AI Readiness Diagnostic · Completed April 22, 2026
How prepared your organization is to deploy and scale AI across product, engineering, and operations.
Overall readiness
Benchmark
Industry
Technology
Org size
180–300 employees
Respondents
4 leaders
Section 1
Above “AI-Curious” (58), below “AI-Native” (79).
Relay Tech Inc. sits in the moderate band: enough infrastructure and tooling in place to be dangerous, but the human-side fundamentals (training, governance, ROI measurement) haven’t kept pace with what’s shipped. Below is where that gap shows up across the 10 KPIs in the framework.
4
Above peer
0
At peer
6
Below peer
Section 2
Each KPI scored 0–100 based on respondent input, weighted by role. Grey bar shows your peer benchmark; the colored bar is your score.
KPI 1
Strategic AI Vision
72
+12 vs peer
KPI 2
Data Infrastructure
81
+16 vs peer
KPI 3
AI Talent & Skills
38
-17 vs peer
Lowest score
KPI 4
Tooling & Platform
76
+14 vs peer
KPI 5
Workflow Integration
51
-7 vs peer
KPI 6
Governance & Risk
42
-8 vs peer
KPI 7
Change Adoption
45
-7 vs peer
KPI 8
ROI Measurement
33
-15 vs peer
Lowest score
KPI 9
Customer-facing AI
78
+18 vs peer
KPI 10
Org-wide Literacy
47
-8 vs peer
Section 3
Three places where your leadership team isn’t aligned on where you stand. These gaps matter: they shape hiring, budget, and how this report gets received in the boardroom.
KPI 3 · Spread 42 pts
Operations sees a critical capability gap; the CEO believes the team is mostly trained. The reality on the floor is closer to Operations' read — and the difference is roughly the size of an annual training budget.
KPI 8 · Spread 38 pts
The CTO knows the team can't yet quantify what AI is actually saving. Leadership has been reporting upward as if it can. That gap will close one way or the other before the next executive review cycle.
KPI 6 · Spread 31 pts
Engineering has shipped AI features without a written use policy; Operations assumes one exists. Two enterprise prospects this quarter asked for it and got conflicting answers from different teams.
Section 4
Relay Tech scores in the upper-mid range on infrastructure (81/100) and tooling (76/100): the tech is in place. Where the score collapses is on the human side — AI Talent & Skills (38), ROI Measurement (33), and Org-wide Literacy (47) all sit well below your peer average.
The pattern is consistent across all four respondents but the magnitude isn't. Your CEO and Operations lead disagree by 42 points on whether the team has the AI skills to deliver. That gap shows up everywhere: in hiring decisions, in tool procurement, and — most consequentially — in how this report will be received in the boardroom.
The encouraging signal is your customer-facing AI score (78). You've shipped real product. The risk is that the org-wide engine that supports those features — training, governance, ROI tracking — hasn't kept pace with what shipped. The fix is sequenceable, not structural.
“You bought 12 AI tools in 2025 but only 23% of your employees feel they know when to use them.”
Section 5
Ranked by combined business impact and feasibility to address.
1
KPI 8
Root cause
No baseline metrics were captured before AI tools were rolled out, and there's no routine cadence for measuring time saved or output quality. Each team uses its own informal definition of "is this AI helping?"
Business impact
When the CFO asks for a defensible AI ROI read, you don't have a clean number. Budget for the next wave is at risk — and the tools that are working will get cut alongside the ones that aren't.
$240K annual AI spend at risk
2
KPI 3
Root cause
Hiring filtered for traditional engineering credentials; training has been ad-hoc and self-directed. The two newly-promoted engineering managers haven't received any structured AI workflow training.
Business impact
Your engineering team is roughly 40% slower to adopt new AI features than your customer-facing team. That gap will compound as the underlying models accelerate.
~$180K/yr in slower delivery velocity
3
KPI 6
Root cause
AI features have shipped to production without a formal review or written use policy. There's no documented stance on customer data, IP, or model behavior.
Business impact
Two enterprise prospects this quarter delayed signing because they couldn't get a clear answer to their AI governance questionnaire. One went to a competitor.
~$420K in delayed/lost ARR
Section 6
3 engagements scoped against your findings. The featured option below is what we’d recommend if you choose only one.
Targets KPI 3, 6, 8, 10
Baseline ROI dashboard, written governance policy, and a 90-day AI skills program for engineering and operations. Closes the three highest-impact gaps in one engagement.
Timeline
8 weeks
Investment
$28,500
Targets KPI 6, 8
Defensible governance doc, executive AI ROI brief for the next board conversation, and audit-ready posture for enterprise sales conversations.
Timeline
3 weeks
Investment
$11,500
Targets KPI 3, 10
Role-specific AI training paths for 180 employees with measurable proficiency gates at 30/60/90 days. Best as a follow-on to the Sprint.
Timeline
12 weeks
Investment
$36,000
Section 7