AI in Business

A Manager's Guide to Finding AI Opportunities

A practical, no-hype guide to identifying real-world AI projects that create business value.

At its core, today's AI excels at three fundamental capabilities:

1. Seeing (Computer Vision)

AI can look at images and videos to inspect products for defects, read handwritten documents, analyze medical images, and monitor security footage.

2. Listening (Speech & Audio Processing)

AI can transcribe meetings, translate spoken languages on the fly, and identify equipment problems from unusual sounds.

3. Reading & Writing (Natural Language Processing)

This is where GenAI shines. It can summarize documents, generate drafts of emails and reports, answer customer questions, and even write code.

Most AI opportunities fall into four categories:

Type 1: Efficiency (Do the Same, But Faster/Cheaper)

Look for: Repetitive tasks, manual data entry, or time-consuming research.
Example: A law firm using AI to review contracts for standard clauses, reducing review time from hours to minutes.

Type 2: Enhancement (Do It Better)

Look for: Areas needing better quality control, error reduction, or personalization.
Example: A retailer using AI for personalized product recommendations, increasing average order value.

Type 3: Expansion (Do New Things)

Look for: Services that were previously too expensive or complex to offer.
Example: A small marketing agency using AI to offer multilingual campaign creation to international clients.

Type 4: Transformation (Reimagine the Business)

Look for: Ways to fundamentally rethink your business model.
Example: A manufacturing company using AI-powered predictive maintenance to sell "uptime as a service" instead of just equipment.

For any potential opportunity, ask these four simple questions to build a business case:

  • The Problem: What specific pain point are we solving, and what is its current cost (in time, money, or quality)?
  • The AI Solution: Which AI capability (Seeing, Listening, Reading/Writing) fits this problem? What data do we need?
  • The Value Creation: How much time or cost will we save? How will we measure success?
  • The Implementation Reality: What's a realistic first step? Can we start with a small pilot project?
  • Starting Too Big: Don't try to transform everything at once. Start with one specific use case, get a win, and then expand.
  • Ignoring the Data Question: AI needs good data. If you don't have it, your first step is to start collecting it.
  • Underestimating Change Management: Involve the people who will actually use the tool from day one. The goal is to make their lives easier.
  • Chasing Perfection: An 80% accurate solution that you can deploy next week is often better than a 99% accurate solution that takes a year to build.