Is “Hi-C” Work the New Operating Model for AI-Era Teams?

AI is flattening the org chart. But without coordination, it may also turn work into bumper cars.

For years, the traditional model of work was built around a clear pyramid: executives set direction, managers coordinated execution, and individual contributors produced the work.

That model (haha) is starting to bend.

AI tools are giving individual contributors more leverage than ever. Product managers can prototype. Designers can generate flows. Developers can scaffold faster. Strategists can research, synthesize, and produce client-ready materials in hours instead of days.

The result is a new kind of worker emerging across modern organizations: someone who is high-context, hands-on, cross-functional, and AI-augmented.

Let’s call it Hi-C work.

1. High-context

2. High-coordination

3. High-contribution

The Pyramid Is Flattening

In many organizations, the middle layers are being compressed.

There may be fewer people sitting purely “above” the work. At the same time, it is harder for junior talent to enter the workforce when AI can already perform many baseline production tasks.

That's why I like taking on at least one intern every year.

What remains is a growing horizontal layer of experienced operators who can move across strategy, execution, communication, and systems.

Managers are becoming player-coaches.

But C's are becoming "Hi-C"'s - they understand the business context, but they can still get close to the work. They can guide a team, but they can also contribute directly. They can use AI to accelerate execution, but they know that speed without alignment creates waste.

AI Makes Coordination More Important, Not Less

One of the biggest misconceptions about AI adoption is that faster output automatically means better performance. Which... yes and no.

AI can help teams generate more:

• More code

• More copy

• More prototypes

• More tickets

• More research

• More documentation

• More ideas

It's all just "more" but not "better"... right?

But more output without shared context can quickly become noise at best, and debt at worst.

When everyone has access to powerful tools, the risk is not that work slows down. The risk is that work accelerates in too many directions at once.

That is where bumper-car work begins.

Teams move fast, but not together - whoops.
People generate assets, but not decisions - more slop.
Agents create artifacts, but not alignment - did I mention slop?
Leaders see motion, but not necessarily progress - so many ideas! But no progress? More mess.

In the AI era, coordination is leverage.

The Rise of the Player-Coach

The strongest teams will increasingly need people who can operate between layers.

A modern player-coach can:

• Understand the strategic goal

• Translate that goal into execution

• Use AI tools to accelerate production

• Identify where the team is blocked

• Step in when hands-on contribution is needed

• Step back when ownership should stay with the team

• Maintain quality, context, and momentum

This is different from micromanagement. I tell myself anyway...

Micromanagement is control without trust.

Player-coaching is contribution with context.

The distinction matters.

A micromanager inserts themselves to approve, correct, or dominate. A player-coach gets close enough to understand reality, support the team, and improve the system.

Why This Matters for Product Teams

Digital product work has always required cross-functional coordination.

Strategy, UX, design, engineering, QA, DevOps, content, analytics, stakeholder management, and user feedback all have to connect. The work breaks down in the handoffs.

It takes a village.

AI makes those handoffs faster, but not automatically better.

A product team using AI without strong coordination can quickly create:

• Prototype sprawl

• Conflicting requirements

• Unreviewed code

• Inconsistent design systems

• Poorly prioritized backlogs

• Redundant documentation

• Client confusion

• QA gaps

• Technical debt disguised as velocity

That is why the new operating model cannot simply be “give everyone AI tools and move faster.”

The better model is:

Give teams AI leverage, then strengthen the human systems around alignment, prioritization, quality, and decision-making.

The New Value of High-Context Work

High-context work is becoming more valuable because AI can produce, but it cannot automatically know what matters.

Someone still needs to understand:

• Why this product exists

• Who the user is

• What the business needs

• Where the technical risks are

• What tradeoffs are acceptable

• Which decisions are reversible

• Which decisions are expensive

• What “good” actually looks like

That context often lives between people, systems, documents, meetings, and decisions.

The player-coach helps connect it.

What Organizations Should Be Doing Next

Companies preparing for AI-enabled work should not only ask, “Which tools should we adopt?”

They should ask:

1. Where does context live today?
   If important knowledge is trapped in meetings, Slack threads, or individual heads, AI will amplify confusion.

2. Who owns coordination?
   Faster teams need clearer decision rights, not looser ones.

3. Which roles need to become more hands-on?
   Some managers may need to get closer to the work. Some ICs may need to become stronger systems thinkers. Create the coaching plan in your 1:1's geared towards these new systems.

4. Where can AI create leverage without creating chaos?
   AI should improve flow, not flood the team with disconnected artifacts.

5. How do we protect quality while increasing speed?
   Reviews, QA, acceptance criteria, and technical standards matter more when production accelerates.

The Bottom Line

The future of work may not be fewer managers.

It may be fewer people who only manage.

The next era of work will not just reward speed. It will reward coordinated speed.