AI Readiness

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First Name*

Last Name*

Business Email*

Does leadership know where AI fits — and is there a plan and an owner behind it?

Q1. How would you describe your organization's current AI strategy? Are you just beginning to explore possibilities, or do you have a clear roadmap for creating business value with AI?*

We haven’t started thinking about it formally

We’re discussing it but nothing is documented

We have a documented strategy

Active roadmap with some defined KPIs and owners

Board-level roadmap, funded, with KPIs tied to the P&L

Q2. Who is currently responsible for guiding AI decisions and helping your organization turn AI opportunities into action?*

Nobody — completely ad-hoc

IT handles it informally

Designated AI lead or working group

Formal AI leadership with exec sponsorship

Chief AI Officer / cross-functional governance

Successful AI starts at the top, with guardrails. How aligned and governed are you?

Q3. How involved is your leadership team in your AI journey? Are executives actively supporting and guiding AI adoption?*

Leadership isn’t involved

Leadership aware but not driving

1–2 executives championing AI

CEO/senior leadership driving company-wide

Leadership publicly champions AI as a differentiator

Q4. What approach do you currently have for AI governance, including security, data usage, acceptable use, and selecting AI tools?*

Nothing — no guardrails at all

Basic inherited IT policies

AI-specific policies drafted, not enforced

Formal governance with compliance

Mature governance with continuous auditing

The biggest barrier to AI isn’t tech — it’s people. How ready is your team?

Q5. How comfortable are your teams with understanding what AI can do, where it can help, and how to use it effectively?*

Very low / unsure — skeptical or unfamiliar

Mixed — a few early adopters, most don’t know where to start

Growing — some training, adoption spreading

Generally strong — most employees use AI daily

High — an AI-first mindset is the norm

Q6. What AI training or enablement have you provided to help employees build confidence and apply AI in their work?*

No training

Ad-hoc / self-directed only

Structured training for some teams

Ongoing, role-based curriculum

Continuous learning — embedded in onboarding

Q7. How would you describe your organization's culture around AI? Are people hesitant, curious, experimenting, or actively embracing AI?*

Resistance — pushback from leadership or staff

Cautious — aware but hesitant

Cautious curiosity — open but unsure where to start

Receptive — people are experimenting willingly

Active enthusiasm — a culture of continuous improvement

AI runs on data, and value only counts if you measure it. How solid is your foundation?

Q8. How prepared is your organization’s data foundation for AI? Consider data quality, accessibility, and how you manage sensitive information.*

Data is scattered; no policy on what goes into AI

Some data organized; sensitive-data handling is informal

Mostly centralized; basic rules for AI data use

Well-organized and accessible; documented data policies

Reliable across systems; governed, audited AI data flows

Q9. How are you measuring AI adoption and business impact today? Are you tracking usage, outcomes, or return on investment?*

Not tracking anything

Anecdotal wins, no formal measurement

Some metrics for specific projects

Defined KPIs, regular leadership reporting

Full measurement framework — in the P&L

What’s actually deployed, connected, and delivering value today?

Q10. What AI tools or platforms is your organization currently using, and how broadly are they being adopted across teams?*

None, or just exploring

1–2 tools, mostly individual (e.g. personal ChatGPT)

Multiple tools across several teams

Integrated AI stack with ownership and automation

Custom / proprietary solutions + managed ecosystem

Q11. How connected are your AI tools to the systems where your teams do their work, such as CRM, ERP, ticketing, or other business platforms?*

No integrations — everything siloed

Minimal or manual only

Some integrations with key systems

Solid integrations with automated workflows

Deep bi-directional + agentic workflows end-to-end

Q12. Where is AI already helping your organization save time, improve processes, or create better outcomes? How are you measuring that impact?*

Not yet — still early stages

A few individual wins, nothing measured

Multiple wins in specific areas; starting to measure

Measurable ROI in several areas; tracked and reported

AI is core to operations — it’s in the P&L

Q13. How are you currently using AI to improve customer experiences, create revenue opportunities, or deliver value externally?*

Purely internal, limited

Internal only, early results

Exploring customer-facing applications

Deploying AI in customer-facing workflows

AI is part of the customer value proposition

Q14. Are there repetitive, multi-step workflows across your systems where AI could help automate work or connect processes more effectively?*

Not really / not sure

A few, all handled manually

Several clear candidates we’d like automated

Some automated; more we’d like AI to connect and act on

Many automated end-to-end; connecting internal systems is a priority

R1. How is your organization currently using Claude or other AI assistants?*

No — not using Claude

Personal / ad-hoc use only

Team licenses, but not standardized

Enterprise, governed deployment

R2. Does your organization have internal engineering or development resources available to support AI initiatives?*

A small/contract dev capability

Yes — a dedicated engineering team

R3. Where would you most like to focus your next AI investment?*

Train our people to use AI well

A plan for where to start

Get the whole org onto one governed AI platform

Connect / coordinate the tools we already have

Build custom AI agents for our workflows