LP‑ready AI maturity assessments, simplified.
In two weeks, we evaluate tooling, processes, metrics, governance, skills, and culture to identify maturity gaps and opportunities for immediate impact and produce an LP-ready report.
One assessment, five executive‑ready assets.
Your assessment includes the following LP‑ready deliverables packaged into a concise, executive‑friendly report that clearly communicates findings, recommendations, and next steps.
Maturity heatmap
A visual 6×4 grid scoring each dimension across four maturity stages with color coding. Current and target states are clearly marked, and the format is built for executive and board discussions.
Gap analysis report
Root cause analysis for each dimension. Identifies what is blocking progress, what would resolve it, and the recommended order of action. Written for a technical audience with practical guidance.
90‑day action plan
A prioritized roadmap organized into Quick Wins (0–30 days), Core Investments (30–60 days), and Foundation Work (60–90 days. Each action is mapped to the dimension it improves.
AI PDLC recommendations
Guidance on which AI PDLC phases and components to implement, in what sequence, based directly on maturity scores. Reduces uncertainty when scoping the next phase.
Business case model
NPV estimate for moving from the current state to the target state using DX Core 4 benchmark gaps. Provides a financial basis for executive approval of future investment.
What are the dimensions of AI maturity?
Our assessment scores six dimensions of AI maturity so leaders can see where AI is creating real impact, and where gaps are slowing adoption.
Tooling
AI tools currently in use, team coverage, adoption relative to license count, and how well those tools integrate into daily workflows. Focus is on real usage, not tools that are merely installed.
Measurement
Whether delivery metrics are tracked, the quality of those metrics, and whether the DX Core 4 signals are present: Diffs per Engineer, Lead Time, Change Failure Rate, and Innovation Ratio.
Process
How AI is integrated across PDLC stages from requirements through deployment. Evaluates whether AI is used in repeatable workflows or only in ad hoc experimentation.
People
Distribution of AI skills across the organization, including prompting ability, agent pipeline development, formal training, and gaps between leadership and individual contributor capabilities.
Governance
Existence and enforcement of AI usage policies, data and IP controls, model selection criteria, and security review processes for AI‑generated code.
Culture
Psychological safety around AI experimentation, leadership adoption behavior, presence of internal champions, and overall appetite for change.
How it works
Data collection
We connect to systems such as GitHub, GitLab, Jira, Linear, CI/CD, and AI platforms. This produces objective baseline signals across Tooling, Measurement, and Process, runs in parallel with interviews, and requires no manual data gathering from client teams.
Stakeholder interviews
Structured 60‑minute sessions with the CTO or VP Engineering (strategy), 2–3 Engineering Managers (process and team structure), and 1–2 Staff or Principal Engineers (technical depth). A CISO or Security Lead may participate when governance review is required.
Developer survey
A 15‑minute anonymous survey sent to engineers in scope. Captures AI tool usage, skills, culture, psychological safety, and friction points. Responses are aggregated and never shared individually.
Scoring and calibration
FullStack analysts score each dimension using an evidence matrix. Scores are triangulated across system data, interviews, and survey results. A peer calibration review ensures consistency.
Gap analysis and roadmap
Root cause analysis identifies what is blocking progress and what actions would resolve each gap. ROI is modeled using DX Core 4 benchmark data.
Readout and report
A 60‑minute executive readout covers the maturity heatmap, the top three blockers, and the recommended AI PDLC entry point. The full written report with all deliverables is delivered within two business days. An optional technical deep dive can follow.
Give LPs a real answer on AI.
In just two weeks, you can move from ad hoc AI talking points to a repeatable, LP‑ready view of AI maturity that strengthens LP trust and focuses your value‑creation work where it matters most.
.avif "Databricks Consulting Partner"){kind=icon}
