The Podcast where AI meets real world decisions
Kenza Ait Si Abbou Lyadini | Scailers GmbH
AI and the Executive Table is the podcast for C-suite leaders and board members navigating the real decisions of AI transformation. Hosted by AI strategist and Spiegel bestselling author Kenza Ait Si Abbou, each episode unpacks the human, organizational, and technological shifts that turn AI from boardroom pressure into lasting business value. No hype. No theory. Just the conversations that matter at the top.
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About Kenza
Kenza Ait Si Abbou-Lyadini is one of Germany’s most sought-after voices on AI transformation and leadership in uncertain times. Born in Morocco, she studied electrical engineering in Spain and industrial engineering in Berlin.
As a former CTO and board member at Fiege Logistik, as well as a senior manager at IBM and Deutsche Telekom, she brings operational credibility across all scales, from global technology conglomerates to complex family-run businesses.
Today, she is the founder and CEO of Scailers, an AI consultancy focused on sustainable transformation. Her work combines AI strategy, leadership and governance.
Kenza is the author of three Spiegel bestsellers, including 'Keine Panik, ist nur Technik' (, 'Menschenversteher' nominated for the German Business Book Award and 'Meine Freundin Roxy', which was adapted into a fulldome film.
She regularly appears on TV, radio and in print as a sought-after expert on AI and digital transformation. In 2020, 'Capital' magazine named her one of the Top 40 Under 40 in Germany, and in 2021, the 'Handelsblatt' named her a “pioneer of transformation”.
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Show Notes
Most 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 Takeaways
Combining 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.
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6 Oct 2026
New Frontiers
Show Notes
Nearly 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 Takeaways
Efficiency 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.
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29 Sept 2026
From Insight to Innovation
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 Takeaways
The 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.
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22 Sept 2026
Data Governance & Trust
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.
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15 Sept 2026
Data Is Organizational Memory
"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.
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8 Sept 2026
AI and the Executive Table | Architecture & Legacy
Legacy systems are not just old technology. They are the accumulated result of past decisions — many of them made for good reasons at the time — that now constrain everything a company wants to do with AI. In this episode, Kenza and Pascal go one layer deeper than IT image and strategy, into the structural reality of architecture, integration, infrastructure, and cybersecurity. They explore why legacy modernization is a leadership decision, not an IT backlog item, and what executives need to understand about the infrastructure gap between a successful AI pilot and production at scale.
Key topics:
- architecture choices that enable or constrain AI at scale,
- why disconnected systems create AI blind spots,
- the infrastructure investment needed for production-grade AI
- why cybersecurity is now an expanded executive responsibility.
Pascal's provocation: Legacy architecture is frozen decision history. The question for every board is which decisions are you still living with, and are they still the right ones?
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1 Sept 2026
IT & Technology | No AI Without IT
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.
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28 Jul 2026
From Regulation to Ambition | Co-Host Dr. Abir Haddad
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.
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21 Jul 2026
Beyond the EU AI Act | Co-Host Dr. Abir Haddad
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 TAKEAWAYS
AI 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.
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14 Jul 2026
AI Risk Management
When executives talk about AI risk, the conversation usually starts with regulation. But many of the real risks appear much earlier — inside everyday decisions, inside teams experimenting with new tools, inside the quiet accumulation of trust in AI outputs that nobody formally approved.
In this episode, Kenza and Naureen explore the full spectrum of AI risk beyond legal exposure: bias, unreliable outputs, data privacy, reputational consequences, and the risk that receives the least attention — gradual, unnoticed reliance. They introduce a practical framework for building risk awareness across the organization, and discuss why silence from employees is often the biggest risk signal of all.
This is also the final episode of Season 2. Kenza thanks Naureen for bringing her perspective to the podcast, and previews what is coming in Season 3: what it actually takes to scale AI inside organizations, and how AI capabilities eventually lead to entirely new business models.
KEY TAKEAWAYS
• AI rarely creates risk through dramatic failures. It creates risk through gradual trust — outputs that quietly shape decisions before anyone formally validated the model.
• The risk leaders most often underestimate: AI becoming embedded in decision-making without clear oversight or accountability.
• A practical framework for managing AI trust: Visible (everyone knows where AI is used), Questioned (outputs are open to challenge), Verified (systems are regularly reviewed).
• Responsible AI is not just about frameworks. It is about how people act within them.
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When compliance enters an AI initiative at the end of the process, it becomes friction. When it is built in from the beginning, it creates clarity — and clarity allows organizations to move faster. That shift, from compliance as checkpoint to compliance as capability, is what this episode is about.
Kenza and Naureen explore why traditional compliance models struggle with AI, what compliance by design looks like in practice, and how the EU AI Act changes what organizations need to prepare for. They challenge the most common misunderstanding: that regulation is designed to stop AI. It is designed to create trust in AI adoption — and companies that prepare early usually find that good compliance is simply good governance.
KEY TAKEAWAYS
• Compliance works best when legal and compliance teams participate in shaping AI initiatives — not when they review them at the end.
• The EU AI Act uses a risk-based approach: not all AI systems carry the same obligations. Classification is the first step.
• High-risk AI systems require documented governance, human oversight, and clear accountability — not just a privacy policy.
• Organizations that treat the AI Act as a trust-building exercise rather than a penalty-avoidance exercise gain a competitive advantage.
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30 Jun 2026
AI Governance by Design
AI responsibility is quietly moving up the executive agenda. The question is no longer whether to use AI — but how to govern it properly while still moving fast. And that is where many organizations hesitate: they assume governance will slow them down.
In this episode, Kenza is joined by Naureen, a governance and compliance expert with over twenty years of experience in complex regulated environments. Together they explore what AI governance actually looks like when it is designed to enable innovation rather than block it — and why organizations that introduce governance early consistently move faster later, because teams already know the boundaries.
They also discuss the CEO's personal role in making governance work, and why conflicting signals from leadership are often the biggest compliance risk of all.
KEY TAKEAWAYS
• Governance creates confidence to move. Without it, people slow down because nobody is sure where the boundaries are.
• The right moment to introduce AI governance is when AI starts influencing real business decisions — not when regulation forces you to.
• Introduce governance one step before you scale AI, not one step after.
• CEOs who treat compliance as part of how the company operates — not as a side function — are the ones whose organizations actually adopt it.
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23 Jun 2026
People & Transformation | Co-Host Amanda Rajkumar
SHOW NOTES
Once organizations move beyond early AI experiments, a new question appears: Do we have the right people to scale this? Eventually internal learning reaches its limits. You need deeper expertise. But hiring AI talent is one of the most competitive challenges companies face today — and the real question is not just who to hire, but how to build the right combination of people.
In this episode, Kenza and Amanda discuss when external AI expertise is actually needed, why the search for a unicorn AI hire so often fails, and what a healthy AI team really looks like. They explore how to integrate new talent without creating an elite separate group — and how to retain people who have endless options elsewhere.
This is also Amanda's final episode as co-host. She hands over to the next season with a clear message: AI transformation starts with the people who already understand your business.
KEY TAKEAWAYS
• Hiring too early is as problematic as hiring too late. The trigger should be scaling from pilots to production — not ambition.
• AI success does not come from one brilliant hire. It comes from a system of people working together.
• The strongest teams combine technical specialists, domain experts, and operational leaders — and pair new external talent with long-tenured internal knowledge.
• AI talent stays where they can see their work matter. Meaningful problems and speed of execution matter more than compensation alone.
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16 Jun 2026
AI Literacy, Upskilling & Co-Creation
SHOW NOTES
When organizations talk about AI transformation, the instinct is often to hire. But that is usually the wrong starting point. The real question is how to enable the people already inside the business.
In this episode, Kenza and Amanda explore what AI literacy actually means inside organizations and why treating it as a training program rather than a business capability is where most companies go wrong. They discuss the three levels of AI literacy (leadership, operational, and technical), why upskilling existing employees should come before external recruitment, and how co-creation across functions accelerates adoption in ways that top-down rollouts rarely do.
KEY TAKEAWAYS
• AI adoption is not primarily a hiring challenge. It is an activation challenge.
• AI literacy only sticks when people apply it to their own decisions and processes - not when it stays in a training room.
• Leaders who visibly participate in AI learning send a signal the rest of the organization cannot ignore.
• Early adopters who pull others along are more valuable than any single AI hire.
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9 Jun 2026
The AI Operating Model: Govern Centrally, Scale Locally
SHOW NOTES
When executives ask about AI transformation, the question is often: what is the right organisational design? Central team or decentralised? The honest answer is: you need both - and the balance shifts as your transformation matures.
In this episode, Amanda and Kenza explore what it actually takes to structure an organisation for AI. They discuss why a dedicated central team is critical in the early stages, how to handle the tension between day jobs and transformation work, and why measuring return on investment in AI is far harder - and more nuanced - than most boards expect.
KEY TAKEAWAYS
Start with a central team close to the CEO to orchestrate initiatives and set the guardrails. Its importance decreases as the organisation matures.
People cannot drive transformation on top of their day jobs. Organisations must explicitly free up capacity or accept that performance targets will temporarily take a back seat.
AI handles structure well. Humans handle chaos. The smartest deployments automate the repetitive 80% and invest in people for the complex 20%.
Before you automate anything, optimise the process first. Automating a broken process just makes the broken parts faster.
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