Transforming Energy Leadership with Dennis Kwok

The most exciting innovation and the most significant challenge are the same: navigating the unknown blue-ocean market created by truly disruptive technology.

In the O&G upstream sector, the next wave of value comes from solutions so entirely new—such as our smart drilling, completion and production technology—that there is no historical reference data. This creates a massive challenge: how do you make high-stakes decisions when traditional justification models don’t exist? The risk is high, the data is incomplete, and every stakeholder’s experience serves as a potential anchor, creating immense resistance to change.

Furthermore, the accelerating rise of AI amplifies this uncertainty while simultaneously demanding faster decisions. AI is the ultimate productivity booster, and companies that learn to integrate AI-augmented judgment effectively will gain a profound competitive advantage. The true challenge is evolving leadership to make efficient decisions not with perfect data, but with Quantitative Intuition in this new market defined by both disruption and artificial intelligence.

It was an unparalleled masterclass in structuring strategic judgment for business leaders—providing frameworks that transform immense risk and uncertainty into innovative actionable opportunity. While other executive programs gave me the language of every C-suite function, this program provided the operating system (OS) and the fundamental framework for turning data into decisions across all of them. It was the missing piece.

Before the program, my balance was heavily skewed toward intuition informed by over 35 years of experience—what we learned is “fast, System 1” thinking. As a patented inventor and COO in upstream O&G, my entire industry background, including my company’s structure, was built on an expert-system approach. This system depends on formalized internal expertise to define the problem, dictate the process, and deliver the answer.

However, operating in a blue-ocean market with truly disruptive completion technology meant my historical data had zero reference points. The expert system was failing because it relied on old knowledge in a new context, making me acutely vulnerable to cognitive biases—specifically overconfidence and anchoring.

The problem wasn’t a lack of data; it was a lack of a rigorous, repeatable framework to validate my intuition and effectively integrate the emerging insights of AI-augmented judgment. I needed a structure to turn my deep experience into a strategic advantage rather than letting the rigid expert system become a source of bias in a high-stakes, unknown market.
 

That necessity came down to two core elements: the increasing complexity of the market and the amplified risk inherent in our disruptive technology.

1. Navigating the Blue-Ocean Risk: For a company like ARECO Technology, operating in an uncontested blue-ocean space, the data we collected revealed what was happening—but not why or what action to take. We were making decisions based on interesting findings, but they weren’t leading to actionable conclusions. I realized that relying solely on our expert system meant that if we encountered something new or unexpected, we would be left lost, confused, or unsure. We lacked the framing necessary to navigate profound uncertainty.
2. The AI-Accelerated Urgency: The rising influence of AI amplified that urgency. AI functions as the ultimate productivity booster, and we needed a straightforward methodology to integrate AI-augmented judgment into our strategic decisions. We risked being left behind if we couldn’t properly evaluate the sophisticated predictions and recommendations AI provides.

The transition to a board-director role also magnified this need. In that governance position—where the stakes are highest—you realize you need a tool to deliberately counteract the risk of analysis paralysis or snap, high-risk, biased decisions. I needed the Quantitative Intuition™ framework to structure our judgment, ensuring we transformed immense risk and uncertainty into actionable, growth-oriented opportunity.
 

Yes. My educational background is anchored by extensive senior-executive education across C-suite functions. I’ve completed programs encompassing the disciplines of AMP (Advanced Management Program), BOD (Board of Director), CEO, CFO, CTO, CDAIO (Chief Data and AI Officer), COO, CMO, and BCP (Business Coach Program)—now including the Leading in a Data-Driven World: Developing Quantitative Intuition™ program.

My prior programs provided me with the language of every executive function, enabling me to understand how each department operates and thinks. The Columbia QI course, however, was uniquely positioned to provide the operating system (OS)—the fundamental framework—for transitioning from data and analysis to actionable strategic decisions across all those functions. It was the missing piece to truly master strategic judgment informed by data.

The most powerful element was its clarity in forcing us to begin with the end in mind. For a leader from an expert-system background, this was a critical shift. Instead of gathering data just to reduce general ignorance, the framework forces you to:

1. Prioritize Action: Define the action you might take before seeking the data, ensuring that the research leads to a decision.

2. Filter Noise: Use the Knowledge Matrix to quickly categorize information and eliminate meaningless or random data—essential for preserving resources in a startup environment.

3. Challenge Bias: The simple act of asking, “What do you wish you knew?” helps reveal and mitigate the overconfidence and anchoring biases that often accompany experience.

In short, the framework armed me with a practical tool to turn ambiguous situations into a series of essential, actionable questions.

The connection lies in moving from analysis paralysis to efficient judgment under uncertainty. The program gave me three tools vital for a high-stakes startup environment:

1. Decision Triage: The Decision Moment Model quickly categorizes decisions by time, risk, and trust, ensuring we don’t overanalyze low-time, high-risk situations (such as crisis management) and instead prioritize action.

2. Focus and Efficiency: The “I Wish I Knew” and Backward Market Research (BMR) methodologies compel us to begin with the end in mind, preventing wasted resources on irrelevant data and ensuring every analysis is directly actionable.

3. Speed to Clarity: The Fermi Method of guesstimating teaches that it’s often better to be roughly right than precisely wrong. This is essential for quickly sizing a blue-ocean market and making confident initial strategic decisions, allowing us to course-correct faster than competitors.

The cohort was the real-world lab that instantly validated the framework. It proved that the challenges facing my company—such as managing asymmetric information—are universal to all high-stakes environments, whether in O&G, medical technology (MedTech), or digital sales.

The diversity immediately broke my reliance on the O&G expert system, pushing me to value unshared information from other sectors. It showed that the Quantitative Intuition methodologies—like the “I Wish I Knew” and Decision Moment Model—transcend industry jargon, providing a single, powerful language for managing risk and making efficient decisions under profound uncertainty.

We began using Quantitative Intuition for a critical go-to-market decision on our new completion technology in the blue-ocean market.

I insisted the team move away from the expert-system mindset and first apply the Backward Market Research (BMR) approach. We defined the successful press release—the final intended action—before doing any analysis.

Next, we used the “I Wish I Knew” framework to identify only the crucial missing information we needed to seek. This process immediately shifted our focus from simply validating the technology to rigorously framing the business risk and implementation strategy required to win the market. It was highly efficient, preventing analysis paralysis and saving critical resources.

Since completing the program, my leadership style has evolved into strategic coaching, where I act as both facilitator and executive coach. I’ve become a data translator for the executive team, guiding them on how to approach uncertainty:

• Precision Questioning: I use the “I Wish I Knew” framework to coach the team in framing problems and defining actionable outcomes. By articulating their “I Wish I Knew,” they focus on acquiring missing information and sharpening critical thinking.

• Challenging Biases: I encourage countering cognitive biases such as overconfidence and confirmation bias by asking, “What surprised you?” This fosters exploration of anomalies and builds trust in their doubts.

My communication has become more top-down, emphasizing synthesis and action, in line with the Minto Pyramid Principle. I emphasize:

• Synthesis Over Summary: I require that meetings present main insights first, supported by data and context. This accelerates decision-making and supports my role as executive coach.
• Decision-Moment Clarity: I apply the Decision Moment Model in discussions, coaching the team to assess risks, time constraints, and trust levels—ensuring efficient decision-making even under VUCA conditions in a high-risk startup.

Absolutely. My co-founding team would describe the change as moving from data consumption to strategic interrogation.

The key shift they’ve noticed is my insistence on precision questioning. When a new problem arises, I no longer allow data collection based on assumptions; instead, we first define our key “I Wish I Knew” questions. This shift achieves two critical objectives:


1. Saves Time and Capital: It eliminates meaningless data collection, focusing instead on the crucial missing information that affects high-stakes decisions in our blue-ocean market.
2. Demands Synthesis: The team knows I require synthesis over dense summaries, with the main insight presented first. This top-down approach saves executive time and ensures strategic judgment precedes detail.

Founders in asset-heavy industries often mistakenly believe their historical data and physical assets are sufficient. QI is the tool that prepares them for the VUCA world. It's the only way to manage multi-billion-dollar risks by integrating top-level strategic governance with technical reality. It turns the art of leadership into a rigorous, data-augmented judgment process capable of managing profound uncertainty.
It’s the only way to manage multibillion-dollar risks—from geopolitical volatility (such as our insights on global bankruptcies) to technical failure—by integrating top-level strategic governance with operational reality. It turns the art of leadership into a disciplined, data-augmented judgment process capable of navigating profound uncertainty.

My advice is twofold, drawn directly from the program’s most essential tools:

• Define the End First: Don’t start a project without clarifying the ultimate decision, the desired outcome, and what the final report should look like to prompt action. This is the Backward Approach to problem formulation.

• Lead the Process, Not the Content: As a leader, your job is to reduce process losses. That means guiding divergent discussions and creating a climate of psychological safety so that unique, asymmetrical information gets voiced and debated before a final decision is made.