“AI will do this but it’s not going to be over the course of a half century, it’s going to happen a lot faster.” – Edmund DeLussey
At machineCON 2024, hosted by AIM Research, we had Edmund DeLussey, Senior Vice President and Global Enterprise Services Leader at Genpact who shared his views on “Breaking through the noise to achieve success with Gen AI”. He recounted a weekend walk with his son, where the young boy heard a high-pitched noise that DeLussey couldn’t perceive. This story served as a perfect analogy for the current state of AI in business – some can hear the signal, while others struggle to cut through the noise.
Clariant, as a company that has been making headlines for its innovative use of AI in customer service. By implementing AI models capable of handling 35 different languages, Clariant has drastically reduced its customer service inquiries by two-thirds. Remarkably, this has not led to a reduction in their customer service team; instead, they now have 2,000 agents working on more complex cases, improving the quality of customer interactions and creating a positive impact on the business. This example underscores that AI isn’t just about automation—it’s about enhancing human roles and driving better outcomes. “Clarinet’s use of AI didn’t just cut down on the number of customer service inquiries—it reshaped the role of customer service agents, allowing them to focus on more complex interactions that require human empathy and understanding,” DeLussey explained.
Surprisingly, a company used AI to expedite the drug development, Lantern Pharma which brought three drugs to the clinical trial stage in just three years – a process that traditionally takes a decade and billions of dollars. “The traditional drug discovery process is a lengthy and costly endeavor. Lantern Pharma’s use of AI has revolutionized this process, significantly speeding up the timeline and bringing crucial drugs to trials in record time,” DeLussey noted. Companies like Unilever and Mars are utilizing AI to innovate, with examples including AI-designed vegan mayonnaise and sustainable packaging solutions like paper M&M packages.
However, not all areas have seen equal success. The slower-than-expected progress in self-driving cars was highlighted, with regulatory complexities and the need for near-perfect accuracy cited as major hurdles.
Based on Genpact’s experiences, the talk outlined five key principles for achieving success with AI. First, prioritization: organizations should create a capability map to identify areas where AI can have the most significant impact. Second, a people-centric approach: AI solutions should be integrated into existing workflows, making employees’ jobs easier rather than adding complexity.
The third principle focused on data strategy. While perfect data is impossible, companies should focus on improving data quality in areas that directly impact key business outcomes. The fourth principle addressed technical architecture, advocating for a robust yet flexible approach with standardized infrastructure, a controlled core component layer for models, and a user-friendly experience layer.
The final principle emphasized the importance of considering operating model changes. Organizations must prepare for rapid shifts in job roles and industry dynamics due to AI implementation.
The session concluded with a clear message: the future of AI is rapidly approaching, and organizations must act swiftly to stay ahead. The comparison to the industrial revolution underscored the magnitude of the transformation at hand, with the accelerated pace of AI adoption requiring immediate and strategic action. As the industry faces both opportunities and challenges, the outlined principles serve as a vital roadmap for companies striving to harness AI’s potential. The next two years will be pivotal, and those who adapt will be well-positioned to thrive in this new era of technological advancement. “AI holds tremendous promise, but it’s essential to approach it with a clear understanding of both its capabilities and its limitations. Organizations must navigate these complexities to fully leverage AI’s potential while addressing its challenges,” he concluded.
