The AI Forecast: Data and AI in the Cloud Era
- Episodios
- 100
- Se publica
- cada semana
- Último episodio
- 12 Nov 2026
Sobre el podcast
The introduction of the first computer. The boom of the dotcom renaissance. Now, the dawn of AI. The throughline across each of these momentous inflections in our digital lives has been data. But the presence of data doesn’t mean immediate insights and results. It’s the architectures and systems in place that determine the true value—and trust—of data. In this podcast by Cloudera, The AI Forecast: Data and AI in the Cloud Era explores the past, present, and future of enterprise AI with today’s leading companies and industry experts. You don’t want to miss this.
Episodios 100
Private AI: Who Owns Your Secret Sauce? | Heard from EVOLVE26 New York
Your company’s secret sauce is becoming part of the AI equation. Marjorie Janiewicz, Chief Revenue Officer at Mistral AI, says enterprises are increasingly asking what happens when proprietary data and …
Notas del episodio
Your company’s secret sauce is becoming part of the AI equation.
Marjorie Janiewicz, Chief Revenue Officer at Mistral AI, says enterprises are increasingly asking what happens when proprietary data and intellectual property are used to customize AI running in environments they don’t fully control. For companies operating mission-critical systems, that question is moving from an IT concern to a board-level issue.
For this special EVOLVE26 New York edition of The AI Forecast, guest host Laura Blewitt sits down with Marjorie to explore why enterprises are rethinking how they deploy AI and how much control they need over the technology.
Marjorie explains why general-purpose AI can fall short when models lack the proprietary context that makes a business unique. She makes the case for open-weight and private AI as a way for organizations to customize models to their own data while maintaining greater control over sensitive information and business continuity.
Laura and Marjorie get into:
- Why proprietary data is becoming central to enterprise AI strategy
- When private or sovereign AI makes more sense than public cloud
- How open-weight models enable deeper customization
- Why data sovereignty and business continuity are becoming board-level concerns
- How model efficiency can change the economics of enterprise AI
When AI touches mission-critical systems and proprietary data, the question of who controls the technology becomes impossible to ignore.
To hear more of the conversation around economics of enterprise AI from EVOLVE26 New York, check out Ep 99 | Rethinking the Cost of Enterprise AI | Heard from EVOLVE26 New York.
Stay in touch with Marjorie:
Marjorie Janiewicz on LinkedIn: https://www.linkedin.com/in/marjorietoucas/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 New York series and stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
Episode 100: What We’ve Learned About AI
After 100 episodes, The AI Forecast has covered a remarkable amount of ground. We’ve learned a lot about what AI can do and what it takes to put it to work in the real world. And the more we’ve uncovered, the …
Notas del episodio
After 100 episodes, The AI Forecast has covered a remarkable amount of ground. We’ve learned a lot about what AI can do and what it takes to put it to work in the real world. And the more we’ve uncovered, the more questions have emerged about what comes next and how we should navigate it.
To mark this milestone on The AI Forecast, Paul Muller looks back at some of the conversations that have shaped the show and asks what they reveal about where enterprise AI is actually heading. Different guests, different industries, but one recurring question: how much responsibility are we willing to hand over to AI?
Paul revisits Jeanette Epps on trust in mission-critical environments, Sol Rashidi on what it takes to get AI into production, Varun Puri on using AI to lead more thoughtfully, and Darlene Newman on the decision logic behind automation. Nell Watson and Theresa Payton explore agentic AI and cybersecurity, while Sergio Gago and Leo Brunnick look at how AI is reshaping enterprise infrastructure.
Episode 100 is a look back at what The AI Forecast has learned so far — and a reminder that after all the hype and breakthroughs, the most interesting AI questions are still the ones we haven’t answered yet.
Revisit the conversations that shaped the show:
- AI, autonomy, and trust in mission-critical environments
- Why enterprise AI is harder than the demo
- Using AI to become a better leader
- Decision logic and the missing context behind enterprise AI
- Guardrails for agentic systems
- The new cybersecurity attack surface
- Why AI is forcing enterprises to rethink their infrastructure
If you’ve been with us for one episode or all 100, thank you for listening. Here’s to the next 100.
Like and subscribe to The AI Forecast, sponsored by Cloudera, and join us for the next 100 conversations about data, AI, and what comes next.
Rethinking the Cost of Enterprise AI | Heard from EVOLVE26 New York
A $6,000 laptop could change the math on a $60,000 AI token bill. That fact makes Patrick Moorhead question how businesses calculate the cost of AI. For suitable workloads, running an open model locally could …
Notas del episodio
A $6,000 laptop could change the math on a $60,000 AI token bill.
That fact makes Patrick Moorhead question how businesses calculate the cost of AI. For suitable workloads, running an open model locally could make an expensive-looking hardware purchase a worthwhile investment. His focus is the cost of producing a reliable result.
For this special EVOLVE26 series, guest host Laura Blewitt takes the mic. In this episode of The AI Forecast, she sits down with Patrick Moorhead, founder and chief analyst of Moor Insights & Strategy, to unpack the economics behind AI’s investment boom and what it will take for businesses to see a return.
Having worked at AltaVista during the dot-com boom and bust, Patrick brings firsthand experience of an investment cycle that promised to change everything. He explains why he believes AI is already in a bubble, alongside the demand that keeps him optimistic about its potential. For enterprise leaders, the discussion comes down to how to make their own AI investments pay off.
Where the AI economics get interesting:
- What the dot-com era can teach us about today’s AI investment boom
- Why the cost of a reliable output matters when evaluating AI ROI
- How open models can change inference economics
- When running AI locally or on-premises could make financial sense
- Why protecting intellectual property belongs in the AI business case
- How to prioritize workflows with a clear connection to business outcomes
Patrick is looking for “proof of life” in the businesses using AI, particularly evidence that non-tech companies are generating new revenue. Faster product launches and services customers will pay for would help demonstrate that the investment is translating into value.
For leaders deciding where to put their AI budget, this conversation offers a sharper way to assess what the business is getting for its money.
To hear more about matching AI model costs to business value, check out Ep 91 | Beyond the POC: AWS’s Playbook for Enterprise AI Success.
Stay in touch with Patrick:
Patrick Moorhead on LinkedIn: https://www.linkedin.com/in/patmoorhead/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 series and stay up to date on the latest episodes. You can watch the video version on The AI Forecast.
Can You Gaslight an AI Model? The New AI Security Threat
You can talk an AI model into breaking its own rules. Sometimes, all it takes is knowing what to say. Dr. Peter Garraghan has spent years studying how attackers exploit AI systems, and some of his findings are …
Notas del episodio
You can talk an AI model into breaking its own rules. Sometimes, all it takes is knowing what to say.
Dr. Peter Garraghan has spent years studying how attackers exploit AI systems, and some of his findings are surprisingly human. AI models can be manipulated through language, using techniques that resemble the social engineering tactics cybersecurity professionals have dealt with for decades.
In this episode of The AI Forecast, Paul Muller sits down with Dr. Peter Garraghan, Chair Professor at Lancaster University and Founder and Chief Science Officer of MindGard, to explore the emerging AI attack surface and what enterprises need to understand to secure increasingly capable systems.
As Peter puts it, “AI is software, and all software has security vulnerabilities.” AI models introduce new complications for cybersecurity; their behavior is difficult to inspect, and their hidden capabilities can give attackers unexpected ways to exploit systems connected to sensitive enterprise data.
Inside the mind of an AI security researcher:
- How attackers can manipulate AI models through language and social engineering
- Why AI introduces new cybersecurity vulnerabilities and attack surfaces
- How humanizing AI can create misplaced trust in its capabilities
- What AI red teaming reveals about model vulnerabilities
- Why traditional security tools struggle with AI behavior
- How enterprises can test AI systems before deploying them
- The risks of connecting autonomous AI agents to sensitive business data
- What CISOs should prioritize when building an AI security strategy
The next cybersecurity vulnerability may be hiding in a conversation with your AI. Peter makes the case for finding it before someone else does.
Want to hear more about securing AI agents? Check out Ep 95 | AI Agents Move Fast. Can Security Keep Up? with Alex Bovee to learn why autonomous systems are forcing enterprises to rethink zero trust and real-time security.
Stay in touch with Peter:
Peter Garraghan on LinkedIn: https://www.linkedin.com/in/pgarraghan/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
AI Agents Move Fast. Who's in Control?
One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive. For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls …
Notas del episodio
One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive.
For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls needed to manage it. And with autonomous AI agents beginning to make decisions on behalf of businesses, the stakes are getting higher.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast follows Paul Muller as he sits down with Todd Trevillion, technology entrepreneur and co-founder of Citadel Edge, to explore whether enterprises are truly ready for agentic AI and what it takes to deploy the technology responsibly.
Working with organizations in highly regulated industries, Todd has seen how quickly AI ambitions can run into operational reality. He questions whether enterprises are prepared to give autonomous agents greater control when the consequences of their actions can be difficult to predict.
Paul and Todd dive into:
- Why agentic AI introduces new risks for enterprise security and governance
- What happens when autonomous agents make decisions on behalf of businesses
- How uncontrolled token consumption can undermine AI ROI
- Why data sovereignty matters in regulated industries
- Why AI observability and human oversight remain essential
- How to balance AI innovation with enterprise accountability
Todd raises an uncomfortable possibility: what happens when AI agents start making decisions based on other agents’ decisions, with no human watching closely enough to catch a mistake? For enterprises, the potential blast radius could extend far beyond a bad answer.
If you're responsible for enterprise AI strategy or governance, this episode will help you think more critically about the authority you're giving AI agents and the safeguards needed to keep them under control.
Want to hear more about responsible AI adoption? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation.
Stay in touch with Todd:
Todd Trevillion on LinkedIn: https://www.linkedin.com/in/toddtrevillion/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes in the EVOLVE26 series. You can also watch the video version on The AI Forecast.
Leaving the Sandbox: AI In the Real World
The AI worked in the sandbox. Then it met the real world. David Hardoon learned that lesson during an ATM optimization project designed to improve cash-delivery routes. Once deployed, the system collided with …
Notas del episodio
The AI worked in the sandbox. Then it met the real world.
David Hardoon learned that lesson during an ATM optimization project designed to improve cash-delivery routes. Once deployed, the system collided with traffic and unpredictable schedules, which are the realities that face the people doing the work.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with David Hardoon, Head of Advanced AI at Accenture, to explore why the human side of AI transformation can determine whether promising technology ever delivers value in production.
Paul and David discuss:
- Why technically successful AI projects can fail when they meet real workflows
- What AI leaders can learn by going directly to the people doing the work
- Why workforce design should be part of AI transformation from the beginning
- How agentic AI could reshape career paths and management structures
- Why familiar change-management principles matter again in the AI era
- How organizations can balance AI innovation with practical implementation
David also reflects on developing the FEAT principles for responsible AI at the Monetary Authority of Singapore and takes a musical detour into Lestrum, his early research connecting patterns in music with brain activity. Both experiences illustrate his belief that AI works best when grounded in how people actually think and operate.
If you’re leading AI transformation, this episode will push you to look beyond the technology and ask whether your organization and its people are ready for what comes next.
Stay in touch with David:
David Hardoon’s website: www.davidroihardoon.com
David Hardoon on LinkedIn: https://www.linkedin.com/in/davidrh/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
AI Agents Move Fast. Can Security Keep Up?
Your AI agent has credentials. Now, what happens when it goes off script? An agent can take action and change course before a traditional security review even begins. For Alex Bovee, identity is only the …
Notas del episodio
Your AI agent has credentials. Now, what happens when it goes off script?
An agent can take action and change course before a traditional security review even begins. For Alex Bovee, identity is only the beginning of the security conversation.
In this episode of The AI Forecast, Paul Muller sits down with Alex Bovee, co-founder and CEO of C1 and longtime cybersecurity and identity leader, to explore how autonomous AI agents are changing enterprise security.
Alex argues that zero trust was built for a world where people remained behind the keyboard. AI agents introduce non-deterministic behavior and can relentlessly optimize toward a goal, sometimes finding routes their creators never anticipated.
Paul and Alex’s discussion examines:
- Why AI agents require a new approach to identity and access control
- How autonomous agents change the zero-trust security model
- Why static permissions struggle at machine speed
- What real-time enforcement looks like for AI agents
- How organizations can identify behavioral drift
- How governed “golden paths” can support secure AI adoption
As AI agents take on more work across the enterprise, security will have to keep pace. If you’re responsible for enterprise security, this episode will show you how to keep pace with AI agents operating at machine speed without creating controls that people will work around.
Stay in touch with Alex:
Alex Bovee on LinkedIn: https://www.linkedin.com/in/alexbovee/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
Why Successful AI Models Can Still Fail the Business
An AI project can deliver a great prediction and still fail the business. Rajesh Kelvalkar has seen it happen firsthand. In one mining project, the model worked, but the team discovered that the downstream …
Notas del episodio
An AI project can deliver a great prediction and still fail the business.
Rajesh Kelvalkar has seen it happen firsthand. In one mining project, the model worked, but the team discovered that the downstream business process wasn’t configured to act consistently on its recommendations. The technology had done its job, but the transformation hadn’t.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Rajesh Kelvalkar, Senior Advisor at Tech Data APJ, to explore what more than 20 years of transformation work has taught him about putting AI into practice.
Rajesh argues that organizations often approach transformation as a technology initiative, even though much of the work happens elsewhere in the business. Clear ownership and measurable outcomes determine whether an AI pilot becomes part of operations or disappears when the project team moves on.
Paul and Rajesh explore:
- Why pilots built on curated data can stumble in production
- How repeatable business processes create strong AI use cases
- What logistics and financial services can teach us about scaling AI
- Why reusable data assets matter beyond a single pilot
- How to measure AI against business outcomes
- Why AI models need continued monitoring after deployment
Rajesh also makes the case for treating data as a product. Projects have an end date, but products have ownership and continue to evolve. That mindset becomes especially important for AI systems, where changing conditions can quickly affect the performance of a model that worked well at launch.
His advice for leaders planning the next phase of AI starts with the outcome. Identify where AI can influence an end-to-end business process, prove value in a focused use case, and build the foundations that allow successful ideas to be reused elsewhere.
If you’re responsible for AI transformation, this episode will help you think beyond the pilot and build AI capabilities designed to scale and sustain.
Stay in touch with Rajesh:
Rajesh Kelvalkar on LinkedIn: https://www.linkedin.com/in/rajesh-kelvalkar-6b33452/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
When Everyone Has AI, Your Data Sets You Apart
After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business? Recorded at EVOLVE26 Singapore …
Notas del episodio
After 23 years in machine learning and data science, Cao Hong has seen plenty of AI projects come and go. His measure of success is simple: did it create value for the business?
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Cao Hong, Principal Director of Data Apps at NCS, to explore how organizations can turn AI experimentation into measurable business impact.
That becomes harder as enthusiasm for AI pushes expectations higher. Cao Hong argues that organizations need to define the outcome they’re working toward early and then understand how AI will fit into how the business actually operates.
Having worked across research and commercial AI projects, Cao Hong has seen how differently the two environments operate. Research can explore problems whose value may be realized years from now. In business, AI ultimately has to earn its investment. That means connecting the technology to a clear use case and establishing how its performance will translate into business value.
Paul and Cao Hong explore:
- How to define value before investing in an AI project
- Why unrealistic expectations can derail AI initiatives
- What leaders should consider when selecting AI use cases
- How data readiness affects AI outcomes
- Why AI governance needs to evolve alongside adoption
- Where enterprises can find differentiation as AI models become widely available
Cao Hong shares how AI helped a water utility predict demand weeks in advance and rethink decisions years ahead. His takeaway for other enterprises: when everyone can access the same models, the advantage lies in what you can do with your own data.
Want to hear more about turning enterprise data into better decisions? Check out Ep 83 | From KPIs to Action: What Comes After The Dashboard?
Cao Hong on LinkedIn: https://www.linkedin.com/in/hong-cao/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
Making AI Fluent in Your Business
Every new AI chat is another first day on the job. Rob Collie describes today’s general-purpose AI as a brilliant new hire who wakes up on their first day every time you start a new conversation. It may …
Notas del episodio
Every new AI chat is another first day on the job.
Rob Collie describes today’s general-purpose AI as a brilliant new hire who wakes up on their first day every time you start a new conversation. It may understand your industry, but your workflows and business context have to be explained all over again.
In this episode of The AI Forecast, Paul Muller speaks with Rob Collie, founder and CEO of P3 Adaptive, former Microsoft product leader for Excel and Power BI, and author of “Fair Game: Bringing AI Into Reach for All Business” to explore why business context can make or break enterprise AI.
Rob brings lessons from decades of business intelligence into the AI era, advocating for starting close to the business and working backward from the decisions people actually need to make in his “Faucet First” philosophy.
Paul and Rob get into:
- Why business context is critical to getting useful results from AI
- What “bot sitting” reveals about off-the-shelf AI adoption
- Why data quality should be evaluated against the business outcome
- How specialized AI tools can outperform a company-wide “super being”
- Why systems thinking is becoming more important in the AI era
- How business leaders can find a practical starting point for AI
If you’re responsible for AI strategy or business transformation, this episode will help you get closer to the work by starting with a real business need and building from there.
Want to hear more about building AI around real business needs? Check out Ep 79 | Why Some AI Products Strike a Chord (and Others Don't)
Learn more:
“Fair Game: Bringing AI Into Reach for All Business” https://fairgamebook.ai/
Raw Data with Rob Collie: https://open.spotify.com/show/1VZLfXgz1hcB8MFG2OSOH9
Rob Collie on LinkedIn: https://www.linkedin.com/in/robcollie/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
Beyond the POC: AWS’s Playbook for Enterprise AI Success
Most AI pilots never make it past the demo phase. AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what …
Notas del episodio
Most AI pilots never make it past the demo phase.
AWS Machine Learning Lead Praveen Jayakumar has seen plenty of promising AI projects get stuck between a successful demo and production. Teams often define what success looks like without deciding what failure looks like, leaving underperforming projects alive long after they should have been shut down. As Praveen puts it, they become “zombie” AI projects.
In this episode of The AI Forecast, Paul Muller sits down with Praveen Jayakumar, who leads Machine Learning Solution Architecture for Amazon Web Services (AWS) across Asia Pacific and Japan, to explore how enterprises can give AI projects a realistic path to production.
Praveen shares what he’s learned working with organizations on machine learning and generative AI deployments, including why teams should build for production while they’re still proving the concept. Observability and evaluation become particularly important as models change, costs scale, and AI systems begin interacting with enterprise data.
Paul and Praveen talk about:
- Why promising AI proofs of concept stall before production
- How to define success and kill criteria before an AI project begins
- Why observability should be built into the proof of concept
- How evaluation frameworks make it easier to test different AI models
- Why the most powerful frontier model may be the wrong choice
- How model selection affects the unit economics of enterprise AI
Recorded at EVOLVE26 Singapore, this episode offers a practical look at what happens after the AI demo, when teams have to decide what’s ready to scale and what’s better left behind.
If you’re responsible for enterprise AI, this episode will help you build a path to production and keep zombie projects from consuming resources long after their expiration date.
Stay in touch with Praveen:
Praveen Jayakumar on LinkedIn: https://www.linkedin.com/in/pjpraveen/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
Can AI Make Sense of Pharma’s Messiest Data?
Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something …
Notas del episodio
Human biology is extraordinarily complex, and researchers often have only fragments of information to work with. Brian Martin compares it to looking at a skyscraper through a keyhole: you can see something clearly, but only a tiny piece of the whole.
Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Brian Martin, CTO of Applied AI at Cloudera and co-founder of Rare Hopes NFP, to explore what one of the world’s most data-intensive industries can teach us about AI and decision-making.
Brian explains how pharmaceutical R&D turns sparse, fragmented data into insights that support drug discovery. With a new drug potentially requiring years of development and billions of dollars in investment, better predictions can have an enormous impact on how quickly promising treatments reach patients.
Paul and Brian explore:
- How knowledge graphs can reveal relationships hidden across fragmented data
- Where AI can connect qualitative patient experiences with quantitative research
- Why patient consent complicates how valuable clinical data can be reused
- How pharma teams can share knowledge without dismantling every data silo
- Why embedding technologists with scientists can accelerate AI adoption
- What other industries can learn from pharmaceutical data strategy
If you’re responsible for enterprise data or AI strategy, this episode will show you how lessons from pharmaceutical R&D can help turn fragmented information into knowledge that drives better decisions.
Stay in touch with Brian:
Brian Martin on LinkedIn: https://www.linkedin.com/in/brianm1028/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.
Running with Scissors: The Enterprise AI Reality
Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement. After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous …
Notas del episodio
Enterprise AI loves a shiny object. Sol Rashidi would rather talk about procurement.
After years of leading AI deployments, Sol has learned that some of the biggest wins come from the decidedly unglamorous parts of the business. Her favorite function to transform? Procurement.
Recorded at EVOLVE26 Singapore, this conversation brings host Paul Muller together with Sol Rashidi, CEO of ExecutiveAI, the world’s first Chief AI Officer, and Chief Strategy Officer of AI Governance & Security at Cyera. A two-time bestselling author and Senior Fellow at Harvard, Sol brings a practitioner’s perspective to what actually happens when enterprise AI meets operational reality.
Sol shares lessons shaped by years of enterprise AI deployments and the postmortems she kept along the way. She challenges the way companies prioritize AI use cases, arguing that business value means little without a realistic path to production.
The conversation turns to:
- Why so many AI projects remain stuck in proof-of-concept mode
- What procurement can teach us about practical AI transformation
- How to choose AI use cases that have a realistic path to production
- Why top-down and bottom-up AI adoption can both stall
- What trust between employees and leadership means for AI adoption
- Why speed is outpacing governance and security
- The hidden trade-offs behind convenient AI tools
- How leaders can keep human agency at the center of AI
Her vision for what comes next is human-led, AI-supercharged. AI can give individuals capabilities that once required entire teams, but Sol believes people still need to protect the creative judgment and agency that make those capabilities valuable in the first place.
As Sol puts it, the goal is to build a world where “AI happens with us and not to us.”
Stay in touch with Sol:
Sol Rashidi’s website: https://solrashidi.com/
Sol Rashidi on LinkedIn: https://www.linkedin.com/in/sol-rashidi-mba-a672291/
Your AI Survival Guide on Amazon: https://www.amazon.com/dp/B0DJGBY1YY?lv=shuf&channelId=520&plpRedirect=mhFallback
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
AI Adoption vs. Adaptation: What Problem Are You Solving?
Paul McDonagh-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented …
Notas del episodio
Paul McDonagh-Smith estimates that many business leaders would struggle to define their organization’s problem clearly in fewer than 25 words. With AI, that lack of clarity can quickly turn into fragmented solutions and misplaced expectations.
In this episode of The AI Forecast, Paul Muller sits down with Paul McDonagh-Smith, a Visiting Senior Lecturer at MIT Sloan School of Management and a Senior Advisor to NASA's Goddard Space Flight Center, to explore how organizations can approach AI with greater clarity and purpose.
Ideas shaping the discussion:
- Why problem framing can determine the outcome of an AI initiative
- The difference between adopting AI and adapting with it
- What the scientific method can teach organizations about AI
- Why AI’s imperfections make human judgment even more important
- How organizational mindsets influence technology outcomes
- Why organizations should invest in capabilities that AI cannot replicate
- The growing importance of trust in AI adoption
From healthcare to energy, Paul sees enormous potential for human imagination and machine intelligence to tackle problems once considered out of reach. The future, in his view, will reflect the choices we make today.
Want to hear more about the organizational side of AI? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail.
Stay in touch with Paul:
Paul McDonagh-Smith on LinkedIn: https://www.linkedin.com/in/paulmcdonaghsmith/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can also watch the video version of this episode on The AI Forecast.
The Great AI Re-Architecture Pushes Workloads to the Data Center
New research points to a major shift in enterprise IT: more than two-thirds of surveyed data leaders are moving some workloads back into the data center. The catalyst? AI. In today’s episode, Paul Muller is …
Notas del episodio
New research points to a major shift in enterprise IT: more than two-thirds of surveyed data leaders are moving some workloads back into the data center.
The catalyst? AI.
In today’s episode, Paul Muller is joined by Cloudera CTO Sergio Gago and CPO Leo Brunnick to unpack Cloudera’s survey of more than 1,500 technology and data leaders about how AI is reshaping their infrastructure. As AI moves into production, agents can generate dramatically more queries than human users do, putting new pressure on the systems beneath them. That is forcing enterprises to reconsider where workloads should run and how much flexibility they will need as AI evolves.
Sergio and Leo join Paul to discuss what they call the “great AIre-architecture” and why the next era of enterprise AI could look decidedly hybrid.
Inside the live conversation:
- What Cloudera’s The Great AI Re-Architecture survey reveals about AI infrastructure
- Why enterprises are moving workloads back on premises
- How agentic AI changes the demands placed on enterprise data
- The economics driving renewed interest in hybrid cloud
- How governance changes when agents access enterprise data
- What the next generation of data engineering could look like
The conversation also introduces Cloudera Anywhere Cloud, announced last week at EVOLVE Singapore. Sergio and Leo explain how a common architecture across public and private cloud environments could give enterprises greater freedom to place AI workloads where they make the most sense.
AI is putting decades of cloud assumptions back up for debate. For enterprise IT, the next big advantage may be the freedom to choose.
Learn more:
Cloudera EVOLVE26: https://www.cloudera.com/events/evolve.html
The Great AI Re-Architecture survey report: https://www.cloudera.com/content/dam/www/marketing/resources/analyst-reports/the-great-ai-re-architecture.pdf.landing.html
Sergio Gago on LinkedIn: https://www.linkedin.com/in/sergiogh/
Leo Brunnick on LinkedIn: https://www.linkedin.com/in/leobrunnick/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 series and stay up to date on the latest developments in enterprise data and AI.
Vibe Coding to Production: Building AI Apps That Actually Scale
Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of …
Notas del episodio
Now that AI coding tools have put development capabilities into more hands, prototypes are becoming business-critical applications almost overnight. Shanea Leven sees an opportunity for a new generation of builders, provided the infrastructure around their applications keeps pace.
In this episode of The AI Forecast, Paul Muller sits down with Shanea Leven, co-founder of Empromptu AI, to explore what it takes to build production-ready AI applications. Shanea explains how organizations can give developers and new technical employees room to build while maintaining the standards required for enterprise software.
Shanea also explores the infrastructure surrounding generative AI applications, including the need to detect drift and evaluate outputs as real-world data flows through a system. As she puts it, the familiar rule still applies: “Garbage in, garbage out.”
In their lively discussion, the pair covers:
- What separates a working AI app from a production-ready one
- How governance should be built into AI development
- Why organizations should fingerprint applications before production
- How non-technical subject matter experts can become effective AI builders
- How AI coding changes the role of software engineers
- What companies should capture today to prepare for custom AI models
The conversation points toward a future where more people can build software themselves. Engineering teams will play a critical role in making that possible by creating the guardrails that allow ideas to move safely from a prompt into production.
Want to hear more about AI governance? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation.
Stay in touch with Shanea:
Shanea Leven on LinkedIn: https://www.linkedin.com/in/shaneak/
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Enterprise AI Success: What Separates Results from Expensive Experiments
Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear? For Mark Ritcey, the answer comes down to disciplined …
Notas del episodio
Most enterprise AI use cases still aren't delivering measurable value. So what separates the projects that work from the ones that quietly disappear?
For Mark Ritcey, the answer comes down to disciplined execution. AI programs need a clear business problem and an organization prepared for how the technology changes the way work gets done.
In this episode of The AI Forecast, Paul Muller sits down with Mark Ritcey, Vice President of AI and Automation Delivery at Latentbridge and lecturer on AI and machine learning, to examine the decisions that shape enterprise AI success.
Mark has spent more than 25 years working across technology and automation, including leading enterprise transformation initiatives in highly regulated industries. He shares what he’s seeing inside AI programs today, where teams commonly go wrong, and why structured experimentation matters as organizations figure out where AI can create real value.
What separates AI success from failure, according to Mark and Paul:
- Why AI projects need a clearly defined business problem
- The risks of experimenting with AI for its own sake
- How unrealistic expectations derail enterprise deployments
- The role of governance as AI moves into production
- How organizational change affects AI adoption
- What CEOs and boards should consider before scaling AI
His advice for leaders is refreshingly straightforward: AI transformation requires diligent, structured work. There are no shortcuts around understanding the business and building the controls required to put AI into production responsibly.
Want to hear another perspective on enterprise AI adoption? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail.
Stay in touch with Mark:
Mark on LinkedIn: https://www.linkedin.com/in/markritcey/
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How AI Is Helping Humanitarian Teams Make Faster Decisions
In a humanitarian crisis, waiting for perfect information isn't an option. Every decision must be made with incomplete data and limited resources in a situation that can change by the hour. For organizations …
Notas del episodio
In a humanitarian crisis, waiting for perfect information isn't an option. Every decision must be made with incomplete data and limited resources in a situation that can change by the hour.
For organizations like Prosper Global (formerly Mercy Corps), AI is helping teams make sense of that uncertainty. By bringing together information from conflict reports, local media, humanitarian data, and environmental sources, AI can surface the context decision-makers need while leaving human judgment firmly in their hands.
In this episode of The AI Forecast, Paul Muller is joined by Josh DeWald, Vice President of Technical Support, Evidence and Program Quality at Prosper Global, and Rob Dickens, AI Technical Lead at Cloudera. Together, they explore how VERA, an agentic AI platform co-developed by Cloudera and Prosper Global, is helping humanitarian teams gather information faster and make better-informed decisions.
Inside their discussion:
How AI supports humanitarian decision-making during fast-moving crises
Why data collection is so difficult in conflict-affected regions
How agentic AI combines structured and unstructured information
The importance of human oversight in AI-assisted decisions
Protecting data privacy and reducing bias in humanitarian AI
Lessons enterprise organizations can learn from operating in information-poor environments
How AI is improving organizational learning across humanitarian programs
The conversation explores what responsible AI looks like when decisions carry humanitarian consequences. Whether you're leading enterprise AI initiatives or working in mission-driven organizations, this episode offers valuable insights into building AI systems that help people make better decisions when the stakes are highest.
Stay in touch with Josh and Rob:
Josh DeWald on LinkedIn: https://www.linkedin.com/in/josh-dewald-10a6178b/?skipRedirect=true
Rob Dickens on LinkedIn: https://www.linkedin.com/in/robert-dickens-73a91340/?skipRedirect=true&originalSubdomain=uk
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From KPIs to Action: What Comes After The Dashboard?
Your dashboard can tell you sales are down, but it can't tell you why or what to do next. Dashboards have become the default way to monitor a business. Bhaskar Sunkara argues they're only the starting point …
Notas del episodio
Your dashboard can tell you sales are down, but it can't tell you why or what to do next.
Dashboards have become the default way to monitor a business. Bhaskar Sunkara argues they're only the starting point. The next step is AI that understands business context and helps leaders move from insight to action.
In this episode of The AI Forecast, Paul Muller sits down with the founder and CEO of Bicycle AI to explore how agentic AI is reshaping enterprise analytics. After helping pioneer application monitoring, Bhaskar now focuses on a different question: how AI can help businesses understand why something changed and what action to take next.
Bhaskar and Paul break down:
- Why traditional dashboards fall short for business decision-making
- How agentic AI moves from reporting problems to recommending actions
- The role of business ontology in connecting data, context, and outcomes
- Why data quality and traceability remain essential for trustworthy AI
- How AI can automate root cause analysis across technical and business systems
- Why human judgment remains central to enterprise decision-making
- What it takes to build proactive, AI-driven business operations
Bhaskar sees AI as a force multiplier for decision-makers, assembling the evidence so leaders can focus on judgment and accountability. The result is faster, more informed decisions backed by business context.
Want to learn more about enterprise AI decision-making? Check out Ep 80 | Decision Logic: The Difference Between an Answer and a Decision
Stay in touch with Bhaskar:
Bhaskar Sunkara on LinkedIn: https://www.linkedin.com/in/bhaskarsunkara/
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Behavior Change: How Startups Are Making AI Stick
Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work. In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and …
Notas del episodio
Behavior change is the biggest hurdle in AI adoption. AI only creates value when people make it part of their everyday work.
In this episode of The AI Forecast, Paul Muller sits down with Varun Puri, CEO and co-founder of Yoodli, to discuss why successful AI adoption starts with changing how people work. Drawing on his experience at Google, Google X, and as the founder of an AI startup, Varun shares practical lessons on embedding AI into everyday workflows and building habits that stick.
From AI-generated leadership briefings to a daily gratitude agent, Varun explains how small behavioral shifts can unlock outsized results, and why the most valuable AI tools are the ones people actually use.
Varun and Paul’s conversation explores:
- Practical ways startups are using AI to improve daily operations
- How AI gives leaders real-time visibility across the business
- Why incentives often undermine successful AI initiatives
- Why emotional intelligence may become more valuable in the AI era
- Personal AI workflows that help leaders stay focused and effective
Throughout the discussion, Varun explains how AI becomes most valuable when it helps people do their best work, rather than simply automating tasks.
Whether you're rolling out AI across an enterprise or scaling a startup, this episode offers practical ideas for integrating AI into everyday work.
Want to hear more about leading successful AI adoption? Check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail
Stay in touch with Varun:
Varun Puri on LinkedIn: https://www.linkedin.com/in/varun-puri001
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AI Guardrails: How to Govern AI Without Slowing Innovation
AI is already inside your organization. The question is whether you know where. As Lisa Pent says, “You can’t govern what you can’t see.” In this episode of The AI Forecast, Paul Muller sits down with Lisa …
Notas del episodio
AI is already inside your organization. The question is whether you know where.
As Lisa Pent says, “You can’t govern what you can’t see.”
In this episode of The AI Forecast, Paul Muller sits down with Lisa Pent, CEO and Founder of PentEdge, to discuss why AI visibility is becoming a board-level issue and what organizations can do to govern AI with confidence. Lisa makes the case that visibility is the foundation of effective AI governance.
Throughout her career in investment banking and fintech, Lisa explains why AI governance must move beyond policies and annual audits. She explains how organizations can gain visibility into AI use and build stronger governance around it.
Their conversation explores:
- Why AI governance must become continuous
- How data lineage creates a stronger audit trail
- The risks created by shadow AI
- How to assess AI risk across critical business systems
- What boards should be asking about AI oversight
- Practical ways to monitor AI across the enterprise
If AI is becoming part of your organization, this conversation provides a practical framework for effective oversight.
To hear more about governing data in the age of AI, check out Ep 72 | The Data Governance Coach: From Data Error to Insight.
Stay in touch with Lisa:
Lisa on LinkedIn: https://www.linkedin.com/in/lisapent/
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Decision Logic: The Difference Between an Answer and a Decision
Ask an AI system a question, and you'll get an answer. Decision logic determines whether you should trust it. In this episode of The AI Forecast, Paul Muller sits down with Darlene Newman, Innovation Lead at …
Notas del episodio
Ask an AI system a question, and you'll get an answer. Decision logic determines whether you should trust it.
In this episode of The AI Forecast, Paul Muller sits down with Darlene Newman, Innovation Lead at Duczer East, to explore the hidden layer that helps AI move from pattern matching to practical decision-making.
From semantic layers and ontologies to knowledge graphs and governance frameworks, Darlene unpacks the often-overlooked structures that sit between AI outputs and real-world decisions. She also shares practical guidance on integrating decision-making logic into AI initiatives without adding complexity.
Paul and Darlene take a closer look at:
- Why decision logic is the “why” behind AI decisions
- How guardrails help prevent hallucinations and unreliable outputs
- Why knowledge design is becoming a critical AI capability
- How organizations can build scalable and auditable AI systems
- Practical approaches for integrating decision logic into AI initiatives
Beyond models and prompts, this conversation is about giving AI the context it needs to make better decisions. For enterprise leaders, it offers a practical look at the structures and knowledge foundations that can help AI deliver consistent business outcomes.
To hear more about turning organizational knowledge into AI capabilities, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management
Stay in touch with Darlene:
Darlene Newman on LinkedIn: https://www.linkedin.com/in/darlenenewman/
Where Innovation Takes Root newsletter: https://www.linkedin.com/newsletters/innovation-through-the-hype-7272737391067459584/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
Why Some AI Products Strike a Chord (and Others Don't)
You recognize the tune, but something feels off. That's how Marlon Davis describes many of today's AI initiatives: AI karaoke. Organizations are rushing to add AI to products, but too often they're layering …
Notas del episodio
You recognize the tune, but something feels off.
That's how Marlon Davis describes many of today's AI initiatives: AI karaoke. Organizations are rushing to add AI to products, but too often they're layering technology onto solutions without fully understanding the customer problems they're trying to solve.
In this episode of The AI Forecast, Paul Muller sits down with fractional Chief Product Officer at Devlnio, Marlon Davis, to explore how organizations can move beyond superficial AI efforts and build products that deliver meaningful customer value.
Paul and Marlon take a closer look at:
- How to identify opportunities where AI genuinely creates value
- Why product teams should focus on customer problems before AI solutions
- The importance of anthropology and observing customer behavior
- How AI can improve product operations and decision-making
- Why understanding customer workflows matters more than adding AI features
- How product managers can navigate the rise of AI-assisted development
If you're deciding where AI belongs in your product portfolio, this episode provides a grounded approach to identifying opportunities that matter to customers.
To hear more about turning AI investments into business value, check out Ep 78 | Mastering Enterprise AI: Why Some Projects Succeed While Others Fail
Stay in touch with Marlon:
Marlon Davis on LinkedIn: https://www.linkedin.com/in/marlondavis/
Froogel Product Manager Newsletter: https://www.linkedin.com/in/marlondavis/recent-activity/newsletter/
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Mastering Enterprise AI: Why Some Projects Succeed While Others Fail
AI may be the most capable intern your organization has ever hired. However, interns still need guidance and clear direction. Enterprise AI is proving no different. In this episode of The AI Forecast, Paul …
Notas del episodio
AI may be the most capable intern your organization has ever hired.
However, interns still need guidance and clear direction. Enterprise AI is proving no different.
In this episode of The AI Forecast, Paul Muller sits down with Michael Gray, CTO of Thrive, to explore the patterns and anti-patterns emerging from real-world enterprise AI deployments.
Drawing on his experience helping organizations implement AI at scale, Michael offers a practical framework for evaluating AI maturity, helping leaders understand where adoption breaks down and what it takes to build momentum across the organization.
Paul and Michael take a closer look at:
- Why AI adoption often stalls despite significant technology investments
- The role of organizational change management in successful AI programs
- Common AI adoption patterns and anti-patterns across enterprises
- How champions inside the organization can accelerate AI success
- How governance and security evolve as AI scales
- The importance of focusing on business outcomes rather than technology alone
The path from AI investment to business value is often more complex than expected. This episode offers advice on turning AI investments into measurable outcomes while building the organizational foundations for successful scaling.
To hear more about the organizational challenges facing AI adoption, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management
Stay in touch with Michael:
Michael Gray on LinkedIn: https://www.linkedin.com/in/michael-gray-4861663/
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The Rise of VibeOps: How AI Is Transforming Network Automation
For decades, network teams have been forced to choose between speed and stability. AI may finally be changing that equation. In this episode of The AI Forecast, Paul Muller sits down with John Capobianco, Head …
Notas del episodio
For decades, network teams have been forced to choose between speed and stability.
AI may finally be changing that equation.
In this episode of The AI Forecast, Paul Muller sits down with John Capobianco, Head of AI and Developer Relations at Itential and author of “Automate Your Network,” to explore how AI is reshaping the future of network operations.
Drawing on decades of experience in network engineering, John explains why network automation has struggled to gain traction and how AI, agents, and Model Context Protocol (MCP) could finally break the bottleneck.
Paul and John take a closer look at:
- Why network automation adoption has lagged behind other areas of IT
- How AI can augment network engineers instead of replacing them
- The role of MCP and AI agents in automating complex workflows
- Why “AI can write and validate scripts as a pair programmer”
- How digital twins can reduce risk and improve network resilience
- The emergence of VibeOps and AI-driven operations
For technology leaders navigating AI adoption, this episode explores how AI-powered automation can help network teams move faster without sacrificing reliability.
To hear more about AI-powered operations and vibecoding, check out Ep 65 | The Vibecoding Liability: How Unchecked AI Can Kill Cloud ROI
Stay in touch with John:
John Capobianco on LinkedIn: https://www.linkedin.com/in/john-capobianco-644a1515/
John’s website: https://www.automateyournetwork.ca/home/
“Automate Your Network” on Amazon: https://www.amazon.com/Automate-Your-Network-Introducing-Enterprise/dp/1799237885
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The Space-AI-Quantum Nexus Challenging Governance
AI governance is already struggling to keep pace. Add quantum computing and space infrastructure, and the challenge becomes exponentially harder. In this episode of The AI Forecast, Paul Muller sits down with …
Notas del episodio
AI governance is already struggling to keep pace. Add quantum computing and space infrastructure, and the challenge becomes exponentially harder.
In this episode of The AI Forecast, Paul Muller sits down with technology governance specialist and researcher Preetha Bedi to explore the growing convergence between AI, space, and quantum technologies—and why this nexus is creating entirely new categories of systemic risk.
From satellite infrastructure and quantum acceleration to agile lawmaking and fractal risk modeling, Preetha makes the case for a fundamentally different approach to technology oversight in the AI era.
Paul and Preetha unpack:
- Why AI, space, and quantum technologies must be governed as interconnected systems
- How traditional governance models are failing to keep pace with technological acceleration
- Why agile lawmaking may become essential for emerging technologies
- The hidden systemic risks tied to satellite infrastructure and orbital congestion
- How quantum computing could dramatically amplify AI development
- Why “fractal thinking” helps model repeating patterns in technological risk
If you’re thinking about the future of AI governance, this episode offers a thought-provoking perspective on what happens when innovation outpaces institutional control.
To hear more about critical infrastructure in space, check out Ep 73 | Out of This World AI: Inside Spaceflight with Jeanette Epps
Stay in touch with Preetha:
Preetha Bedi on LinkedIn: https://www.linkedin.com/in/preethabedi/+++
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Why Enterprise AI Still Breaks at Scale with Ravit Jain
As organizations rush to scale AI, many are learning that better models can’t compensate for weak data foundations. AI hype is everywhere, but operational readiness still isn’t. In this episode of The AI …
Notas del episodio
As organizations rush to scale AI, many are learning that better models can’t compensate for weak data foundations. AI hype is everywhere, but operational readiness still isn’t.
In this episode of The AI Forecast, Paul Muller sits down with Ravit Jain, founder of The Ravit Show and one of the leading voices in the global data and AI community, to explore the trends shaping the future of enterprise AI.
Drawing on conversations with hundreds of AI and technology leaders, Ravit shares why the industry is shifting away from experimentation and toward measurable business outcomes. From agentic systems and contextual AI to governance and responsible adoption, he explains why organizations must get the fundamentals right before AI can scale effectively.
Together, Paul and Ravit delve into:
- Why data quality remains the foundation of successful AI
- How AI is moving from assistant to operator
- The rise of agentic AI and contextual systems
- How governance and responsible AI shape long-term adoption
- Why operational readiness matters as much as the technology itself
If you’re building or scaling enterprise AI, this episode offers a practical perspective on separating hype from impact, and what leaders should prioritize as AI moves into its next phase.
To hear more about the future of enterprise AI, check out Ep 69 | Industrial Enterprise AI: Growing Value and Organizational Risk Management.
Stay in touch with Ravit:
Ravit Jain on LinkedIn: https://www.linkedin.com/in/ravitjain/Ravit Jain’s website: https://www.theravitshow.com/
The Ravit Show on YouTube: https://www.youtube.com/channel/UC4yopSSlBfw2WAykLPTYH-w
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
Fan Intelligence: Preparing for the World Cup Surge
With the World Cup on the horizon, global attention is about to shift back to football. For clubs, that spotlight represents a rare opportunity to activate millions of fans. The question is whether they’re …
Notas del episodio
With the World Cup on the horizon, global attention is about to shift back to football. For clubs, that spotlight represents a rare opportunity to activate millions of fans. The question is whether they’re ready.
As AI reshapes how organizations understand and engage their customers, sports clubs are beginning to rethink what a “fan” really is. In markets like Brazil, where membership programs are a core part of club revenue, teams hold vast amounts of fan data, but it is often fragmented. Ticketing systems, membership programs, e-commerce, and social channels often operate in silos, making it nearly impossible to build a unified view of the fan.
In this episode of The AI Forecast, Paul Muller sits down with Caio Nogueira, co-founder of Lupa Data, to explore how AI and data are transforming fan engagement—and why most clubs are still leaving value on the table.
Caio and Paul explore the implications of:- Why fan data is becoming a measurable business asset
- The shift from static databases to dynamic, behavior-driven segmentation
- Using AI to predict churn, personalize experiences, and drive loyalty
- How leading organizations are moving toward behavior-based segmentation, using data to understand how fans engage (and when they disengage)
For any organization managing subscribers or members, the tools to understand your audience already exist. The challenge is bringing the data together in a way that makes it usable. Because when the next global moment arrives, the advantage goes to the teams that already know their fans.
To explore another data-driven transformation in sports, check out Ep 62 | How Moneyball's Billy Beane Changed Baseball Forever with Data Analytics
Stay in touch with Caio:
Caio Nogueira on LinkedIn: https://www.linkedin.com/in/caio-nogueira-gon%C3%A7alves-52529837/
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Out of This World AI: Inside Spaceflight with Jeanette Epps
Human spaceflight is one of the few domains in which data and human judgment must work together flawlessly under extreme pressure. That makes it a powerful lens for understanding what it takes to build …
Notas del episodio
Human spaceflight is one of the few domains in which data and human judgment must work together flawlessly under extreme pressure. That makes it a powerful lens for understanding what it takes to build resilient, intelligent systems here on Earth.
In this Women Leaders in Technology spotlight episode of The AI Forecast, Paul Muller sits down with former NASA astronaut Dr. Jeanette Epps to explore what complex, high-stakes environments can teach us about AI.
Drawing on her 235-day mission aboard the International Space Station, Jeanette shares firsthand insights into how tightly integrated systems must function when precision is critical, and uncertainty is unavoidable. From working with autonomous robotic systems like Astrobee to managing unexpected mission anomalies, her experience proves that automation works best when paired with human judgment, not separated from it.
On today’s mission, Jeanette and Paul chart a course through:
- What human spaceflight reveals about managing complex systems
- Why precision and trust are non-negotiable in mission-critical environments
- The role of human-in-the-loop decision-making in AI systems
- Lessons in leadership, resilience, and decision-making under pressure
- Why baseline testing and known data are essential for trusting AI outputs
- How to design systems that balance automation with human oversight
For leaders building AI-driven systems, this episode offers a rare perspective from one of the most demanding operational environments imaginable. When the stakes are high enough, there’s no room for guesswork—only systems you can trust.
To listen to another episode from the WLIT series, check out Ep 66 | Women Leaders in Technology: AI Agents Are Your New Team– Now What?
Stay in touch with Jeanette:
Dr. Jeanette Epps on LinkedIn: https://www.linkedin.com/in/jeanette-epps-phd-812aba50/
Dr. Jeanette Epps on Instagram: https://www.instagram.com/jeanette.epps/
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
The Data Governance Coach: From Data Error to Insight
In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data …
Notas del episodio
In the world of enterprise AI, the pressure on data has changed. What used to be “good enough” now gets amplified by faster decisions, and therefore, faster mistakes. Governance is fundamental in ensuring data trust and integrity. In this episode of The AI Forecast, Paul Muller sits down with The Data Governance Coach, Nicola Askham, to share her pragmatic perspective and assert that governance only delivers value when it’s simple enough for people to use and embedded into day-to-day work.
Here’s what business leaders need to know:
- Why data governance has shifted from compliance to value creation
- How AI is raising the stakes for data quality and trust
- Practical ways to start and scale governance without overengineering it
- Why data governance is more about people than technology
- Common anti-patterns, from “IT-owned governance” to overly complex frameworks
- The role of data literacy and communication in driving adoption
- How to design fit-for-purpose frameworks that evolve with the organization
Nicola’s advice is refreshingly direct: start small, keep it simple, and focus on outcomes. In a world shaped by AI, governance evolves alongside the business, sharpening decision-making and protecting against costly mistakes.
For more on data governance, listen to Ep 68 | Agentic AI Is Forcing a Governance Reset.
Stay in touch with Nicola:
Nicola Askham on LinkedIn: https://www.linkedin.com/in/nicolaaskham/
Nicola’s website: https://www.nicolaaskham.com/
Nicola’s book on Amazon: https://www.amazon.com/Effective-Data-Governance-Framework-Organization-ebook/dp/B0FY7KWSM8?ref_=ast_author_dp&th=1&psc=1
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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes. You can watch the video version of this episode on The AI Forecast.
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