Where are you on the AI Maturity Curve?

A 5-minute diagnostic based on the Ultrathink AI Maturity Curve. Get a clear, honest view of where you are on the journey from AI pilots to production systems that actually move the P&L.

Identify your current AI maturity stage (1–5)
See how you score on People, Process, and Technology
Get practical next steps you can use with your team tomorrow

The Ultrathink AI Maturity Curve showing 5 stages: AI Mandate No Mechanism, Pilot Purgatory, Connected Copilots on a Shared Platform, Production AI Systems, and AI-Native Operating Model

What you'll get in 5 minutes

This isn't a generic "AI readiness" quiz. It's a quick, opinionated snapshot of how mature your ability is to turn AI into production systems that move the P&L.

You'll walk away with:

Your AI Maturity Stage on the Ultrathink AI Maturity Curve
A People / Process / Technology breakdown
3–5 specific recommendations tailored to your stage

What we actually measure

Most AI maturity models obsess over tools and data warehouses. We care about something simpler:

Can you repeatedly ship AI systems that survive contact with production and move the P&L?

To get there, we look at three dimensions:

People

Who owns AI outcomes? How AI-literate are your leaders and domain experts? Are they empowered to drive bottom-up innovation inside clear guardrails?

Process

Do you have a real path from idea → experiment → production → iteration, or are you stuck in pilot purgatory? How are AI initiatives prioritized and governed?

Technology

Do you have a modern AI application stack (Axon™-style) that many teams can build on, or does every pilot reinvent the plumbing?

Why we built the AI Maturity Curve and this assessment

The market is flooded with AI pilots and strategy decks. Research from firms like BCG and McKinsey shows that only a small minority of companies are actually seeing material P&L impact from AI, even though almost everyone has a mandate and a roadmap.

In our work with clients, we kept seeing the same pattern:

• Lots of AI activity, very few systems anyone would bet a KPI on
• Partners paid by the hour, not by the outcome
• Confusion about where to start, and what "good" looks like

The Ultrathink AI Maturity Curve and this assessment are our way of cutting through the noise. It's a fast, honest conversation starter about where you really are today—and what "one stage up the curve" could be worth to your business.

Takes about 5 minutes

Question 1 of 12 People

Leadership & ownership

Which best describes leadership's involvement in AI today?

a We have an AI "mandate," but no clear owner or accountability for outcomes.

b We have a central AI/data team, but business leaders mostly watch from the sidelines.

c Specific executives sponsor AI initiatives tied to their KPIs.

d AI outcomes are embedded in leadership scorecards, and domain leaders co-own results with tech.

Question 2 of 12 People

Team skills & literacy

How AI-literate are the teams who would use or own AI systems?

a A few enthusiasts; most people are confused or skeptical.

b We've run some training, but AI is still seen as "someone else's job."

c Key teams (support, ops, eng, etc.) have hands-on experience with AI in their own workflows.

d AI-augmented work is normal; most teams have clear ideas for where AI can help next.

Question 3 of 12 People

Org structure

How is AI work currently organized?

a It's ad hoc. Whoever has time runs experiments.

b We have a central AI/DS team handling most requests.

c We have a central platform/COE plus "AI champions" in a few business units.

d We run a hybrid model: central platform + embedded product/AI teams in major domains.

Question 4 of 12 People

Trust & adoption

How much do business stakeholders trust AI systems today?

a They mostly see AI as risky or a toy; they won't stake KPIs on it.

b They trust AI for internal experiments, but not for customer-facing decisions.

c They trust specific copilots/workflows that have proven themselves.

d They treat well-governed AI systems like any other production system: trusted, monitored, and escalated when needed.

Question 5 of 12 Process

Path from idea to production

What happens when someone has a new idea for using AI?

a It gets discussed in a meeting, then dies in someone's inbox.

b It may become a pilot, but there's no consistent process or criteria.

c We have a known path: idea → experiment → go/no-go → scale, at least in some teams.

d All major AI initiatives run through a portfolio process with owners, business cases, and kill criteria.

Question 6 of 12 Process

Portfolio and prioritization

How do you decide which AI initiatives to fund?

a We don't. It's opportunistic—whatever vendor or stakeholder shouts loudest.

b We prioritize pilots, but not systematically against P&L impact or feasibility.

c We prioritize based on impact vs. effort in a few domains.

d We deliberately rank opportunities based on P&L impact, feasibility, and risk, and revisit that ranking regularly.

Question 7 of 12 Process

Governance & guardrails

How do governance and risk show up in your AI work?

a Either we don't think about it, or it shows up as "no, you can't do that."

b We have policies, but they're hard to interpret and slow experiments down.

c We have clear guidelines and approval flows for different risk levels.

d Governance is built into how we design, review, and ship AI systems; it enables many teams to move fast safely.

Question 8 of 12 Process

Change management & rollout

What happens after an AI system is "done"?

a "Done" usually means we showed a demo. Adoption is an afterthought.

b We do some training, but usage fades after the launch email.

c We have playbooks for rollout, training, and feedback in a few teams.

d Every production system has an adoption plan, feedback loops, and clear mechanisms to iterate based on real-world use.

Question 9 of 12 Technology

Platform & stack

Which best describes your AI tooling and platform today?

a We mostly use off-the-shelf tools and web UIs (ChatGPT, etc.).

b Each pilot spins up its own architecture (APIs, RAG, front-end).

c We have the beginnings of a shared AI platform (connectors, retrievers, basic MLOps).

d We have a robust modern AI stack (Axon-style) that multiple teams build on, with shared components and standards.

Question 10 of 12 Technology

Integration into workflows

Where does AI "live" in your organization?

a In the browser—separate from the tools people actually use.

b We've embedded AI into one or two workflows as a sidebar or add-on.

c Several core tools (CRM, ticketing, IDEs, internal portals) have integrated AI copilots.

d AI is deeply embedded into critical workflows, owning specific steps end-to-end with clear handoffs.

Question 11 of 12 Technology

MLOps & observability

How do you monitor and manage AI systems in production?

a We don't have AI systems in production yet.

b We log some things, but there's no consistent monitoring or rollback strategy.

c For some systems, we track performance, errors, and user feedback, and can roll back changes.

d We have CI/CD/CT pipelines, automated evaluation, dashboards, and alerting for all major AI systems.

Question 12 of 12 Technology

Breadth & depth of AI systems

How many workflows have "real" AI systems in production today?

a None. We're still in pilot / exploration mode.

b One or two workflows have partial automation or copilots.

c Several workflows across multiple functions rely on AI for day-to-day work.

d AI is part of how we operate in multiple core value streams (e.g., CX, underwriting, claims, ops, finance).

Free, 5-minute assessment

Get your AI Maturity Curve results

See exactly where you landed on the Ultrathink AI Maturity Curve—plus how you scored on People, Process, and Technology, and the next moves we'd recommend for your stage.

We'll also email you a summary so you can share it with your team.

Your AI maturity stage (1–5)
A People / Process / Technology breakdown
3–5 practical recommendations tailored to your answers

Stage 3

What This Means

Your Scores by Dimension

People 0%

Leadership, skills, org structure, and trust

Process 0%

Path to production, prioritization, governance, and rollout

Technology 0%

Platform, integration, MLOps, and system breadth

The Synapse Cycle™

Ultrathink Axon™

The Outcome Partnership

AI Maturity Assessment

The Signal

The Modern AI Application Stack