Evaluate AI Capabilities

Know What the Tech Can (and Can’t) Do. Now that you’ve defined the problem, it’s time to get real about the technology. What’s possible? What’s practical? What’s just hype?

If your AI product strategy is a house, this is where you figure out whether you’re building with bricks or straw.

The AI Toolkit

Start by understanding the core AI capabilities available. You don’t need to be a data scientist, but you do need to speak the language. Most product use cases fall into a few broad categories:

• Classification: Categorize inputs (e.g., spam vs. not spam, intent detection)

• Regression: Predict numeric outcomes (e.g., price estimates, risk scores)

• Generation: Create new content (text, images, audio, code)

• Clustering: Find groups or patterns in messy data

• Recommendation: Suggest next-best actions, products, or content

• Vision: Interpret images, video, or spatial data

• Speech: Convert audio to text or text to voice

• Natural Language Processing: Understand or transform text meaningfully

Foundation Models vs. In-House Training

Not every company needs to train their own model. In fact, most shouldn’t. That leaves you with two options:

Use Foundation Models

• Fast to implement (OpenAI, Claude, Gemini, etc.)

• Great for general intelligence

• Limited customization unless you fine-tune

Train In-House Models

• Tailored to your data and domain

• Full control over behavior

• Requires infrastructure, expertise, and data hygiene

A hybrid approach often works best: start with a foundation model, then layer in your own logic or lightweight fine-tuning over time.

Data Readiness Check

You can’t have good AI without good data. Before you build anything:

• Do you have labeled training data?

• Is your data structured, clean, and representative?

• Are there risks of bias, gaps, or leakage?

• Can you track inputs, outputs, and corrections over time?

If the data isn’t ready, the model won’t matter.

Don’t Chase Capabilities. Map Them.

It’s not about what AI can do. It’s about what you should do.

Match each AI capability to a specific, user-facing outcome. Then ask:

Is this the best solution, or just the most technical?

Coming Up Next

Next up: Aligning your AI product strategy with real business objectives. Because cool doesn’t pay the bills and keep the lights on.