A synthesis of the ideas the team generated — clustered into strategic themes, prioritized by impact and effort, and sequenced into a practical roadmap leadership can act on. Anchored to the digital commerce pillar Kristin is leading.
The LI-COR + Onset team sees AI as the lever to move from individual-level adoption to enterprise-level capability — and to make the digital commerce experience match the precision of the instruments behind it. Across 52 ideas, three patterns stood out: the website acting as a salesperson (proactive outreach, compatibility mapping, an AI advisor, a responsive purchase pathway), turning proprietary depth into a moat (73K research papers, instrument service history, internal documentation — all activated as AI inputs), and operating at the speed Kristin wants (collapsing 8-hour product pages, automating quote and order entry, unifying data that today lives in personal hubs).
The strongest opportunities aren't technology projects in isolation — they're places where AI unlocks something the team has been wanting to do for a long time. This report organizes those opportunities so leadership can decide where to invest first.
When we clustered the workshop output, the same big ideas kept showing up under different functional banners. These are the strategic threads worth pulling.
From personalized outreach based on what a customer already owns, to anonymous-visitor retargeting and SMS — the team wants the relationship to start before the buyer ever opens a chat window.
Kristin's exact framing. Compatibility mapping, an AI advisor that coaches and educates, a customer-responsive purchase pathway, improved search, checkout UX that adapts to who's standing in front of it.
An AI agent on the instruments themselves — built-in support, data interpretation, education, and direct purchase of consumables & services. Plus quick access to complete service-life history and triage to subject-matter experts.
The 73,000-paper citation corpus is a moat. Mining publications and funding opportunities, auditing content for combined brand position, competitive analysis agents, citation-driven positioning. Depth becomes leverage.
Kristin's "AI is being adopted individually, not enterprise-wide." AI-ready datasets, integrated info access, financial summarization, SRO analysis, in-house knowledge from multiple sources — the unification layer Tom and Kristin are already building toward.
ISO routing & approvals, Tuesday meeting slide generation, supply-chain sourcing, order entry through form builders, requirement & test-criteria development from product-ideation transcripts. The "give-everyone-an-extra-half-day-a-week" theme.
LI-COR University for career development, just-in-time learning replacing "laborious onboarding reading sessions," internal product training, interactive instrument-use training, easier access to internal business information.
After generating 52 ideas, participants voted on the ones they most wanted LI-COR + Onset to pursue. These seven rose to the top — and a clear pattern emerged about where the team's energy is most concentrated.
Proactive, personalized outreach based on purchase history — identify customers who bought "X" and surface upgrades or things to consider next.
Map compatibility and solutions to match customers in their journey.
An AI agent on the instruments themselves — built-in support, data review and education, and the ability to purchase consumables, professional services, etc.
A personal AI advisor on the web that coaches and educates.
Mine and find research publications and funding opportunities that shape future product directions and sales strategies.
A customer-responsive purchase pathway — the website behaves like a salesperson.
Improved search across e-commerce and the website.
Five of the seven top-voted ideas live in Customer Acquisition & Retention. The team's energy is pointed squarely at the commerce experience — the website, the buyer's journey, and the proactive moment that turns a past purchase into the next one. This validates Kristin's framing exactly: the workshop is the commerce pillar of an enterprise AI strategy, and the room agrees on where to start.
Two ideas reach beyond pure commerce — the AI agent on instruments (which extends the customer relationship into the product itself) and research-publication mining (which turns the 73K-paper corpus into product & sales leverage). Both are differentiators no off-the-shelf tool can replicate, and both deserve their own track even as commerce takes the lead.
Each dot is a strategic theme, placed by the value it can unlock and the effort required to get there. Hover for detail. Quick Wins are where to start; Strategic Bets are where to plan.
If leadership only acts on a handful of opportunities in the next year, these are the ones we'd build a business case around first. Each is anchored in a real idea the team named — six of them appear in the top-voted seven, plus two strategic stretches Kristin flagged on the call.
A tool that renders a custom page and value proposition dynamically based on who the visitor is — new end-customer, returning lab manager, distributor, or internal customer-service user — and what they're trying to do. Pulls from every content source LI-COR + Onset owns (inventory, specs, product attributes, sales trends, customer metadata) so the same shop URL feels different to every visitor. This is the use case the team chose to take through the AI Canvas.
→ View AI Canvas Deep DiveAgents that scan order history and identify the right "next conversation" with each existing customer — a consumables refill, a related instrument, a relevant new release. The highest-voted idea in the room, and a natural sibling pilot that runs alongside the dynamic page builder.
A researcher describes their study and the system returns the ideal instrument configuration, with compatibility validation against eddy covariance, HOBOnet, and the rest of the catalog. Solves "what fits with what" without a sales-engineering call — and feeds directly into the role-aware page builder.
Built-in support, data interpretation, education, and direct purchase of consumables & professional services — turning every deployed instrument into a relationship surface. The most differentiated long-term play and the one no commerce platform can replicate.
Mine the 73,000-paper corpus continuously to surface emerging applications, funding opportunities that shape product strategy, and citation trends that refine positioning and messaging. The team's depth becomes a strategic asset, not a marketing footnote — and a source the dynamic page builder can cite.
Semantic + technical search that handles scientific terminology, unit conversions, and application-first queries. Table stakes but foundational — every other commerce idea works better when search works. Fastest near-term win to ship.
Kristin's "8 hours per page × 50 products" pain. A multi-agent pipeline ingests spec sheets, manuals, and photos and produces complete, SEO-ready product pages — copy, tech specs, applications, cross-sells, citations — in minutes. The backstage complement to the dynamic page builder.
The enterprise version of what Kristin is building individually in her GenSpark hub. A natural-language agent connected to pipeline, orders, inventory, catalog, and web analytics. Answers commerce questions in plain English — and lays the foundation for the broader enterprise AI build-out.
After generating the use-case list, the team chose to take "a tool that responds by application and builds a custom page by pulling from all content sources" through the AI Use-Case Canvas — a structured exercise that pressure-tests an idea across thirteen dimensions before resources get committed. Here's what surfaced, in the team's own words.
Built to balance momentum with foundation-laying. The Now bucket gives the team early wins and confidence. Next builds the platform layer. Later is where the visionary bets — the instrument-as-AI-surface — land.
All 52 ideas, preserved in the team's own language. Filter by pillar to see what came up where.