AI-Assisted Competitive Quote Ecosystem

Lowe's Pro quoting workflows historically relied on fragmented operational systems, inconsistent associate experiences, manual SKU-by-SKU processing, disconnected estimation services, and vendor-dependent workflows that created operational friction at enterprise scale.

As Lowe's expanded its investment in Pro sales and estimation capabilities, the organization began exploring how AI-assisted estimation, OCR processing, and intelligent SKU matching could transform the quoting experience. But the challenge wasn't simply adding AI to an existing workflow. It was operationalizing AI safely inside a complex retail ecosystem where quoting accuracy, pricing integrity, customer trust, and associate adoption all directly impacted business performance.

I helped shape and operationalize an enterprise-scale Competitive Quote ecosystem — transforming fragmented workflows into scalable, AI-enabled operational systems supporting store associates, remote quoting teams, and future digital customer experiences. The initiative is projected to drive over $300M in business impact through improved quoting workflows, operational efficiency, competitive pricing intelligence, and increased Pro sales conversion.

Associates were navigating multiple disconnected systems while manually searching products SKU-by-SKU, interpreting customer-submitted material lists, coordinating across fragmented operational workflows, and managing quote communication through inconsistent channels. Different stores operated with different levels of maturity and process consistency, while estimation often depended heavily on manual intervention and vendor-supported processing.

In parallel, Lowe's was investing more heavily in AI-assisted estimation. OCR processing, AI-assisted SKU matching, intelligent recommendations, and future conversational workflows all introduced a new layer of operational and organizational complexity — and a set of strategic questions:

I worked at the intersection of product strategy, systems design, workflow orchestration, AI operationalization, and cross-functional execution alignment. A large part of the role involved helping teams move from fragmented feature thinking toward more cohesive platform and workflow thinking.

I worked across Product Management, Engineering, UX, Store Operations, Pricing, Analytics, Program Management, and external estimation vendors to align roadmap direction, structure phased rollout strategies, and reduce ambiguity around operationally complex initiatives.

A core strategic tension was balancing automation with operational trust. The system needed to support rapid quote generation and scalable AI-assisted processing while accounting for exception-heavy realities, incomplete data, varying associate expertise, and the risks of AI-generated pricing recommendations.

To support this, we introduced concepts around confidence scoring, exception handling, operational review states, associate verification layers, routing logic, and human-in-the-loop AI review systems. The strategy wasn't full automation — it was intelligently augmenting associate decision-making while maintaining pricing integrity and operational accountability.

Because AI outputs directly impacted quoting accuracy, pricing integrity, customer trust, and sales execution, the workflows required significantly more operational rigor than a typical AI assistant experience.

I contributed to workflow strategies involving OCR + LLM-assisted estimation, AI-based SKU matching, confidence thresholds, explainability patterns, operational review models, and exception-handling systems. A major focus was helping teams determine where automation should happen, where human review remained critical, how associates should understand AI confidence, and how trust could be maintained in partially automated systems.

Operationalizing AI inside enterprise systems is rarely just a model problem — it's fundamentally a workflow orchestration problem. The challenge isn't generating intelligent outputs; it's designing systems that integrate AI into real human workflows in ways that remain understandable, reliable, scalable, and operationally trustworthy.

The initiative operated at enterprise retail scale across nationwide store operations, remote quoting teams, Pro Desk workflows, vendor-supported estimation systems, and future digital self-service experiences. Different stores operated with different processes, operational maturity, and staffing realities. Inventory variability, regional pricing, vendor coordination, and rollout sequencing all shaped how workflows needed to behave in practice.

In parallel, a significant portion of the work involved organizational orchestration: aligning historically disconnected teams, structuring execution priorities, coordinating dependencies, simplifying fragmented ownership models, and helping create clearer operational direction. This was as much an operational systems design challenge as a product design challenge.

This initiative reshaped how I think about AI product design. The future of enterprise AI isn't replacing people with automation — it's designing systems that intelligently augment human workflows while maintaining trust, operational clarity, and organizational scalability.

The most interesting problems were rarely visual design problems alone. They were systems problems — orchestration problems, operational trust problems, ambiguity problems, governance problems, and workflow design problems. That intersection, where product strategy, operational systems, AI behavior, and human decision-making converge, is the work I'm most energized by.