Organizational Integration
Why It Matters
Individual skill without team practices leads to inconsistent quality. DORA 2025: AI amplifies existing capabilities—strong teams get stronger, struggling teams get worse. Microsoft research via GitHub: It takes 11 weeks for developers to fully realize productivity gains from AI tools—plan for this ramp-up when calculating ROI.
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
Measuring Impact of GitHub Copilot - GitHub Resources
Critical source for setting realistic expectations around AI tool adoption. The 11-week ramp-up finding counters expectations of immediate productivity gains. The guidance to measure for 6+ months prevents 'honeymoon phase' bias in ROI calculations. Essential for organizational_integration dimension.
- Microsoft research indicates 11 weeks for full productivity gains
- Initial productivity dip during learning phase is normal
- 81.4% of developers install IDE extension on first day of license
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Assessment Questions (9)
Maximum possible score: 49 points
○ Q1 single choice 5 pts
Does your team have guidelines for when/how to use Copilot?
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
State of AI Code Quality 2025
This is the most comprehensive 2025 survey on AI code quality (609 developers). The key insight is the 'Confidence Flywheel' - context-rich suggestions reduce hallucinations, which improves quality, which builds trust. The finding that 80% of PRs don't receive human review when AI tools are enabled is critical for our agentic_supervision dimension. NOTE: The previously cited 1.7x issue rate and 41% commit stats were not found in the current report.
- 82% of developers use AI coding tools daily or weekly
- 65% of developers say at least a quarter of each commit is AI-generated
- 59% say AI has improved code quality
○ Q2 single choice 5 pts
How does your team handle code review for AI-generated code?
Note: 2025 update: AI-assisted code review (CodeRabbit, etc.) is emerging practice for managing volume
State of AI Code Quality 2025
This is the most comprehensive 2025 survey on AI code quality (609 developers). The key insight is the 'Confidence Flywheel' - context-rich suggestions reduce hallucinations, which improves quality, which builds trust. The finding that 80% of PRs don't receive human review when AI tools are enabled is critical for our agentic_supervision dimension. NOTE: The previously cited 1.7x issue rate and 41% commit stats were not found in the current report.
- 82% of developers use AI coding tools daily or weekly
- 65% of developers say at least a quarter of each commit is AI-generated
- 59% say AI has improved code quality
October 2025 Update: GenAI Code Security Report
Primary source for AI code security statistics: 45% overall failure rate, 72% for Java specifically. The 'bigger models ≠ more secure code' finding is critical for model_routing - security scanning is needed regardless of model. Java's 72% rate makes it the riskiest language for AI-generated code.
- AI-generated code introduced risky security flaws in 45% of tests
- Java was the riskiest language with 72% security failure rate
- XSS (CWE-80) defense failed in 86% of relevant code samples
○ Q3 single choice 5 pts
Does your organization have policies for agent mode / autonomous AI coding?
GitHub Copilot Agent Mode Documentation
GitHub Copilot's agent mode enables autonomous multi-file editing, allowing AI to plan and execute complex changes across a codebase without step-by-step human approval. This capability requires careful supervision practices since agents can introduce cascading errors across multiple files. Critical for agentic_supervision dimension - assessing how organizations manage autonomous AI coding.
- Agent mode can edit multiple files autonomously across a codebase
- Requires explicit approval before applying changes (diff review checkpoint)
- Supports iterative refinement: review, request changes, re-generate
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
○ Q4 multi select 9 pts
What AI coding tool training have you received?
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
State of AI Code Quality 2025
This is the most comprehensive 2025 survey on AI code quality (609 developers). The key insight is the 'Confidence Flywheel' - context-rich suggestions reduce hallucinations, which improves quality, which builds trust. The finding that 80% of PRs don't receive human review when AI tools are enabled is critical for our agentic_supervision dimension. NOTE: The previously cited 1.7x issue rate and 41% commit stats were not found in the current report.
- 82% of developers use AI coding tools daily or weekly
- 65% of developers say at least a quarter of each commit is AI-generated
- 59% say AI has improved code quality
○ Q5 single choice 5 pts
How does your team share AI coding tips, tricks, and learnings?
State of AI Code Quality 2025
This is the most comprehensive 2025 survey on AI code quality (609 developers). The key insight is the 'Confidence Flywheel' - context-rich suggestions reduce hallucinations, which improves quality, which builds trust. The finding that 80% of PRs don't receive human review when AI tools are enabled is critical for our agentic_supervision dimension. NOTE: The previously cited 1.7x issue rate and 41% commit stats were not found in the current report.
- 82% of developers use AI coding tools daily or weekly
- 65% of developers say at least a quarter of each commit is AI-generated
- 59% say AI has improved code quality
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
○ Q6 single choice 5 pts
Does AI-generated code have different testing requirements?
Note: Given 45% security flaw rate in tests (Veracode 2025), security scanning for AI code is recommended
October 2025 Update: GenAI Code Security Report
Primary source for AI code security statistics: 45% overall failure rate, 72% for Java specifically. The 'bigger models ≠ more secure code' finding is critical for model_routing - security scanning is needed regardless of model. Java's 72% rate makes it the riskiest language for AI-generated code.
- AI-generated code introduced risky security flaws in 45% of tests
- Java was the riskiest language with 72% security failure rate
- XSS (CWE-80) defense failed in 86% of relevant code samples
○ Q7 single choice 5 pts
Does your organization track AI coding tool effectiveness?
Note: DORA 2025 recommends three-layer measurement: adoption, behavior, outcomes. Leading AI tool vendors track ARR growth as adoption proxy.
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
Cognition-Windsurf Acquisition and Consolidation
The July 2025 Cognition-Windsurf deal illustrates rapid AI coding market consolidation. The bidding war (OpenAI $3B, Google $2.4B acqui-hire, Cognition acquisition) shows the strategic value of AI coding tools. Cognition's $10.2B valuation post-merger signals enterprise confidence in agentic coding.
- Cognition acquired Windsurf July 2025 after Google hired CEO in $2.4B deal
- OpenAI's $3B Windsurf offer expired just hours before Google deal
- Acquisition included IP, trademark, product, and $82M ARR
○ Q8 single choice 5 pts
Does your organization provide dedicated time for AI tool learning?
Note: DORA 2025: Dedicated learning time leads to 131% increase in team AI adoption
State of AI-Assisted Software Development 2025
The 2025 DORA report introduces the 'AI Capabilities Model' identifying seven practices that amplify AI benefits. The core insight is that AI is an 'amplifier' - it magnifies existing organizational strengths AND weaknesses. Key stats: 89% of orgs prioritizing AI, 76% of devs using daily, but 39% have low trust. The trust research is critical: developers who trust AI more are more productive, but trust must be earned through organizational support (policies, training time, addressing concerns). The 451% adoption increase from acceptable-use policies is remarkable - clarity enables adoption.
- 89% of organizations prioritizing AI integration into applications
- 76% of technologists rely on AI for parts of their daily work
- 75% of developers report positive productivity impact from AI
○ Q9 single choice 5 pts
Has your organization redesigned workflows specifically for AI tools?
Note: McKinsey 2025: High performers are 3x more likely to redesign workflows for AI. Rakuten achieved 79% time reduction with Slack-integrated AI coding.
The State of AI in 2025: Agents, Innovation, and Transformation
McKinsey's 2025 survey shows AI use is common (88%) but enterprise value capture is rare (only 39% see EBIT impact). The key differentiator is workflow redesign - high performers are 3x more likely to fundamentally redesign workflows. The 62% experimenting with agents stat is critical for agentic_supervision. Key insight: most organizations are still in pilots, not scaled adoption.
- 88% report regular AI use in at least one business function (up from 78%)
- Nearly two-thirds still in experimentation or piloting phases
- 62% experimenting with AI agents; 23% scaling agents
Claude Code in Slack: Agentic Coding Integration
Claude Code's Slack integration represents the 'ambient AI' pattern: AI agents triggered from natural team conversations, not dedicated coding interfaces. The $1B revenue milestone and enterprise customers (Netflix, Spotify) validate the market. Rakuten's 79% timeline reduction is a standout case study.
- Claude Code in Slack launched December 8, 2025 as research preview
- @Claude tag routes coding tasks to Claude Code on web automatically
- Analyzes Slack context (bug reports, feature requests) for repository detection