AI for Engineering
The Performance Benchmark
Is your AI actually moving lead time, MTTR, and developer toil?
Take the quiz to see where your AI stack is improving engineering velocity, where it’s falling short, and what to adjust to see better results.
This self-assessment is designed for:
- Staff and principal engineers
- Platform and developer productivity teams
- Engineering leaders responsible for AI strategy in software development
What respondents get:
- An AI Stack Score (0–100) and a clear benchmark profile
- A directional estimate of the hours their team burns today and guidance on where to optimize
- A personalized time-savings range and tailored workflow recommendations
Question category goes here
Question 1/14
10%
Select the workflow that best reflects a real point of friction on your team today.
We’ll use your answers to model where time is lost and where AI could have the biggest impact
We’ll use your answers to model where time is lost and where AI could have the biggest impact
For your most important engineering workflows, how much of the surrounding context does AI need to see to be useful?
Select all that apply
Congratulations!
You’ve completed the quiz.
Download your resultsYou’ve completed the quiz.
Here’s your AI Stack for Software Engineering Snapshot - including your AI Stack Score, where your team is losing time today, and the workflow most worth tackling first.
Your AI stack profile
Fragmented
Emerging
Connected
Based on your responses, engineers are still losing meaningful time to fragmented context across code, tickets, logs, and chat. AI is present, but key workflows still rely on manual reconstruction.
01
At a glance
AI Stack Score
[x]
of
100
Estimated Annual Cost of Fragmented Context
$2.7M
Equivalent Capacity Lost
16
full-time engineers
Starting Workflow
Start with production incidents because your responders already move quickly, and a shared context layer can further compress MTTR by linking alerts, changes, and ownership in one place.
02
Maturity profile
Where you are today
Adoption Stage
Fragmented Growth
Governance Posture
Enterprise-Grade
AI Depth
Surface-level
Segment Label
Tool-rich, context-poor
Your team is under pressure to [top_pressures_display], and AI is already part of the picture. Today, you are in the [adoption_stage] stage, with the biggest friction coming from [main_friction]. The best next move is [best_next_move].
03
Architecture pattern
How close you are to the two-layer model
Context Layer
Missing
AI Surfaces
Enterprise-Grade
Fragmentation Risk
Surface-level
Maturity Band
Tool-rich, context-poor
Your team is under pressure to [top_pressures_display], and AI is already part of the picture. Today, you are in the [adoption_stage] stage, with the biggest friction coming from [main_friction]. The best next move is [best_next_move].
04
Workflow friction
Onboarding & unfamiliar code
Avg time to safe change
Missing
Avg helpers per event
Enterprise-Grade
Knowledge fragmentation index
Surface-level
Annual hours burned
Tool-rich, context-poor
Your team is under pressure to [top_pressures_display], and AI is already part of the picture. Today, you are in the [adoption_stage] stage, with the biggest friction coming from [main_friction]. The best next move is [best_next_move].
Workflow friction
Production incidents
Avg time to root cause
Missing
Avg engineers involved
Enterprise-Grade
engineers
Tool switching index
Surface-level
Annual hours burned
Tool-rich, context-poor
Your team is under pressure to [top_pressures_display], and AI is already part of the picture. Today, you are in the [adoption_stage] stage, with the biggest friction coming from [main_friction]. The best next move is [best_next_move].
Workflow friction
Spec clarity & decision reconstruction
Avg time to clarification
Missing
Stall probability
Enterprise-Grade
Async stall index
Surface-level
Annual hours burned
Tool-rich, context-poor
Your team is under pressure to [top_pressures_display], and AI is already part of the picture. Today, you are in the [adoption_stage] stage, with the biggest friction coming from [main_friction]. The best next move is [best_next_move].
Section 1
Economic impact
Estimated context cost
Engineers in scope
Missing
Estimated context-chasing time per engineer per week
Enterprise-Grade
hours
Estimated annual engineering hours lost
Surface-level
Estimated salary-weighted cost
Tool-rich, context-poor
This estimate is derived from workflow frequency, coordination depth, tool surface area, and clarification lag. A simple explainer can also show the math as frequency × duration × headcount × loaded cost.
Section 2
How close you are to a unified context model
High-performing AI stacks increasingly converge on a two-layer model:
- A governed enterprise context layer
- Multiple AI surfaces that call into it
You are currently in:
Missing
Context Coverage
Enterprise-Grade
Governance Depth
Surface-level
Surface Orchestration
Tool-rich, context-poor
At your current configuration, each AI tool is reconstructing partial organizational context independently.
Section 3
Workflow deep dive
Onboarding & unfamiliar code
Avg time to safe change
Missing
Avg helpers per event
Enterprise-Grade
Knowledge fragmentation index
Surface-level
Annual hours burned
Tool-rich, context-poor
Dominant friction driver: Change discovery delay
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Workflow deep dive
Production incidents
Avg time to root cause
Missing
Avg escalation depth
Enterprise-Grade
Tool switching index
Surface-level
Annual incident hours
Tool-rich, context-poor
Dominant friction driver: Change discovery delay
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Workflow deep dive
Spec clarity & decision reconstruction
Avg time to clarification
Missing
Stall probability
Enterprise-Grade
Async stall index
Surface-level
Annual hours burned
Tool-rich, context-poor
Dominant friction driver: Change discovery delay
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Modeled upside with a governed context layer: Unifying change, code, and incident context could reduce MTTR by 20-35%.
Workflows where AI can help most
WORKFLOW
TODAY
WITH A CONTEXT LAYER
Onboarding & service understanding
Slow orientation across docs, tickets, and tribal knowledge
Faster ramp, fewer "who owns this?" interruptions
Day-to-day coding & small safe changes
Good local productivity in the IDE, weak cross-system grounding
Scoped changes tied to tickets, docs, and prior decisions
Incidents & production support
Too much manual digging across logs, Slack, Jira, GitHub, and dashboards
Faster root cause analysis and more reusable incident history
Section 4
Your evaluation checklist
Your answers suggest you care most about coverage, accuracy, security, and workflow fit.
Pressure-test any AI tool or platform against this checklist.
Pressure-test any AI tool or platform against this checklist.
Coverage
Can it see code, tickets, incidents, design docs, and owners, not just a single repo?
Can it see code, tickets, incidents, design docs, and owners, not just a single repo?
Trust & accuracy
Does it ground answers in your artifacts with citations, not just model guesses?
Does it ground answers in your artifacts with citations, not just model guesses?
Security & governance
Can it run in your VPC or single-tenant environment, with strict egress controls and end-to-end ACLs?
Can it run in your VPC or single-tenant environment, with strict egress controls and end-to-end ACLs?
Workflow fit
Does it plug into IDEs, Git, Jira, and chat, or force yet another standalone interface?
Does it plug into IDEs, Git, Jira, and chat, or force yet another standalone interface?
At your current configuration, each AI tool is reconstructing partial organizational context independently.
Bring context into your coding layer
Get the guide
A complete context layer would enable all your different coding assistants and agent frameworks to coexist. By maintaining a single trusted source of truth for organizational knowledge, permissions, and history, your teams would reclaim context time and accelerate your goal of reducing incident frequency and severity by approximately 10-14 additional engineer-months per year.
Looking to design a complete AI stack that matches how your engineers actually work, all while staying in control of your systems and data? Our latest guide, “The software engineer’s field guide to the AI stack,” walks through what modern engineering stacks need for teams to ship faster with AI while keeping systems reliable.
Congratulations!
You’ve completed the quiz.
You’ve completed the quiz.
Complete the form to review your results.
AI for Engineering
Results