The Next Frontiers of AI

As enterprises rapidly embrace AI agents and agentic systems to revolutionize decision-making and automation, the question arises: Are Software Development Lifecycle (SDLC) solutions critical to the future of Agentic AI? Join Scott Hebner, host of the Next Frontiers of AI, and Paul Nashawaty , renowned industry analyst and expert in Application Development, modernization, and Software Development Lifecycle (SDLC) solutions, as they consider the role of SDLC in agentic systems development. • Are software development skills essential? • Will software developers also need to become data scientists? • Does the complexity spectrum—from no-code to autonomous AI—dictate SDLC platforms? • When will SDLC platforms be critical, beneficial, or potentially overkill? • Will we see an integration of Jira, GitHub, Docker, Kubernetes, MLOps, etc. with AI platforms? Tune in for a thought-provoking conversation on bridging traditional software engineering practices with the revolutionary demands of AI-driven innovation.

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In this episode of the Next Frontiers of AI Podcast, I am joined by Irving Wladawsky-Berger, an MIT research affiliate, adjunct professor, senior fellow, advisory board member, and a legendary, game-changing executive from IBM. We discuss how the last 50 years of transformative technologies serve as a guide for the future of AI. You’ll gain unique insights from an industry innovator who has repeatedly helped reshape the business landscape with technological innovations spanning the eras of mainframes, PCs, client-server systems, e-business, cloud computing, IoT, and now AI. Are we watching the same movie over and over again, just with different characters? Tune in and find out!


00:00 - Intro
00:06 - Exploring Insights with Irving Wladawsky-Berger: A Podcast Introduction
03:34 - Reflecting on Technology's Evolution
06:15 - Irving's Affiliation with MIT
08:55 - Navigating Cybersecurity Challenges: Insights from the MIT CIO Symposium and Mentorship Perspectives
14:19 - Irving's Career Journey in Technology
17:10 - The Shift from Research to Product Development
20:59 - E-business and Internet Era at IBM
23:36 - IBM's Near-Death Experience and Internet Revival
26:59 - Leadership and AI: Driving IBM's Transformative Journey
32:18 - The Power of AI in Research
40:34 - Lessons from Technology Transformation
43:55 - Agentic AI: Insights and Future Perspectives

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In this episode of the Next Frontiers of AI Podcast, I go solo to address an array of questions I have received about how to build decision intelligence capabilities in AI Agents and agentic systems. As the market is quickly realizing, generative AI and LLMs are insufficient to fuel these AI Agents, and businesses must build an extended ecosystem of specialized AI models. You’ll learn my point-of-view that has been informed by dozens of AI experts and pioneering companies. I am eager to hear your views!


00:00 - Intro
00:06 - Exploring the Evolution and Future of AI Agents: An Introduction
05:54 - Limitations of Generative AI and LLMs
08:32 - AI Ecosystem and the Iceberg Metaphor
11:21 - Building AI through Domain and Semantic Knowledge
14:47 - Causal Reasoning and Advanced AI Capabilities
20:13 - Translation to Agent Framework
22:39 - Embracing Causal AI: Shaping the Future and Industry Collaborations

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In this episode of the Next Frontiers of AI Podcast, I am joined by Ishansh Gupta quality management at BMW Group , the lead data scientist for . We discuss how advancements in AI are being used to create more resilient and trustworthy supply chains and manufacturing processes. You’ll also learn more about Causal AI, why Ishansh became an early Ph.D. in the rapidly expanding field, how he earned his management's trust, and how he is now nurturing the next generation of AI and data science talent.


00:00 - Intro
00:05 - Unveiling AI's Impact: An Overview and Insights with Key Guests
04:14 - The Motivations and Distinctions of Causal AI
06:53 - Building Managerial Trust: Causal AI for Decision Making
10:06 - Advancing AI: The Convergence and Applications of Causal and Generative AI
12:42 - Integrating Tribal Knowledge for Robust AI Model Development
16:24 - Causal AI in Supply Chains
19:00 - Building Resilience: Harnessing Causal Knowledge in Supply Chains
22:17 - Building and Validating Causal Models
26:10 - Business Impact of Causal AI
29:28 - Causal AI Hackathon and Community Impact
34:21 - Closing and Future Directions

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In this episode of the Next Frontiers of AI Podcast, I am joined by Stuart Frost, the CEO and founder of Geminos. We discuss the future of AI-powered business decision-making, which understands why outcomes occur. You’ll learn how this industry pioneer has delivered next-generation AI platforms that incorporate gaming Causal Knowledge Graphs. By integrating causal relationships, Causal Knowledge Graphs transform knowledge graphs from passive repositories into dynamic, self-reinforcing systems that provide a foundation for more intelligent decision-making AI agents.


00:00 - Intro
00:05 - Unveiling the Impact: AI Innovations and Entrepreneurial Success
02:47 - Stuart Frost's Journey
05:12 - Navigating AI Complexities: Challenges and Analytical Distinctions
07:49 - The Role of LLMs and Generative AI
10:15 - Building Better Decision-Making Models
13:42 - Accelerating Enterprise Intelligence: Decision-Making and Knowledge Capture
21:29 - Causal Knowledge Graphs
30:42 - Enterprise-Wide Knowledge Models
37:42 - Connecting Enterprise Decisions
41:38 - Conclusion and Call to Action

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In this episode of The Next Frontiers of AI Podcast, Scott Hebner is joined by Jayeeta Putatunda, the director of the AI Center of Excellence at the Fitch Group, to discuss the unique needs of financial services organizations and how institutions are addressing the limitations of today's AI. You'll learn about the power of advanced RAG frameworks, agent-based architectures knowledge graphs, and how Causal AI combined with RAG represents the next frontier for actionable, interpretable, and reliable AI decision-making.

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In this episode of The Next Frontiers of AI, Scott Hebner, principal analyst at theCUBE Research, speaks with Marc Le Maitre, CTO of Scanbuy, about the critical role of trust in AI decision-making and how causal AI is addressing this challenge.

Check out the latest from theCUBE, including upcoming tech events https://www.thecube.net/

Traditional AI models, reliant on statistical correlations, often lack transparency and reasoning, making it difficult for businesses to trust their outputs, Le Maitre explained. Causal AI enables a deeper understanding of cause-and-effect relationships, allowing for more explainable and reliable decision-making, he added.

Visit theCUBE Research for the latest in tech news: https://thecuberesearch.com/

Scanbuy has successfully implemented causal AI in programmatic advertising, achieving a 10x return on investment by improving transparency and user trust. The conversation explores how causal AI is evolving from a niche approach to a mainstream solution, with growing adoption as tools and AI agents make it more accessible to businesses beyond specialized AI engineers.

Follow theCUBE's wall-to-wall coverage as the roving news desk for SiliconANGLE reports live from tech's top events: https://siliconangle.com/category/cube-event-coverage/

Catch up on all episodes of The Next Frontiers of AI Podcast: https://www.youtube.com/playlist?list=PLenh213llmcYQRnnZYJAFCns3BaKkA7dv

#theCUBE #TheNextFrontiersOfAI #theCUBEresearch #AI #CausalAI #ROI #podcast


00:00 - Intro
00:07 - Exploring AI: Future Frontiers, Trust Challenges, and Industry Insights with Marc Le Maitre
02:51 - Navigating the Challenges and Opportunities of AI in Advertising
07:46 - Importance of Trust and Causal AI in AI Integration
14:16 - Understanding Causal AI: Concepts and Philosophical Roots
23:47 - Capabilities of Causal AI in Decision-Making
32:32 - The Future of Causal AI in Advertising: Growth, Applications, and Insights

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Scott Hebner hosts an enlightening episode of the Next Frontiers of AI podcast, featuring George Gilbert, principal analyst at theCUBE Research. The discussion navigates the transformative journey towards agentic Artificial Intelligence (AI), shedding light on the intricacies of data platforms and agentic frameworks. Hebner and Gilbert delve into the rising trend where software learns business processes, a shift poised to redefine data storage and business operations, predicated on recent insights from industry leaders such as Deloitte and McKinsey.

Listeners gain insight into the potential benefits and challenges of deploying AI agents, as Hebner mentions. Gilbert articulates the need for integrating domain knowledge, decision intelligence, and explainability into AI models. Highlighting the importance of a harmonized view within agentic systems, they explore how agent networks could revolutionize business decision-making and adaptation to real-time conditions. Key paradigms include multi-agent reinforcement learning and Swarm intelligence. Engage with further discussions through theCUBE Research and explore more at SiliconANGLE.com, #CyberResiliencySummit, #AI, #AgenticAI, #theCUBE, #theCUBEResearch.


00:00 - Intro
00:07 - Navigating the Evolution and Impact of AI: Insights with George Gilbert
03:58 - Defining Agentic AI
06:25 - Building the Future with Agents
09:28 - The Ladder to Agentic AI
12:35 - Key to Domain Knowledge
17:14 - Decision Intelligence and Explainability
22:00 - Building AI Agents
25:28 - Developing Agentic Systems
30:31 - The Continuous Learning Loop
34:13 - Evolution and Future Directions in IT: Insights and Preparations

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Scott Hebner introduces the New Next Frontiers of AI podcast which discusses the future of AI in business and provides top predictions for 2025. Tim Sanders, VP of Research Insights at G2, joins the discussion to provide insights on the predictions.

Prediction 1: LLMs will run out of gas for enterprise ROI. LLMs have limitations in decision-making and adaptability. Correlation-based outcomes may lead to inaccuracies and hallucinations. Complementary specialized models will be needed for agentic AI.

Prediction 2: AI reasoning will be democratized. Reasoning involves chain of thought, semantic reasoning, causal AI, and swarm intelligence. Vendors are creating user-friendly environments for businesses to apply reasoning techniques.

Prediction 3: AI explainability will be crucial for innovation. Slowing deployments due to lack of trust in AI calls for explainability. Rote and effective explainability will be important for building trust and overcoming trust gaps in AI. Understanding the chain of thought in AI systems will enhance trust and decision-making.
In the upcoming year, there will be advancements in explainability in AI. If explanations are not possible, there may be a slowdown in AI deployments, especially in agents and generative AI. Generative AI may follow a fate similar to the browser wars of the early internet age, where it becomes part of the infrastructure rather than the center of innovation. Andrew Ng emphasized the importance of the application layer over the foundation layer in technology. Software developers will increasingly need to become data scientists to stay relevant. The gap between supply and demand for AI talent will drive the need for talent augmentation. Trust, talent, and rapid advancements in explainability and decision intelligence will be crucial in the AI industry in 2025. The industry is moving quickly, so individuals should prioritize learning about AI to stay ahead.


00:00 - Intro
00:06 - Introduction and Podcast Overview
02:17 - Prediction 1: LLMs and Enterprise ROI
11:45 - Prediction 2: AI Reasoning Democratization
18:48 - Prediction 3: AI Explainability as Innovation
25:59 - Prediction 4: Gen AI and Browser Wars
30:13 - Prediction 5: Software Developers as Data Scientists
35:23 - Prediction 6: AI Talent Augmentation
40:10 - Conclusion and Final Thoughts

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