The Challenge
Trek's Director of Advanced Technologies wanted to bring AI into the organization but faced a sprawling value chain, frozen budgets, hiring freezes, and post-COVID travel restrictions. With too many potential AI entry points and no clear prioritization framework, the team needed a fast, credible path to executive approval — one that could survive scrutiny from Trek's president without requiring a multi-year, high-risk investment.
What They Built
Loft Design audited Trek's full value chain to identify and prioritize 35 AI opportunity areas, built the executive business case that secured a new AI budget during financial austerity, then designed a physical AI-embedded handlebar system housing a small language model running locally on a microcontroller for CES 2025.
Loft Design deployed their Growth Edge sprint methodology to audit Trek's full value chain systematically, surfacing 35 distinct AI opportunity areas across the business. These were evaluated against agreed metrics balancing prototyping feasibility with ROI potential, narrowing to three finalists. Loft then constructed a compelling executive business case for the highest-priority opportunity — one designed to survive scrutiny from Trek's president in a period of hiring freezes, travel restrictions, and post-COVID financial austerity. The business case secured a significant new AI budget. Phase two used that budget to design and build an AI-embedded handlebar system for CES 2025: a physical prototype housing a small language model running locally on a microcontroller, with no internet connectivity required. The on-device SLM functions as a real-time AI cycling companion — answering questions about terrain, battery life, and trail conditions from data available on the device. Running a capable language model on a resource-constrained microcontroller without cloud dependency required significant edge AI engineering work, combining embedded systems expertise with hardware-software integration across a twelve-plus-month timeline.
AI Role
Growth Edge sprints mapped Trek's entire value chain, identified 35 AI opportunity areas, and narrowed the field to three high-ROI priorities — giving the director a crisp, defensible recommendation for the president.
Infrastructure
• Small language model (SLM, on-device) • Microcontroller hardware (embedded systems) • Generative AI design tools • Edge AI optimization stack
Integration Points
• On-device sensor data → SLM inference engine (microcontroller) • SLM → real-time handlebar interface (terrain, battery, trail queries) • No external API or internet connectivity — fully edge-deployed
Best for organizations with a dedicated innovation function committed to shipping real physical-digital products — in categories like mobility, consumer hardware, medtech, or life sciences — that need a structured process to quickly prioritize AI opportunities and build an executive-approved investment case.
