Do Not Use AI in Product for the Sake of It

It's tempting to add AI everywhere, but using it without a clear strategy is a surefire way to waste resources and disappoint users. So, how do we use AI smartly? Lets discuss!

Hey there, product leaders! 👋

Ever feel like everyone's jumping on the AI bandwagon without a plan?

You're not alone.

It's tempting to sprinkle AI on everything, hoping it'll magically boost your product.

But let's be real – using AI just because it's trendy is a recipe for wasted resources and, frankly, a mediocre product.

Let's talk about dodging that bullet and using AI like a sharp weapon.

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Why the "AI for AI's Sake" Trap is So Easy to Fall Into

We've all been there. The pressure to innovate, the fear of missing out (FOMO is real, people!), and the allure of cutting-edge tech can lead us down the wrong path.

Maybe you've seen competitors launch AI features and felt the urge to keep up.

Or perhaps you're confused by the potential of AI without a clear understanding of how it truly benefits your users.

Here are some common scenarios where product managers might integrate AI without a clear strategy:

1. The "Me Too" AI Integration: You see a competitor roll out an AI-powered chatbot, and suddenly, you feel like you need one too. Even if your users haven't expressed a need for it.

2. The "Shiny Object Syndrome": A new, powerful AI model drops, and you're itching to use it, even if it doesn't solve a real user problem. You are drawn to the technology's potential without figuring out its true application for your product.

3. The "Over-Engineering" Nightmare: You spend months developing an intricate AI feature that, in the end, is too complex for users to understand or appreciate. The result? Low adoption, high development costs, and a lot of frustration. For example, you spent time developing AI-powered product onboarding, instead of using simple, effective tooltips.

The "AI for AI's sake" trap often stems from a lack of clear objectives.

If you don't know why you're using AI, you won't know how to use it effectively.

Strategic AI: Finding the Sweet Spot

So, how do we shift from reactive AI adoption to proactive, strategic implementation?

It's all about finding the intersection of user needs, business goals, and AI capabilities. Think of it as a three-way Venn diagram.

1. User Needs: What Problems Really Need Solving?

First and foremost, put your users at the center.

❓ What are their biggest pain points?

❓ Where do they struggle with your product?

Don't assume AI is the answer – dig deep to understand the root of the problem. Sometimes, a simple UX tweak can be more effective than a complex AI solution.

For example, if users complain about slow customer service, an AI chatbot might seem like the obvious fix. But what if the real issue is a confusing FAQ section? In that case, improving content and navigation might be the better path. This is where the deep understanding of user needs and pain points comes.

2. Business Goals: Where Can AI Drive Real Impact?

Next, align AI initiatives with your core business objectives.

❓ Are you aiming to increase user engagement, boost conversions, or reduce operational costs? Identify areas where AI can truly move the needle.

For instance, if your goal is to personalize the user experience, AI-powered recommendations can be a game-changer.

Or, if you're looking to streamline internal processes, AI can automate repetitive tasks, freeing up your team for more strategic work.

3. AI Capabilities: What's Actually Possible and Practical?

Finally, consider the realistic capabilities of AI.

Don't get caught up in the hype – focus on what's achievable and sustainable.

Assess the data you have (or need), the resources required, and the potential ROI.

For example, building an in-house LLM model might sound impressive, but do you have the necessary data to train it properly? Is the investment worth the potential return? Or it might be better to use available solutions for now?

Making it Actionable: Your Strategic AI Checklist

Ready to put this into practice?

Here are some tips:

1. Start with a clear problem statement: Define the user pain point or business challenge you're trying to solve.

2. Evaluate if AI is truly the best solution: Consider alternative approaches and weigh the pros and cons.

3. Define measurable success metrics: How will you know if your AI feature is successful?

4. Prioritize based on impact and feasibility: Focus on high-impact, achievable initiatives first.

5. Iterate and learn: Start small, test your assumptions, and adapt based on user feedback.

6. Stay on top of users' needs and expectations: they will change over time, and it is important to quickly adapt to them. Do not assume that user research results from 2-3 years ago are still valid, it is most likely that they are not.

The Takeaway:

Ditch the "AI for AI's sake" mentality and take a smarter, more intentional approach. Your users – and your bottom line – will thank you (maybe your manager too, but this largely maybe 😉)

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