Borrowed Time (Part 2)

What Happens When Machines Sprint and Organizations Stall

In Borrowed Time: Part 1, we looked at the countdown to when superintelligent ‘everywhere agents’ change everything. A synthesized view of forecasts and insider perspective peg it at roughly 30 months. For leaders, a hard question warrants attention: What happens when your company runs on an analog clock? The machines are sprinting. Orgs are lagging. Time, not tech, is the real disruption to watch.

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In his visionary book Present Shock, Douglas Rushkoff describes a world where digital technologies collapse time into an ever-present now. In this space, everything feels everywhere, all at once.

He warned that as digitally mediated life accelerated, we’d struggle to keep pace with algorithmic time—a runaway clockwork of machine logic and feedback loops that, left unchecked, would fuel widespread anxiety and disorientation.

Today, that tension is pervasive in the workplace.

Tempo Shock is the organizational version of Present Shock. It goes beyond emotional overload to structural fractures in organizations we rely on. New shocks reverberate from the growing gap between machine velocity and the comfortable rhythm of institutional work.

AI doesn’t just move fast—it compounds. Each output becomes feedback. Every update makes the next one smarter. Inside the machine, speed builds on itself.

Organizations, by contrast, are built for stability and consensus. They run on meetings, approvals, and alignment, not momentum. It’s not that they lack vision—it’s that their systems weren’t built for speed.

In music, a tempo mismatch throws the whole composition off-key. In companies, it breaks coordination, alignment, and impact. The result isn’t a loud failure. It’s quiet drift. When this happens, the advantages of AI evaporate.

The 30-Second Management Report

To illustrate Tempo Shock, consider the “30-minute” board packet.

This is the briefing document prepared ahead of quarterly board meetings. It includes financial results, strategic updates, key performance indicators (KPIs), and risk assessments. It’s compiled across finance, operations, legal, investor relations, and communications.

Board packets are more than reports or paperwork. They’re the story a company tells itself about what’s happening, what matters, and what comes next.

How it’s built is a revealing signal.

Typically, creating a board packet absorbs around 120 hours of human labor. Finance digs through spreadsheets. Ops rebuilds forecasts. IR and comms stitch together mismatched reports into a coherent narrative.

Now, a first draft can be produced in 30 minutes.

An AI agent connects to NetSuite, Salesforce, and internal analytics. It flags anomalies, drafts commentary, generates visuals, and anticipates board questions using past transcripts. It operates at algorithmic speed—orders of magnitude faster than humans.

The insights haven’t changed.
The decisions haven’t changed.
The clock has.

When AI sprints at 200× speed but approvals stay analog, the bottleneck isn’t technical—it’s structural. In less time than it takes for VPs to access a DocuSign, the packet is ready for review.

Tempo awareness and expertise—building, managing, and making decisions at machine speed—is a new competitive edge.

Seeing Shocks Up Close

Technologies like AI don’t just speed things up—they reshape how work is made, what moves it, and why it matters.

The deeper I’ve gone into the guts of corporate operations, the clearer the signal becomes: speed is the thing. With AI, work compression is clear. What’s less so is the ability to make speed stick—to institutionalize it.

Again and again, the same pattern emerges: as technology accelerates, structure becomes a bigger barrier—a mental, organizational, and creative drag.

Diagnosing Tempo Shocks makes that friction visible. Think of two clocks ticking inside the same company:

• Clock One: The organization moves faster than its leadership.

• Clock Two: Leadership moves faster than the organization.

Figure 1 maps the symptoms and actions needed to close the gap.

In one clock, frontline teams often surge ahead—experimenting, deploying, learning—while leadership hesitates. In the other, visionary leaders set bold AI agendas. The systems beneath them lack the structure, talent, or workflows to deliver.

One marketing executive I work with witnessed firsthand how quickly AI can accelerate content creation and research. Energized, she issued a clear mandate: her team and agencies needed to get on board. But both remained stuck at the ‘test’ level, lacking the infrastructure and expertise to act on her vision. A wave of ambitious initiatives floundered, leaving frustration on all sides.

The inverse is just as flawed. At a leading pharmaceutical company, innovation teams launched over two dozen AI pilots across various therapeutic areas, from predictive modeling to molecule discovery.

What they lacked was a system to capture, synthesize, and scale the knowledge they gained. Despite impressive results, there was no mechanism to reallocate budgets based on AI performance, no shared knowledge base, and no structured way to communicate progress to increasingly curious investors.

When an organization’s clocks fall out of sync, it’s not just a communication problem. It’s a reality distortion. Different parts of the business operate in fundamentally different ways, using different language, at a different pace.

Speed Brakes Inside the Org

Despite all the hype, most AI investments underdeliver—and executives know it. According to Cisco’s AI Readiness Index, organizational preparedness declined year over year.

Across sectors and industries, similar patterns emerge. They’re not technical—they’re structural. They act as speed brakes, preventing organizations from progressing, even as they invest heavily in AI.

You can see it at the executive level. You experience it through fragmented governance, sluggish workflows, and misaligned communication. You feel it in operating norms and culture that prohibit machine-speed change.

These are the most common friction points where the promise of AI gives way to operational drag. Let’s go deeper.

Executive Depth: Leaders vary widely in their AI literacy and vision. When leadership competence lags, it becomes a chokepoint—one of the most significant barriers to AI-enabled impact.

A leader’s perspective shapes where attention goes, which investments get made, and how teams interpret AI mandates. If you lead a team, department, or business, where do you fall on the spectrum in Figure 2?

Organizational Limbo: Internal paralysis often sets in when functional departments await slow, centralized AI direction. In that liminal space, opportunities evaporate.

Consider what happens in many companies:

Technology and strategy teams can spend up to a year crafting an enterprise AI roadmap. They often collaborate with consultants to evaluate and approve AI systems, such as Microsoft Copilot. They develop governance frameworks that struggle to keep pace with AI’s rapid advancements, and target productivity improvements primarily in manufacturing, supply chain, and R&D.

Meanwhile, vital functions—stakeholder-facing groups like public policy, investor relations, communications, and corporate risk—are left waiting.

By the time top-down guidance trickles down, it’s both obsolete and misaligned. Months-long planning often fails to account for newer, more powerful AI agents that can execute tasks more effectively than selected technologies—or employees.

Top-down strategies also overlook task-specific opportunities where AI can excel. Employees begin to sense their replaceability, prompting many to use unauthorized AI tools. The most talented depart for organizations that offer greater autonomy, sparking a secondary talent crisis.

Here, tempo is the silent cause. When strategy lacks coherence, operations fall out of sync, and speed isn’t modeled from the top down, liminality turns into apathy. Gallup reports that employee engagement has sunk to its lowest level in a decade—only 31% of workers now feel engaged at work.

As a simple diagnostic, consider tempo as a critical ingredient, as shown in Figure 3.

Turning Tempo into Advantage

Can Tempo Shock be turned into an advantage?

That’s the ‘non-obvious’ question facing executives today. It’s no longer a question of whether to adopt AI, but rather how to accelerate its impact without compromising models that keep the company alive.

In my work, I’ve found that many leaders aren’t just paralyzed. They’re overwhelmed.

They understand the urgency. They know AI requires iteration, experimentation, and continuous adaptation. However, they’re also running complex businesses, navigating daily volatility, fragility, and financial risk.

Now, they face a new dimension of management: time compression.

It’s an ‘Accelerator’s Paradox.’ Leaders are being asked to move faster, inside systems that were built to slow things down.

The solution isn’t another strategy off-site.

It’s not a vendor demo.

It’s something structural by design.

‘Labs’ as a Way Forward

AI Labs are a solution. Not technical showrooms, but organizational accelerators. Unlike labs focused on tech incubation, working AI Labs inside companies create:

• Safe spaces to experiment with new workflows.

• Accelerators of understanding, literacy, and hands-on credibility.

• Translators between machine logic and institutional rhythm.

Labs both accelerate and protect operational models until the organization is ready to absorb new ones.

In 2016, our team examined how the Brexit vote and the U.S. presidential election caught traditional institutions off guard. We didn’t see them as isolated political shocks—they were symptoms of something deeper: a system-wide misalignment between how information moved through networks and how we’d been trained to interpret it.

The tools we relied on were built for a different era. They weren’t calibrated to the dynamics of networked influence, algorithmic amplification, or fractured attention.

Our agenda focused on a big, paradoxical question:

How do you make sense of the world when the world no longer makes sense?

We didn’t do a study or write a deck. We built a lab.

We designed it to study network graphs, modes of digital influence, and the new actors shaping narratives. It became our reconnaissance from the future.

We tracked how stories traveled through networks, from ‘zero to zeitgeist.’

We identified the early breakdown of social coherence, well before COVID made it clear.

And when generative AI arrived, we were already ready. We had shifted the lab’s focus to AI acceleration before the launch of ChatGPT.

The most important thing we discovered wasn’t which AIs to use. It was the energy and metabolism that our teams needed to work effectively with AIs. Once we got in sync, it became a center for growth. The ability to see early, act quickly, and learn rapidly became a competitive edge when ChatGPT gained prominence in business circles.

Labs are organizational speed machines—pockets of the future inside the present. Not for innovation theater. Not isolated skunkworks. They’re temporal bridges, connecting today's reality with tomorrow’s operating mandate: building for speed.

Seeing Structure as an Innovation

Organizations rarely fail due to a lack of intelligence, ambition, or resources.

They fail because they try to force tomorrow’s logic into yesterday’s systems.

It’s not a capability problem. It’s a perspective problem.

A dangerous instinct is to centralize—to wait until things feel clearer, safer, more manageable. But AI won’t wait for your org chart. It will continue to learn, compound, and reshape the landscape, ready or not.

That’s why successful organizations won’t scale AI by committee. They’ll focus on specifics, on autonomous units doing critical work, on compatible clocks. They’ll create new rhythms, new workflows, and new forms of value creation—without being crushed by institutional drag.

If you realize your organization needs to move faster than it does today, the time to build this capacity is now.

Disruption isn’t a technology. It’s a mismatch of clocks. In the AI era, the winners won’t be the ones with the most data scientists or best AIs.

They’ll be the ones who best keep and accelerate time.