“In an AI-driven media environment, judgment is the most valuable metric we have.“

I’ve led teams through multiple waves of platform change—from homepage-driven news consumption to social-first distribution, mobile alerts, streaming and now AI-influenced discovery.

What hasn’t changed is this: metrics are signals, not strategy. And algorithms, no matter how sophisticated, don’t understand purpose, ethics or community impact.

That’s leadership work.

When Algorithms Became Editors

In my years leading digital newsrooms, algorithmic platforms increasingly dictated visibility. Story placement, headline structure, video length and publishing cadence all began to influence performance in measurable ways.

It was tempting—and sometimes profitable in the short term—to optimize everything for reach.

But we learned quickly that algorithmic success without editorial intention led to:

• Audience fatigue
• Brand erosion
• Inconsistent voice
• Short-lived gains with no loyalty

Instead, we learned to treat algorithms as distribution partners, not editors-in-chief.

Metrics helps us understand how stories travel. Editorial judgment determines which stories are worth amplifying.

This approach helped my digital team at KRON 4 News (Bay Area) and ABC 10News (San Diego) exceed KPIs — the latter over five consecutive years — while still strengthening trust and long-term audience engagement in these competitive markets.

AI Changes the Scale, Not the Responsibility

Today, AI and machine learning tools accelerate content creation, testing and distribution. They can suggest headlines, predict engagement and surface trends faster than any human team.

But AI systems optimize for what they can measure, not for what matters.

They don’t recognize:

• Ethical risk
• Cultural context
• Institutional responsibility
• Long-term reputation

That’s why leadership matters more — not less — in AI-influenced environments.

At San Diego State University, where institutional voice and public trust are paramount, metrics guide planning … but deciding how far optimization should go, and where it should stop, was my job as an editorial leader.

The Role of Metrics in an Algorithmic World

In algorithm-driven ecosystems, metrics should help content leaders ask better questions, not chase better numbers.

Used responsibly, analytics help teams:

• Identify where audiences meaningfully engage
• Understand which formats serve different platforms
• Detect when optimization begins to distort mission
• Balance reach with relevance

Used irresponsibly, metrics become a shortcut, replacing judgment with automation and confusing visibility with value.

Good Metrics vs. Bad Metrics Questions

Bad Metrics Questions (Algorithm-Driven, Short-Term)

• “Why didn’t this story go viral?”
• “How do we make this headline more clickable?”
• “What time should we post to game the algorithm?”
• “Can we automate more of this content?”

These questions focus on manipulating systems rather than serving audiences.

Good Metrics Questions (Audience-Driven, Strategic)

• “Who engaged with this content—and why?”
• “Did this story reach the audience it was intended for?”
• “What formats best served the story’s purpose?”
• “Where did optimization enhance clarity—and where did it dilute meaning?”
• “What does sustained engagement tell us that spikes don’t?”

Good questions lead to better decisions, even when the numbers are uncomfortable.

Protecting Mission in a Measurable World

One of my core leadership responsibilities has been translating analytics for both creative teams and stakeholders, ensuring metrics inform strategy without overpowering it.

That means:

• Pushing back when data is oversimplified
• Advocating for context in performance discussions
• Creating space for experimentation without fear of metric-based punishment

Teams do their best work when metrics are tools, not threats.

Why This Perspective Matters Now

As AI continues to shape content discovery, organizations face a choice:

• Let algorithms define success
• Or define success clearly and use algorithms in service of it

The strongest teams I’ve led didn’t reject data or AI. They framed them. They understood that leadership — not automation — ultimately decides what success looks like.

Metrics don’t tell stories. Algorithms don’t either.

People do.