How it works

From talking about it
to building it — in ~30 days.

"We keep talking about this." In about 30 days, your team will have done something about it.

Get started  →
A different approach

Everyone's working.
Nothing's compounding.

Most organizations are running several AI initiatives at once — a committee, a fellows program, a center of excellence, individuals figuring it out alone. None of it connects. Pend brings the same people into one engagement that compounds.

What most organizations are doing right now
COMMITTEETEAMSDEPARTMENTSINDIVIDUALS

  • Each group produces its own version: policies, tools, methods, one-off workshops. Nothing connects.
  • Six months in, you have documents. Twelve months in, they're out of date.
  • People are working hard. Nothing compounds.
The engagement

How the work flows.

Four phases, configured to your organization. People-first AI enablement, built with your team in real time.

01
Align
1–2 weeks

Run the diagnostic. Surface where leadership and staff actually stand. Build the shared baseline.

02
Configure
90-min session

Walk the findings together. Decide which four artifacts your team will build in the working sessions.

03
Build
4 working sessions

Your team builds the tools, policies, and methods in the room. Same day, same week — not a project plan.

04
Compound
Ongoing

What your team built spreads to other departments and teams. Re-run the diagnostic next year to measure what moved.

The starting line

Find out where everyone is with AI.

Every AI initiative is built on assumptions about where people stand — how they use AI, what they trust, what they need. The diagnostic replaces those assumptions with measurement, and gives every part of your organization the same starting line.

01 — Mindset

How people think about AI.

Five tensions structure how people make AI decisions through their actual work — Priorities, Strategy, Expertise, Trust, Identity. The diagnostic measures where each individual lands, then aggregates into orientation profiles and role-level patterns.

  • Where the organization sits on each of the five tensions
  • Which tensions are unified and which are split
  • Where leadership and staff diverge
  • The orientation breakdown across leadership and staff
Mindset · sample excerptFrom the organization report
Orientation distribution
24%31%45%HUMAN-LEDADAPTIVEAI-FORWARDcareful, deliberatesituationalcore to their work
02 — Capability

How people actually work with AI.

Three working relationships emerge from how people work with AI — Author (directs the work), Collaborator (figures it out together), Delegator (assigns and moves on). The diagnostic identifies who's who, and what's compounding underneath each pattern.

  • Who's building real fluency vs. who's just moving faster
  • Which types concentrate in which roles
  • Where invisible capability gaps will show up later
  • What use cases are core practice and which are still occasional
Capability · sample excerptFrom the organization report
Type distribution
43%
Author
Directs the work. AI extends their thinking.
36%
Collaborator
Works with AI as a thinking partner.
21%
Delegator
Assigns work to AI. Uses what comes back.
Manager layer · type breakdown
Author
38%
Collaborator
24%
Delegator
38%
03 — Usage

What you provide vs. what people build on their own.

The diagnostic measures what the organization has put in place — tool access, training, support — alongside what people are doing independently: shadow tools, self-directed learning, self-built workflows. The gap between the two is where the real story lives.

  • What people say the organization actually provides
  • What they're using outside the official stack
  • The shadow gap — and what it's telling you
  • The lowest-scoring item, named directly
Usage · sample excerptFrom the organization report
The shadow gap
PROVIDED BY THE ORGANIZATIONBUILT BY PEOPLE THEMSELVES58%88%"I know how to apply the tools to my work""I use AI tools the organization hasn't provided"+ 30 PTSTHE GAP100 dots · 100 percent100 dots · 100 percent

Same population. Same scale. The score for using tools the organization hasn't provided runs 30 points ahead of the score for knowing how to apply the tools it has. They're the same problem.

Step 2 — The opportunity set

Design the work around your people.

The diagnostic gives everyone the same starting point. This session uses it. In 90 minutes, leadership and staff look at the findings together and decide: which problems are worth solving first, in which domains, for which people. No more AI decisions made by whoever yelled loudest. The data is in the room.

  • Which domains your data points toward
  • Where the highest-leverage interventions are
  • What your team will build in the working sessions
  • Who needs to be in the room for each one
VP
"How do we find the balance between using AI to reduce costs and retaining a people-first team?"
VP Marketing · Consumer Brand
FA
"How do we coach students to use it as a tool and not an end product?"
Faculty · Regional University
IT
"How concerned should we be about data security?"
Head of IT · Professional Services Firm
VP
"We are drowning in academic integrity violations — help us dig out and plan forward."
VP Academic Affairs · Independent School
Step 3 — The use cases

Build what your organization actually needs.

Four working sessions. Each one produces something your team uses immediately — not a recommendation to implement later. A tool, a policy, a workflow, a playbook. Built in the room and deployed the same week. What you build depends on where your data pointed.

  • Governance frameworks your team can actually use
  • Tools your staff picks up immediately
  • Workflows that eliminate the recurring admin work
  • Decision filters that route AI proposals without a committee
Governance Matrix · sample toolBuilt in session
Click one from each grid. See your approach.
Step 1 — Stakes
Monitor
High people · Low cost
High risk
High people · High cost
Low concern
Low people · Low cost
Investigate
Low people · High cost
Step 2 — Leverage
Aware but exposed
High visibility · Low control
Full leverage
High visibility · High control
Uncharted
Low visibility · Low control
Untapped
Low visibility · High control
Experiment zone
Principles-based
Efficiency zone
Standards-based
Accountability zone
Policy + Contract
Danger zone
Increase visibility first

Built in one session. Routes every AI decision that lands on a desk — without re-litigating the same questions each time.

What it actually looks like

What people-first AI enablement actually looks like.

Day 1
You finally see where everyone actually stands.
The diagnostic runs across your team. For the first time you're not working from assumptions about where leadership and staff are with AI, what leadership actually believes, or why every AI conversation stalls in the same place. The data is specific to your organization. You read it and recognize your colleagues in it.
Week 1
You make the decisions you've been circling.
One 90-minute session. Your team looks at the findings together and decides which problems are worth solving first — not by consensus or whoever speaks loudest, but by what the data actually shows. You leave the room with a prioritized direction. The committee conversation is over.
Weeks 2–4
Your team has built something real.
Four working sessions. Each one produces something your team uses immediately — a tool, a policy, a workflow, a playbook. Not a deck. Not a recommendation. The thing itself, built in the room and deployed the same week.
30 days and beyond
It compounds.
What your team built spreads. Other departments ask for it. You invite a new team. You run the diagnostic again in six months and you can see exactly what moved. You're not starting over — you're building on what's already there.
Common questions

What people ask before they start.

How much of our team's time does this actually take?

The diagnostic is 15 minutes per person. The configure session is 90 minutes for the leadership team. Each working session runs about two hours. Total time commitment for someone at the center of the engagement is roughly one day spread across 30 days. Everyone else touches it once — the diagnostic — and gets to use what their colleagues built.

Can we start with just the diagnostic?

Yes. Some organizations run the diagnostic first to understand where they are before committing to the working sessions. The report stands on its own. Most find that the configure session is the natural next step — the data surfaces questions that are hard to resolve without a structured conversation.

How is this different from bringing in a speaker or forming a committee?

A speaker leaves. A committee produces a document. Pend produces something your team built themselves — which means they understand it, own it, and actually use it. The diagnostic also replaces the assumptions that drive most committee work: instead of debating where the organization is with AI, you start from data.

What do we actually own at the end?

Everything your team builds in the working sessions is yours — the tools, the frameworks, the policies, the workflows. We don't retain any rights to what gets created. The diagnostic report is also yours and remains confidential.

Can we run the diagnostic again to measure what changed?

Yes, and this is how the data becomes genuinely useful over time. A second deployment six to twelve months later shows you exactly what moved — did the leadership gap narrow? Did shadow tool usage shift after the working sessions? We discount repeat engagements significantly.

How is anonymity protected at small organizations?

Reports are presented at the group level — role and department, never individual. Where group sizes are small enough that individuals could be identifiable, we suppress or aggregate those cells. No individual is identifiable from any finding in the report.

What happens to our data?

Individual responses are confidential and never shared. Your organization's results are yours — we hold them in confidence and don't sell or publish them. With explicit permission, anonymized aggregate findings may inform published research. Without it, your data informs only your organization.

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