From Algorithms To Outcomes: 10 Lessons That Help AI Engineering Drive Growth In Modern Retail

Forbes

a day ago

Keshav Agrawal, Senior Director of Product, AI-Powered Search Experience, Fortune 10 Retailer.
In today's commerce landscape, AI is everywhere—but tangible business impact is not. The real challenge for retail and customer experience (CX) leaders isn't whether to use AI, but how to engineer systems that consistently deliver business outcomes, not just insights or prototypes.
Over the past decade, I've had the privilege of leading AI-powered engineering initiatives at a Fortune top-10 retailer, where I've helped scale intelligent search, transaction and e-commerce systems used by hundreds of millions of customers. Along the way, I've learned that turning AI into sustained growth requires more than model performance or the latest frameworks. It takes systems thinking, organizational clarity and an unwavering focus on customer outcomes.
Here are 10 key lessons I've learned from the front lines—lessons that can help retailers and CX leaders transform AI from a buzzword into a results engine.
1. Start with customer problems, not data or models.
Every successful AI initiative starts by solving a real customer pain point, not by deploying a model for its own sake. Is the shopper struggling with irrelevant search results? Are product filters too rigid? Is personalization creating a confusing experience? If we don't anchor our efforts in human problems, our solutions—no matter how sophisticated—risk being irrelevant. AI should reduce friction and increase delight. Period.
2. Define success by business outcomes, not model metrics.
A model with a 92% accuracy rate might look great on paper, but what does that mean for the bottom line? Instead of celebrating precision or recall scores, I've learned to align teams around customer-centric key performance indicators (KPIs) like search abandonment rate, conversion, average order value (AOV) or customer satisfaction. If your AI investment doesn't move these needles, it's not delivering value—no matter how elegant the math behind it.
3. Think probabilistically, not deterministically.
AI systems aren't designed to give perfect answers. They're designed to make informed predictions under uncertainty. The best-performing systems I've built—particularly in search and ranking—work by nudging decisions with a sense of probability, not absolute confidence. We trade black-and-white answers for flexible, context-sensitive suggestions. That's more aligned with how real customers behave, and ultimately leads to better outcomes.
4. Invest across three buckets for momentum and resilience.
Not all AI bets pay off equally. I've found it helpful to allocate investments across three types. This approach has helped my teams achieve early wins to build trust while still making space for riskier, long-term breakthroughs. It's like managing a product portfolio—diversification matters.
1. High-confidence, medium-impact (e.g., summary generation)
2. Medium-confidence, high-impact (e.g., re-ranking with deep models)
3. Low-confidence, breakthrough potential (e.g., generative UX, multimodal intent inference)
5. Build automated feedback loops into the system.
Every customer interaction is a learning opportunity if we design for it. Whether it's a shopper refining their search, abandoning a cart or clicking a similar item, their actions generate valuable behavioral signals. The best AI systems I've led don't just 'go live,' they get smarter every day. Continuous learning loops help align system behavior with evolving user intent, and are often the hidden driver behind compounding improvements in relevance and satisfaction.
6. Build for human-in-the-loop—not just automation.
AI can fail quietly or spectacularly, especially in long tail or ambiguous use cases. This is why human-in-the-loop design is essential. In practice, this means building fallback mechanisms, surfacing model confidence levels and enabling business teams to intervene when needed. Humans remain vital for curating, escalating and auditing outputs. We don't eliminate human judgment—we scale it.
7. Evaluate models like you'd evaluate a business decision.
Before greenlighting any new model or feature, ask: "Does it materially improve a business KPI?" "Does it introduce latency or complexity?" "Is it resilient under load or user diversity?" I've seen teams chase fractional accuracy gains that increased infra costs and decreased UX performance. AI systems should be judged like any major investment—through the lens of cost, risk and return.
8. Build infrastructure that supports experimentation.
No matter how good your ideas are, they'll never ship without the right infrastructure. That's why I've championed early investments in machine learning operations (MLOps) tooling, model versioning, monitoring and rollback systems. At scale, experimentation velocity is everything. The ability to test, measure, learn and iterate safely has been a key differentiator in every AI win I've been part of.
9. Design org structure to accelerate AI learning cycles.
AI is not just a technical function—it's a cross-functional discipline that thrives at the intersection of engineering, product, analytics and design. In the best setups I've seen (and built), these roles are co-located, aligned on shared metrics and empowered to iterate without handoffs. Structure either accelerates intelligence or strangles it. Choose wisely.
10. Make AI everyone's job—not just the data team's.
Some of the most impactful moments in my AI career came not from engineers, but from product owners, designers or operations leads who asked the right questions. AI shouldn't live in a silo. It should be embedded in how your company thinks, solves problems and measures success. When everyone understands the 'why' behind a model's behavior and what it's optimizing for, your organization stops reacting to AI and starts steering it.
AI in retail is not about novelty anymore—it's about execution. At a time when digital shoppers expect more and competition is only a click away, it's not enough to have intelligent models. You need intelligent systems, organizations and leadership.
Whether you're a product executive, a CX strategist or a technologist, these lessons can help you move beyond theory and toward traction. AI's true power lies not in its complexity, but in its ability to generate outcomes that matter—to your customers and your business.
When done right, AI can make your company faster, sharper and more responsive to what your customers need—often before they even ask.
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