AI SDLC Assessment
Vention is a global software development and AI services company that helps engineering organizations evaluate how effectively AI supports software delivery. In about two weeks, our AI SDLC Assessment places your organization on the 5-Stage AI SDLC Maturity Model, identifies what limits AI’s impact across the SDLC, and shows what to improve first.
Companies that have chosen Vention as their software engineering partner
When to choose AI SDLC Assessment?
Your challenge | What you’re seeing | What the assessment answers | Evidence from Vention’s experience |
|---|---|---|---|
AI tools are in, but delivery metrics haven’t improved | Engineers use Copilot, Cursor, or other AI tools and report working faster. Lead time, PR review latency, and regression cycles have barely changed. | Where are individual productivity gains getting lost before they translate into faster delivery? | In one engagement, we found one to three QA→Dev→QA cycles per story. Releases planned monthly were taking three to four months. |
AI investment needs a clear business case | Leadership expects AI to lower delivery costs while keeping the roadmap on track. You need evidence of where AI could reduce effort and which improvements to prioritize. | Where can AI reduce delivery effort and cost, and what should you change first? | Vention’s AI-enabled delivery work with Kafene increased engineering efficiency by 15-20% and reduced PR review time by 30%. |
AI adoption is outpacing governance | Your teams use different AI tools, sometimes outside the approved stack. AI spend, data exposure, and responsibility for reviewing AI-generated changes are becoming harder to track. | What’s your organization’s status against Vention’s 5-Stage AI SDLC Maturity Model and seven assessment areas? | Vention was the first organization assessed against the 5-Stage AI SDLC Maturity Model. We used our own engineering environment to test and refine the approach before bringing it to clients. |
Facing a different AI challenge?
- If you need to determine whether your data, infrastructure, and organization are ready for AI, start with an AI readiness assessment.
- If you have an AI product or use case in mind and need to define what to build and how to build it, start with an AI discovery workshop.
What Vention’s AI SDLC Assessment evaluates
Vention’s AI SDLC Assessment scores seven areas of your software delivery system. Each area has a current score and a recommended target based on your goals, constraints, and supporting evidence.
Assessment area | What we evaluate | Key insight |
|---|---|---|
Engineering foundations |
| Whether the engineering foundation can support AI-assisted development reliably and at scale |
Quality safety net |
| Whether teams can verify and safely integrate AI-generated changes |
Knowledge and context accessibility |
| Whether context and engineering knowledge is accessible to both people and AI or it is dependent on individual team members |
Process and flow |
| Where AI can accelerate work and where targeted improvements may simply create a downstream bottleneck |
People and adoption |
| Whether best practices are shared or stay individual |
Governance and risk |
| Whether sufficient guardrails exist before scaling AI adoption |
Value measurement and sponsorship |
| How AI investment is tied to measurable delivery outcomes, ownership, and executive support |
The findings place your organization on Vention’s 5-Stage AI SDLC Maturity Model:
- Stage 1: Individual AI exploration
- Stage 2: Consistent team usage
- Stage 3: Integrated AI workflow
- Stage 4: Orchestrated AI development
- Stage 5: AI-driven development.
Many engineering organizations we see sit between Stage 1 and Stage 2 of the 5-Stage AI SDLC Maturity Model, even when they describe themselves as ‘AI-enabled.’
Adoption often varies within the same organization: one team integrates AI into delivery workflows, while another relies on individual tool use. Our assessment places the overall delivery system on the model and highlights differences between teams where the evidence supports them.”
Viktoria Balakshina
Delivery Director
How Vention’s AI SDLC Assessment rolls out
Vention completes the standard AI SDLC Assessment in about two weeks. We combine technical evidence with interviews across delivery roles, typically requiring 5-8 hours of your team’s time when repository access is available.
Repository access is optional. An interview-led assessment uses technical discovery and broader interviews, typically requiring 10-20 hours of your team’s time. Assessments covering multiple teams or repositories may require more participation and a longer schedule, confirmed during scoping.
Stage | Time interval | Who’s involved | What Vention does |
|---|---|---|---|
Kickoff and scoping | Days 1-2 | Your assessment lead and relevant engineering stakeholders | We agree on the teams and roles to include, define the assessment scope, and establish the repository access route if access is available. |
Evidence collection | Week 1 | Primarily Vention’s assessment team, with limited input required from your engineers. | When repository access is available, Vention reviews a codebase clone, including relevant documentation, configuration, prompts, agents, workflows, and SDLC artifacts. A separate read-only Git history collector analyzes the commit chronology without modifying the repository. Repository access is read-only: no repository changes, and no network calls beyond the initial clone. Collector output is removed after the report is validated, and raw commit data is never included in the report. If repository access is unavailable, a 50-minute technical discovery call and structured interviews become the primary sources of evidence. The interview-led approach typically requires 10-20 hours of your team’s time. |
Interviews | Weeks 1-2 | Relevant representatives from product and business analysis, architecture, development, QA, DevOps/SRE, security and compliance, delivery management, and engineering leadership. | We examine how delivery works across roles and clarify the technical findings. The participant mix and interview format are agreed upon during scoping. Each finding is labeled to show its evidence source and whether it is confirmed, inferred, requires confirmation, or remains unknown. |
Report and readout | End of week 2 | Decision-makers responsible for AI investment and delivery priorities | We consolidate the evidence into a Full Assessment Report and Executive Report, then present the findings, including AI adoption assessment results, identified gaps, and priorities for improvement. |
For operating partners: Vention can apply the same methodology and reporting structure across portfolio companies to identify recurring patterns, company-specific constraints, and investment priorities. Each company is assessed against its own evidence and recommended targets.
Find out where AI can improve your software delivery
Start with a 30-minute scoping call to discuss your current AI adoption, delivery priorities, and assessment scope.
What you receive
Vention’s AI SDLC Assessment delivers two documents: a Full Assessment Report for engineering teams and an Executive Report for leadership. Both are presented live to your decision-makers.
Each finding shows its supporting evidence. Where evidence is insufficient, the report says “unknown.”
Full Assessment Report
- Current and recommended target scores across seven assessment areas (Engineering foundations, Quality safety net, Knowledge and context accessibility, Process and flow, People and adoption, Governance and risk, Value measurement and sponsorship), with evidence behind every score.
- Overall AI SDLC maturity placement on Vention’s 5-Stage AI SDLC Maturity Model
- Systemic findings, each with severity and a recommendation
- Directional cost of standing still
- Baseline KPIs: Relevant measures may include cycle time impact, throughput, review time, defect rate, escaped defects, test coverage, documentation completeness, bug-versus-feature ratio, team velocity, and acceptance or rework rates for AI outputs. When a metric cannot be established reliably, it is recorded as not currently tracked rather than estimated.
- Directional improvement sequence: What can begin now, what requires stabilization first, and what depends on larger prerequisites. An illustrative sequence, not a committed implementation roadmap.
- Gate conditions: The conditions that should be met before AI adoption expands into the next phase.
- Scoring rubric: Clear stage definitions alongside each scorecard, supported by a consistent scoring scale and detailed methodology in the appendices.
Executive Report
- Readiness summary
- 3-5 quick wins, including effort, expected value, and cost of standing still
- Target state: A comparison of today’s maturity with a 6- to 9-month target, including an illustrative sequence of two or three improvement waves.
- Quantified value of reaching the target
- KPIs for measuring progress
- Leadership decision block: The specific decisions and approvals needed to move into the next phase.
Why trust Vention’s AI SDLC Assessment
- Applied within Vention first. We used the 5-Stage AI SDLC Maturity Model to assess our own engineering organization before applying it to client environments. Our internal AI transformation includes Atlas AI, a context engine for AI-enabled delivery that has saved 10,000+ engineering hours and 2-3 days per typical feature.
- Findings backed by evidence. We combine repository and git history analysis, where access is available, with interviews across delivery roles. Each finding records its evidence source and level of confirmation, so you can trace how we reached a specific conclusion.
- The assessment lead can stay involved. The AI Transformation Manager who leads your assessment can continue into subsequent transformation work. An AI Transformation Architect may also participate where additional technical depth is needed.
- Vendor-agnostic approach. We work with your existing AI development framework and toolchain, including BMad Method, Spec Kit, OpenSpec, Kiro, or your own standards.
Quick facts about Vention
AI specialists
AI projects
Years of experience in custom software development
Founding partner of the BMad Method
What follows the AI SDLC Assessment
The assessment is complete in itself, with no obligation to continue with Vention. If you move forward, the specialists who assessed your delivery can stay involved through implementation.
- Roadmap and pilot: Turn assessment findings into a focused implementation plan and test priority changes against baseline and target metrics.
- AI SDLC transformation: Redesign workflows, establish governance, train teams, and embed transformation specialists to scale validated practices across software delivery.
- Agentic engineering at scale: Extend proven AI-enabled workflows into coordinated agentic engineering across teams and SDLC stages.
Each stage provides the evidence and decisions needed for the next.
Vention’s work with Kafene illustrates the results of AI-enabled delivery: a 15-20% increase in engineering efficiency and a 30% reduction in PR review time after integrating AI into development workflows.
What is it like to work with Vention?
Hear from clients who trusted us to question their assumptions, challenge their thinking, and tell them what needed to change.
James Schuler
Co-Founder at Kafene
"I made the difficult choice to end our previous partnership and transition to Vention. In hindsight, that was the best decision I ever made."
Bobby Gilbert
Senior Director of Digital Transformation at Sperry Rail
"Vention truly challenged our thinking: this PDW [product discovery workshop] was the first time I've seen a complex software project finish under budget and ahead of schedule."
Jeff Frey
Co-Founder at WiseOwl Innovations
"Vention didn't just act as order takers … they approached the project like true partners. Their team brought thoughtful questions, creative problem-solving, and a clear understanding of our goals."
Jun Seki
CTO at Healthera
"They are very honest and constructive and provided invaluable feedback to us as well."
FAQs
What is an AI SDLC Assessment?
Vention’s AI SDLC Assessment evaluates how AI is used across the software development lifecycle. It scores seven areas of your delivery system and maps your posture to Vention’s 5-Stage AI SDLC Maturity Model. In about two weeks, you get a detailed Full Assessment Report and an Executive Report for leadership. Findings are based on repository evidence (when available) and interviews with individuals across delivery roles.
The assessment is not a code audit, tool benchmark, or quantitative productivity study. Detailed solution design and a scoped transformation roadmap are part of a separate engagement.
How much does the assessment cost?
The assessment pricing depends on the size of your engineering organization and the scope of the evaluation. We confirm the scope and pricing during a short scoping call.
How is an AI SDLC Assessment different from an AI readiness or AI maturity assessment?
An AI readiness assessment evaluates whether an organization has the use cases, data, infrastructure, skills, and governance needed to adopt AI. Broader AI maturity assessments typically evaluate AI adoption across the entire organization.
An AI SDLC Assessment focuses specifically on software delivery. It examines how AI is used across the SDLC, where it improves engineering workflows, where gains are lost between stages, and what needs to change to scale AI effectively across engineering teams.
How long does the assessment take, and how much time does our team need to invest?
The assessment typically takes about two weeks and requires 5-8 hours of your team’s time, spread across relevant delivery roles. If repository access is not available, we rely more on interviews and technical discovery, which increases your team’s involvement to roughly 10-20 hours. Larger organizations with multiple teams and repositories may require additional time.
Do you need access to our code?
Repository access is optional, but it provides stronger technical evidence. Vention reviews a clone of the codebase and uses a read-only git history collector to identify patterns such as rework and development phases. There is no write access, no change to your repository, and no network calls beyond the initial clone. Collector output is removed after the report is validated, and raw commit data is not included in the report.
If repository access is not available, the assessment runs on an interview-led route instead.
What do we actually receive?
You receive a Full Assessment Report with evidence, findings, maturity evaluation, and inputs for your transformation roadmap, as well as an Executive Report that summarizes priorities, expected value, and the decisions leadership needs to make next.
Do we get a maturity score?
Vention’s AI SDLC Assessment returns the scores across the seven areas and a placement on Vention’s 5-Stage AI SDLC Maturity Model, with evidence behind each rating. If there is not enough evidence to score an area reliably, it is marked as “unknown.”
How is this different from a Copilot dashboard or an online AI maturity quiz?
Adoption dashboards measure AI usage: prompts, licenses, generated code lines. Online maturity quizzes rely largely on self-reported answers.
The AI SDLC Assessment examines whether AI use translates into measurable improvements in delivery. Findings are grounded in evidence from your engineering environment and interviews.
What happens after the assessment?
The report provides the inputs for the next step: a focused pilot tied to baseline and target metrics. If the results support broader adoption, Vention can help scale the approach through AI SDLC transformation. However, there is no obligation to continue with Vention after the assessment.
More AI needs? Explore more ways we can help
Need | Recommended Vention’s materials |
|---|---|
To know more about AI in SDLC | Explore AI SDLC transformation and see how Vention applies AI-enabled software delivery. |
To explore the opportunities that AI can bring across the specific parts of the SDLC | Explore AI Quality Engineering |
To benchmark your approach | Read the State of AI in Software Development 2026 for broader data on how engineering organizations are adopting AI. |
Your engineers are faster. Find out why your releases aren’t.
Find out where AI saves effort, where gains get lost, and what to improve first with Vention’s AI SDLC Assessment.