A Case Study on Applied AI Research in the Consumer Discretionary Sector

If All You Have is a Prompt...

A smarter approach to AI decision-making reveals how broader tool usage drives better results across complex, multi-step customer interactions.

Rachel leaned back in her chair—staring at the dashboard on her screen. As fictional head of customer insights at WidgetWave (a fictional player in the gadget e-commerce space), she was no stranger to analytics. The company had poured millions into a sleek, AI-powered shopping experience: smart recommendations, dynamic pricing, and a chat interface that could field everything from tech specs to delivery updates. Yet something wasn’t clicking.

Customer satisfaction scores had plateaued. Cart abandonment rates were creeping up. And although WidgetWave’s website and app looked impressive on the surface, the numbers told a different story. Shoppers were visiting, browsing, and even interacting with the AI assistant—but far too many were leaving before hitting “buy.” More troubling still, returning customers were becoming a rarer breed.

As Rachel dug into customer feedback, a theme emerged. Shoppers felt like they were talking to a clever intern, not a trusted advisor. “Sure, it sounds smart,” one reviewer wrote. “But it doesn’t do anything I can’t figure out myself.” Another noted: “The chatbot recommended a charger that wasn’t even in stock.” The AI systems were technically functioning, but they weren’t delivering value in the way customers expected.

When Smart Isn’t Smart Enough

WidgetWave’s AI assistant (like those of many competitors) had become a creature of habit. It relied heavily on a few familiar tools, like chain-of-thought responses, basic search, and a sales-oriented product database. But it ignored other valuable internal systems: the real-time inventory API, the discount-bundling logic engine, and the customer loyalty database. Each of these tools existed for a reason, but the AI rarely touched them.

Meanwhile, fictional rivals like GizmoGo had started rolling out tailored flash-sale bundles and real-time alerts about price drops or low stock. Word was spreading on Reddit and in customer forums: GizmoGo “knew what you needed before you did,” while WidgetWave “felt like last season’s tech.”

Internally, WidgetWave’s data landscape wasn’t helping. Different teams managed their own dashboards: marketing controlled campaign data, inventory was monitored by ops, and customer behavior lived in CRM platforms. The AI assistant—sitting in the middle—pulled most of its recommendations from a general-purpose product database. It lacked the orchestration layer to intelligently reach out to other tools, and it had no built-in incentive to try.

Rachel knew the risk wasn’t just technical; it was strategic. In e-commerce, personalization isn’t a “nice-to-have.” It’s the new default. And in this crowded marketplace, attention spans are short and switching costs are nearly zero. Customers don’t give second chances to AI that wastes their time.

The Cost of Standing Still

Left unaddressed, these frictions would continue to eat into WidgetWave’s performance. The marketing team could launch as many email campaigns as it wanted, but without smart follow-through from the shopping assistant, customers would continue bouncing. The AI (built to delight) was instead contributing to churn.

If WidgetWave couldn’t evolve its AI assistant to use the full depth of its internal tooling (especially at key moments like checkout or cart building), it would remain stuck. Conversion rates would lag, order values would remain flat, and customer satisfaction would erode further. Worst of all, competitors who learned to tap their full stack of tools more creatively would race ahead—converting WidgetWave’s window shoppers into loyal customers.

Rachel’s challenge wasn’t just technological. It was existential. Could WidgetWave’s AI evolve into something more resourceful, strategic, and effective (before the brand’s perception was locked in as “almost useful”)?

The good news? There was a growing body of research suggesting it could. But only if the company was willing to rethink how its AI made decisions (and why it kept reaching for the same tired tools).

Rethinking the AI’s Game Plan

Rachel knew the AI wasn’t failing because it lacked knowledge. It was failing because it lacked strategy. It wasn’t that the assistant didn’t have access to useful tools; it just didn’t know when to use them. Worse, it kept reverting to safe defaults, even when other options were available. To change this, she needed to move the AI from reactive to resourceful.

The solution wasn’t simply more training data or a better prompt. It was a better policy, a smarter internal decision-making engine that could guide the AI to ask, “What’s the best tool for this job?” and not just, “What have I used before?”

That’s when Rachel and her team turned to an emerging AI research (from Stanford): Step-wise Policy for Rare-tool Knowledge (SPaRK). It wasn’t a new model or a new tool. It was a new approach to training models to use what they already have, more intelligently. At its core, SPaRK teaches models to consider not only the correctness of a decision but also whether they’re relying too heavily on a narrow set of tools. It rewards accuracy; but it also rewards exploration.

For a company like WidgetWave, that meant more than just technical elegance. It offered a strategic advantage. If the AI assistant could learn to tap into overlooked APIs (such as real-time stock alerts, time-sensitive discount engines, or bundle-matching services), it could offer customer experiences no competitor was matching at scale. The challenge was to implement this kind of learning without breaking the existing stack.

Making Tool Diversity a Priority

The implementation started with something deceptively simple: a full inventory of WidgetWave’s internal tools. Rachel’s team partnered with engineering leads across inventory, marketing, pricing, and customer success to map out which APIs were available, which were reliable, and which had been historically underused by the AI assistant.

Next, they created a scoring system… not just for how often a tool was used, but how much value it could add if used appropriately. This allowed them to identify “hidden gems”, tools that could meaningfully enhance the customer experience but had been ignored due to model bias or lack of integration logic.

But discovery was only the beginning. The real breakthrough came when the team adopted SPaRK’s “rarity-first” strategy. Instead of blindly encouraging the AI to try new things, they taught it to choose from tools that had proven useful in training simulations (but to favor the ones it used the least). This approach struck a balance; it prevented randomness, but still pushed the model out of its comfort zone.

To support this learning, Rachel’s team generated thousands of synthetic customer interactions—realistic journeys with layered questions like: “Is this product in stock?” “Can I bundle this with another device and get a discount?” “What’s the fastest shipping option for my location?”

Each of these questions had multiple possible tool paths. Using reinforcement learning (RL) methods, the AI was trained to recognize not just what tools were valid, but which combinations delivered the best results (especially when those combinations included underused APIs).

Finally, the team launched a controlled pilot. A subset of the site’s traffic interacted with the newly trained policy. In each session, the AI had more freedom to explore its toolset (but with a framework that nudged it toward better decisions over time).

In this way, SPaRK became less of a technical experiment and more of a business strategy. By aligning the AI’s incentives with customer value—choosing the best answer, even if it comes from a less-used path—WidgetWave took the first real step toward making its AI assistant not just responsive, but truly adaptive. And that, Rachel believed, was where the competitive edge would come from.

When Smarter Decisions Start to Scale

Within weeks of the pilot rollout, Rachel’s team started to see the early signs of a turnaround. Shoppers engaging with the new AI assistant were not only more likely to complete a purchase; they were also spending more time interacting, asking deeper questions, and showing less frustration in feedback surveys. Something had shifted.

It wasn’t about the model sounding smarter. It was about the model acting more resourcefully. Instead of offering one-size-fits-all responses or falling back on static product suggestions, the assistant was tailoring its responses based on a fuller understanding of what tools could be used. If a product was low in stock, it was flagged early. If a bundled discount could be applied, it was proactively offered. The result was a sharper, faster, and more customer-aligned shopping experience.

But performance alone wasn’t the only goal. Rachel had framed this initiative around clear, measurable objectives (and those, too, were showing positive movement). Cart abandonment rates dropped in the pilot cohort. Repeat-purchase indicators (especially among customers served by the new policy) showed upward trends. And, critically, customer service tickets related to product availability or promotional confusion saw a visible dip.

These weren’t just metrics. They were signs that the AI system was finally closing the loop between user expectations and actual delivery.

Measuring More Than Accuracy

What made the pilot stand out internally wasn’t just that the AI was more accurate; it was that it was more agile. The analytics team developed a simple way to track what they called “tool diversity”: the range and combination of APIs used per session. With the old assistant, usage graphs had looked like spikes around just two or three endpoints. With SPaRK-inspired policy in place, those graphs now resembled a broader, flatter curve… more tools being used, in more balanced combinations, depending on the user’s needs.

Rachel emphasized this shift in every stakeholder meeting. “It’s not that we added new tools,” she reminded colleagues. “It’s that we finally trained the system to trust them (and to know when to use them).”

The evaluation wasn’t framed as a binary of success or failure. Instead, the team looked at outcomes in stages. A good result meant fewer drop-offs and faster responses. A better result meant seeing these outcomes across a wider range of query types. But the best outcome (the one Rachel had her eye on) was a cultural one: the AI becoming a trusted advisor, not just an automated clerk.

They weren’t quite there yet. But they were closer than ever.

From Experiment to Expectation

Of course, not everything went smoothly. Some of the underused tools introduced new complexity: data freshness issues, latency concerns, and a few unexpected conflicts in promotional logic. Early on, a pricing tool accidentally offered discounts in markets it wasn’t authorized for. But because the framework emphasized deliberate exploration, the AI’s behavior remained understandable and correctable. The system wasn’t “going rogue”; it was learning where the edges were.

The biggest lesson learned? Diversity isn’t a technical feature; it’s a business competency. Encouraging AI systems to take calculated risks with how they use available resources mirrors what good analysts, marketers, or product managers already do: they assess, explore, adapt. SPaRK didn’t force the AI to be smarter. It incentivized it to be wiser.

For WidgetWave, this shift brought with it more than just near-term ROI. It opened the door to future applications: personalized post-purchase journeys, real-time promotion planning, even customer service triage. But more than that, it reestablished trust… between the system and the customer, and between the product team and what the AI could become.

As Rachel reflected on the rollout, she framed it not as a technical upgrade, but as a mindset shift. “Tool choice is decision quality,” she told her team. “And better decisions are our business.”


Further Readings

Free Case Studies