AI-Assisted Developer Productivity — Resource Management
Difficulty: Intermediate. Target Role: Manager.
Key Points for AI-Assisted Developer Productivity
- AI coding tools reduce completion time by 25-55% for well-defined tasks like boilerplate, tests, and documentation, but show minimal improvement for novel architectural design work
- Measure productivity impact through controlled experiments with clear baselines, not vibes or anecdotes from enthusiastic early adopters
- Establish clear usage policies covering data sensitivity, code ownership, and approved tools before rolling AI tools out broadly
- AI shifts the bottleneck from writing code to reviewing code. Teams that do not adjust their review practices will ship more bugs faster
- ROI calculations must include all costs: licensing, training time, increased review burden, security tooling, and potential quality regression
Common Mistakes with AI-Assisted Developer Productivity
- Mandating AI tool adoption without investing in training, which leads to frustration, poor outputs, and engineers quietly abandoning the tools within weeks
- Not reviewing AI-generated code with the same rigor as human-written code, assuming the AI 'got it right' because the code looks clean and compiles
- Measuring only coding speed while ignoring quality metrics like bug rates, test coverage of generated code, and long-term maintainability
- Ignoring the security implications of sending proprietary source code to external AI services without evaluating data handling policies
Related to AI-Assisted Developer Productivity
Tech Debt Negotiation, Engineering Roadmaps
AI Strategy for Engineering Leaders — Technical Strategy
Difficulty: Advanced. Target Role: Director.
Key Points for AI Strategy for Engineering Leaders
- Start with use case identification, not technology selection. The question is 'what problems can AI solve for us' not 'how do we use GPT-4'
- Classify every AI initiative by risk level and reversibility before committing resources. Low-risk internal tools first, customer-facing features later
- Build an evaluation framework that scores initiatives on three axes: technical feasibility, business impact, and organizational risk
- Apply a 70/20/10 portfolio approach: 70% on proven AI patterns, 20% on emerging capabilities, 10% on experimental bets
- Staff an enabling team that upskills product teams on AI, not a centralized AI silo that becomes a bottleneck for every request
Common Mistakes with AI Strategy for Engineering Leaders
- Chasing every new AI trend without a prioritization framework, leading to a scattered portfolio of half-finished experiments that deliver no business value
- Trying to build AI-powered features without first investing in the data infrastructure required to support them
- Creating a centralized AI team that becomes a bottleneck, with every product team waiting in a queue for AI resources
- Measuring success by number of models deployed rather than business outcomes improved, which incentivizes shipping models nobody uses
Related to AI Strategy for Engineering Leaders
Building Technical Vision, Technology Radar Creation
Build vs Buy Framework — Decision Making
Difficulty: Intermediate. Target Role: Manager.
Key Points for Build vs Buy Framework
- Total cost of ownership over 3 years is the true comparison — include maintenance, integration, training, and opportunity cost
- Build only when the capability is a strategic differentiator — if it does not create competitive advantage, buy it
- Assess team capability honestly — building requires not just initial development but years of operational expertise
- Factor in time-to-market — buying gets you to production in weeks while building may take quarters
- Plan for the exit — whether you build or buy, design integration layers so you can switch later without rewriting consumers
Common Mistakes with Build vs Buy Framework
- Comparing build cost against license cost only — ignoring the ongoing maintenance burden of custom software
- Letting engineer preference drive the decision — 'we could build that' is not a business justification
- Not evaluating vendor lock-in risk — some SaaS products make data extraction deliberately difficult
- Assuming build means better fit — custom software has bugs too, and you are the only support team
Related to Build vs Buy Framework
Technology Radar Creation, Tech Debt Negotiation
Building Technical Vision — Technical Strategy
Difficulty: Advanced. Target Role: Director.
Key Points for Building Technical Vision
- A technical vision document should cover a 2-3 year horizon with concrete milestones at 6-month intervals
- Ground the vision in business outcomes — every architectural decision should trace back to revenue, reliability, or velocity
- Separate principles (timeless) from bets (time-bound) — principles guide decisions when you are not in the room
- Include explicit non-goals to prevent scope creep and set clear boundaries for the engineering organization
- Socialize the draft widely before finalizing — a vision nobody helped shape is a vision nobody follows
Common Mistakes with Building Technical Vision
- Writing a technology wishlist instead of a strategy — listing tools without explaining the problems they solve
- Making the vision too abstract to be actionable — engineers should be able to derive quarterly goals from it
- Skipping the current-state assessment — you cannot chart a course without knowing your starting coordinates
- Failing to revisit and update the vision as business context changes — a stale vision is worse than no vision
Related to Building Technical Vision
Technology Radar Creation, Engineering Roadmaps
Communicating Strategy to Execs — Communication
Difficulty: Advanced. Target Role: VP.
Key Points for Communicating Strategy to Execs
- Lead with the business impact — executives care about revenue, risk, and velocity, not technology choices
- Use the pyramid principle — state your recommendation first, then provide supporting evidence in decreasing detail
- Quantify everything — replace 'significant improvement' with '40% reduction in deploy time, saving 12 engineer-hours per week'
- Prepare for the 'just make it faster' response — have a menu of options with trade-offs ready
- Keep the presentation to 5 slides maximum — executives make decisions in minutes, not hours
Common Mistakes with Communicating Strategy to Execs
- Starting with the technical details instead of the business outcome — you lose the room in the first 60 seconds
- Presenting one option instead of a menu — executives want to choose, not rubber-stamp
- Using jargon without translation — 'microservices migration' means nothing without 'enables 3x faster feature delivery'
- Not having a clear ask — every executive presentation should end with a specific decision request and timeline
Related to Communicating Strategy to Execs
Building Technical Vision, Engineering Roadmaps
Engineering Roadmaps — Resource Management
Difficulty: Intermediate. Target Role: Manager.
Key Points for Engineering Roadmaps
- Outcome-based roadmaps outlast feature-based roadmaps — define what success looks like, not just what to build
- Build in 20-30% buffer for unplanned work — if your roadmap assumes 100% utilization, it will fail by week 3
- Map dependencies explicitly and resolve them before committing to timelines — hidden dependencies are the top roadmap killer
- Use a rolling 3-month detail window with 6-12 month directional themes — precision decreases with distance
- Review and adjust the roadmap monthly — a roadmap that never changes is a fantasy document
Common Mistakes with Engineering Roadmaps
- Treating the roadmap as a promise instead of a plan — stakeholders should understand that timelines are estimates, not commitments
- Including only feature work — platform improvements, tech debt, and developer experience initiatives deserve roadmap space
- Not involving the team in estimation — top-down timelines without engineer input are fiction
- Overloading the roadmap to show ambition — a roadmap with 40 items communicates nothing about priority
Related to Engineering Roadmaps
Building Technical Vision, Communicating Strategy to Execs
Tech Debt Negotiation — Communication
Difficulty: Intermediate. Target Role: Manager.
Key Points for Tech Debt Negotiation
- Quantify tech debt in business terms — hours lost per sprint, incident frequency increase, or deployment lead time degradation
- Use the risk framing: tech debt is not about code quality, it is about the probability and cost of future incidents
- The 20% rule works — dedicate 20% of each sprint to tech debt reduction without asking for permission on specific items
- Make tech debt visible with a debt register — categorize by impact, effort, and risk with regular reviews
- Frame debt paydown as enabling velocity — 'investing 2 sprints now saves 1 sprint per quarter forever'
Common Mistakes with Tech Debt Negotiation
- Using developer-centric language with product stakeholders — saying 'the code is messy' instead of 'deploys take 3x longer than they should'
- Trying to pay down all debt at once with a big rewrite — incremental improvement is almost always better
- Not tracking the impact of debt paydown — if you cannot show results, you lose credibility for future asks
- Treating all tech debt as equal — not all debt carries the same risk or blocks the same business outcomes
Related to Tech Debt Negotiation
Build vs Buy Framework, Engineering Roadmaps
Technology Radar Creation — Technical Strategy
Difficulty: Advanced. Target Role: Director.
Key Points for Technology Radar Creation
- Use four rings — Adopt, Trial, Assess, Hold — to classify technologies by organizational readiness
- Run quarterly review sessions with senior engineers to keep the radar current and reflective of real experience
- Include not just languages and frameworks but also techniques, platforms, and architectural patterns
- Publish the radar openly so every team can make consistent technology choices without bottleneck approvals
- Track adoption metrics — a technology in 'Adopt' that nobody uses signals a communication or tooling gap
Common Mistakes with Technology Radar Creation
- Treating the radar as a top-down decree instead of a collaborative assessment — engineers ignore mandates they did not help create
- Putting too many items in 'Adopt' — if everything is recommended, nothing is prioritized
- Never moving items to 'Hold' — failing to sunset technologies leads to fragmentation and maintenance burden
- Skipping the narrative — each blip needs a one-paragraph rationale explaining why it is in that ring for your specific context
Related to Technology Radar Creation
Building Technical Vision, Build vs Buy Framework