Engineering Leadership's Whack-A-Mole Problem

I've been interviewing engineering leaders recently, listening to their stories about improving software development effectiveness. What started as conversations about AI adoption, team scaling, and delivery optimization revealed a fascinating pattern: regardless of company size, tech stack, or industry, virtually every leader described the same exhausting cycle consuming their time and organizational focus.

They all seemed trapped in what I've come to think of as engineering leadership's whack-a-mole problem.

The Pattern Everyone Recognizes (But Nobody Talks About)

Here's what I keep hearing in these conversations. A concerning metric surfaces—deployment frequency drops, cycle times increase, or the relationship between story points and actual delivered value starts looking questionable. What happens next is remarkably consistent across organizations.

Investigation Phase - Someone needs to dig through fragmented data across multiple systems, trying to understand what's actually happening versus what was planned. The challenge is that developing accurate hypotheses requires substantial experience—understanding how tools interact, recognizing team dynamics, and drawing from historical patterns that only come with time in the trenches.

Analysis Phase - Those hypotheses need validation through comprehensive data analysis. Teams must build a compelling case that demonstrates the severity of the problem and propose specific corrective actions with measurable outcomes. This often involves creating dashboards, conducting trend analysis, and preparing presentation materials that can withstand executive scrutiny.

Action Phase - Organizations implement changes and monitor metrics to validate effectiveness. Then they wait to see if their hypothesis was correct.

The cruel irony? By the time this cycle completes, new issues have emerged. The original metric that initiated the investigation may not have been the most critical organizational risk—it was simply the one making the most noise. It's like whack-a-mole: solve one problem, and three more pop up somewhere else.

The Engineering Operations Response

Forward-thinking organizations have created specialized Engineering Operations roles to address this challenge. These positions focus on productivity, efficiency, team health, and release progress—essentially becoming the connective tissue that enables engineering effectiveness while reporting insights back to engineering leadership.

Many of these Engineering Ops professionals are former project managers who understand the critical importance of providing organizational "glue" that keeps teams functioning smoothly. Meanwhile, engineering leadership finds themselves spending most of their time in 1:1s and synthesizing insights from their Engineering Ops teams.

The typical workflow involves leadership identifying potential issues and tasking Engineering Ops with validation and analysis. While this approach definitely accelerates investigation compared to already-overloaded engineering managers doing the analysis themselves, it still perpetuates the reactive cycle. We've just gotten better at being reactive faster.

Engineering Ops teams can execute investigations more efficiently than busy engineering managers, but they're still playing the same whack-a-mole game—just with better reflexes.

The Eternal Management Challenges (Now With Extra Pressure)

What's interesting is that the core challenges haven't changed much over the years:

• Investment visibility - Where are our engineering hours and budget actually going?
• Strategic alignment - Are teams working on what matters most to the business?
• Delivery predictability - Can we anticipate and prevent problems before they become expensive?
• Process optimization - Which workflows help versus hinder our progress?
• Team effectiveness - How do we measure and improve developer productivity?
• Organizational health - Are our practices sustainable for the long term?

These aren't new problems created by AI-era development. But AI has dramatically increased the stakes. When AI coding tools represent significant budget line items, executive questions become more urgent: "Is AI actually making us faster? Can you prove we're maximizing these investments?"

Here's the kicker: 71% of leaders can't quantify AI coding tool impact. These tools require real financial investment, creating intense pressure for objective measurement of both tool effectiveness and overall engineering performance.

The Experience Bottleneck Gets Worse

The investigate-analyze-act cycle creates what I call the "experience bottleneck." Effective problem diagnosis requires a combination of factors that are hard to scale:

• Industry experience for pattern recognition across different contexts
• Deep knowledge of team capabilities and interpersonal dynamics
• Historical understanding of how systems and processes have performed over time
• Awareness of how different tool integrations affect workflow efficiency

This knowledge concentration means only senior leadership and experienced Engineering Ops professionals can effectively navigate the complexity. Junior team members get trapped in investigation loops that consume time without producing insights, while senior leaders spend strategic thinking time on work that should be systematized.

It's like having your most expensive consultants spend their time on data entry.

The Competitive Reality

Organizations that figure out how to break the whack-a-mole cycle will have Engineering Ops teams focused on strategic optimization while their competitors remain stuck in reactive mode. When AI can comprehensively analyze development data relationships that manual analysis cannot practically explore, teams can transition from playing detective to being strategic partners.

The most valuable resource in any engineering organization isn't code—it's the strategic focus and analytical capacity of engineering leadership and operations teams. The question is whether we'll continue to waste these resources on manual investigation cycles that AI could eliminate.

Breaking the Cycle

If you're ready to explore what it looks like to stop playing whack-a-mole and transform your engineering operations from reactive investigation to strategic optimization, I'd love to hear from you. Reach out to me directly—let's discuss your journey.

The whack-a-mole game is optional. Strategic advantage isn't.