A practical guide to using AI (the often-overlooked fundamentals)

Right now, many companies are racing to incorporate AI into their products and services. It’s common to see announcements highlighting “AI-powered” features, even when the underlying functionality may simply be an enhancement to existing tools.

Amid all the excitement, however, some of the most practical and effective steps toward using AI well are sometimes overlooked. These are the foundational approaches that help teams build solutions that genuinely create value.

Below are a few straightforward—but important—principles to keep in mind when thinking about how AI fits into a product or organization. Consider these more as reminders as they may seem very obvious.

1. Talk to your customers

One of the most effective ways to build meaningful products is also one of the simplest: talk to the people who use them.

Customer conversations can reveal how and why people actually use a product, what problems they are trying to solve, and where they experience friction. These insights often highlight opportunities where AI can genuinely help—or where it may not be necessary at all.

There is also a craft to effective customer interviewing. Rather than asking leading questions like “would you use this?”, it’s usually more valuable to explore real behaviors:

• what problem were you trying to solve?
• what tools did you use?
• what was frustrating about the process?

Understanding why customers “hire” a product to solve a problem provides a strong foundation for identifying thoughtful and practical applications of AI.

2. Stay aware of your industry

AI development is moving quickly, and it’s helpful to stay informed about how others in your space are experimenting with it.

Watching what competitors and adjacent industries are doing can help identify patterns: what seems to be working, what customers respond to, and what might simply be hype.

The goal isn’t to copy every new feature that appears in the market, but to remain informed and thoughtful about where AI may provide meaningful advantages.

3. Understand the limitations of your platform and data

AI systems rely heavily on the quality and structure of the data they interact with.

Organizations with well-organized, meaningful datasets often have more opportunities to apply AI effectively. On the other hand, if data is fragmented, inconsistent, or difficult to interpret, it can limit what AI systems are able to accomplish.

Before building new AI-driven features, it’s often helpful to ask a few practical questions:

• is our data structured in a way that models can use effectively?
• does the data represent meaningful patterns or signals?
• would the results make sense to users outside the internal system?

Taking time to understand and improve data quality can significantly increase the usefulness of any AI-driven initiative.

4. Create an environment that's conducive to experimentation

Innovation tends to thrive in environments where teams are able to experiment, learn quickly, and iterate.

Developing new AI capabilities often requires exploration and testing. Not every idea will succeed, but each attempt can provide valuable insight that improves the next iteration.

Organizations that encourage responsible experimentation—and give teams room to learn—often make faster progress than those that expect every new initiative to deliver immediate results.

5. Connect new ideas to real business impact

When evaluating a new AI concept, it can be helpful to step back and consider the broader impact.

A simple framework is to ask:

If we invest in this idea, what outcome will it improve, and how will that improvement benefit both the business and the customer?

Clear connections between innovation, customer value, and business outcomes help ensure that AI projects move beyond interesting demos and toward meaningful improvements.

AI isn’t a cure-all for organizational challenges, and it can’t replace strong product thinking, solid research, or reliable data. But when teams focus on these fundamentals—understanding customers, learning from the market, improving data foundations, encouraging experimentation, and tying ideas to real outcomes—AI becomes far more likely to create value.

In many ways, consistently applying these simple principles is what turns new technology into genuinely useful innovation.