With enterprises now moving beyond their initial explorations in Generative AI, the question turns to how companies can scale their AI efforts while maintaining governance, compliance, efficiency, and business impact.
In this episode of the Analytics Insight podcast, host Priya Dialani speaks with Shashank Sharma, Global Head of Intelligent Operations, AI and Automation at Brandtech Plus, about the evolution from Generative AI to GenOps. The discussion explores AI-RPA integration, intelligent business operations, human oversight, enterprise governance, automation strategies, and how organizations can maximize ROI while scaling AI responsibly.
Shashank explains why artificial intelligence and RPA work well together, how companies can strike the right balance between automation and human control, the biggest obstacles to artificial intelligence, and why good data and process management are key to success. Here are the key excerpts from the interview.
GenAI primarily focuses on creating content such as text, images, and videos, while GenOps uses AI in business operations. GenOps integrates AI with governance, automation, compliance, and operations to increase efficiency, reduce costs, and ensure the effective functioning of AI in enterprises.
AI provides intelligence and reasoning, whereas RPA handles rule-based tasks consistently. By combining both, organizations can automate complex business processes, minimize errors, secure their information assets, and balance the probabilistic aspects of AI with the deterministic aspects of RPA execution.
Productivity, faster process execution, reduced costs, enhanced compliance, improved data accuracy, and higher returns on investment can be achieved through automation. Automation enables people to stop performing routine tasks and focus on more important functions within an organization.
AI can make smart decisions, but it can also make wrong or dangerous ones. Human intervention is required to confirm important decisions, govern, prevent unnecessary errors, hold people accountable, and keep business transactions safe until AI technology becomes reliable.
Budget constraints, staff resistance, poor-quality data, and a lack of clarity about the organization's processes are among the issues. The recommendations to overcome these problems involve focusing on the most significant AI use case, experimenting, engaging subject-matter experts, and improving data quality.