Glenn Gow, The Scaling Executive Coach and host of The Scaling Executive Podcast, spoke with Sheldon Fernandez — AI strategist, co-founder, former CTO, and former CEO of Darwin AI — about the single most important shift CEOs must make to unlock AI as a scaling engine. Fernandez’s core argument: when AI is used in a genuinely transformative way, it does not add to an existing process. It forces you to reinvent that process entirely. CEOs who make that distinction will separate themselves from competitors who are still layering technology onto old workflows.
This episode is for CEOs who want to move past AI experimentation and identify where AI will produce a genuine competitive advantage in their business model.
Key Takeaways
- CEOs who ask “where can I add AI?” are asking the wrong question. The right question is: “Which process in my industry can AI force me to completely reimagine?” — that is where a disruptive opportunity lives.
- Agile startups have a structural advantage over large enterprises in AI adoption: incumbents carry operational weight that makes swift AI adoption nearly impossible.
- Explainability — understanding how an AI model reached a conclusion — is a deployment requirement, not an optional feature, for CEOs in healthcare, financial services, and any regulated sector.
- Critical thinking has shifted from task execution to two new locations: the quality of the prompt that sets up the AI, and the judgment applied to what the AI produces.
- Executives who communicate with precision get better outputs from AI tools — the skill that makes a strong writer also makes a strong prompter, and the gap between the two compounds over time.
- The competitive unit to scale is not headcount — it is the machine. CEOs who define narrow domains and direct AI agents within them build leverage their competitors cannot replicate by hiring alone.
CEOs Who Reimagine Processes with AI Beat Those Who Only Add AI to Existing Workflows
The most common mistake Fernandez sees in both startups and large enterprises is treating AI as a technology add-on — layering it on top of an existing workflow without changing how the work actually gets done.
“When AI is used in a disruptive or transformative way, it’s not just a technological add-on to an existing process. It necessitates re-imagining that process in a given domain.”
Fernandez draws the parallel to a missed decade that most executives recognize in hindsight. When the internet arrived, magazines simply published their content online. No business model changed. No workflow changed. The result was a lost decade of opportunity for incumbents while new entrants built entirely different content consumption models around the same technology. Fernandez uses this pattern to help CEOs diagnose their own AI posture: are you digitizing an existing process, or are you rebuilding it?
The question Fernandez puts to CEOs is not “where can we use AI?” It is: “Am I reinventing a process in some way that really accelerates the application of AI in this domain — and is the industry open to that?” When the answer to both is yes, Fernandez sees a genuine disruptive opportunity.
Startups hold a structural advantage here. Large enterprises carry operational weight — complex processes, legacy systems, organizational inertia — that makes swift AI adoption difficult. Agile companies can move faster, experiment more freely, and capture market positions before incumbents recognize what has changed.
Fernandez’s Uber example names the mechanism precisely: when Steve Jobs held up the iPhone, nobody immediately connected the inefficiency of taxi service with GPS and a smart computer in your pocket. Uber made that connection. Fernandez’s point to CEOs: the technology was not new. The process of reimagination was. The company that wins is the one that asks what existing inefficiency a new capability makes solvable — not the one that installs the new capability inside the old inefficiency.
CEOs in Regulated Industries Must Understand AI Explainability Before They Deploy It
Fernandez built Darwin AI around a specific technical problem: AI systems are often a mystery even to their designers. Deep learning models contain billions of parameters. The chain of logic that produces any given output — what inputs, what memory context, what floating-point arithmetic — is not transparent. The field calls this the explainability problem.
For most applications, this is an inconvenience. For regulated industries, it is a blocker.
Healthcare and financial services demand traceability. They require that a decision made by an AI system can be explained, audited, and defended. Without explainability capability, AI cannot be legally or ethically deployed in those environments.
Darwin AI’s explainability tools surfaced a vivid example of what happens without them. A client running an autonomous vehicle system noticed their self-driving car turned left with increasing frequency when the sky was a particular shade of purple. The cause was invisible inside the neural network. Darwin AI’s tools revealed that the turning scenario had been trained exclusively in the Nevada desert at a specific time of day — when the sky was exactly that color. The AI had learned a nonsensical correlation: sky color predicts turning direction.
“If you don’t know how something works, you don’t know when it’ll fail. And if you don’t know when it’ll fail, there are boundary cases, edge cases like this lurking in the system that can manifest with disastrous consequences.”
For CEOs deploying AI in regulated sectors, explainability is not a future consideration. It is a deployment requirement today.
CEOs Who Master Prompting Treat AI as a Scalable Analyst — Not a Search Engine
Fernandez’s framework for AI scaling comes down to a single question he poses to his clients: “If you had access to an analyst, 24 hours a day, 7 days a week, with mathematical PhD-level reasoning, what would that analyst be doing?”
The framing is specific by design. The AI agent he describes needs to be taught. It produces good results only in narrow domains. But once it is taught and confined to a defined scope, it executes with a speed no human team can match.
The scaling implication for CEOs is that critical thinking has moved. It no longer lives in the execution of the task. It now lives in two places: the prompt that sets up the AI, and the judgment applied to what comes out.
Gow described his own process: building a serious prompt for a complex research question takes roughly 30 minutes. He then runs the same prompt through two separate large language models simultaneously, receives two independent long-form answers, and asks a third model to synthesize and evaluate them. The output is research he could not produce alone, delivered faster than any human analyst could.
Fernandez added a point that sharpens the competitive implication: “We thought large language models would make writing obsolete. What we’ve learned is that it most benefits those of us who can write really well and clearly and crisply and lucidly.”
Strong writing is now a prompting asset. CEOs and executives who communicate with precision get more from their AI tools than those who do not. The communication skill is the multiplier.
The Scale AI 2025 State of AI Report predicts a billion-dollar unicorn with just one employee directing AI agents across all functions — an outcome Fernandez acknowledges may or may not materialize in this specific cycle. The lesson for CEOs is not the extreme case. It is the direction. The competitive unit to scale now is the machine, not the headcount.
The Scaling CEO Framework from Sheldon Fernandez
| Principle | What it means in practice | Named evidence from this interview |
| Reimagine, don’t add on | AI’s real value appears when it forces a process to be rebuilt from scratch — not when it speeds up an existing one | Darwin AI’s client work consistently shows that companies layering AI onto entrenched processes see incremental efficiency gains; companies that rebuild the process around AI’s capabilities create positions competitors cannot easily replicate |
| Explainability before deployment | In any regulated sector, an AI system whose reasoning cannot be traced creates compliance exposure and unpredictable failure modes | Darwin AI’s explainability tools revealed that a client’s autonomous vehicle turned left based on sky color — a nonsensical correlation that was invisible inside the neural network and had no human-readable explanation without interpretability tooling |
| Prompt quality is the leverage point | Critical thinking no longer lives in task execution — it lives in the quality of instructions given to AI and the judgment applied to its output | Fernandez advises CEOs to define a narrow domain, teach the AI within it, and ask what a 24/7 PhD-level analyst would do for the business — the CEO who poses that question well gets compound returns on every AI interaction |
| Clear writing scales AI output | The executives who benefit most from large language models are those who communicate with precision — the writing skill and the prompting skill are the same skill | Fernandez: “It most benefits those of us who can write really well and clearly and crisply and lucidly” — the gap between a precise prompter and an imprecise one widens as AI tools grow more powerful |
| Scale the machine, not the headcount | The competitive unit to build is AI agent capacity, not team size — CEOs who define the scope and direct the agents accumulate leverage that hiring cannot replicate at the same speed | The Scale AI 2025 State of AI Report predicts a billion-dollar unicorn with one employee directing AI agents; Fernandez notes that specific outcome may not materialize in this cycle, but the directional logic is already operating |
Quotes from This Episode
- “When Steve Jobs held up that iPhone, nobody — nobody — went home and said: you know what, taxi service is really inefficient, and there’s a GPS in my pocket and a smart computer — let me see what I can do about this. That connection was not immediately made. And of course, Uber figured it out.” — Sheldon Fernandez, AI Strategist and former CEO, Darwin AI
- “If you had access to an analyst, 24 hours a day, 7 days a week, with mathematical PhD-level reasoning, what would that analyst be doing?” — Sheldon Fernandez, AI Strategist and former CEO, Darwin AI, on the question every CEO should ask before deploying an AI agent
- “That which makes you good will keep you from being great.” — Sheldon Fernandez, quoting his former CEO on the trap of relying on a narrow set of strengths
- “Your thing to scale now is going to be a machine if you do it right.” — Sheldon Fernandez, AI Strategist and former CEO, Darwin AI
- “It was that confluence of experience and timing that was just really appropriate.” — Sheldon Fernandez, AI Strategist and former CEO, Darwin AI, on the decision to step into the CEO role at Darwin AI’s founding
Frequently Asked Questions
How should a CEO identify where AI will actually create competitive advantage in their business?
The test is not where AI saves time — it is where AI forces a process to be rebuilt from scratch. Sheldon Fernandez advises CEOs to ask whether they are reinventing a process in a way that accelerates AI’s impact in a given domain, and whether the industry is open to that reinvention. When both conditions are true, the opportunity is disruptive, not incremental. Industries with entrenched incumbents and ossified processes are the most attractive targets, because agile companies can move faster than large enterprises burdened with operational complexity.
What does AI explainability mean for a CEO who is not an AI engineer?
Explainability refers to the ability to understand how an AI model reached a specific conclusion — not just what it concluded. For CEOs in healthcare, financial services, or any regulated sector, this is a deployment requirement, not a technical detail. Darwin AI’s work revealed an autonomous vehicle that turned left based on sky color — a nonsensical correlation invisible inside the neural network without interpretability tools. Sheldon Fernandez’s rule: if you cannot trace how an AI system reached its output, you cannot know where or how it will fail, and that uncertainty creates both regulatory and operational risk.
How do CEOs develop the prompting skills needed to get real value from large language models?
Glenn Gow’s approach — taking 30 minutes to build a serious prompt for a complex question, running it through two separate models simultaneously, then asking a third to synthesize and evaluate the outputs — treats LLMs as competing analysts rather than single sources. Sheldon Fernandez adds the foundational skill: clear, precise writing. CEOs who communicate with precision get more accurate and useful AI outputs because the writing skill and the prompting skill are the same skill. Building that capability now compounds over time as AI tools grow more powerful.
CEOs Work with Glenn Gow to Scale Their Companies by Scaling Themselves First
Glenn Gow is The Scaling Executive Coach — he coaches ambitious executives into the CEO seat and CEOs into successful exits. With 25 years as a CEO and 5 years in venture capital, Glenn helps leaders scale their companies by scaling themselves first. If this conversation was useful, you can apply for executive coaching with Glenn Gow or apply to be a guest on The Scaling Executive Podcast.
