The hard part of applying AI is rarely the model; it is choosing a problem where AI genuinely helps and then proving it before you bet on it. This play is a structured method for finding where AI creates value, prioritizing the candidates honestly, and de-risking the winner with a pilot instead of a big-bang commitment.

When to use this play#

Use it when there is appetite to apply AI but no specific, validated use case yet, which is the moment teams most often pick the flashy option over the valuable one. It works whether you are scanning a whole organization for opportunities or evaluating a single proposed use case against the bar.

Match the technology to the task first#

Each AI technique has its own strengths and limits, and the most common early mistake is starting from a technique you find exciting and hunting for somewhere to apply it. Reverse that. Start from the task, understand what it actually requires, and choose the technique that fits. A capable technique pointed at the wrong task produces an impressive demo and no value.

How to run it#

1. Match the technology to the task. Understand the task's real requirements, then select the technique whose strengths fit. Respect each technique's limits.

2. Identify opportunities. Look in three places where AI tends to pay off: cognitive applications, knowledge that is siloed or has never been scaled, and analysis-heavy work that consumes human attention.

3. Prioritize with four questions. For each candidate, ask:

• How critical is it to the strategy?
• How hard is it to implement?
• Is the benefit worth the effort?
• Which cases give the best value in both the short and the long term?

4. Launch a pilot. Take the top candidate and prove it small before committing. The pilot exists to surface the data, integration, and capability problems while they are still cheap to fix.

The three capability types#

It helps to recognize which kind of capability a use case needs. Adapt the examples to your domain:

• Insights through data analysis — sentiment from reviews, purchase prediction, fraud detection, risk modeling.
• Engaging customers and employees — chatbots, recommendations, around-the-clock support, self-service, onboarding.
• Process automation — routing and optimization, extracting information from documents, moving data between systems, back-office tasks, summarizing reports, drafting communications.

Implementation challenges to plan for#

A use case that looks clean on a slide still has to clear these:

• Picking the right solution among several plausible ones.
• A data strategy: what data, how much, how it is collected, stored, accessed, and who owns it.
• Building the team.
• Training and testing the model.
• Integrating into day-to-day operations.
• Monitoring and evaluating it once it is live.
• Continuous improvement over time.

Common pitfalls#

• Insufficient or low-quality data. The most common reason promising use cases stall. No model overcomes data that is not there.
• Outdated infrastructure. A use case that the surrounding systems cannot support is a use case that will not ship.
• Integration difficulty. Standing up a model is often easier than wiring it into the workflow it is supposed to improve.
• Lack of AI talent. Without people who can build and maintain it, even a validated use case stalls.
• Overestimating the system's capability. Expecting more than the technique can deliver leads to disappointment and abandoned projects.
• Underestimating cost. The model is rarely the expensive part; data, integration, and ongoing operation are.

Signals it's working#

• Use cases are chosen because the task needs them, not because a technique is fashionable.
• The four prioritization questions actually eliminate candidates rather than rubber-stamping the favorite.
• The pilot surfaces a data or integration problem early, while it is still cheap to address.
• The opportunities you pursue cluster in cognitive, siloed-knowledge, and analysis-heavy work, where AI genuinely earns its place.