How Generative AI and Emerging Technologies Can Help Enterprises Achieve Their Organizational Goals
Insights from Bharathi V [Chief Digital and Information Officer] and Jagan Mohan Jami [Chief Operating Officer] Acuvate.
About This Episode
Join Bharathi V., Chief Digital and Information Officer SBF International of Suntory Beverage & Food and Jagan Mohan Jami, COO of Acuvate of 8th Episode of Coffee Conversations for a deep dive into how Generative AI and other emerging technologies are transforming the way enterprises operate and achieve their strategic goals.
Drawing on her leadership experience across global organizations like Coca-Cola and P&G, Bharathi shares her perspective on how AI is becoming an integral part of decision-making, cost optimization, and employee engagement. Jagan brings a practitioner’s lens to how enterprises can systematically adopt these innovations—while balancing speed, governance, and long-term impact.
Together, they explore the real-world possibilities of AI Agents—beyond buzzwords—and how it fits into the broader fabric of digital transformation.
How Generative AI and Emerging Technologies Can Help Enterprises Achieve Their Organizational Goals
Key Topics Discussed
- Aligning AI with Enterprise Goals
How emerging technologies can directly impact strategic objectives like efficiency, agility, and customer experience. - Culture & Innovation at Suntory
The role of heritage, discipline, and experimentation in driving digital change. - The Impact of Automation and AI on Enterprise Productivity
Boosting productivity and time to market through smart automation and AI workflows. - Impact and Adoption of Generative AI as a Business leaders
Real-world strategies for unlocking business value and scaling AI adoption. - Balancing Pragmatism and User Experience Across the Organization
How to strike the right balance between practical decisions and user-centric design.
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Generative AI and Emerging Technologies Related - FAQs
Problem: Employees often spend up to 50% of their time on “grunt work” or “gray work” that is neither interesting nor core to their role.
Solution: By prioritizing Data Readiness for Agentic AI, organizations can deploy AI assistants to handle these routine tasks. The initial goal is to focus on personal productivity using AI to automate mundane activities like itemizing hotel bills for expense reports or booking meeting rooms. This approach not only boosts efficiency but also serves as a low-risk entry point for Implementing AI Minimum Viable Product (MVP), alleviating employee fears about job loss by automating tasks nobody wants to do.
Problem: Teams waste significant hours on mind-numbing reconciliation tasks, such as manually matching bank statements with ERP records.
Solution: Moving beyond basic automation to Multi-Agent Frameworks for Enterprise allows for intelligent process transformation. Unlike simple RPA, these systems can handle complex reasoning. For instance, in accounts receivable, intelligent agents can match bank statements with ERP lines reducing a half-day task to just five minutes. This level of efficiency is only possible when you start Breaking Data Silos for GenAI, allowing the system to access and correlate financial data across different platforms.
Problem: New technology initiatives often fail due to a lack of trust from senior management and resistance from end-users.
Solution: To ensure a project is believable, leaders must go to the “Gemba” (the actual place of work) to understand ground truths. However, for advanced implementations like Enterprise Autonomous AI Agents, trust is built through Governance for Autonomous Systems. By demonstrating that these agents operate within safe, monitored bounds, leaders can assure stakeholders that the technology is reliable. Grounding boardroom conversations in real user needs backed by robust governance makes the business case undeniable.
Problem: There is often a disconnect between technical experts and general users, leading to resistance against complex tools like predictive maintenance systems.
Solution: The conversational nature of Generative AI has made technology accessible to non-technical staff, easing the pain of adoption. For industrial contexts, this is critical for adopting an AI for Predictive Maintenance Strategy. By offering clear training like Suntory’s “Manabi” program and simplifying interfaces, companies can empower floor staff to use advanced tools. This also involves IT OT Convergence for AI Decisions, ensuring that insights from technical machinery (OT) are presented in a way that is understandable and actionable for business users (IT).
Problem: A common failure mode is focusing too much on backend “plumbing” (infrastructure) or too much on frontend “fixtures” (apps), ignoring how they connect.
Solution: Success requires architecting both layers concurrently. In complex environments, Industrial Data Contextualization is the critical link between the plumbing and the fixtures. It ensures that the underlying data (identity, networks, cloud) is organized and understandable, allowing the user-facing applications to function smoothly. Without this contextualization, even the best-designed user interface will fail because the “plumbing” cannot deliver the right data to the right person at the right time.