SharkNinja and BU Questrom Launch a Dedicated AI & Analytics Lab - What Product Teams Can Do With It
Date: November 20, 2025
SharkNinja and Boston University's Questrom Consulting Lab are standing up a dedicated AI & Analytics Lab aimed at real business impact. The model is simple: pair faculty-led expertise and a hands-on graduate team with a company that ships products at scale. The outcome product leaders care about is faster decisions, clearer signal from data, and tighter execution from concept to launch.
This isn't about research for research's sake. It's a structure to design, test, and deploy AI-driven and analytics-backed solutions that move core product metrics.
What the Lab Will Do
- Consumer insight mining: synthesize reviews, support tickets, and social chatter with NLP to surface needs, friction points, and language that converts.
- Demand and supply forecasting: improve accuracy at SKU and feature level to cut stockouts and overproduction.
- Feature prioritization: use model-driven scoring (impact, effort, margin lift, risk) to focus roadmaps.
- Experimentation at scale: build a test-and-learn pipeline for pricing, packaging, and channel tactics.
- Design and quality analytics: detect early failure modes, predict returns, and reduce warranty exposure.
- Operations insight: track build readiness, lead times, and vendor risk with real-time dashboards.
A dedicated graduate team, guided by experienced faculty and Lab leadership, will co-develop solutions with SharkNinja stakeholders. Think of it as a standing pod you can brief with well-formed problems and get working prototypes, then production-grade handoffs.
Why it matters for product development
Mark Barrocas, CEO of SharkNinja, points to using AI to make smarter decisions, move faster, and improve how teams collaborate. The expectation is simple: better end products because decisions are driven by data that's actually used.
Professor Peter Howard at Questrom highlights the value of pairing academic rigor with live industry challenges. Dean Susan Fournier underscores that hands-on work like this prepares teams to deliver measurable outcomes from day one.
Practical implications for product leaders
- Shorten discovery cycles: compress weeks of qualitative review into hours with structured VOC pipelines.
- De-risk big bets: run scenario models on margin, cost, and demand before committing to tooling or inventory.
- Tighten feedback loops: route post-launch data straight into the backlog, not just reports.
- Operationalize insights: ship model outputs to the systems that matter-PLM, ERP, CDP-not just slides.
- Scale what works: codify playbooks for experiments, approvals, and model monitoring so wins repeat.
Use cases you can pilot in 90 days
- Voice-of-customer pipeline that tags themes, urgency, and sentiment across reviews, support, and social.
- Idea scoring model that ranks concepts by expected value, effort, risk, and differentiation.
- Feature-level margin model to show trade-offs across materials, suppliers, and SKU complexity.
- Predictive maintenance for connected devices using telemetry to preempt failures and returns.
- Usability analytics that auto-summarize test sessions into clear issues, severity, and design actions.
- Retail media and promo optimization that ties spend to unit velocity and incremental margin.
How to work with a lab like this
- Frame problems clearly: "We need to improve forecast MAPE by 20% at SKU level within 2 quarters."
- Secure data access early: owners, schemas, permissions, sample sets, compliance requirements.
- Co-own success metrics: decide target deltas up front (NPS, returns, MAPE, margin, cycle time).
- Embed a product liaison: one decision-maker who unblocks, prioritizes, and accepts deliverables.
- Plan the handoff: define production path, MLOps, monitoring, and ownership before building.
- Cover guardrails: privacy, model bias checks, and change management for teams using the outputs.
Metrics that matter
- Experiment cycle time: brief to decision.
- % of roadmap decisions informed by model outputs.
- Model adoption rate: active users and usage frequency.
- Forecast accuracy delta vs. baseline.
- Build vs. buy savings realized.
- NPS/CSAT shift and return-rate reduction post-release.
- Service-level and inventory turns improvements.
Common risks and how to avoid them
- Proof-of-concept purgatory: scope to one metric, one flow, one team-ship version one.
- Data quality gaps: add basic validation, lineage, and well-defined owners from day one.
- Model overfitting: enforce out-of-sample testing and ongoing drift checks.
- Adoption stalls: design outputs for existing tools and workflows; train the closest operators.
- Shadow builds: publish playbooks and standards so teams don't rebuild the same thing three times.
The Lab is set up as a center of excellence and a proving ground. Try more ideas, kill weak ones faster, and scale the few that move the metrics you actually track.
If you want the official announcement, you can read the press release on Business Wire.
If your team needs structured upskilling to make use of this kind of work, explore role-based AI learning paths here: AI courses by job.
About the partners
The Questrom Center for Action Learning gives students real projects, real timelines, and real accountability-so employers get contributors who are work-ready. The Questrom Consulting Lab operates dedicated student teams led by faculty advisors to tackle strategic problems with measurable outcomes.
Questrom Experiential Learning (QXL) moves students beyond lectures into consulting, live portfolio management, venture building, and industry exploration. The focus is skill growth through doing, so graduates contribute faster and with more confidence.
SharkNinja is a global product design and technology company behind the Shark and Ninja brands, known for highly rated household products. With more than 3,600 associates and distribution across major retailers worldwide, the company continues to grow through a steady cadence of new, category-shifting products.
Your membership also unlocks:
