AI Strategy That Delivers: The Product-First Playbook
Stop building AI that goes unused. In this article by Director of Product Brittany Langosch, learn how a product-first approach to implementing your AI strategy can help you solve real-world problems and drive measurable business outcomes.
The Problem with Most AI Projects
AI implementation isn’t just a technical lift, it’s also a product challenge. Too often, organizations treat AI as an engineering-first initiative, resulting in models that don't solve meaningful problems or deliver measurable outcomes. Without strong product leadership, even the best AI fails to create value.
Here are five essential steps to maintain a product-first mindset when implementing a successful AI strategy.
1. Start with the Right Problem
AI should be tied to a clear user or business pain point. Whether you're aiming to accelerate decision-making, automate workflows, or personalize experiences, the solution must be anchored in a real-world need, not a shiny algorithm in search of a use case. The product team’s role is to uncover the unmet user needs that AI can solve during the discovery process.
2. Design for Trust and Usability
If your AI doesn't integrate cleanly into the user experience, it becomes noise. Users need to understand and trust what the AI is doing. This means providing clear insights, not black-box recommendations. Product teams are critical in shaping how AI shows up in the workflow.
For example, a well-designed AI feature might say, “High churn risk due to 3+ support tickets in the last month and a drop in usage.” This transparency helps users trust the system and act on its insights, rather than simply flagging a “high-risk customer” with no context.
3. Get Your Data House in Order
Not all data is AI-ready. High-quality, unbiased, and well-structured data is a prerequisite, and product teams must help evaluate what data matters and whether it's fit for purpose. The output you receive from AI is only as impactful as the data used to train it.
4. Think Beyond the Launch
AI isn’t a one-and-done project. Models need continuous monitoring, tuning, and governance. Product design leaders should define success metrics, feedback loops, and ethical guardrails from day one, building upon their existing practices for successful product launches.
5. Drive Cross-Functional Alignment
A successful AI project requires tight collaboration across product, engineering, data science, and the business. Product teams serve as the connective tissue, keeping everyone aligned on outcomes, not just outputs.
Ensuring Value at Every Step: If you want your AI investment to drive real value, you must keep product at the center. If it’s not embedded in a real workflow, lacks context, or doesn’t solve a meaningful problem, it won’t be used, or worse, it will cause confusion. Product teams ensure that models are part of usable, valuable experiences that truly drive outcomes.
Brittany Langosch brings nearly 15 years of product management experience and a deep background in digital product consulting. She’s known for turning complex business challenges into outcome-driven solutions that fuel growth and impact. At Sparq, Brittany leads with strategy, empowers cross-functional teams, and helps organizations level up their product practices – all while keeping users at the center of the process. A proud Wisconsin native, she’s also a wife, mom, and group fitness instructor who’s led over 700 classes (and still has energy to spare).
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