The AI Forecast: Data and AI in the Cloud Era

Cloudera

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.

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#98
Yesterday · 51 min
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 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 MindGuard, 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/ +++ 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.
#97
October 21 · 31 min
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 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/ +++ 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.
#96
October 14 · 27 min
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 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/ +++ 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.
#95
October 7 · 37 min
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 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/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
#94
October 1 · 21 min
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 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/ +++ 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.
#93
September 29 · 25 min
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, 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/ +++ 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.
#92
September 23 · 1 hr 2 min
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 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/ +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
#91
September 17 · 20 min
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 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/ +++ 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.
#90
September 15 · 38 min
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 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/ +++ 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.
#89
September 9 · 46 min
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 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 +++ Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest conversations about enterprise data and AI.
#87
September 2 · 57 min
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 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/ +++ 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.
#88
August 26 · 42 min
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 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/ +++ 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.
#86
August 19 · 52 min
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 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/ +++ 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 Cloudera’s YouTube Channel.
#85
August 12 · 42 min
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 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/ +++ 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.
#84
August 5 · 52 min
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 like 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 Mercy Corps, and Rob Dickens, AI Technical Lead at Cloudera. Together, they explore how VERA, an agentic AI platform co-developed by Cloudera and Mercy Corps, 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 +++ 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.
#83
July 29 · 49 min
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. 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/ +++ 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.
#82
July 22 · 32 min
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 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 +++ 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.
#81
July 15 · 34 min
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 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/ +++ 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.
#80
July 8 · 35 min
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 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/ +++ 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.
#79
June 24 · 50 min
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 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/ +++ 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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