Acme AI IQ organizational baseline scorecard: maturity distribution, dimension scores, team-level heatmap, at-risk and model teams, within- and between-team variation, key findings, and recommended actions.
Where to lean in, where to intervene, and what to do in the next 30–60 days.
The raw ingredients are here: Identity & Roles (4.06/6) and Effective Teaming (4.06/6) lead the five dimensions. AI wins are still individual achievements, not yet a team capability. More than half of the variation in perceived maturity is within teams, placing you at Level 3: Defined maturity, with an overall AI IQ of 58/100. Find out more about maturity levels and the 5 pillar AI enablement model here.
Maturity estimates, where our people are
Five dimensions of AI enablement (avg out of 6)
NOTABLE TEAMS
| Team | IDENTITY | TEAMING | PROCESS | LEARNING | VISIBILITY | OVERALL |
|---|---|---|---|---|---|---|
| Applied Science (n=2) | 3.50 SD 3.54 | 4.50 SD 2.12 | 3.50 SD 2.12 | 4.00 SD 1.41 | 4.00 SD 1.41 | 3.90 |
| Delivery Solutions (n=3) | 4.67 SD 1.15 | 4.33 SD 2.08 | 3.67 SD 2.52 | 3.33 SD 1.15 | 3.33 SD 2.08 | 3.87 |
| Communications (n=2) | 5.00 SD 1.41 | 5.00 SD 1.41 | 3.00 SD 1.41 | 3.50 SD 0.71 | 2.50 SD 0.71 | 3.80 |
| Management (n=4) | 4.25 SD 0.96 | 4.50 SD 1.00 | 2.50 SD 1.00 | 3.75 SD 0.96 | 3.75 SD 1.89 | 3.75 |
| Product, Data and Engineering (n=10) | 4.90 SD 1.60 | 4.40 SD 1.51 | 2.70 SD 1.34 | 3.40 SD 0.97 | 3.30 SD 1.16 | 3.74 |
| Strategic Partnerships (n=3) | 3.67 SD 1.53 | 3.67 SD 1.53 | 4.00 SD 1.00 | 3.33 SD 2.08 | 3.33 SD 1.53 | 3.60 |
| Country Engagement (n=6) | 4.17 SD 0.75 | 4.50 SD 0.55 | 2.83 SD 0.41 | 3.67 SD 1.03 | 2.67 SD 1.75 | 3.57 |
| People & Culture (n=3) | 4.00 SD 0.00 | 4.00 SD 1.00 | 2.67 SD 1.15 | 3.67 SD 0.58 | 2.67 SD 0.58 | 3.40 |
| Connectivity Credits (n=4) | 2.50 SD 2.65 | 4.75 SD 1.26 | 2.50 SD 1.73 | 3.75 SD 1.89 | 3.25 SD 2.06 | 3.35 |
| Tech Product, Data and Engineering (n=2) | 4.00 SD 0.00 | 4.00 SD 0.00 | 3.00 SD 1.41 | 3.50 SD 0.71 | 2.00 SD 1.41 | 3.30 |
| Partnerships and Fundraising (n=4) | 3.25 SD 2.22 | 3.25 SD 2.75 | 3.00 SD 2.94 | 3.50 SD 1.29 | 2.25 SD 1.71 | 3.05 |
| Tech Advocacy (n=2) | 4.50 SD 0.71 | 3.50 SD 0.71 | 2.00 SD 0.00 | 2.00 SD 0.00 | 3.00 SD 1.41 | 3.00 |
| Enablement (n=4) | 4.00 SD 1.41 | 2.25 SD 2.06 | 2.25 SD 0.96 | 3.50 SD 0.58 | 2.75 SD 0.96 | 2.95 |
| Finance (n=2) | 3.00 SD 0.00 | 3.50 SD 0.71 | 3.00 SD 0.00 | 2.50 SD 2.12 | 2.50 SD 0.71 | 2.90 |
| All Teams (n=51 respondents) | 4.06 | 4.06 | 2.84 | 3.45 | 3.00 | 3.48 |
Where does the variation appear?
Which teams are DIVERGENT, and which are CONSISTENT?
Identity & Roles (4.06) and Effective Teaming (4.06) are the two highest dimensions. People know what they bring to a human–AI team and they work together to apply it, the foundation everything else builds on.
Process Orientation is the lowest dimension at 2.84/6, and most of the variance is within teams. Many team members are limiting themselves to automating tasks, but some are already thinking about outcomes.
Among teams with n>2, Enablement is the lowest scoring team (14.8/30), and Partnerships & Fundraising (15.3/30) sit just above. Both could benefit from structured support before org-wide plays land.
82% Experimenters or Shapeshifters, but 0 Optimizers, 0 Observers, and only 2 Integrators among 51. Lots of experimentation but results may be lacking due to gaps in process design, governance and integration.
Acme AI Value Creation Signals: where value is showing up, who to elevate, blockers to remove, and what people want next.
A look at wins by team, colleagues peers point to, the blockers to remove, and what people want to do next.
AI use is concentrated in writing, docs & presentations and research, data & insights; blockers are mostly tool access & licenses and budget, credits & cost. A small group of innovators is already leading the way.
WIN THEMES (share of 50 answers)
Wins by team, most concrete wins shared
Our read: the single most promising thread in this data
Marco Bellini (Product, Data and Engineering) is the clearest value signal in this survey. When people were asked what AI work deserves scaling, 13 colleagues across 9 different teams (Enablement, Strategic Partnerships, Country Engagement, Management, Communications, Tech Product, Data and Engineering, Product, Data and Engineering, Delivery Solutions, Connectivity Credits) independently named Marco — the widest reach of any name cited — and what they credit is consistent: prototyping & building and training & enablement.
In a colleague's words: “I think a lot of people have been using AI to automate a lot of their workflows, building POCs, creating internal tools which everyone can use like Lucia, Marco, etc.”
Marco's own account of the win: “Yes. I created a brand asset manager that helped the communications teams at Acme and ITU access brand resources and social media materials more quickly, reducing dependencies on the design team…”
Most-cited colleagues, interview shortlist
| Colleague | Mentions | Peers | Department / team |
|---|---|---|---|
| Marco Bellini | 13 | 13 | Product, Data and Engineering / Product, Data and Engineering |
| Oliver James Grant Hughes | 4 | 4 | Connectivity Credits / Connectivity Credits |
| Diego Alberto Sanz Moreno | 3 | 3 | Applied Science / Applied Science |
| Emre Kaan Yıldız | 3 | 3 | Applied Science / Applied Science |
| Rohan Mehta | 3 | 3 | Product, Data and Engineering / Product, Data and Engineering |
| Jonathan Fisher | 2 | 2 | Management / Management |
| Julien Antoine Marat Fontaine | 2 | 2 | Connectivity Credits / Connectivity Credits |
| Lucia Carmen Vega Flores | 2 | 2 | Tech Product, Data and Engineering / Tech Product, Data and Engineering |
| Sabine Kellner | 1 | 1 | Partnerships and Fundraising / Partnerships and Fundraising |
| Rafael Costa Almeida | 1 | 1 | Country Engagement / Country Engagement |
| Otgonbayar Enkhbat | 1 | 1 | Country Engagement / Country Engagement |
| Selin Demir | 1 | 1 | Management / Management |
"Getting access to tools, e.g., subscription to ClaudeCode, as well as time/budget to experiment with AI"
"Enterprise level licenses, access to subscriptions like cowork copilot"
"We are encouraged to use all AI tools but everything but copilot is forbidden by Acme policy."
"Confidentiality and security concerns arise if I vibecode"
"Ability to tap into enterprise level systems (SharePoint and email)"
"Not being able to touch Acme's Microsoft package (Teams, sharepoint, etc) with Claude Code"
"Besides project management, not sure what other AI I can use in learning & development"
"No team wide best practices"
"Resharing of prompts"
"Explore where Acme systems allow me to use agents, new software, new platforms (much seems barred on our proprietary systems)."
"Generating high-quality donor intelligence reports and key messages that do not require hours of fact-checking and fixing"
Repository of approved materials (mission, capabilities, impact metrics, key messages adapted to various users) to build our local LLM model
"Lack of time during work hours to explore. i have been spending my money on external training"
"To share AI projects with others"
Acme personas and people: AI persona by maturity and dimensions, what energizes people, career stage, and persona synergies.
AI Personas describe different styles of engaging with AI, and the distribution of AI Personas on a team reveal collective strengths and gaps. Find out more about AI Personas here.
Acme AI Persona distribution vs. global benchmark
Overall AI Maturity and Dimensions of AI Enablement by AI Persona
| Persona | Overall | IDENTITY | TEAMING | PROCESS | LEARNING | VISIBILITY | |
|---|---|---|---|---|---|---|---|
| Shapeshifter (n=21) | 2.95 | 4.05 | 4.00 | 2.76 | 3.48 | 2.86 | |
| Experimenter (n=21) | 2.90 | 4.19 | 4.24 | 2.81 | 3.52 | 2.90 | |
| Steward (n=4) | 3.00 | 3.75 | 4.00 | 3.00 | 3.00 | 3.00 | |
| Stabilizer (n=3) | 3.00 | 4.00 | 3.67 | 3.00 | 3.33 | 4.33 | |
| Integrator (n=2) | 3.50 | 3.50 | 3.50 | 3.50 | 3.50 | 3.50 | |
| Optimizer (n=0) | — | — | — | — | — | — | |
| Observer (n=0) | — | — | — | — | — | — |
Energizes Me
Career stage
Superpowers: Cross-functional fluency, versatility, adaptability, quick learning.
Brings out their best: Experimenter, Optimizer · Possible friction: Optimizer, Steward.
Superpowers: Curiosity, imagination, boundary-pushing, rapid prototyping.
Brings out their best: Optimizer, Stabilizer, Steward · Possible friction: Observer, Steward.
Superpowers: Ethics, thoroughness, trust-building, advocacy, responsible innovation.
Brings out their best: Optimizer, Integrator · Possible friction: Experimenter, Optimizer.
Superpowers: Strength under pressure, resilience, clear-headedness, steadiness.
Brings out their best: Shapeshifter, Optimizer, Integrator · Possible friction: Experimenter, Shapeshifter.
Superpowers: Collaboration, facilitation, team onboarding, systems thinking.
Brings out their best: Experimenter, Shapeshifter, Optimizer · Possible friction: Experimenter, Observer.
Superpowers: Structure, process improvement, metrics, quality, best practices.
Brings out their best: Shapeshifter, Integrator · Possible friction: Experimenter, Steward.
Superpowers: Risk awareness, critical thinking, thoroughness, thoughtful evaluation.
Brings out their best: Shapeshifter, Integrator, Stabilizer · Possible friction: Experimenter, Shapeshifter.
Observers are the natural risk-spotters — globally the most common persona (21%), entirely absent here. Until you hire or develop these instincts, explicitly assign ownership of risk review and critical evaluation of every major AI initiative to someone on the team.
Acme AI SWOT: strengths, weaknesses, opportunities and threats synthesized from the four free-response survey questions.
Strengths, weaknesses, opportunities and threats synthesized from what 51 people wrote about their wins, what's worth scaling, what's blocked, and what they wish they could do.
S · Strengths — what is already working
“we now use AI for presentations always, this saves alot of time We have a big project in procurement and now have approval to use AI in this process. this is excellent.”
“Yes, I used it to analyze multiple sources of information quickly and develop a report and to quickly compare and align excel files + create new data. The work was faster.”
“Built my own agent on Claude to help me manage my emails. However, i did it after working hours - at work, there is no time I can spend on learning and building with AI.”
W · Weaknesses — where value leaks today
“Not being able to touch Acme's Microsoft package (Teams, sharepoint etc.) with Claude Code (and other AI tools). No Claude Code Enterprise accounts.”
“enterprise level licenses, coordination, access to subscriptions like cowork copilot, clarity on data privacy issues”
“working in HR I would need integration with our core data. confidentiality and security concerns arise if I want to just vibecode something but also lack of integration.”
O · Opportunities — what people are asking for
“explore where Acme systems allow me to use agents, new software, new platforms (much seems barred on our propriatory systems).”
“Generating high-quality donor intelligence reports and key messages that do not require hours of fact-checking and language-fixing, and that do not end up quickly using up Claude Pro tokens”
“I would like AI to help me organize my tasks better. Go through my Acme emails / teams meetings / Slack and Click up, pull out a short summary for each country. Highlight key tasks / action items for me.”
“Luck of time during the worl hours to explore. Mostly, i have been spending my money on external training to learn how to use Claude.”
T · Threats — what could stall the program
“Getting access to tools, e.g., subscription to ClaudeCode and most importantly time to experiment with AI”
“We are encouraged to use all AI tools but formally everything but copilot is forbidden by Acme policy. This makes it very awkward and stressful to use non-copilot types of AI”
“lack of time”