You don't need to understand how neural networks work to make good AI decisions for your business. You need to understand what AI can do, what it can't, and how to tell the difference. This playbook is your no-jargon guide.
What AI is actually good at
- Pattern recognition at scale (finding needles in haystacks)
- Generating human-like text, images, and code
- Automating repetitive cognitive tasks
- Processing and summarizing large amounts of information
- Personalizing experiences based on user behavior
What AI is bad at
- Anything requiring true understanding or consciousness
- Tasks where being wrong occasionally is unacceptable
- Creative work that requires genuine originality
- Decisions that require empathy or ethical judgment
- Anything outside its training data
The 80% rule
If AI can do 80% of a task reliably, that's usually enough to be valuable. The remaining 20% stays with humans. This isn't a failure of AI; it's how AI works best. Think of it as a very capable assistant, not a replacement.
The best AI applications augment human capability. The worst ones try to replace human judgment entirely.
Questions to ask before any AI project
- What specific problem are we solving?
- Do we have the data this requires?
- What's the cost of AI being wrong?
- Who will use this, and will they trust it?
- How will we know if it's working?
Red flags to watch for
Be skeptical of anyone who promises AI can solve vague problems, guarantees specific accuracy numbers before seeing your data, can't explain in plain English how their solution works, or suggests fully automating high-stakes decisions.