Building an AI product is an exciting endeavor, but also one fraught with potential pitfalls. With AI being hyped in the media, it’s tempting to jump in without proper planning. However, taking shortcuts usually ends badly.
In this post, I’ll share 5 of the most common mistakes teams make when building AI products, and how to avoid them. Get these right from the start and you’ll set your project up for success.
Mistake #1: Not Defining Your Goals
It’s easy to get enamored with AI capabilities and lose sight of what problem you’re trying to solve. Before exploring solutions, clearly define your goals and success metrics.
Ask yourself:
- What specific user needs will my product address?
- How will my product deliver substantially more value than existing options?
- What key results define success for this product (e.g. revenue, user engagement)?
A shared understanding of your objectives will prevent the team from meandering or building features that don’t move the needle. Revisit your goals frequently to ensure you stay on track.
Mistake #2: Neglecting the Training Data
No algorithm is smarter than the data used to train it. Unfortunately, many teams realize this too late, after they’ve built their models.
From the start, invest heavily in collecting and labeling quality training data that accurately represents real-world scenarios. Work closely with subject matter experts who intimately understand your use cases. If the examples you feed your algorithm don’t generalize well, neither will your AI.
Mistake #3: Over-Engineering the Solution
With cool new AI frameworks popping up regularly, it’s tempting to stitch together complex pipelines. However, simple algorithms applied to high-quality data often outperform intricate ones applied to poor data.
Start with a simple model that solves a subset of your problem. Then expand the data and refine the algorithms. Don’t architect complex systems until you’ve validated product-market fit. The most elegant machine learning architecture won’t matter if customers don’t use your product.
Mistake #4: Underestimating Infrastructure Needs
Many AI models require serious computing resources, like multi-GPU servers for training and low-latency access to massive datasets. Neglecting infrastructure needs can grind your project to a halt.
Work with engineering leadership early on to understand infrastructure requirements. Over-provision access to data, compute power, and engineering support. The last thing you want is your team sitting around waiting for jobs to finish.
Mistake #5: Launching Without a Plan for Updating Models
Unlike traditional software, AI systems require ongoing maintenance and improvement. As your product accumulates usage data, model performance can degrade over time. New data may reveal edge cases your algorithm doesn’t handle.
Have a plan in place to continuously monitor your system’s performance, retrain models on new data, and handle versioning. Also budget for data acquisition and labeling costs after launch.
The Hype Around AI
With AI being hailed as a cure-all in the media, it’s tempting to dive in headfirst. But building AI products requires planning and diligence. Avoid common mistakes like unclear goals, poor data, over-engineering, resource constraints, and neglecting post-launch model governance.
Do the groundwork up front to set your team up for delivering real value to customers with AI. With a laser focus on solving real problems and leveraging data effectively, you’ll build products that users love.
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