George Gilbert sits down with Pratik Desai, Head of Applied AI, ListEngage / TCS. Enterprise adoption of agents faces a triple challenge: Large organizations must navigate not only a data unification problem but also an organizational alignment problem where different departments optimize for conflicting KPIs (like marketing optimizing click-throughs while returns spike elsewhere). Bottom-up adoption through workflow-level champions works better than top-down mandates in these environments. Commercial companies have a structural advantage: Smaller, often digitally-native companies where founders remain operationally involved can align technology, operations, and business outcomes more quickly. Their reduced technical debt and flatter organizational structures enable faster experimentation and pivots. Salesforce is making its most profound pivot since founding: The company is shifting from software-as-a-service to "service-as-software," repositioning Data Cloud (now Data 360) from a marketing CDP to a data foundation for the agentic enterprise. This represents a move from CRM-centric company to a data-and-AI company that happened to start with CRM outcomes. Context is the missing ingredient in most agent strategies: While there's no shortage of agent development tools and SDKs, they lack enterprise and customer context. The real value lies in unified data (context) and workflow integrations (actions), not in the agent harness itself. This is the decade of agents, not the year: Product-market fit for agentic technology remains elusive, and organizations are still experimenting. The path to becoming an "agentic enterprise" requires solving long-standing data harmonization and organizational change management challenges that AI is magnifying rather than creating. 00:00 - Intro 00:02 - Welcoming Pratik Desai: Navigating AI Adoption and Enterprise Solutions 02:32 - Aligning Technology and Business Models 04:59 - Navigating Data and Change: Overcoming Departmental Challenges 11:02 - The Impetus for Cross-Functional Outcomes 14:59 - The Role of Build vs. Buy in Enterprises 19:16 - Experimentation in AI Models 26:39 - Differences between Enterprises and Commercial Accounts 34:28 - Salesforce's Pivot to Data and AI 40:47 - The Future of Agent-Based Technologies 47:09 - Salesforce Agent Tools and Technologies 50:32 - The Role of MuleSoft and Informatica in AI 54:06 - Strategic Oversight: Integrating Agent Observability with Future Workflow Management

Join George Gilbert as he talks with Alexis Steinman and Raphael Steinman about Maxa.ai 00:00 - Intro 00:06 - Exploring Maxa AI: Transforming Business Intelligence for Diverse Audiences 02:22 - Exploring Maxa AI: Methodologies and Capabilities 04:57 - From Historical Insights to Real-Time Business Intelligence 08:40 - Maxa's Innovative Approach: Bridging Traditional and Modern Data Modeling 12:35 - Exploring the Depths of Maxa's Innovative 4D Data Modeling 17:45 - A Step-by-Step Look at Maxa's Data Modeling Process 20:00 - Simplifying Data Complexity with AI 23:23 - Foundational Models and Software Integration 25:44 - How AI Facilitates Data Assembly 28:10 - Empowering Data Transformation: The Human-AI Synergy 32:02 - Harmonization and Metrics Enrichment in Data Modeling 34:39 - Creating Business Process Metrics with Unique Modeling 40:08 - AI in Business: Current Implementations and Future Innovations with Maxa's Technologies 45:33 - Advanced Analytical Techniques with Maxa AI 47:36 - Strategic Evolution: Implementing AI for Tomorrow

Road to Service as Software | Best Practices for Deploying Agents in Production 00:00 - Intro 00:07 - The Evolution of AI in Salesforce: Setting the Stage for Innovative Implementations 03:19 - Core Components and Considerations for Implementing Salesforce AI 08:36 - Challenges in AI Integration 11:38 - The Role of Organizational Dynamics 15:54 - Emerging Frameworks and Tools for AI 18:16 - Day One vs. Day Two Problems in AI 29:58 - Comprehensive Observability and Testing 33:59 - The Future of AI Governance 37:11 - Closing Insights and Future Outlook

George Gilbert & Dave Vellante talk with Carsten Thoma on this edition of The Road to Service as Software. 00:00 - Intro 00:05 - Evolution of Enterprise Software: From Service to AI Integration 04:51 - Interview with Carsten Thoma, President of Celonis 10:34 - Organizational Challenges and Adoption 18:26 - Optimizing End-to-End Outcomes 28:48 - Investing in AI and Productivity Gains 32:48 - Siloed Agents vs. End-to-End Processes 41:40 - Outcome-Based Models and Future Business Strategies 44:12 - Legal Insights and Final Thoughts

The Salesforce Data Cloud business, managed by EVP and GM Rahul Auradkar, is the tip of the spear of Salesforce's reinvention as a software-only hyperscaler. In the age of AI, how effectively you model your data determines how effective your agents are. Data that models business operations is the new agent platform Raw analytic data is the new infrastructure. Modeled data is the new platform. Traditional BI metrics and dimensions lets agents answer questions about what happened. However, because Data Cloud models the customer and her engagement journey, agents can also ask why something happened, what's likely to happen, and decide, optionally with human supervision, what should be done. Data as a 4D map enables agents and new personas to work Rather than a 2D map of raw snapshots of events typical of analytic data, this 4D map allows teams of agents to perceive the state of the customer and the business, perform analysis, make a plan, and operationalize decisions in the Customer 360 apps or external systems. And most important, this integration allows agents to learn from the outcome of their actions, something much harder for vendors without a combination of a data platform, agents, and operational applications. Data Cloud coexists with Snowflake, Databricks, and other data platforms Because it comes with a built-in customer model, new personas can work with it. It uses zero copy access to bring together customer-related data in existing platforms. For example, a marketing person can track how their customers move across channels and what makes them purchase. They don't have to build data pipelines or work with data products. 00:00 - Intro 00:05 - Understanding Rahul Auradkar's Vision and the Value Proposition of Salesforce Data Cloud 05:49 - Buyer Personas for Data Cloud 09:38 - 4D Mapping and System of Intelligence 17:07 - Role of Semantics and Models in Salesforce Data Cloud 23:59 - Harmonization and Application Logic 31:17 - Practical Applications and Use Cases of Data Cloud 36:10 - Transition to Digital Labor and Pricing Models 41:50 - Flexibility in Data Cloud Pricing 47:21 - Envisioning the Future: Integration and Dialogue