The Tactical Trap: Focusing AI on the Wrong Skillset
Organizations invest heavily in AI tools to "make developers faster" (tactical) while missing their true power: augmenting the strategic capabilities of engineering leadership (strategic).
An engineering manager's core skillset—synthesis, abstraction, planning, risk assessment, and communication—is a native fit for the strengths of a Large Language Model (LLM). Conversely, a developer's core need—flawless, deterministic, context-perfect logic—is the LLM's single greatest weakness (as seen in all the previous pain points). This explains why many ICs report frustration with AI, while managers often find it highly effective for their own coding and planning tasks. The organization is misaligning the tool. It's forcing a powerful strategic synthesis engine (the AI) to act as a junior developer, where it's error-prone. This ignores its potential to act as a force-multiplier for managers and leaders, where its skills are a 1:1 match.
This is the single biggest missed ROI opportunity in AI-assisted development. While the company fights for a 10% velocity gain in code-writing (and gets "almost-correct" code in return), it's leaving 10x gains on the table. Managers are left to manually perform the very tasks AI excels at: reading oceans of data (Jira, Confluence, support tickets), forecasting timelines, and identifying systemic risks. The company is using a "super-brain" to write boilerplate but is still using spreadsheets and human intuition to de-risk multi-million dollar projects.
Wasted Potential
A company spends $1 million on co-pilot licenses for 5,000 developers, who use it to write unit tests. The 500 engineering managers, who are trying to plan the next year's roadmap, are still manually reading 30-page "Voice of the Customer" reports and building Gantt charts by hand.
The "Red Team" Multiplier
An engineering manager asks a grounded AI, "Read our last 12 sprint retrospectives and this new project proposal. Identify the top 5 systemic risks and suggest resource allocations." The AI flags that the 'Payments' team is always a bottleneck in Q4, a risk no human could have synthesized that quickly.
True "Data-Driven" Planning
A manager asks, "Analyze our PagerDuty logs and the last 500 customer support tickets. What is the real, underlying cause of our platform instability?" The AI bypasses human opinions and provides a data-backed answer (e.g., "A database query in the 'Auth' service is timing out under peak load"), allowing the EM to plan the right fix.
Strategic Scenario Planning
A Director of Engineering uses AI to "red team" a product launch, asking, "What are 10 different ways this new feature launch could fail?" The AI provides a comprehensive list covering technical debt, market competitors, infrastructure scale, and poor user adoption—all in 30 seconds.
The problem isn't the AI; it's the lack of a human-in-the-loop verification and governance system. These workflows are the perfect antidote.
AI Governance Scorecard
View workflow →The Pain Point It Solves
This directly addresses the missed ROI opportunity by providing leadership with concrete metrics on AI adoption, risk, and value. Instead of guessing whether AI investments are paying off, this scorecard shows exactly where AI is creating value (strategic planning, risk assessment) versus where it's struggling (code generation).
Why It Works
It shifts the conversation from "AI makes developers faster" to "AI helps managers de-risk projects." The scorecard tracks adoption metrics for strategic use cases (sprint retrospective analysis, risk assessment, timeline forecasting) versus tactical use cases (code generation, unit tests). This provides the data needed to reallocate AI investments from low-ROI tactical tasks to high-ROI strategic tasks.
Strategic AI Planning Workflow
The Pain Point It Solves
This workflow directly attacks the "managers using spreadsheets" problem. Instead of managers manually reading 30-page reports and building Gantt charts, this workflow provides a systematic approach for using AI to synthesize data from multiple sources (Jira, Confluence, support tickets) and generate strategic insights.
Why It Works
It leverages AI's native strengths (synthesis, abstraction, pattern recognition) for tasks that managers actually need. The workflow guides managers through using AI to: analyze sprint retrospectives for systemic risks, forecast timelines from historical data, identify bottlenecks from incident logs, and generate strategic scenario plans. This transforms AI from a "junior developer" into a "strategic analyst," delivering 10x ROI gains instead of 10% velocity gains.
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Engineering Leader & AI Guardrails Leader. Creator of Engify.ai, helping teams operationalize AI through structured workflows and guardrails based on real production incidents.