Finding the Right AI Opportunities

Every credible AI opportunity should connect to an observable business objective. That objective gives the team a reason to invest, a way to prioritize competing ideas and a basis for measuring whether the eventual solution creates value.

Avoid objectives such as “use generative AI” or “deploy agents.” Those describe technologies, not outcomes. Ask what must become faster, better, safer, less expensive or more scalable.

View the organization as a system rather than a collection of departments. Follow the flow from suppliers and partners through the enterprise to customers and stakeholders.

Look for delays, rework, information gaps, handoffs, capacity constraints, service failures and decisions that materially affect the objective.

Identify processes that consume significant time, generate frequent exceptions, depend on specialized knowledge or constrain throughput.

Processes are particularly interesting when conventional automation has struggled because the work contains judgment, ambiguity, unstructured information or cross-system investigation.

A process map tells you what happens. A decision map explains why work moves in one direction rather than another. Intelligent systems create value when they improve the quality, speed or consistency of these decisions.

For each important decision, document the trigger, owner, information required, available options, constraints and consequence of a poor or delayed decision.

Exceptions occur when the standard path can no longer continue: inventory is unavailable, information is missing, a supplier is late, a transaction looks unusual or two systems disagree.

These situations frequently require humans to interpret context, gather evidence and choose among imperfect alternatives.

Experienced employees investigate before deciding. They open systems, read notes, compare transactions, search documents, contact colleagues and reconstruct context.

Each information-gathering action is a candidate for a governed agent tool.

Enterprise decisions depend on policies, procedures, product knowledge, contracts, historical cases, customer context and operational experience.

Identify which sources must be available at decision time and which source is authoritative when information conflicts.

Map systems that provide context and systems where actions ultimately occur. Distinguish read access from write access.

An agent may safely investigate an ERP while still requiring explicit approval before creating a transfer, changing a price or contacting a customer.

Not every opportunity requires an LLM, and not every intelligent capability requires machine learning. Classify the work according to the computation it actually requires.

Complex solutions can combine approaches: an agent investigates, calls a forecasting model, invokes optimization, explains the result and requests approval.

Estimate frequency, effort, delays, revenue affected, avoidable cost, capacity released and risk reduced.

Early estimates do not need false precision. Their purpose is to establish an order of magnitude and expose assumptions that a pilot can test.

Evaluate data availability, integration complexity, reliability, security, privacy, human oversight and organizational readiness.

A solution can be technically possible but operationally unacceptable if ownership is unclear or consequential actions cannot be governed.

High-value, high-feasibility opportunities are natural pilot candidates. High-value but difficult opportunities may require architecture or data groundwork first.

Also consider strategic learning: a modest pilot can establish reusable identity, tools, data access, approvals and observability.

Define users, trigger, information, intelligence, tools, actions, human approvals and success measures.

Keep the pilot narrow enough to learn quickly but complete enough to exercise the real operating workflow.

Scaling requires ownership, monitoring, security, cost controls, data governance, auditability, support and organizational change.

Design reusable boundaries. Agents should call business capabilities rather than vendor-specific functions, and consequential actions should remain governed.