Mick Hollison of Cloudera and Scott Hebner of theCUBE Research discuss how artificial intelligence, AI, drives conversational platforms that redefine advertising. They unpack distinctions between paid search and AI advertising and examine how AI engines interpret buyer intent to influence purchase decisions.Hebner explains that marketers must own the conversation and build brand authority with AI engines to appear in the silent shortlist and influence buyer decisions. They outline practical advertising strategy adjustments required to align messaging for machine and human audiences.Hollison emphasizes compressing differentiated value into machine-readable signals through a concise checksum statement that identifies buyer, category, differentiators and quantified outcomes. They present frameworks such as Signal Briefs, SignalWatch and the AEO Advantage Index as tools for ongoing monitoring and message management to avoid misrepresentation and improve the efficacy of ad spend.This conversation delivers actionable insights for marketers, ad tech professionals and brand strategists focused on AI advertising, conversational platforms, buyer intent and brand authority.

This Next Frontiers of AI episode features Mark McNally of Nobody Studios and host Scott Hebner of theCUBE Research examining how artificial intelligence reshapes startup economics and creates repeatable pathways to wealth creation beyond the traditional unicorn model. McNally brings nearly 30 years of startup experience, and they describe how agentic AI reduces cost and time to market and enables venture studio approaches to accelerate company creation across industries. Hebner highlights how cross-company economies of scale and acquisitive innovation support earlier and smaller exits and expand opportunities for founders and investors.Key takeaways include a shift from unicorn-or-bust financing toward earlier and smaller exits and acquisitive innovation. AI makes product development cheaper and faster, shifting defensibility to unique data partnerships and go-to-market strategies. Venture studio models enable repeatable company creation by leveraging shared resources and operational playbooks, accelerating scale and improving investor returns. The episode provides practical insights for founders investors and builders seeking scalable approaches to startup formation and value creation.

Stas Levitan of LightSite joins Scott Hebner of theCUBE Research to examine why many brands remain invisible to large language models LLM and how answer engine optimization AEO diagnostics address visibility gaps in artificial intelligence AI search. Levitan explains technical readiness issues, LLM crawling behavior, structured endpoints versus unstructured pages and content formats such as Q&A and schema, and they demonstrate how diagnostics reveal root causes behind differences in citation, recommendation and discovery across major AI engines. Hebner frames the discussion within operational priorities and they highlight the need for cross-team collaboration.Key takeaways include actionable diagnostics and technical fixes. Levitan reports that approximately 90% of sites are not technically prepared for LLM search and that approximately 30% actively block access. They show that structured data and Q&A-formatted content markedly improve extraction and citation, and that assigning skills can guide bots deeper into sites. Hebner emphasizes an AEO influence chain to prioritize schema, recency, evidence and cross-team alignment among marketing, IT and security.This episode provides practical guidance for improving site visibility in AI search and for aligning technical, content and governance efforts to support discovery and citation across LLMs.

This episode examines how to operationalize artificial intelligence coding to reduce organizational chaos and convert individual AI gains into measurable team productivity in software development. Wells Burke of CodeVine joins Scott Hebner of theCUBE Research to discuss agentic AI, preventing code duplication and aligning individual workflows to team objectives.Burke outlines CodeVine's capture-correlate-compound framework and explains methods to measure token and repository level costs and to surface and share productivity unlocks. They emphasize capturing large language model activity, correlating it to code and releases, and automatically redeploying effective workflows to scale best practices. Hebner highlights tracking activity, correlating inputs to velocity and quantifying business-level return on investment to justify broader AI adoption. The conversation addresses implications for engineering leadership, enterprise maturity and the shift from isolated wins to team aligned workflows.

Roland Boulos of UnifyApps joins Scott Hebner of theCUBE Research to discuss production-grade agentic artificial intelligence, advancing beyond chatbots that rely solely on large language models toward accountable digital workers. Boulos explains the role of knowledge graphs, persistent memory, connectivity and governance in creating agentic AI. They examine architecture, real-world supply chain use cases and platform strategies for scaling.Key takeaways emphasize treating generative AI as a strategic transformation and building an AI operating system layer that unifies data, context and actions. Boulos states trusted digital workers require grounded enterprise knowledge, multi-type memory and strict governance. Hebner highlights that trust is the currency of enterprise AI and recommends an assembly-first value-driven approach to deliver measurable return on investment.This conversation provides practical insights for enterprise technology leaders and solution architects on data architecture, governance and deployment strategies to scale agentic AI across supply chains and other mission-critical domains.

This episode, "The AI Velocity Trap: Why 85% of Enterprises Stall," examines artificial intelligence adoption and explains why most enterprise initiatives stall despite heavy investment. Nitesh Bansal of R Systems, chief executive officer and managing director, joins Scott Hebner of theCUBE Research to discuss practical approaches that accelerate deployment in complex enterprise environments. Bansal draws on decades of systems and product engineering experience, unpacks the concept of engineering velocity, contrasts model-centric thinking with execution architectures and explores layered approaches such as connectors, governance, evaluations, financial operations and agent templates that accelerate AI deployment in brownfield enterprise environments. They emphasize that engineering velocity rather than model selection determines whether AI scales.Key takeaways include setting clear return on investment objectives, reimagining value chains with an AI-first lens and designing human-in-the-loop workflows. theCUBE Research underscores that governance and trust are necessary but insufficient for scale.Action points include building reusable governance and connector libraries, adopting evaluation-first and FinOps practices and prioritizing high-impact use cases for brownfield environments.

Magnus Revang of Openstream appears on the Frontiers of AI podcast hosted by Scott Hebner of theCUBE Research to discuss transparent trustworthy multiagent artificial intelligence and architectures that move beyond large language model black boxes. Revang explains how specialized plan-based multiagent systems deliver explainability, provenance and operational reliability for high-stakes enterprise workflows. They outline Openstream's multimodal agent approach, which combines symbolic reasoning and knowledge graphs with large language models to support event-triggered high-control deployments and contrast them with low-control prompt-driven copilot scenarios.Revang emphasizes architectural trust controls such as data provenance, explainability, human-in-the-loop collaboration and policy alignment as prerequisites for operationalizing agentic AI. They describe how integrating LLM fluency with symbolic AI, knowledge graphs and specialized agents reduces hallucination and increases determinism. Hebner and theCUBE Research cite survey results showing enterprise leaders prioritize governance and trust as strategic investments.Listen to the full conversation to explore architecture patterns, governance frameworks and deployment strategies for enterprise agentic AI and decision intelligence.

In this episode Scott Hebner of theCUBE Research and Christophe Bertrand of SiliconANGLE and theCUBE present four predictions for enterprise artificial intelligence in 2026. Hebner emphasizes agentic decision intelligence and the need for semantic and causal layers to ensure defensibility and to reshape enterprise architectures; they address implications for explainability and decision systems. Bertrand stresses that cyber resiliency and data governance are prerequisites for large-scale AI deployment and become focal points for attackers and backup strategies; they call for tighter integration across security, storage and data services.The conversation examines agentic architectures, semantic and causal layers, explainability, data management and backup and recovery gaps, and considers evolving vendor dynamics such as DeepSeek-R1 and other platforms. Key implications include stronger governance and compliance requirements, integrated security and storage strategies, improved data protection and operational approaches to minimize risk while enabling innovation in enterprise AI.Listeners gain practical insight into architecture design, governance frameworks and cyber resiliency strategies for 2026 deployments. The episode highlights factors to consider for deploying agentic systems at scale, the importance of defensible models and explainability, and the need for comprehensive backup and recovery planning.

In this insightful episode, we delve into the intricate world of AI agent development with George Gilbert, principal analyst for data and AI at SiliconANGLE and theCUBE. The discussion centers on constructing AI systems that enterprises can rely on, audit, and verify by building on existing large language model frameworks with additional semantic and causal layers.George Gilbert, a distinguished expert in the field, joins Scott Hebner, principal analyst for AI at theCUBE Research, to explore the necessity of developing AI architectures beyond large language models. They discuss the challenges of ensuring AI decisions are logical, accurate, and explainable, and the imperative shift from mere trust to verifiable decision logic anchored in semantic knowledge.The episode outlines the limitations of large language model-based agents, highlighting studies from prestigious universities that demonstrate how such models can mislead with seemingly logical but ultimately unfaithful explanations. Gilbert underscores the vital role of semantic layers and knowledge graphs as the foundation for reliable agentic AI, which allows systems to understand and interact in a meaningful context while causal layers provide solid grounds for defensible decision-making.As the conversation progresses, Gilbert and Hebner emphasize key takeaways such as the importance of semantic architecture for trustworthy AI systems. They stress that building AI agents requires more than fluency; it demands a robust structure that assures enterprise stakeholders of the systems' reliability and accountability. This episode is essential for those seeking to innovate responsibly in AI.

Join us for an insightful episode from the Next Frontiers of AI Podcast, hosted by Scott Hebner, principal analyst for artificial intelligence at theCUBE Research. This episode explores the impending rise of AI decision intelligence and its anticipated mainstream adoption in 2026. Our discussion features an expert perspective from Joel Sherlock, CEO of Causify, focusing on revolutionary changes in AI decision-making technologies.In this engaging discussion, Sherlock shares their journey and expertise in causal modeling. As the CEO of Causify, Sherlock provides unique insights into how causal decision intelligence can transform enterprise decision-making. The episode, facilitated by Hebner and the theCUBE Research team, examines critical market trends, including the shift from generative AI to agentic workflows in businesses.Key takeaways from this episode include the impact of AI decision intelligence on trust and reliability in AI-driven decisions. Sherlock discusses how causal AI is essential for creating explainable and auditable decision-making processes, a notion echoed by analysts at theCUBE Research. The dialogue underscores the necessity of a robust AI architecture beyond large language models to make AI truly decision-grade.