Creating an AI-Powered Analytics Platform in Six Weeks
Turning years of engagement data into actionable intelligence
Who They Are & Why This Moment Mattered
A large workplace technology provider had built a strong business managing employee recognition and rewards programs for enterprise clients. Their platform supported some of the largest companies in the country, but the experience had begun to stagnate. Administrators wanted deeper insight. Prospects were looking for something more modern. Competitors were introducing AI-driven features, and the market was shifting toward intelligence, automation, and data-led decision making.
The company reached an inflection point. They had years of rich recognition and engagement data, but no way for program administrators to explore it, learn from it, or surface meaningful trends. If they didn't modernize, they risked losing ground with enterprise accounts and missing opportunities with new clients.
The Real Problem & What Was at Stake
On the surface, they asked for help designing and prototyping an AI-powered analytics experience. Underneath, the stakes were larger. Their challenge was not just a lack of dashboards. It was a lack of leverage:
- •Their data was trapped in the system with no easy way to interpret it
- •Administrators had no real-time insight into how recognition behavior affected engagement
- •Leadership couldn't easily tell the story of program impact
- •Sales struggled to differentiate the platform in a crowded category
If they couldn't turn data into intelligence, they risked being seen as a commodity tool instead of a strategic solution.
Constraints & Starting Point
The constraints were clear from day one:
- •A six-week timeline
- •A platform that hadn't yet integrated AI into its product workflow
- •Limited internal engineering support during the sprint
- •A need for a working, convincing prototype, not another slide deck
How We Approached It
We treated the work like a focused, high-velocity mission. A small team, tight communication, and weekly momentum. AI played a central role: exploring data patterns, generating early insight summaries, accelerating concept exploration, and helping us iterate quickly.
Human judgment stayed focused on what mattered: designing an experience that genuinely helped program administrators, identifying the highest-leverage use cases, and ensuring the prototype told a compelling story.
Every week, we delivered something tangible the team could react to, building alignment and confidence along the way.
What We Did
Discover & Align
We interviewed program administrators and internal customer success leaders to understand their workflows, pain points, and decision-making patterns. We mapped where recognition programs broke down and where administrators spent the most time. This revealed a clear insight: the real value was not in reporting, but in helping administrators understand what the data meant.
Define the Strategy & Experience
We articulated the strategy for an AI-powered analytics experience centered around a conversational interface. Administrators would be able to ask the platform questions in natural language - "Which teams are under-recognizing each other?" "Where are we seeing early signs of disengagement?" We defined the core capabilities, the insight types that would create the most value, and the scope for the six-week prototype.
Design & Prototype
We designed the experience end to end: the conversational interface, insight patterns, recommendation logic, and the interaction model. AI tools accelerated early drafts of insight summaries, recommendations, and visual patterns. We then built a fully functional prototype connected to sample datasets, generating real insights with real structure.
Validate & Demonstrate
We prepared the prototype for customer demos and internal executive sessions. Seeing the experience live transformed the conversation. Administrators immediately recognized how much manual effort it would save. Executives saw how it could reposition the entire platform around intelligence rather than administration.
Extend & Enable
The prototype created momentum. The company extended the engagement to begin building the production version and weave AI-driven insights into their long-term product roadmap.
The Outcome
Before
A platform with valuable data but no meaningful way to surface insights, differentiate in the market, or help administrators understand engagement patterns.
After
A functioning AI-powered analytics experience that demonstrated real value in six weeks, reframed the platform's positioning, strengthened enterprise sales conversations, and renewed internal energy and alignment around innovation. The prototype became a catalyst for a more strategic, intelligence-led platform direction.
What Changed Inside the Organization
Beyond the product itself, the engagement reshaped how the organization thought about AI and innovation. Teams saw how AI could augment their platform in practical, high-impact ways. Leadership gained conviction around a new product narrative. Product and engineering teams became more aligned on what "intelligence" should mean for customers. They moved from talking abstractly about AI to building AI-driven value directly into the core of the experience.
Why This Story Matters
This reflects a broader pattern across many mid-sized B2B platforms: years of valuable data, but no system for turning it into insight. When companies unlock that data through carefully designed AI experiences, they shift from being tools to being strategic partners. This project shows what becomes possible when a clear strategy, a small focused team, and AI-accelerated workflows come together to solve a real business problem.
Key Takeaways
Trapped data becomes a liability until it becomes intelligence
Conversational AI interfaces lower barriers to insight access
Prototypes that work beat presentations that promise
Intelligence-led positioning differentiates in commodity markets
Services Delivered
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