.svg)
Any business leader with an intense drive to innovate feels like they should be moving faster, smarter, and more strategically. The subtle art of allowing oneself to feel "behind" or underachieving is the innate value that makes success possible. In the age of AI, it's easy to think that every organization from enterprise down to SMB is implementing agentic solutions that tap into the latest and greatest models that will springboard their company beyond competitors. But the rush to implement AI "for the sake of AI" is often done without a clear understanding of true impact.
For Jim Iyoob, Chief Customer Officer at Etech Global Services and a 38-year veteran of the contact center industry, it is time for a reality check. He argues that while AI offers significant potential in the near-term, its current application is frequently misguided, and the key to success lies in a human-centric approach that prioritizes genuine customer needs and ethical considerations.
The AI illusion: "The whole 'myth of AI' is misguided because everybody is talking about I have to have it. They have no idea why they want to have it," Iyoob states. He notes that while tens of thousands of AI companies have emerged, many will fail because they misunderstand the technology's role. "People look at AI as it is going to solve all my problems. That is a lie." Iyoob firmly believes AI is not a "set it and forget it" solution, referencing the old Ron Popeil rotisserie slogan. "That is the biggest lie in the industry. Machine learning and AI has to be tuned, it has to be coded, and it has to be kept up with. That is where most people get it wrong."
Humans make the machine work: Central to Iyoob’s philosophy is the "human in the loop" concept. "I believe that it is human in the loop. Human beings are what makes the machine work well," he says. This perspective challenges the notion that AI will entirely replace human agents. Instead, he sees AI as a tool to enhance human capabilities, saving customers money and improving metrics like first contact resolution and handle time when applied correctly.
Personalization comes from reality, not boardrooms: When it comes to predictive AI and personalization, Iyoob points to a critical flaw: a lack of domain expertise in development. "The problem is the guy who coded that machine for this personalization never took a call a day in his life," he explains. Effective personalization, he argues, stems from analyzing the vast dataset businesses already possess – their customer interactions. "You already know what your customer said, what your best agent said and responded to, what worked, what did not work."