AI and the Executive Table

Show NotesMost executive decisions are forecasts in disguise. 'Will this market grow?' 'Will this AI initiative deliver ROI?' 'Will this acquisition integrate well?' These are all probability estimates — and most leadership teams make them without any structured method, relying instead on experience, intuition, and the confidence of whoever speaks loudest in the room.In this episode, Kenza and Pascal explore two landmark studies on AI-augmented forecasting and what they mean for executive decision-making. The first, by Halawi et al. (NeurIPS 2024), shows that a retrieval-augmented AI system can approach the forecasting accuracy of competitive human forecasters — and that combining AI and human judgment always outperforms either alone. The second, by Schoenegger et al. (2025, co-authored with Philip Tetlock, the world's leading expert on forecasting), shows that AI assistance improves human forecasting accuracy by 24–28% — even when the AI assistant is deliberately biased and overconfident.The key insight: the benefit is not just informational. It is cognitive. Engaging with an AI perspective — even an imperfect one — forces assumption-surfacing, reduces overconfidence, and makes human judgment measurably more accurate. The Brier Score — a rigorous calibration metric that measures how well-calibrated probability estimates are, not just whether someone was right or wrong — confirms the improvement is real and quantifiable.For boards making high-stakes decisions under uncertainty, this is not a hypothetical. It is a demonstrated, quantifiable capability that most executive teams are not yet using systematically.Key TakeawaysCombining AI forecasts with human judgment always produces better outcomes than either alone — confirmed across thousands of forecasting questions.AI assistance improves human forecasting accuracy by 24–28%, measured by the Brier Score — the gold standard for calibration.The benefit is cognitive, not just informational: AI forces you to surface assumptions you didn't know you were making and reduces overconfidence even in experienced forecasters.AI performs best exactly where boards struggle most — in the 30–70% uncertainty range where nobody is sure and overconfidence is highest.Most executive teams use AI to summarize and report. The studies suggest they should be using it to challenge assumptions before the decision is made.The practical starting point: pick one recurring decision type, introduce probability language, track outcomes, and score calibration over time.

Show NotesNearly 9 in 10 organizations report using AI regularly. Only 37% say AI has made a positive contribution to their EBIT. And only 6% qualify as genuine AI high performers. The rest? Optimizing. Automating. Saving costs. Declaring victory.In this episode Kenza and Pascal ask themselves: when does AI actually grow the top line?And close with a board checklist and a final provocation:"Do not ask what AI can do for your current business. Ask what business you can build because AI now exists."Key TakeawaysEfficiency asks 'how.' Growth asks 'what, for whom and why us?' These are different questions requiring different organizational conditions.Capability without mandate produces demos. Mandate without capability produces PowerPoint. Both without a business model hypothesis produce expensive activity.A Copilot license is not a strategy.The winners will not be the companies that bought the most AI. They will be the ones that used AI to become a different company while they still had the luxury of choice.

Every week, AI teams deliver insights. Dashboards fill up. Reports multiply. And in most organizations nothing changes. Not because the AI didn't work. Because the insight never became a decision.In this episode, Kenza and Pascal tackle the hardest part of AI transformation: the gap between having insight and acting on it. Pascal brings a provocation that cuts to the heart of how most companies measure AI ROI: an insight is not an innovation. It only becomes innovation when it changes a product, process, customer experience, or business model.Key TakeawaysThe last mile of AI is not prediction. It is authority — who owns the decision that needs to change.An insight is not an innovation. If no decision, process, product or customer outcome changes, you produced information, not value.Process intelligence exposes the difference between how a process is described and how it actually behaves. That gap is usually where the real AI opportunity sits.A process map can be political. A timestamp is less diplomatic. Use process evidence to redesign the system, not to publicly grade departments.You cannot ask people to challenge the status quo while measuring them only on how reliably they preserve it.Data without decision rights is just expensive observation.

Most organizations have data governance policies. Far fewer have data governance that actually works when it meets day-to-day operations. In this episode, Kenza and Pascal move beyond the theory and into the uncomfortable reality: why governance frameworks fail, why data quality is a leadership responsibility that nobody owns, and why the difference between having data and trusting data is exactly where most AI projects quietly fall apart.The episode closes with a line worth writing down: a dashboard can be technically correct and still be organizationally untrustworthy. And another: trust should be earned by the system, not requested by the project team.Key Takeaways•      Data governance only works when it protects value and speeds decisions — not when it is purely a compliance exercise people route around.•      The business owns the meaning of data. Technology owns the enablement. Governance connects the two.•      AI did not repeal garbage-in, garbage-out. It just made the garbage more eloquent. LLMs can mask data quality problems — which makes them more dangerous, not less.•      Trust in data must be built progressively: low-stakes decisions first, visible provenance, measurable error rates, and explicit human override.•      The opposite risk is equally dangerous: executives who trust the data too much. Confidence is not evidence. Mature organizations institutionalize questioning.

"Data is the new oil." It's one of the most repeated phrases in every AI conversation - and one of the most misleading. Data is not valuable in itself. It is dead storage space until it is connected, trusted, contextualized, and governed in a way that actually improves decisions.In this episode, Kenza and Pascal move from technology foundations into the data layer and make the case that most data problems are not technical problems at all. They are cultural, political, and leadership problems. And often, they are the result of organizations not knowing what they actually want to know.Key topics: the DIKW pyramid (Data, Information, Knowledge, Wisdom) as a framework for understanding where companies get stuck, why data silos are political artifacts rather than technical accidents, how to move from raw data to decisions that actually change something.Data silos are rarely technical accidents. They are political artifacts,  fiefdoms inside organizations that protect data as a source of power.

Most companies talk about AI strategy. Far fewer ask the more uncomfortable question: is our technology foundation actually ready for it? In this first episode of Season 3, Kenza and her new co-host Pascal challenge the persistent view of IT as a cost center with no seat at the table. They make the case that in an AI-driven company, IT is not the basement, it defines the strategic degrees of freedom a company actually has. And underinvesting in it is not a budget decision. It is a leadership decision.Key topics: why IT perception inside the organization determines AI success, process clarity as a precondition for automation,how to make the investment case for technology readiness when boards are focused on short-term efficiency.AI does not repair broken processes. It industrializes them.

The Middle East is not just a market to expand into; for the right founders, it may be the best place to build. Countries like the UAE and Saudi Arabia are making AI a national strategic priority, moving faster, investing bigger, and building governance frameworks designed to attract tech talent.In this episode, Kenza continues her conversation with Dr. Abir Haddad, who works extensively in the region, for a ground-level view of what is actually happening. They explore what Europe can learn from the Gulf's approach to AI and why the region is uniquely attractive for startup founders and growth-stage CEOs: direct access to governments as first customers, national AI strategies that actively seek technology partners, and an innovation culture that moves at a speed most European ecosystems simply cannot match.KEY TAKEAWAYS The Gulf is investing heavily in AI ecosystems and talent.Start-ups gain easier access to funding and government projects.Regulation relies more on guidelines, sandboxes and experimentation.Central AI governance coordinates activities across institutions.Human oversight and continuous upskilling remain essential. 

In this episode, Kenza speaks with Dr. Abir Haddad, legal futurist and advisor to governments and regulators across multiple jurisdictions, for a clear-eyed view of what the global regulatory landscape actually looks like and what it demands from leadership. From the EU's risk-based AI Act to the US sector-specific approach, China's state-led model, and emerging frameworks in the Gulf, this episode gives global executives the map they need to navigate regulatory fragmentation without losing competitive agility.KEY TAKEAWAYSAI regulation is becoming global.EU, US and China follow different models.The use case determines the actual risk.Clear governance enables faster AI adoption.Executives must know how AI is governed, documented and controlled.