Identifying AI Opportunities and Building a Proof of Concept
From workflow analysis to working AI prototype in six weeks
Who They Are & Why This Moment Mattered
A well-established research insights company supported major brands with consumer testing, sensory research, and analytical reporting. Their reputation was strong, but their internal operations had become increasingly strained. Researchers spent large amounts of time on repetitive tasks - synthesizing responses, formatting reports, prepping studies - leaving less time for high-value analysis and storytelling.
With AI advancing quickly across the industry, leadership recognized they needed to modernize. Competitors were beginning to automate parts of the research workflow. Clients were asking for faster turnaround times. Internal teams were hungry for better tools. The company knew AI could unlock meaningful efficiency, but they lacked clarity on where to start.
The Real Problem & What Was at Stake
The initial request was to explore how AI could help. But the underlying challenges were structural and operational:
- •Researchers were bogged down by manual, repetitive work
- •Insights generation took too long, affecting speed to client delivery
- •Workflows varied widely across teams, creating inconsistency
- •Leadership lacked a clear roadmap for applying AI responsibly
- •The organization needed to demonstrate progress quickly to build internal momentum
If they didn't take action, they risked losing competitive edge, burning out researchers, and falling behind in an industry rapidly adopting AI-enabled workflows.
Constraints & Starting Point
We entered the project with a defined set of constraints:
- •A six-week timeline to analyze workflows, prioritize use cases, and build a working AI prototype
- •Limited AI infrastructure and no existing automation tools
- •Highly specialized research processes that required careful understanding
- •The need to build trust with internal teams who were unsure how AI would impact their roles
How We Approached It
We approached the engagement as a focused discovery-to-prototype sprint. A small team embedded with researchers and operations staff to understand how projects moved from brief to delivery, where friction accumulated, and where AI could create the most leverage.
AI helped accelerate workflow analysis, pattern recognition, and early prototype exploration. Human judgment centered on prioritizing use cases that balanced feasibility, impact, and adoption potential.
Each week produced tangible outputs - opportunity maps, workflow diagrams, and prototype concepts - allowing leadership to make fast, informed decisions.
What We Did
Map Workflows and Identify Pain Points
We interviewed researchers, project managers, and operations staff across multiple teams. We mapped the end-to-end research workflow: study design, recruitment setup, data collection, analysis, reporting, and delivery. Patterns emerged immediately: repetitive tasks consumed disproportionate time, and bottlenecks slowed down insights delivery.
Prioritize High-Impact AI Use Cases
We analyzed opportunities across effort and impact. The highest-value areas included summarizing open-ended responses, structuring raw data for analysis, drafting initial report outlines, automating repetitive formatting tasks, and standardizing deliverables across teams. We built a prioritized opportunity roadmap aligned with strategic goals.
Define the Future Workflow and Success Criteria
We translated insights into a future-state workflow, showing how AI would support - not replace - researchers: AI accelerates early synthesis, researchers refine insights, automated tools format drafts, and humans focus on interpretation, nuance, and storytelling. This framework reduced fear, increased buy-in, and clarified value.
Build a Proof of Concept to Demonstrate Real Value
We built a working prototype that accepted raw research data, summarized responses using customizable lenses, generated structured insights, created editable draft reports, and highlighted patterns researchers could refine. We tested the tool with real research data to validate accuracy and usefulness.
Deliver an AI Opportunity Roadmap and Next-Phase Plan
We synthesized learnings into a pragmatic AI roadmap that included short-term automation opportunities, medium-term workflow redesign, long-term platform opportunities, and capability-building recommendations for internal teams. This allowed leadership to plan adoption responsibly and strategically.
The Outcome
Before
Research teams were overwhelmed by manual tasks, inconsistent workflows slowed project delivery, and the organization lacked clarity on how to adopt AI responsibly.
After
A validated set of AI opportunities and a functional POC that demonstrated immediate time savings, streamlined early-stage analysis and report creation, improved consistency across teams, built internal confidence in AI adoption, and established a clear roadmap for future investment. The company gained momentum and proof that AI could meaningfully improve operations.
What Changed Inside the Organization
The engagement created alignment and clarity. Teams understood where AI could help and where human expertise remained essential. Leadership gained a clear, realistic path for introducing automation without disrupting the organization. Most importantly, researchers saw AI not as a threat but as a tool that could eliminate repetitive work and give them more time to focus on the deeper insights clients valued most.
Why This Story Matters
Many research organizations know AI could dramatically improve their operations but don't know where to begin. Without clarity, AI adoption becomes abstract or stalled by fear. This project shows how a focused, six-week sprint can turn uncertainty into direction, prove value through a real prototype, and empower teams to embrace AI in ways that enhance - rather than replace - their expertise.
Key Takeaways
AI works best when it augments expertise rather than replacing it
Workflow mapping reveals where automation creates the most leverage
Internal buy-in grows when teams see AI as a tool rather than a threat
Prioritized roadmaps prevent scattered AI experimentation
Services Delivered
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