AKKR Ascend · Capstone · GTM Functional Group·Fielded June 2026
An aggregated read of the AI Readiness Survey across 168 go-to-market respondents at 6 AKKR portfolio companies — and the case for which transformation levers move a PortCo from one adoption stage to the next.
The objective
Use the survey to decide where the group should focus its 1–2 deep-dive transformation processes — generic enough to apply at any AKKR PortCo — that move an organization from one AI-adoption stage to the next.
The decision in front of us
Should we focus on education (training the workforce on AI), or on best-practice sharing & an AI repository (capturing and distributing what already works)? The data answers this clearly.
Sections 01–04 update to the selected PortCo. The company comparison and strategy sections below stay portfolio-wide.
01 — Who responded
Sample
Appspace is ~39% of the sample — aggregate read is weighted toward it
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 74% use AI daily or most days (47% every single day) and 79% say they understand the governance guardrails. Yet 43% have had no formal AI training in a year, and understanding of how AI works sits at just 3.51/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
74% use it daily or most days — adoption is real and habitual
Capability
Mean 3.51/5 — competent users, shallow foundations
Capability
Mean 3.28/5
Sentiment
Mean 3.62/5 — tools are fine; the gap is enablement
Investment
68 of 168 have had none
Appetite
60% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 16% say their team uses standardized AI approaches. 31% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 69% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (61%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 49% top-3 selection. Stand-alone training & office hours sits well below at 30%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 68% use AI daily or most days (47% every single day) and 65% say they understand the governance guardrails. Yet 66% have had no formal AI training in a year, and understanding of how AI works sits at just 3.6/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
68% use it daily or most days — adoption is real and habitual
Capability
Mean 3.6/5 — competent users, shallow foundations
Capability
Mean 3.2/5
Sentiment
Mean 3.22/5 — tools are fine; the gap is enablement
Investment
39 of 65 have had none
Appetite
61% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 12% say their team uses standardized AI approaches. 37% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 68% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (63%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 49% top-3 selection. Stand-alone training & office hours sits well below at 25%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 96% use AI daily or most days (54% every single day) and 100% say they understand the governance guardrails. Yet 24% have had no formal AI training in a year, and understanding of how AI works sits at just 3.38/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
96% use it daily or most days — adoption is real and habitual
Capability
Mean 3.38/5 — competent users, shallow foundations
Capability
Mean 3.52/5
Sentiment
Mean 4/5 — tools are fine; the gap is enablement
Investment
7 of 31 have had none
Appetite
61% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 21% say their team uses standardized AI approaches. 21% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 64% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (54%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 44% top-3 selection. Stand-alone training & office hours sits well below at 39%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 71% use AI daily or most days (29% every single day) and 92% say they understand the governance guardrails. Yet 13% have had no formal AI training in a year, and understanding of how AI works sits at just 3.35/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
71% use it daily or most days — adoption is real and habitual
Capability
Mean 3.35/5 — competent users, shallow foundations
Capability
Mean 2.92/5
Sentiment
Mean 3.67/5 — tools are fine; the gap is enablement
Investment
3 of 24 have had none
Appetite
48% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 9% say their team uses standardized AI approaches. 32% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 71% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (62%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 53% top-3 selection. Stand-alone training & office hours sits well below at 39%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 73% use AI daily or most days (59% every single day) and 91% say they understand the governance guardrails. Yet 23% have had no formal AI training in a year, and understanding of how AI works sits at just 3.5/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
73% use it daily or most days — adoption is real and habitual
Capability
Mean 3.5/5 — competent users, shallow foundations
Capability
Mean 3.68/5
Sentiment
Mean 4.23/5 — tools are fine; the gap is enablement
Investment
5 of 22 have had none
Appetite
57% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 32% say their team uses standardized AI approaches. 27% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 86% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (77%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 42% top-3 selection. Stand-alone training & office hours sits well below at 27%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 87% use AI daily or most days (67% every single day) and 60% say they understand the governance guardrails. Yet 62% have had no formal AI training in a year, and understanding of how AI works sits at just 3.69/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
87% use it daily or most days — adoption is real and habitual
Capability
Mean 3.69/5 — competent users, shallow foundations
Capability
Mean 3.2/5
Sentiment
Mean 3.53/5 — tools are fine; the gap is enablement
Investment
10 of 16 have had none
Appetite
67% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 20% say their team uses standardized AI approaches. 20% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 53% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (36%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 52% top-3 selection. Stand-alone training & office hours sits well below at 20%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
01 — Who responded
Sample
This PortCo's slice of the survey
Sample
Front-line, customer-facing roles dominate — this is a revenue-team read
Sample
Two-thirds are individual contributors — the people who actually run AI in the workflow
02 — The paradox: heavy use, thin scaffolding
Read of the room
Usage is high — 44% use AI daily or most days (22% every single day) and 78% say they understand the governance guardrails. Yet 50% have had no formal AI training in a year, and understanding of how AI works sits at just 3.56/5. The workforce has adopted AI faster than the organization has equipped it. That gap is not closed by awareness campaigns — it's closed by giving people vetted, reusable structure.
Behavior
44% use it daily or most days — adoption is real and habitual
Capability
Mean 3.56/5 — competent users, shallow foundations
Capability
Mean 3.22/5
Sentiment
Mean 3.67/5 — tools are fine; the gap is enablement
Investment
4 of 9 have had none
Appetite
78% would commit 2+ hrs — real appetite, but capped
Pairs with the training gap on the left: the appetite is there, but capped. Design the program for a realistic 1–4 hr/month budget — short, hands-on, in-workflow.
Behavior
% of respondents selecting each — research, content and meeting prep lead; deep workflow automation lags
03 — The real problem: fragmentation
Read of the room
Only 0% say their team uses standardized AI approaches. 50% say most people have built their own methods, and another set has no visibility into peers at all. When a colleague builds something useful, 67% find it hard or only neutral to discover and reuse — and the top worry about self-built tools is quality and accuracy (75%). This is the signature of an organization that needs a shared, governed knowledge layer — not more individual upskilling.
Maturity
Fewer than 1 in 6 teams operate with shared approaches
Friction
Difficult + neutral outweigh easy almost 2 to 1
Friction
% selecting each — inconsistent output and lack of training top the list
Risk
% selecting each — the cost of no shared standard
04 — The decisive signal: what would move the needle
The question, answered
Asked to rank what would most help scale AI adoption, respondents put reusable, shared assets at the top. The four highest-ranked levers — team playbooks, a central repository of approved tools, a standardized prompt library, and best-practice documentation — average 61% top-3 selection. Stand-alone training & office hours sits well below at 33%. The signal is clear: people don't primarily need to be taught AI in the abstract — they need to find, trust, and reuse what their peers have already proven works.
The preferred formats are hands-on and self-serve — live workshops and self-paced modules — not lecture courses. Education works best when it's embedded in the repository: worked examples, guided practice, office hours tied to real plays.
05 — Differences between the companies
Read of the room
The top three value levers — playbooks, a central repository, and a prompt library — are the same at every single company, which is why a generic transformation process is the right bet. But the starting points differ sharply. Appspace and StoreForce combine low governance clarity (~56-60%) with high untrained populations (60-62%) — they need the enablement engine first. Kantata, ESO and Abrigo have governance largely understood (90%+) and lower untrained rates — they're ready for the knowledge exchange now. VisiQuate is the most fragmented (50% build their own way, 0% standardized) despite small size — a sharing layer would pay off fastest there.
Benchmark
Green = ahead · Amber = mixed · Red = needs attention. Sorted by sample size.
| PortCo | n | Usage /5 | Underst. /5 | Cust. conf /5 | Gov. clarity | No training | Build own way | Hard to discover |
|---|---|---|---|---|---|---|---|---|
| Appspace | 65 | 4 | 3.6 | 3.2 | 60% | 60% | 37% | 46% |
| Kantata | 31 | 4.46 | 3.38 | 3.52 | 90% | 23% | 21% | 36% |
| ESO | 24 | 3.88 | 3.35 | 2.92 | 92% | 12% | 32% | 50% |
| Abrigo | 22 | 4.27 | 3.5 | 3.68 | 91% | 23% | 27% | 32% |
| StoreForce | 16 | 4.53 | 3.69 | 3.2 | 56% | 62% | 20% | 27% |
| VisiQuate | 9 | 3.33 | 3.56 | 3.22 | 78% | 44% | 50% | 11% |
06 — A generic maturity model
Framework
Every PortCo sits somewhere on this line. The transformation processes below are the bridges between stages.
Process A AI Knowledge Exchange — the sharing & repository engine. Bridges Stage 2 → 3 and sustains 3 → 4.
Process B Embedded Enablement Engine — practical, role-based capability building. Bridges Stage 1 → 2 and feeds the exchange.
07 — Two deep-dive transformation processes
Synthesis
Why these are one system, not two separate projects
The two processes are not sequential — they compound. The Knowledge Exchange supplies the vetted plays the Enablement Engine teaches; people apply them in real work, and graduates publish new and better plays back into the Exchange. Every turn raises the floor, makes the next turn cheaper, and makes the advantage harder for competitors to copy.
08 — Individual PortCo recommendations
Two high-level value-creation opportunities per company, sequenced to its current stage. These are the headline bullets; the full process docs accompany this analysis.
09 — What to expect: ROI outlook
Read of the room
For a value-creation audience the hero isn't time saved, it's revenue. GTM teams exist to win deals, and customer-AI confidence sits at just 3.28/5 today — so consistent, vetted plays have real room to move win rate. Framed as a share of pipeline, a balanced +2 points of win rate is ~2% of new-business pipeline in incremental ARR (e.g. $1.0M/yr on a $50.0M pipeline). Underneath it, the efficiency floor (~$3.8K per GTM user) makes the program self-funding regardless. Scaled across the portfolio, the floor alone runs into the millions before a single point of win rate is counted.
The prize
Pipeline-agnostic by design: pick your own pipeline row. Baseline win rate ~15%.
Each +1 point of win rate (e.g. 15% → 16%) converts roughly 1% of new-business pipeline into incremental ARR. A balanced +2 points — a ~13% relative lift in close rate from consistent, vetted customer-AI plays — is 2% of pipeline.
| New-biz pipeline / yr | +1 pt · 1% | +2 pts · 2% | +3 pts · 3% |
|---|---|---|---|
| $10.0M | $100K | $200K | $300K |
| $25.0M | $250K | $500K | $750K |
| $50.0M | $500K | $1.0M | $1.5M |
| $100.0M | $1.0M | $2.0M | $3.0M |
No pipeline figure is asserted — the grid is a sensitivity range. Find your PortCo's pipeline row and read across. Reclaimed selling time and ~30% faster ramp add further capacity on top, not modeled here.
The floor
Efficiency alone covers the program. Built bottom-up so it's trivially defensible.
The floor, itemized
The self-funding floor broken out. Every lever ties back to a specific survey signal, not assumed in a vacuum.
| Value lever | Survey signal it draws on | Conservative | Expected | Upside |
|---|---|---|---|---|
Reuse over rebuild Vetted playbooks & prompt library replace per-person trial-and-error. |
#1 barrier is time; ~62% can't easily find peers' work | $72K | $144K | $252K |
Faster onboarding New hires reach productive AI use sooner via embedded modules. |
40% untrained today; preferred delivery is onboarding + LMS | $25K | $60K | $110K |
Tool & build de-duplication One approved-tool catalog ends redundant licenses & parallel builds. |
36% see duplicate solutions; 15% duplicate tools | $15K | $40K | $90K |
Quality & rework Approved, rated assets cut inconsistent output and re-dos. |
42% cite inconsistent outputs; 35% don't trust results | $20K | $55K | $120K |
| Efficiency floor — per 100 GTM users / yr | $132K | $299K | $572K | |
This is the floor only — it deliberately excludes the revenue lever above (win-rate + capacity), which is larger but more assumption-sensitive. Together they make the case both ways: self-funding on efficiency, transformative on revenue.
Balanced posture: the efficiency floor is grounded in survey signals; the revenue lever is expressed as a % of pipeline so nothing financial is invented. Win-rate lift is the largest and most assumption-sensitive lever — shown as a 1–3 point range, not a point estimate.
10 — Token economics: standardization as a cost lever
Read of the room
Most token waste in a GTM org isn't from too much usage — it's from unguided usage: the same prompt re-attempted 4–5 times, context re-pasted every session, the same workflow rebuilt and re-run by five teams, and defensive re-runs because nobody trusts the first output (35% say exactly that). Every one of those is removed by a vetted prompt library, an approved repository, and shared playbooks. The enablement engine compounds it by teaching people to prompt and model-select well the first time.
Mechanisms
Five concrete paths from fragmentation to fewer, cheaper calls
Illustration
A worked example of the trial-and-error sink
Illustrative figures for a single task class; multiply across the dozens of repeatable tasks a GTM team runs. The point is directional: the savings scale with how repeatable and standardizable the work is — which is most of it.