The excitement around artificial intelligence (AI) is impossible to ignore. We see headlines every day about new AI tools, companies racing to integrate machine learning, and industries being disrupted. It’s enough to make any business leader wonder: Should we be using AI, too?

It’s a great question—because AI can unlock innovative solutions that once felt out of reach.

But before you decide to invest in an AI-powered digital product, you need to confirm that AI really is the right solution to your problem at hand. The promise of AI is high, and a thorough validation process will help you see real results—rather than finding out later that a different path might have worked better.

In this article, we’ll explore why product validation always matters, especially when deciding how to incorporate AI into your product strategy.

Interested in more? We’ve previously written about the benefits and practical applications of AI.

(It’s also worth noting that here, we’re talking about incorporating AI into the products we build—not how AI might change our internal development process.)

Common reasons that leaders ask about AI

When prospects and friends in our network tell us, “We’re thinking about AI,” they often give one of these key motivations:

Data as an untapped asset: Organizations frequently have large datasets—including customer transactions, operational metrics, or production-line data, for example. They want to turn the data into actionable insights, and AI is seen as the catalyst for unlocking this potential.

Increased efficiency: Leaders looking to streamline operations see AI as a way to automate labor-intensive tasks or uncover insights they can’t easily obtain otherwise. The expectation is a more productive workflow overall.

Keeping pace with competitors: Companies notice others experimenting with AI—or hear success stories in their industry—and worry they’ll be left behind. Even without a fully defined use case, the desire to remain competitive can prompt an early exploration of AI.

Driving innovation: AI also makes it possible to tackle challenges that were once out of reach. Organizations can use advanced analytics and predictive modeling to explore new business opportunities or invent solutions that weren’t previously realistic.

That said, AI works best when you have clear objectives and quality data. If you’re still defining your business challenge or refining your datasets, it may be more effective to address those fundamentals first. Once you have a strong foundation, AI can really shine.

If you take away one thing from this article, let it be this: while AI can be incredibly powerful, it’s still just one tool among many. The real question is whether it’s the right tool for the challenge you’re trying to solve.

You need to validate your idea through product strategy work first—to ensure you’re solving the right problem with the right toolkit. Sometimes, AI offers a unique edge, but you won’t know until you’ve done the legwork to confirm it’s the best path forward.

Why product validation matters

Validation is the process of confirming that a product idea is both feasible and valuable before you make significant investments into its design and development. In the context of AI, validation ensures you:

• Reduce risk: By testing assumptions early, you can avoid committing large budgets or development cycles to an unverified idea.

• Focus on solving the right problem: The excitement around AI can overshadow your real business challenge. Validation keeps your team grounded, ensuring technology choice addresses actual user needs.

• Ask the questions that matter: This includes exploring your data readiness, the complexity of the model you might need, and how you’ll define success.

Think of validation as a reality check that helps you pinpoint the true problem you’re solving and confirm whether AI is the best solution to address it.

Red flags of skipping AI validation

Even if you have good reasons for exploring AI, skipping proper validation can lead to unintended consequences.

Be on the look out for these warning signs within your team:

• Misaligned expectations: If everyone expects instant, dramatic results, they may be disappointed when AI requires incremental testing, data cleanup, and iteration.

• Vague ROI strategy: AI can involve significant and ongoing costs for data processing, cloud infrastructure (such as AWS or Azure), or licensing. Without a clear plan to measure value, your project could become a financial burden.

• Poor data quality or accessibility: AI models rely on high-quality inputs. If your data is incomplete, disorganized, or difficult to obtain, the model’s performance—and your results—will suffer.

• No defined problem statement: If nobody can articulate the specific challenge AI is meant to address, it’s easy to fall into the trap of exploring AI for its own sake—rather than using it to solve a real need.

So, how do you confirm your idea for using AI is on the right track? By making validation part of your process from the start.

How to approach product validation

At MichiganLabs, we incorporate AI exploration into the same product strategy framework we use for any digital product. AI isn’t treated as a special or separate process—it’s simply one more powerful tool we evaluate when searching for your best solution.

The process includes:

• Discovery workshops: We start by sitting down with key stakeholders to identify core business needs. What’s driving your interest in AI? What metrics will define success?

• Market and technical research: Next, we look at the available tools, solutions, and data. Is AI genuinely the most effective approach? Could a simpler approach achieve your goals? We also assess whether your data is clean, accessible, and relevant enough to train or power an AI model.

• Prototyping and testing: If your business case supports an AI-powered digital product, we don’t jump straight into large-scale development. Instead, we build lightweight prototypes—often using off-the-shelf AI services or pre-trained models—to quickly test feasibility. These low-cost, narrowly-scoped experiments help check user acceptance and technical viability before any major investment.

• Iteration and validation: We gather feedback from real stakeholders and analyze success metrics. This allows us to refine our approach (or pivot entirely) before you invest fully into development. By the end of this phase, you should know whether AI will truly deliver on your business goals.

Want a step-by-step look at how easy it can be to produce a low-cost AI prototype? Read our rapid sentiment analysis example, where one of our developers shows how easily we can spin up a quick prototype using a free, pre-trained model.

Embracing AI with confidence

AI can be powerful. It can improve your customer experiences and boost your operational efficiency.

Yet, without a clear business problem, reliable data, and defined success metrics, even the best AI model may fall short.

Product validation ensures you’re focusing on the right digital solution and helps you feel confident that AI deserves a place in the toolkit.

Ready to explore AI-powered products further? Let’s discuss if AI is the best way to reach your goals. Start a conversation by completing our simple contact form or by reaching out to me on LinkedIn.