Find the AI and Automation Opportunities Actually Worth Pursuing

Start with the work, not the technology

Do not automate something simply because it can be automated, and do not add AI simply because AI is available. Both decisions are easy to justify and expensive to unwind.

The first question is not “where can we use AI?” It is “where is the work predictable, information-heavy or repetitive, and where does it genuinely require human judgment?” Once that line is drawn, the opportunities usually name themselves.

Where AI and automation tend to earn their place

Across operations and service environments, the same categories keep showing real return because the work is high-volume, rule-shaped or context-assembly heavy:

• Triage — classifying and prioritizing incoming work consistently
• Routing — getting work to the right owner without a human relay
• Summarization — turning long threads, tickets and incidents into usable context
• Knowledge retrieval — surfacing the answer instead of the document library
• Workflow orchestration — moving work between steps without manual nudging
• Information gathering — assembling the facts a decision needs
• Documentation — drafting records people currently write from memory
• Repetitive task execution — the predictable actions that fill a queue
• Status communication — keeping stakeholders informed without a meeting
• Support workflows — deflection, assisted response and follow-up
• Escalation workflows — triggering escalation on criteria rather than instinct
• Operational visibility — continuous reporting no one has to assemble

“Where should we use AI in our operation?”

Start where three things overlap: high volume, low judgment and clear inputs. Triage, summarization and knowledge retrieval usually qualify first because they reduce effort without removing accountability from a person.

Be more careful where judgment, risk or customer relationship is involved. There, AI works best as assistance — preparing the context and the draft — while the decision stays human.

“What work should we automate first?”

The highest-volume predictable task with a stable input and a clear definition of done. Volume gives you return, predictability gives you reliability, and a clear definition of done gives you something you can verify.

Avoid starting with the most visible process. Start with the one that repeats most often, because that is where small savings compound.

“Should we automate the process or redesign it first?”

Redesign first when the process contains steps that exist only to compensate for another problem. Automating a bad process can simply give you a faster bad process — and a more expensive one to change later.

A short test: remove the technology question entirely and ask whether a well-informed person would design the workflow this way today. If the answer is no, redesign comes first.

“How do we identify practical AI use cases?”

Follow the effort, not the hype. Find where people spend time assembling information, repeating explanations, copying between systems or writing the same thing in different words. Those are AI-shaped problems.

Then check three constraints before committing: is the input consistent enough, is the output verifiable, and does anyone lose accountability if the system is wrong? Those three questions filter out most of what looks promising in a demo.

Questions leaders ask

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