A.I. Archives • NYC Executive Coaching

From my associate Janice Giannini.

A.I. is not just changing how work gets done. It is quietly changing what it means to lead.

A widely circulated essay by A.I. entrepreneur Matt Shumer, “Something Big Is Happening,” argues that recent advances in generative A.I. represent a structural inflection point rather than incremental progress (Shumer, 2026). Whether one agrees with every projection, the broader signal is difficult to ignore: generative systems have moved into the operational core of organizations.

As A.I. embeds itself into analysis, decision support, and execution, leaders face a more personal question:

Who are we when machines can perform parts of the work that once defined our authority?

For many leaders, authority has long rested on experience, judgment, and the ability to synthesize information faster or more comprehensively than others. When intelligent systems begin to share that cognitive terrain, the shift is not merely operational. It touches professional identity.

Few executives discuss this openly. Yet privately, many acknowledge a quiet recalibration underway. This is not primarily a technology story. This is a leadership story.

A.I. Is Embedded – Not Approaching

Organizations now use generative A.I. to draft contracts, write code, synthesize research, analyze financial performance, and support operational decisions. McKinsey’s State of A.I. in 2023 reports that more than one-third of organizations already use generative A.I. regularly in at least one business function (McKinsey & Company, 2023).
Regulatory bodies have also formalized expectations. The European Union’s A.I. Act establishes a risk-based governance structure that categorizes A.I. systems by potential harm and assigns compliance requirements accordingly (European Parliament, 2024).

In the United States, the National Institute of Standards and Technology (NIST) released the A.I. Risk Management Framework (Version 1.0), outlining structured categories such as reliability, transparency, and accountability (NIST, 2023).

A.I. no longer sits at the periphery of strategy. It operates inside governance, risk, and execution.

Why This Wave Feels Different

Organizations have navigated prior technological revolutions — electrification, computing, and the internet. Leadership adapted each time. This transition differs in one important respect.

Earlier waves primarily automated physical labor or routine cognitive tasks. Generative A.I. systems now participate in analysis, synthesis, pattern recognition, and elements of creative reasoning, domains historically associated with managerial and executive authority.

Research from MIT and Harvard shows that structured hybrid teams outperform either humans or A.I. operating alone under many circumstances. The challenge for leaders is recognizing where that boundary is. (Dell’Acqua et al., 2023).

A.I. redistributes cognition inside organizations. It alters who holds analytical leverage and how decisions are formed. That redistribution does not eliminate leadership. It changes its center of gravity.

A Necessary Counterpoint

It is reasonable to question whether this moment differs fundamentally from past cycles. Economists have long observed that productivity gains often lag behind technological breakthroughs. At the macro level, many organizations still struggle with integration complexity and uneven results with A.I. integration.

From this perspective, A.I. may represent a significant but manageable evolution rather than a redefining force.

Yet the more subtle shift may not lie in aggregate data. It lies in how leaders experience the redistribution of cognitive authority inside their own teams — and whether that experience quietly reshapes their role before macro indicators fully register the change.

Productivity Gains and Shifting Expertise

Empirical evidence illustrates how A.I. alters performance patterns.

A 2023 National Bureau of Economic Research study of more than 5,000 customer support agents found that generative A.I. tools increased productivity by 14 percent overall — with gains exceeding 30 percent among less-experienced workers (Brynjolfsson, Li & Raymond, 2023). A.I. compressed performance gaps by amplifying certain capabilities.

For leaders, this introduces a reflective tension. If A.I. elevates baseline analytic performance, traditional signals of expertise evolve. Authority may rely less on possessing answers and more on structuring better questions.

Leadership has historically fostered the expectation that leaders see further, synthesize faster, and decide with greater clarity. When analytic capability becomes distributed, leaders may find that their value shifts from owning answers to architecting inquiry.

That transition can feel destabilizing before it feels empowering.

The Illusion of Stability

Organizations often equate stability with preservation. Research on resilience suggests that stability more often emerges from adaptive capacity.

Amy Edmondson’s work on psychological safety shows that teams adapt more effectively when leaders create space for experimentation without fear (Edmondson, 2018).

A.I. integration will test this. Early missteps are inevitable. Leaders who respond with rigidity may unintentionally slow learning. Leaders who combine accountability with curiosity strengthen resilience.

Stability in an A.I.-integrated organization increasingly means consistency amid change, not insulation from it.

Leadership Across Levels

First-line leaders guide experimentation without losing standards.
Middle leaders translate strategy while absorbing multi-directional pressure.
Senior executives assume formal governance responsibility for algorithmic risk.

Across all levels, one pattern repeats:

Leaders remain steady while the cognitive ground shifts.

That steadiness does not mean certainty. It means composure in ambiguity.

Calibrated Urgency

A.I. introduces bias risk, cybersecurity exposure, and regulatory scrutiny. Excessive hesitation carries consequences. McKinsey’s research indicates that organizations scaling A.I. capabilities report measurable operational and revenue effects (McKinsey, 2023).

Leaders may approach A.I. integration as disciplined experimentation:

Launch bounded pilots
Define explicit accountability
Establish human override authority.
Reflect openly on lessons learned.

Such practices allow forward movement without escalating anxiety.

What Remains Constant

A.I. changes tools and tempo. It does not eliminate trust, meaning, or human judgment.

As systems grow more complex, leaders’ interpretive role may become more visible.

Teams do not expect omniscience. They will expect steadiness — and honesty about uncertainty.

Conclusion: Leadership Starts in the Mirror

A.I. defines the current operating environment. It invites leaders to reconsider how authority, expertise, and judgment function in their organizations.

The weightier shift may not be technological. It may be internal.

Before processes change, before structures evolve, leadership orientation changes first.

For many leaders, the first step may not involve deploying a new system. It may involve noticing their internal posture toward what is unfolding.

Do I treat A.I. primarily as a disruption to manage, or as a capability to integrate?
Am I waiting for certainty before experimenting?
When I speak about A.I. with my team, what tone do I convey—guardedness, neutrality, or possibility?
Have I created a structured space to explore how A.I. might amplify our strengths rather than automate our tasks?

Most leaders may not instinctively know where to begin. That is understandable. The starting point is rarely technical. It is intentional reflection.

Spending disciplined time understanding how A.I. intersects with strategy, talent, and personal leadership identity is not a delay. It is the essential preparation.

Organizations tend to mirror their leaders’ internal posture. If leaders approach A.I. with steadiness, curiosity, and calibrated experimentation, their teams are more likely to do the same. If leaders hesitate without reflection, uncertainty often multiplies.

The organizations most likely to grow healthily in this environment may not be those that move fastest. They may be those whose leaders calibrate their mindset early, approaching A.I. neither as a threat nor as a silver bullet, but as leverage. A.I. will continue to evolve. Markets will adjust. Capabilities will expand.

The more relevant question may be this:

Am I choosing to evolve alongside it, thoughtfully, visibly, and with the composure that allows others to do the same?

Leadership, as always, starts with the person in the mirror.

References

https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-A.I.-in-2023

https://eur-lex.europa.eu/eli/reg/2024/1689/oj

https://www.nist.gov/itl/A.I.-risk-management-framework

(Dell’Acqua et al., 2023).
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4573321

(Brynjolfsson, Li & Raymond, 2023).
https://www.nber.org/papers/w31161

From my associate Grant Tate.

Let’s be honest: the traditional organizational chart is starting to look a little… dusty. You know the one—the classic pyramid where the CEO sits at the apex, flanked by a rigid line of “C-level” silos. The CFO handles the money, the CIO handles the tech, the COO keeps the trains running, and they all meet once a week to report on their respective kingdoms.

It worked for the industrial age, but in a world where market shifts happen in minutes rather than months, that “stay in your lane” mentality is becoming a liability. It’s time we trade the rigid ladder for something a bit more fluid, collaborative, and—frankly—smarter.

The End of the Executive Silo

The biggest flaw in the traditional hierarchy isn’t the people; it’s the bottlenecks. When critical policy decisions are filtered through isolated departments, you end up with “tunnel vision” leadership. The CFO might see a cost-saving measure that the CIO knows will cripple their infrastructure, but those insights often don’t collide until it’s too late.

Instead of a top-down hierarchy, imagine an Agile Leadership Circle. In this model, while each executive still brings their specific “superpower” to the table (be it finance, operations, or strategy), they operate as a unified strike team for critical and policy-level decisions.

In an agile structure, the “C-Suite” functions more like a high-performing product team. They don’t just “check in”; they collaborate in real-time. This doesn’t mean management by committee (which can be a slow-motion train wreck); it means leveraging the collective intelligence of the group to ensure every major move is viewed through multiple lenses simultaneously.

Enter the Silent Partner: AI at the Table

If collaborative decision-making sounds like it might lead to endless meetings and “analysis paralysis,” you’re right—if you’re doing it the old-fashioned way. This is where the game changes. The modern management team isn’t just human; it’s AI-augmented.

Think of AI not as a tool for the IT department, but as a “Chief Synthesis Officer” that sits at the center of the executive circle. Here is how that looks in practice:

Real-Time Data Synthesis: Instead of waiting for a quarterly report, the team uses AI dashboards that aggregate financial, operational, and market data in real-time. When a policy shift is proposed, the AI can immediately simulate the impact across all departments.
The “Red Team” Algorithm: AI can be used to play devil’s advocate. By feeding a proposed strategy into a Large Language Model (LLM) trained on market failures and competitor data, the team can receive an objective “stress test” of their ideas before they ever go live.
Bias Detection: We all have blind spots. AI tools can analyze executive meeting transcripts or strategy documents to identify cognitive biases—like over-optimism or “sunk cost” fallacies—helping the team stay grounded.

Making the Shift: Focus vs. Collaboration

You might be wondering: “If everyone is involved in everything, who actually does the work?” The key is a balance of Individual Focus and Collective Governance.

Individual Focus: The CFO still owns the ledger; the CMO still owns the brand. They lead their respective teams with autonomy.
Collective Governance: For “Level 1” decisions—those that shift company culture, involve high-risk investments, or alter the long-term roadmap—the team enters “Agile Mode.”

This creates a culture of extreme transparency. When the management team uses shared AI platforms to track goals and data, there are no “hidden agendas.” The focus shifts from protecting my department to optimizing the whole system.

The Future is a Network, Not a Ladder

Transitioning to an agile, AI-supported management structure isn’t just a “nice-to-have” creative experiment. It’s a survival strategy. The pace of change today requires a leadership team that can pivot as one, backed by data that is processed at the speed of thought.

By breaking down the silos and inviting AI to the decision-making table, we aren’t just making the C-suite more efficient—we’re making it more human. We’re freeing up leaders to do what they do best: innovate, inspire, and navigate the complex human elements of business, while the machines handle the heavy lifting of data synthesis.

So, the next time you look at your company’s org chart, ask yourself: is this a map of how we actually work, or is it just a relic of how we used to work? It might be time to redraw the lines.