Moving Enterprise AI From Hype to Accountable Results With Freshworks

Has enterprise AI finally reached the point where impressive demonstrations are no longer enough? In this episode, I speak with Murali Swaminathan, CTO at Freshworks, about the growing pressure on AI investments to deliver measurable business value. Murali has over 30 years of enterprise software experience, including roles at ServiceNow and CA, and now leads engineering and architecture teams at Freshworks. Murali believes the AI hype cycle is being replaced by an accountability cycle. Buyers want to understand reliability, governance, total cost of ownership, traceability, and the return generated by every deployment. They also want the ability to audit decisions, override outcomes, and use feedback to improve performance. Productivity alone provides an incomplete measure. Within service operations, companies can examine time to resolution, the volume of repetitive work automated, the number of issues completed without human intervention, and the quality of the employee's experience. Murali describes the difference between service-level agreements and experience-level agreements. Resolving a ticket within two minutes means very little if the employee's problem remains. The better question is whether AI completed the workflow and restored the person's ability to work. We also discuss why mid-market and agile enterprises provide a demanding test for AI. These companies have complex requirements but cannot absorb lengthy implementation programs, unclear pricing, or failed experiments. Murali recommends beginning with a limited process, measuring the result, establishing whether it can be repeated, and expanding only after it has proved reliable. Architecture plays an important role. Murali argues that ease of use begins beneath the interface. Configuration-led platforms can be upgraded as new capabilities arrive, while heavily customized systems can leave companies trapped on older releases. Autonomous service operations do not require removing people from every process. Murali uses the example of a printer incident. AI can read the ticket, classify the problem, route it to IT or facilities, and apply an automated fix when a trusted process exists. People retain responsibility for unusual, uncertain, or higher-risk decisions. Scaling this model requires cloud infrastructure that respects regional data residency, privacy, encryption, routing, and audit requirements. AI requests and diagnostic logs must remain within the correct geographic and regulatory boundaries. The conversation concludes with engineering skills. AI coding tools can generate software quickly, but engineers must understand architecture, usability, testing, and customer requirements. Companies also need rules determining which code can be reviewed by AI and which changes require human approval. Is your company measuring whether AI genuinely improves service operations, or is it counting deployments and calling that progress? Listen to the episode and share your thoughts with me.