modeling at least three scenarios:

Scenario	Assumption	Executive Question
Conservative	Limited adoption and high review burden	Does the project still justify its cost?
Expected	Moderate adoption with normal exceptions	What operational benefit is likely?
Upside	Broad adoption and improved process design	What must be true for this result to occur?

For instance, a network operations tool may identify optimization opportunities accurately, but its economic value depends on whether teams trust the recommendations and can apply them without creating new reliability risks. The operating implications of AI-driven network optimization illustrate why technical performance and safe adoption must be evaluated together.

Evaluate Risk, Control, And Long Term Economics

Ask What Failure Costs And Whether It Can Be Contained

Every AI opportunity should have a failure-economics model. Ask: If the system is wrong, how often might it be wrong, how serious is the impact, who detects it, what does recovery cost, and can the action be reversed?

This makes risk concrete. A mistaken draft email is usually easy to catch and reverse. A mistaken credit decision, sanctions alert, medical recommendation, trade, or wallet transaction may have costly or irreversible consequences.

A useful way to frame the question is:

Expected failure exposure = likelihood of error × impact of error × time before detection × recovery difficulty.

This is not a universal financial formula. It is a disciplined way to expose assumptions. If the organization cannot estimate the values with confidence, that uncertainty itself should influence pilot scope.

Containment and reversibility should shape investment choices. Early deployment is safer when the AI recommends rather than acts, operates within low-impact cases, records its reasoning and inputs, and can be disabled quickly. Human-in-the-loop review is appropriate when each recommendation needs approval. Human-on-the-loop supervision may fit high-volume work where people monitor thresholds, trends, and exceptions instead of reviewing every output.

Ask Who Is Accountable For Governance And Decisions

AI governance becomes practical when decision rights are explicit. Ask: Who can approve the use case, validate the evaluation set, override outputs, pause operations, approve model changes, and accept residual risk?

Cross-functional involvement is not bureaucracy for its own sake. The business owner understands value and workflow. Technology teams manage integration and reliability. Security, legal, compliance, privacy, and risk teams assess obligations and exposure. Frontline users determine whether the system works under actual conditions.

Stanford Graduate School of Business’s guidance for senior leaders emphasizes connecting AI investments to business outcomes and cross-functional adoption while addressing bias, privacy, and security risks early with clear accountability.

Governance should also address algorithm bias. The question is not merely whether a model has bias, because any system trained on historical data may inherit uneven patterns. The practical question is whether outcomes differ materially across affected groups, whether the organization can detect that difference, and what corrective action is available.

Ask What The Full Investment And Opportunity Cost Will Be

A low subscription price can conceal a high total cost of ownership. Ask: What will this initiative cost to implement, operate, secure, evaluate, maintain, and eventually replace or exit?

Include licensing, infrastructure, data preparation, integration, security controls, expert review, user training, support, monitoring, vendor management, and change management. Then compare that total with alternatives competing for the same budget and leadership attention.

The critical comparison is not “AI versus doing nothing.” It is often AI versus process simplification, conventional automation, data remediation, additional staff, or a different strategic investment.

Ask vendors for evidence calibrated to your environment:

• Which comparable organizations achieved the claimed outcome?

• What baseline did they use?

• Over what period was the result measured?

• Which implementation and operating costs were included?

• What performance limitations, exclusions, or human-review requirements applied?

• Can the organization export its data, evaluation artifacts, and workflow configuration if it changes vendors?

Vendor lock-in may be acceptable when speed matters and contractual protections are strong. It is less attractive when the system controls critical workflows, proprietary data, or decisions that require long-term flexibility.

Key Takeaways

Use A Decision-Specific Investment Test

The best AI opportunities are not defined by a fashionable model or an impressive demo. They are defined by a clear decision, a measurable baseline, and a credible pathway from system output to business outcome.

Before approving a pilot, I recommend confirming that the organization can answer these questions:

• What decision or workflow changes?

• Why is AI better than redesign or rules-based automation?

• What evidence will prove quality before deployment?

• What happens when the system is wrong?

• Can the system be paused, audited, and contained?

• Who owns adoption, operations, and accountability?

• Does the value remain positive under conservative adoption assumptions?

• Is this the best use of capital and executive attention?

Treat Pilots As Evidence Programs

A pilot should not be a vague trial. It should be an evidence program with scope, owners, evaluation data, success measures, stop criteria, and a scaling decision.

Approve a pilot when uncertainty can be reduced safely. Defer or reject it when the workflow is low value, the data cannot support evaluation, failure is difficult to contain, or a simpler solution produces most of the benefit.

Frequently Asked Questions

What Makes An AI Opportunity Worth Pursuing?

An AI opportunity is worth pursuing when it improves a specific, valuable decision or workflow and the improvement can be measured. It should also have usable data, a viable operating model, acceptable failure economics, and a realistic path to adoption. A technically impressive use case without a credible business pathway is not a strong investment case.

Which Business Problems Are Good Candidates For AI?

Good candidates often involve unstructured information, repeated judgment, pattern detection, forecasting, classification, or large volumes of content that people must review. Avoid AI when the problem is primarily caused by unclear ownership, broken approvals, inconsistent data entry, or rules that conventional software can handle reliably.

How Should Executives Calculate AI ROI?

Start with the current baseline: cost, time, quality, error rates, revenue leakage, delay, or risk exposure. Then estimate the benefit under conservative, expected, and upside adoption scenarios. Subtract full lifecycle costs, including integration, security, data preparation, support, human review, and maintenance. ROI should be revisited after deployment because initial assumptions often change.

How Accurate Does An AI System Need To Be?

There is no universal percentage. Required accuracy depends on the decision’s consequences, the ability to detect mistakes, and the cost of correction. A content assistant can tolerate more errors than a system influencing payments, compliance actions, or customer eligibility. Define quality criteria using representative evaluation cases before deployment.

What Happens When AI Makes A Mistake?

Executives should know who detects the error, how quickly it is detected, who can override the output, how the issue is recorded, and whether the action can be reversed. If those answers are unclear, restrict the system to recommendations or lower-risk cases until controls improve.

Should We Build Or Buy An AI Solution?

Buy when a vendor product closely fits the workflow, can meet security and integration needs, and offers acceptable control and exit options. Build when the decision logic, data, workflow, or competitive differentiation is unique enough that generic tooling creates material limits. In many cases, a hybrid approach is practical: use proven model infrastructure while building the workflow, controls, and integrations around it.

What Should A Pilot Prove Before Scaling?

A pilot should prove more than technical accuracy. It should show that users adopt the system, outputs meet predefined quality criteria, integration works, controls are effective, costs remain within expectations, and the organization can operate the solution responsibly. It should also have stop criteria that prevent sunk-cost escalation.

Sources And References

Executive Strategy And Governance

1. Stanford Graduate School of Business — What Senior Leaders Need to Understand About AI
   https://www.gsb.stanford.edu/exec-ed/difference/what-senior-leaders-need-understand-about-ai-beyond-hype-across-every-function

2. Tandem Coach — AI Opportunity Evaluation Framework for Executives
   https://tandemcoach.co/evaluate-ai-opportunities-framework/

Assessment And Validation

3. DECODE — How to run an AI opportunity assessment in 6 steps
   https://decode.agency/article/how-to-run-an-ai-opportunity-assessment/

4. TechTarget — How to verify the value of AI-powered business analytics
   https://www.techtarget.com/news/How-to-verify-the-value-of-AI-powered-business-analytics/842070/

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