The AI Forecast: Data and AI in the Cloud Era

Latest Episodes:

Industrial Enterprise AI: The Missing Piece is Organizational Apr 15, 2026

Show notes

Enterprise AI is running into a familiar problem in the energy and manufacturing industries: the technology is moving faster than the organizations around it.

In this episode of The AI Forecast, Paul Muller sits down with Patrick Bangert, VP and Chief of AI at Occidental Petroleum, and author of “Leading Enterprise AI Programs: Optimize AI Teams for Value Creation,” to unpack the complexities of rolling out AI at enterprise scale in highly regulated industries with serious physical risks and 24/7 operations.

Together, Paul and Patrick take a closer look at:

How machine learning solves complex physical problems with predictive maintenance
Organizational change management when responses are emotional
Why reinventing organizational processes is key to AI success
The rise of shadow AI and startups as drivers of innovation
Why responsible AI and governance are essential for scaling safely
How to balance experimentation with long-term AI effectiveness

For leaders navigating enterprise AI in industrial settings, this episode offers a grounded, practical perspective on what separates hype from impact, and how to build an AI strategy that actually delivers.

Want to know more about the obstacles facing enterprise AI? Check out Ep 48 | Why Most Enterprise AI Projects Fail and How to Fix Them with Tom Harshbarger

Stay in touch with Patrick:
Patrick Bangert on LinkedIn: https://www.linkedin.com/in/patrick-bangert/

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Agentic AI Is Forcing a Governance Reset Apr 08, 2026

Show notes

What happens when AI stops advising and starts acting?

Agentic AI promises autonomy, speed, and a new level of intelligence in how systems operate. But as these systems begin to pursue goals and make decisions, the risks become harder to predict.

In this episode of The AI Forecast, Paul Muller sits down with futurist Nell Watson, AI ethics expert and co-author of “Safer Agentic AI: Principles and Responsible Practices,” to explore what safe, responsible AI looks like in this new era.

In this engaging conversation, Nell shares insights from her journey in AI, her work on ethics and safety, and why agentic systems represent a fundamental shift in how we think about risk. From data provenance to governance frameworks, she explains why organizations must move from reactive oversight to proactive design—and why cooperation will be critical to navigating what comes next.

Together, Paul and Nell delve into:

Why “AI is the butler for the brain”
The importance of data provenance in building trustworthy systems
How AI ethics and safety must be embedded from the start
Why governance needs to anticipate risks, not just respond to them
The role of “AI supervising AI” in maintaining control
How science fiction can help us prepare for real-world AI challenges

If you’re a business or technology leader deploying AI, this episode offers a clear, practical lens on balancing innovation with responsibility—and why safety is now a core requirement for scaling AI systems.

Stay in touch with Nell:

Nell Watson on LinkedIn: https://www.linkedin.com/in/nellwatson/
Nell’s website: https://www.nellwatson.com/
Nell Watson’s books on Amazon: https://www.amazon.com/stores/author/B0CWS75KPG

To hear more about AI governance, listen to EP 67 | The “Wobbly” Nature of AI: Governing an Unpredictable Technology

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The “Wobbly” Nature of AI: Governing an Unpredictable Technology Apr 01, 2026

Show notes

AI governance is the Achilles heel of most enterprises. As organizations accelerate AI adoption, boardrooms face urgent questions about cybersecurity, compliance, resilience, and regulatory risk.

In this episode of The AI Forecast, Paul Muller meets with Shoshana Rosenberg, author of “Practical AI Governance: Building a Program for Oversight and Strategy,” and creator of the Prism AI Governance Framework, about how leaders can build adaptable AI governance programs that strengthen their resilience to this susceptibility.

Drawing parallels with cybersecurity, Shoshana explains why AI is a “wobbly” technology: predictive, non-deterministic, and fundamentally different from traditional software. She explores why governance cannot be owned by a single department and why boards must shift from a compliance mindset to an organizational strategy rooted in adaptability and AI literacy.

Their conversation goes in-depth on:

Why AI governance is becoming a board-level responsibility
How AI differs from traditional deterministic systems
Why resilience and adaptability matter more than box-checking compliance
The importance of AI literacy across the organization
How to balance innovation with risk management
Preparing for the evolving AI regulatory environment

Board members, CIOs, legal leaders, and AI governance pros: get practical guidance on building AI governance programs that demand trust, defy regulatory scrutiny, and dominate rapid technological change.

Stay in touch with Shoshana:

Shoshana on LinkedIn
Shoshana’s website

For more on AI governance and cybersecurity, give this episode a listen: Ep 56 | When AI Moves Fast, Security Can't Lag Behind w/ Jessica Hammond

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Women Leaders in Technology: AI Agents Are Your New Team– Now What? Mar 25, 2026

Show notes

In this special Women Leaders In Technology episode of The AI Forecast, Wayfound.ai CEO Tatyana Mamut, PhD, makes a bold claim: AI is already acting like a workforce—and organizations are unprepared for what that means.

From econometrics to anthropology to leading roles at Salesforce, AWS, an d Nextdoor, Tatyana shares how her background shaped a fundamentally different approach to leadership. Drawing on her unconventional journey, she explains why agentic AI is forcing leaders to rethink how they manage technology, shifting from systems to a focus on teams, culture, and governance.

Together, Tatyana and Paul share their perspectives on:

Why agentic AI needs to be managed like human teams
The rise of multi-sapien workplaces (humans + AI agents)
How culture and leadership frameworks shape AI outcomes
Why unstructured data drives the most valuable insights
The role of ethics, law, and governance in controlling AI systems
Why intellectual curiosity beyond technical skill defines great leaders

This conversation goes beyond technology; Tatyana also reflects on leadership and representation in tech, challenging assumptions about opportunity, and exhibiting why diverse ways of thinking are critical in an AI-driven world.

Stay in touch with Tatyana:
Tatyana Mamut on LinkedIn: https://www.linkedin.com/in/tmamut/

Tatyana’s website: https://www.tmamut.com/

Links & Resources

Full Series Playlist: The AI Forecast: Data & AI in the Cloud Era - YouTube

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The Vibecoding Liability: How Unchecked AI Can Kill Cloud ROI Mar 18, 2026

Show notes

Cloud computing promised efficiency, scalability, and reliability. But as AI workloads grow more complex, many enterprises are learning the hard way that these promises don’t come automatically.

In this episode of The AI Forecast, Paul Muller sits down with Linthicum Research founder David Linthicum to talk through the real state of hybrid cloud strategy and enterprise architecture in the age of cloud computing and AI.

Together, Paul and David delve into:

Why cloud governance and resilience are now board-level concerns
The hidden costs of hybrid cloud and multi-cloud environments
Why workload repatriation is accelerating
How vibe coding can generate inefficient, high-cost AI applications
The difference between reliability and true operational resilience
Why enterprises need a common operational control plane

If you’re a technology leader scaling AI systems, this episode offers practical guidance on building governed, cost-efficient cloud architectures that won’t fail as complexity rises.

To go deeper on cloud resilience, hybrid cloud strategy, and AI workloads, listen to Ep 59 | The Secret to Creating the Cloud-Like Experience Anywhere with Adam Skotnicky.

Stay in touch with David:
David Linthicum on LinkedIn: https://davidlinthicum.com/about-david-linthicum

David’s website: https://davidlinthicum.com/about-david-linthicum

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Links & Resources
Full Series Playlist: https://www.youtube.com/playlist?list=PLe-h9HrA9qfAmGHgsmXUZgLL-T4Xjhlq8
Learn more about The AI Forecast: https://www.cloudera.com/resources/podcast/the-ai-forecast.html Connect with Cloudera:
Website | LinkedIn | YouTube

AI Managed Services: A Smarter Path for SMEs Mar 10, 2026

Show notes

AI adoption is accelerating across small and medium-sized enterprises (SMEs), but many businesses lack the in-house expertise to build and manage AI infrastructure effectively.

In this episode of The AI Forecast, Paul Muller speaks with Hyve’s Marketing and Operations Director, Charlotte Webb, about how managed service providers (MSPs) are reshaping AI adoption for SMEs. They explore the build vs. buy debate in AI solutions and why cloud computing alone doesn’t guarantee lower costs, better performance, or compliance.

Charlotte explains how Hive takes a consultative approach to AI managed services—helping businesses align AI initiatives with long-term business strategy, digital transformation goals, and operational efficiency.

Charlotte and Paul dive headfirst into:

The risks of unmanaged AI workloads in the cloud

The role of managed services in reducing complexity and cost

Data sovereignty and regulatory compliance in AI deployments

Misconceptions about cloud computing and AI performance

Hybrid cloud and GPU infrastructure for scalable AI workloads

Real-world AI success stories, including e-commerce growth and operational gains

For those navigating AI adoption, evaluating cloud strategy, or exploring managed AI services, this episode provides actionable insights into building sustainable AI capabilities without overextending internal resources.

Stay in touch with Charlotte:

Charlotte on LinkedIn

Want to hear more about the build vs buy debate and lessons from the cloud computing era? Check outEp 28 | Engineering for GenAI: Lessons from Past Hype Cycles with Ryan Ries of Mission Cloud

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Open Lakehouse Architecture: How to Scale AI to Production Mar 04, 2026

Show notes

Open lakehouse architecture is becoming the foundation for production AI and enterprise AI at scale.

In this episode of The AI Forecast, Dipankar Mazumdar, Director of Developer Relations at Cloudera and co-author of the book “Engineering Lakehouse with Open Table Formats,” joins Paul Muller to explain why open lakehouse architecture is critical for moving from AI pilot to production AI.

They break down:

How Apache Iceberg and open table formats decouple storage from compute
How schema evolution enables change without costly data rewrites
How multiple engines can securely access the same data without duplication
How to prevent small-file performance bottlenecks
How to control AI compute costs at scale
How to embed governance, metadata, and data lineage into AI workloads

Production-ready AI requires scalable data architecture and governance built in from day one. AI and GenAI pilots may be everywhere, but your architecture is what truly decides what survives.

Stay in touch with Dipankar:

Dipankar Mazumdar on LinkedIn: https://www.linkedin.com/in/dipankar-mazumdar/

Dipankar’s website: https://dipankarmazumdar.github.io/

Dipankar’s book on Amazon: https://www.amazon.com/Engineering-Lakehouses-Open-Table-Formats-ebook/dp/B0DKJD39X8

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How Billy Beane Changed Baseball Forever with Data Analytics Feb 25, 2026

Show notes

For decades, success in baseball was built on instinct. Scouts trusted their guts and managers leaned on tradition, meaning experience and unwritten rules shaped decisions that often went unquestioned.

Then Billy Beane changed the game.

The former Executive VP of Baseball Operations for the Oakland Athletics and pioneer of the Moneyball philosophy joins host Paul Muller on this special episode of The AI Forecast. Together, they explore how evidence-based decision-making disrupted one of the most tradition-bound industries in the world. Billy shares how shifting from intuition to analytics made data the ultimate competitive advantage in baseball.

Drawing on hard-won lessons from the front office, he explains how constraints can fuel innovation, why challenging assumptions is essential to performance, and how organizations can use data to redefine the way decisions are made. From talent evaluation to resource allocation, Billy makes the case that optimizing for success means building systems that reward evidence over ego.

Want to hear more about data-driven strategy and transformation? Check out Episode 17: How to Win in the Data Economy with Jan-Willem Middleburg.

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The AI Shake-Up Telecom Can’t Ignore with Mike O’Sullivan Feb 18, 2026

Show notes

As telecom operators invest billions in next-generation networks, many are racing to deploy AI to cut costs and unlock new revenue. But beneath the push for automation and smarter infrastructure lies the deeper challenge of rethinking how connectivity itself should be trusted.

In this episode of The AI Forecast, Mike O’Sullivan, Head of Member Solutions at TM Forum, joins host Paul Muller to explore how autonomous networks could reshape customer experience and retention. Mike breaks down the financial pressure facing the industry from soaring network build costs, costly legacy systems, and flat revenues, and why cost reduction is only the first step in telecom’s AI journey.

Drawing on decades in the industry, Mike paints a clear picture: reactive telecom is out and self-optimizing, AI-powered networks are in. The future of telecom is adaptive and intelligent, and it depends on turning vast amounts of data into immediate, informed action.

If you enjoyed this conversation, check out Episode 56: When AI Moves Fast, Security Can’t Lag Behind with Jessica Hammond for more on real-time decisioning and regulated industry AI.

Stay in touch with Mike:

Mike O’Sullivan on LinkedIn: https://www.linkedin.com/in/mike-o-sullivan-35845b/

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How AI FOMO and Lost Focus on Culture Harm ROI with Jen Stirrup Feb 11, 2026

Show notes

As AI and BI projects race to deliver quick wins, many organizations overlook what truly determines long-term success: the people behind the data.

In this episode of The AI Forecast, Jen Stirrup, author, speaker, and founder of Data Relish, joins host Paul Muller to dissect the difference between data literacy (reading the numbers) and data fluency (speaking the language of business). Jen challenges the industry’s obsession with AI FOMO, warning that rushing to deploy models on shaky cultural foundations is the fastest way to derail ROI.

Drawing on real-world data horror stories, she explains why tools alone cannot fix a broken culture and how mentorship, collaboration, and practical skills can empower teams to work independently and intelligently with data.

Listen in to discover:

Why poor data culture is the silent killer of AI ROI.
How to move your team from passive observation to active questioning.
Practical steps to build an environment where it is safe to challenge the data.

Did you enjoy this deep dive on data culture? Check out Episode 5: Data Doesn’t Have To Be So Complicatedwith Jordan Morrow for another perspective on the literacy debate.

Stay in touch with Jen:
Jen Stirrup on LinkedIn: https://uk.linkedin.com/company/jenstirrup
Jen Stirrup’s AI and data books: https://jenstirrup.com/books/

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