LI-COR + Onset · Pre-Workshop Field Notes · May 2026

Eight voices.
One inflection point.

Before we walk into the workshop together, your team told us where they stand on AI — honestly, unevenly, and with a clarity that's harder to find than you'd think. This is what the responses say, read together, with the contradictions left in.

Responses 8 of 14 invited
Window April 24 → May 12, 2026
Companies LI-COR + Onset HOBO
Headline signals
4.4/10 Average clarity of long-term AI vision (range: 1 → 7)
88% Cite accuracy as a top-3 concern — the single most shared anxiety
100% Agree leadership is ready to redesign roles & processes for AI
62% Self-rate the team as novice or basic on AI skill
01 · Where the org sees itself

A team that has started, but hasn't yet scaled.

Half of respondents place LI-COR in the "Activation" stage — running early pilots to see what sticks. Just under 40% feel parts of the business are already operational. Only one voice is still in pure awareness mode. That distribution alone tells you the workshop's job: move energy from scattered pilot to shared playbook.

12.5%
N = 1
Awareness
Interest is growing, real adoption is minimal.
50%
N = 4
Activation
Early pilots are underway to test value and learn fast.
37.5%
N = 3
Operational
Scaling AI at production level across the org.
"ChatGPT is used for content generation… GenSpark is used for data analysis. Small group of users, not good enough enterprise adoption yet."
From a respondent in · Activation
02 · The competitive lens

No one thinks LI-COR is winning the AI race. And no one thinks it's losing badly.

Asked to benchmark against their nearest competitors, the team clusters tightly around the middle. Three say "about the same," three say "slightly ahead," only two see the company as behind. The shape of this answer matters more than the number — it's a team that believes the gap is closable in 12 months.

03 · The vision paradox

Some leaders see a documented strategy. Others see nothing at all.

On a 0–10 scale of long-term AI vision clarity, the team's responses range from 1 to 7 — an enormous spread for a single organization. The split isn't random. Three respondents rated clarity at 6 or 7. Four rated it at 3 or below. The same company, the same year, two different mental models of "what we're doing."

Individual vision-clarity scores (0–10)
1
2
2
3
6
7
7
7
01234 5678910
Mean: 4.4 · Median: 4.5 · Range: 1–7
The distribution is bimodal — there's no consensus middle, only a fork.
"Non-existent."
1 respondent · Rated vision clarity 1/10
"Documented and communicated."
3 respondents · Rated vision clarity 6 or 7/10
What this implies for the workshop:

The exercise isn't "create a vision." It's surface the one that already exists, decide who owns it, and make sure it's the same vision being told in finance, marketing, science, and IT.
04 · The capability honesty check

The team is refreshingly self-aware about its AI skill level.

Asked to rate their team's overall AI skill from novice to advanced, half called themselves novices. Only one respondent claimed "advanced." This isn't a problem — it's an asset. Skill modesty paired with leadership ambition is the cleanest setup for an enablement-led plan.

Self-rated team skill (1 = Novice → 4 = Advanced)
1 · Novice
50%
4 of 8
2 · Basic
13%
1 of 8
3 · Intermediate
25%
2 of 8
4 · Advanced
13%
1 of 8
Team-wide mean: 2.0 (Basic). Even the most generous reading puts this at the bottom of the maturity curve — and the workshop is the moment to acknowledge it without flinching.
"Skill level is a coaching problem, not a hiring problem. Eight people who can tell you exactly where they are is a faster starting point than eight people pretending to be further along."
CoPilot Innovations · workshop framing note
05 · The foundation audit

Strong on leadership and culture. Weak where it costs the most: data, processes, governance.

Seven foundational components were rated by every respondent. The pattern that emerges is sharp: leadership and innovation-friendly culture come out strong, while the underlying scaffolding — data infrastructure, documented processes, IT governance, budget — is where the work hasn't happened yet. These are the dependencies the workshop has to make visible.

Component R1R2R3R4R5R6R7R8 Avg
Leadership sponsorship of AI 3 4 4 5 5 4 3 4 4.00
Company culture for innovation 3 4 3 5 3 4 3 4 3.63
IT Security & AI governance 2 2 3 3 3 3 2 5 2.88
Budget for AI talent & tools 3 2 2 4 3 3 2 4 2.88
Legacy technology infrastructure 2 1 3 3 3 3 4 4 2.88
Documented processes & procedures 1 1 3 4 2 3 2 5 2.63
Data infrastructure (cloud, lakes) 2 1 2 3 2 3 2 2 2.13
1 · Very Poor 2 · Needs Improvement 3 · Adequate 4 · Strong 5 · Excellent R1 – R8 represent the eight individual responses, ordered chronologically and anonymized
▲ TWO STRONGEST
Leadership sponsorship and culture for innovation are the two highest-rated foundations — both above 3.6/5.
▼ TWO WEAKEST
Data infrastructure (2.1) and documented processes (2.6) are the lowest — exactly the foundations AI scales on.
06 · What worries them

Accuracy is the shared anxiety. Security is the close second.

Asked to name their top-three concerns about AI adoption, the team converges on a tight pattern. Seven of eight respondents flagged accuracy. Five flagged security. The combination tells you something specific: the team isn't worried about whether AI will work — they're worried about whether they can trust the output in a scientific instruments business where being wrong has consequences.

Accuracy
7 mentions 88%
Security
5 mentions 63%
Bias
3 mentions 38%
IP Leakage
3 mentions 38%
Change Management
2 mentions 25%
Cost
1 mention 13%
Unclear ROI
1 mention 13%
Each respondent could select up to three concerns. Percentages = share of respondents who included that concern.
07 · What would unlock adoption

Three asks tied for first place — and they're not technical.

The team's enablement priorities cluster around training, approved tools, and a use-case playbook. These are the three signals that AI adoption gets stuck when the org skips the "what's allowed and how" conversation — not when it lacks raw capability.

5 / 8
Training
Structured learning paths. Cited by 63% of respondents — the most-named enabler.
5 / 8
Approved Tools
Clarity on what is sanctioned for what use. The "what can I actually use?" question, answered.
4 / 8
Use-Case Playbook
Concrete, applied examples. "Show me what good looks like in my function."
3 / 8
Internal Champions
Peers leading from within their function — not consultants pushing from outside.
2 / 8
Technical Support
Help when something breaks or a workflow needs a developer's hands.
2 / 8
Leadership Mandate
A clear "yes, do this" from the top. Not optional. Not later.
"
Digestible, demonstrative successful internal "test" project samples for inspiration and examples.
Custom write-in answer from one respondent
08 · Where AI could move the needle

A three-way tie at the top: customer experience, data, marketing.

Across all eight respondents, three areas tied for first place as the most significant competitive advantages AI could unlock in the next 12 months. They map almost perfectly to the three POCs already on the workshop agenda — Page Factory, Instrument Concierge, Ask the Business.

Customer / Member Experience
6 / 8
Data Analysis / Business Intelligence
6 / 8
Marketing & Sales Personalization
6 / 8
Operational Efficiency / Process Optimization
4 / 8
Product / Service Innovation
1 / 8
Supply Chain & Logistics
1 / 8
Risk Management / Compliance
1 / 8
UX optimization / friction-point fixes
1 / 8
Adding new capabilities
1 / 8
09 · Workflows on the wishlist

What they want fixed, in their own words.

The team was asked for the top three workflows they'd most want to improve with AI or automation. These are the actual responses, lightly grouped — and a remarkably consistent picture of customer-facing motion, content velocity, and internal knowledge access emerges.

Respondent 01
  • Customer education as sales front line
  • Smooth, pleasant online ordering
  • Rapid web & marketing updates
Respondent 02
  • Jira ticket building
  • Marketing automation
  • Marketing team processes
Respondent 03
  • Updating & pruning outdated content
  • Building FAQs in strategic places
  • Amazon optimization (limited hours)
Respondent 04
  • Customer onboarding
  • Technical support
  • Customer outreach
Respondent 05
  • Tender / contracts review
  • ISO metrics & documentation
  • Science & support
Respondent 06
  • Reporting
  • Sales process
Respondent 07
  • Customer engagement
  • Prospecting
  • Order entry
Respondent 08
  • Finding info internally & synthesizing
PATTERN 01
Customer-facing motion
Onboarding, support, outreach, engagement, prospecting, ordering. Mentioned by five respondents — the workshop's most defensible POC space.
PATTERN 02
Content velocity
Web updates, FAQs, Amazon listings, marketing automation, content pruning. The "Page Factory" thesis lives here.
PATTERN 03
Internal knowledge access
"Finding info internally and synthesizing," ISO docs, reporting, tender review. The "Ask the Business" thesis lives here.
10 · Already in flight

AI is in the building. It's just not enterprise yet.

When asked for an AI use case already implemented or in progress, every respondent had an answer. That's the punchline of the entire survey: adoption isn't zero — it's individual. The workshop is the forcing function to convert these scattered pilots into a shared, executive-sponsored program.

ChatGPTGenSpark
"Content generation, blog posts, social reuse… and GenSpark for data analysis, modeling, Excel and PowerPoint development."
— Anonymous respondent
Data Analysis
"Analyzing data sets for trends, opportunities and strategy enhancements."
— Anonymous respondent
Web Funnels
"Using AI to analyze web page sales funnels and suggest enhancements."
— Anonymous respondent
Commerce Data
"Data analysis — order trends."
— Anonymous respondent
ComplianceInventory
"Security breach and risk to GDPR; data analysis around inventory management."
— Anonymous respondent
GenSpark
"PowerPoint creation with GenSpark."
— Anonymous respondent
ClaudeChatGPT
"Code development utilizing Claude. Market research utilizing ChatGPT."
— Anonymous respondent
Meeting Capture
"Automated note-taking."
— Anonymous respondent
11 · The leadership mandate

Every respondent agrees the leadership is ready. Not one disagreed.

The survey closed with a single statement to react to: "Our leadership is ready & willing to challenge and redesign roles, processes, and decision-making models when AI offers a better way forward for the organization." The result is unambiguous.

100%
Agree or Strongly Agree that leadership is ready to redesign roles, processes, and decision-making for AI.
4
Strongly Agree
4
Agree
0
Neutral or Disagree
12 · The throughline

A team aligned on why, divided on how, and waiting for someone to own the playbook.

Read together, the eight responses describe a single posture. Leadership intent is settled. Customer and commerce ambition is clear. The blockers are foundational — data, processes, governance — and the asks are practical — training, approved tools, a real playbook. The workshop's job is to translate that posture into a program.

01 · WHERE TO START
Commerce + customer is the wedge.
Six of eight respondents named customer experience, BI, and marketing personalization as the biggest 12-month opportunities. The three workshop POCs — Page Factory, Instrument Concierge, Ask the Business — line up exactly with where the team is already pointed.
02 · WHAT TO RESOLVE
Vision clarity is the silent gap.
A 1-to-7 range on vision clarity means the org isn't telling itself one story. The deliverable from this workshop has to be a single articulated AI thesis that finance, marketing, science, and IT all recognize as theirs.
03 · WHAT TO BUILD NEXT
Foundations, then velocity.
Data infrastructure (2.1/5), documented processes (2.6/5), and governance (2.9/5) are the lowest rated foundations. They are also the prerequisites for the agentic and enterprise-scale work that comes after this workshop. Sequence them in.