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:  

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: 

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

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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: 

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:  

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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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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:

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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:

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

Stay in touch with Jen:

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Data complexity is the enemy of innovation. Adam Skotnicky, VP of Engineering at Cloudera and founder of Taikun (acquired by Cloudera), joins host Paul Muller to explain how engineering teams can reclaim simplicity without sacrificing flexibility or control.

Together they unpack why most teams are overwhelmed by tooling and operational overhead, how platform engineering can abstract complexity away from users, and what it really means to deliver “cloud-like” agility across hybrid environments.

For those dealing with hybrid cloud sprawl, or simply wanting your data platform to do more for you, this episode is a must listen.

Stay in touch with Adam: 

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AI is reshaping how sales teams find prospects, build relationships, and close deals. Frank O’Dowd, Cloudera’s Chief Revenue Officer, joins to discuss Cloudera’s approach to AI in the sales function. 

Frank details his philosophy, which is that rather than replacing the human touch, AI is helping sales professionals work smarter, offering insights, personalization, and efficiency at scale. It’s a complementary tool that can help sales teams make themselves relevant to their target audience.  As Frank says in the episode, “The person with the most information always wins.” 

Stay in touch with Frank: 

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One common cause of concern around AI is how its computing may negatively impact the environment.

In this episode of The AI Forecast, Julie Kae, VP of Sustainability and Social Impact at Qlik and Executive Director of Qlik.org, joins host Paul Muller to reframe the conversation: when designed intentionally, AI isn’t a threat to ESG: it can be one of its most powerful enablers.

Drawing on more than 15 years leading data-for-good initiatives, Julie explains how AI and analytics can help organizations reduce waste, lower emissions, and create measurable social impact, without sacrificing profitability. She shares real-world examples from global nonprofits, climate initiatives, and purpose-driven enterprises, illustrating how better data, not perfect data, is often the key to progress.

The conversation also explores how by integrating sustainability, diversity, and social impact into AI as core infrastructure, businesses can create meaningful change that extends beyond profit. 

Stay in touch with Julie: 

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As AI adoption accelerates, so do the risks. In this episode of The AI Forecast, Jessica Hammond—Senior Director Product Management of GenAI at Protegrity—joins host Paul Muller to unpack how organizations can move fast with AI without compromising security, privacy, or compliance.

Drawing on her background in product, engineering, security, and compliance, Jessica explains why sensitive data exposure is one of the most underestimated risks in AI systems—from user-generated inputs leaking PII to agents inheriting permissions they shouldn’t have. She walks through how field-level data protection, policy-based controls, and governance embedded directly into the AI pipeline can protect data transparently, without slowing developers or re-architecting applications.

The conversation also explores why governance can’t be bolted on at the end, how data protection impacts AI evaluations and non-deterministic outputs, and what it really takes to secure AI across hybrid and multi-cloud environments. 

Stay in touch with Jessica: 

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The AI Forecast welcomes back John Santaferraro, host of The Digital Analyst Podcast and CEO of Ferraro Consulting, for a candid  look at what’s really coming next for AI in 2026.

John argues that despite nonstop headlines, most organizations are still dabbling with AI, not truly using the technology. He predicts that 2026 will be defined by AI literacy, strategic adoption, and a shift away from trivial use cases toward executive-level decision support. John and host Paul Muller explore why private AI will become essential for protecting intellectual property, how asking the right questions may soon matter more than having the right answers, and why agentic AI is poised to fail loudly before it succeeds.

The discussion also dives into data and agent governance, the coming wave of platform and data unification, and why causal AI—not just GenAI—may unlock the next leap in predictive power. 

If you’re planning your AI strategy for 2026, this conversation will help you separate signal from noise and prepare for what’s next.

Stay in touch with John: 

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