Revolut PRAGMA: The Foundation Model for Money

Building AI-Ready Customer Data with Tealium CEO Jeff Lunsford

Artificial intelligence is only as good as the data behind it. In this episode, we sit down with Jeff Lunsford, CEO of Tealium, to discuss why customer data has become one of the most strategic assets for enterprises embracing AI. As organizations race to deploy AI applications, digital assistants, predictive analytics, and agentic workflows, many discover that fragmented, outdated, or poorly governed customer data becomes the biggest obstacle—not the AI model itself. Jeff shares how enterprises can move beyond traditional Customer Data Platforms (CDPs) to create real-time customer intelligence that powers meaningful AI outcomes. During our conversation, we explored how the customer data landscape has evolved from the early days of tag management into today's world of real-time data orchestration, AI activation, and predictive decisioning. Jeff explains where Tealium fits within the modern enterprise architecture alongside data warehouses, cloud platforms, reverse ETL, and customer engagement systems. We also discuss the importance of creating real-time customer context, enabling AI systems to make faster, more intelligent decisions while maintaining strong governance, privacy, consent management, and regulatory compliance. Jeff provides a practical overview of AIStream and explains how organizations can deliver AI-ready data to applications, models, and autonomous agents in real time. The conversation also explores: * Why data quality—not AI models—is often the biggest barrier to successful AI deployments * The role of real-time customer context in improving personalization and customer experiences * Predictive intelligence and AI-driven decisioning * AI at the edge and real-time activation * Building trusted AI through strong governance, privacy, and consent management * Partner ecosystems spanning cloud providers, data platforms, and AI technologies * Emerging trends including Model Context Protocol (MCP) and agentic AI workflows * Practical advice for CIOs, CMOs, CDOs, and CEOs preparing their organizations for the next generation of AI Jeff also shares career advice for students entering the workforce, discussing the skills that will remain valuable as AI continues to reshape nearly every industry. Whether you're leading AI strategy, modernizing your customer data architecture, or simply trying to understand how AI creates business value beyond the model itself, this episode offers practical insights into one of the most important foundations of enterprise AI: trusted, real-time customer data. Topics Covered * Tealium overview and enterprise strategy * Customer Data Platforms (CDPs) * Real-time customer data and context * Data orchestration and activation * AI readiness * AIStream * Predictive intelligence * AI decisioning * Customer experience personalization * Privacy, consent, and governance * Data quality for AI * Agentic AI and MCP * Enterprise AI strategy * AI careers and future workforce If you enjoyed this episode, be sure to subscribe to The Macro AI Podcast, leave a review, and share it with colleagues interested in AI, enterprise architecture, customer data, and digital transformation. Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [https://www.macroaipodcast.com/] Macro AI LinkedIn Page:   https://www.linkedin.com/company/macro-ai-podcast/ Gary's Free AI Readiness Assessment: https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

AI Isn’t Eliminating Work. It’s Moving the Bottleneck

In this episode of the Macro AI Podcast, Gary Sloper and Scott Bryan examine one of the most important questions facing business leaders today: is AI eliminating work, or is it changing where work gets stuck?  While much of the public conversation focuses on job replacement, the bigger strategic issue may be that AI is moving the bottleneck. AI can make individual tasks faster — from writing and research to coding, customer support, forecasting, and design — but that does not automatically make the entire enterprise faster. In many cases, AI simply exposes the next constraint: approvals, data quality, governance, implementation capacity, supplier readiness, field labor, compliance, or physical infrastructure.  Gary and Scott discuss why the labor market is not yet showing a simple AI-driven job-loss story, why entry-level career paths may be one of the first pressure points, and why individual productivity gains do not always translate into enterprise-wide economic gains. They also explore how AI can create new work by making ideas, experiments, and business models cheaper to pursue.  The episode highlights examples across healthcare, manufacturing, banking, retail, telecom, and software, showing how AI shifts the constraint from knowledge production to workflow absorption. The discussion also explains why physical bottlenecks — including data centers, power, cooling, manufacturing capacity, clinical capacity, logistics, and supplier readiness — will matter more as AI accelerates planning, design, analysis, and demand generation.  The key takeaway: AI is not just a labor replacement technology. It is a throughput technology. The companies that win will be those that map their workflows, anticipate where bottlenecks will move, redesign early-career training, modernize their workflow layer, and use AI for growth — not just cost cutting.    Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [https://www.macroaipodcast.com/] Macro AI LinkedIn Page:   https://www.linkedin.com/company/macro-ai-podcast/ Gary's Free AI Readiness Assessment: https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

McDonald's ArchIQ and the Future of AI in Business Operations

Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [https://www.macroaipodcast.com/] Macro AI LinkedIn Page:   https://www.linkedin.com/company/macro-ai-podcast/ Gary's Free AI Readiness Assessment: https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

Does Claude Learn from your Code?

The concern is understandable. If your team is building a specialized AI product on Claude — with custom agent logic, refined system prompts, proprietary data pipelines, and hard-won product insight — it is natural to wonder whether that work could somehow make the model smarter and eventually benefit a competitor.  Gary and Scott break down the issue clearly and practically. They explain the difference between three things that are often confused: in-conversation context, Claude’s account-level memory features, and the underlying model weights. The key takeaway: API usage does not update Claude’s model weights, and a competitor does not gain access to what Claude remembers within your account.  The episode also walks through Anthropic’s commercial data protections, including the default policy that commercial API inputs and outputs are not used to train generative models unless a customer opts in. Gary and Scott also discuss API data retention, zero data retention options for enterprise customers, and the practical areas where teams can accidentally create risk — including browser-based prototyping, feedback buttons, and partner program opt-ins.  Most importantly, the conversation turns this into an operational playbook for business leaders:  Use the API for serious development.  Audit whether developers have disabled model training in browser settings.  Avoid feedback buttons on proprietary workflows.  Create a clear approval process before joining partner or beta programs that involve data sharing.  Gary and Scott close by reframing the strategic question. For most AI products, the durable moat is not the prompt itself. The real competitive advantage comes from proprietary data, customer relationships, execution speed, product insight, and the feedback loops that compound over time.  This is a practical episode for executives, founders, product leaders, developers, and investors who want a clear answer to one of the most important AI business questions: where is the real IP risk, and what should teams actually do about it?  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [https://www.macroaipodcast.com/] Macro AI LinkedIn Page:   https://www.linkedin.com/company/macro-ai-podcast/ Gary's Free AI Readiness Assessment: https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog

What is an AI Harness

In this episode of the Macro AI Podcast, Gary and Scott break down an important emerging concept in enterprise AI: the AI harness.  For the last few years, most of the AI conversation has focused on the model — GPT, Claude, Gemini, Grok, Llama, and which one is smartest. But in the enterprise, the model is only part of the story. The real question is what has been built around the model to make it useful, controlled, repeatable, and safe.  Gary and Scott explain that the model is the “brain,” while the harness is the operating layer that allows that brain to do real work. A harness can give the model access to tools, manage workflow state, control permissions, enforce guardrails, log activity, route decisions to humans, and connect AI to actual business systems.  They also explain why this matters as companies move from chatbots to AI agents. Once AI can take action — opening tickets, updating CRM records, drafting customer responses, approving invoices, or triggering workflows — businesses need a control layer. That control layer is the harness.  The episode also distinguishes between three uses of the term: the agent harness, the evaluation harness, and the broader enterprise harness. For business leaders, the enterprise harness may be the most important because it includes identity, permissions, governance, compliance, auditability, monitoring, and human oversight.  The key takeaway: enterprise AI success will not come from model selection alone. The companies that get the most value from AI will be the ones that design the best systems around the model. The model gives you intelligence. The harness gives you reliability.  Send a Text to the AI Guides on the show! [https://www.buzzsprout.com/2454256/fan_mail/new] About your AI Guides Gary Sloper https://www.linkedin.com/in/gsloper/ [https://www.linkedin.com/in/gsloper/] Scott Bryan https://www.linkedin.com/in/scottjbryan/ [https://www.linkedin.com/in/scottjbryan/]   Macro AI Website:  https://www.macroaipodcast.com/ [https://www.macroaipodcast.com/] Macro AI LinkedIn Page:   https://www.linkedin.com/company/macro-ai-podcast/ Gary's Free AI Readiness Assessment: https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness [https://macronetservices.com/events/the-comprehensive-guide-to-ai-readiness] Scott's Content & Blog https://www.macronomics.ai/blog