ISG Software Research Analyst Perspectives | Conversational Intelligence

Most of the discussion and planning around artificial intelligence (AI) tools for contact centers and CX has focused on finding appropriate use cases and understanding how to deploy these tools. There are already plenty of success stories about enterprises reducing friction for agents, saving time and expanding the breadth of interactions that can be handled automatically. Now that most people are convinced of the utility (and inevitability) of AI applications, perhaps the industry can look forward and examine some of the potential downstream consequences of such a dramatic reshaping of the customer care environment. By 2028, most customer communications, both inbound and outbound, will be created and overseen by conversational AI systems. So, how will consumers respond downstream of this massive shift? Consumers are mercurial but, ultimately, fairly predictable in their behavior. That assessment underpins much of the consensus thinking about using AI in the first place, assuming that things like automated sentiment analysis can predict buyer behavior. From the consumer point of view, some of the consequences of extreme automation are positive. Wait times go to near zero, and service can be more consistent because you remove the variation inherent in human responses. Some consumers will likely continue to seek out human help. But the funnel that moves interactions through escalations will become tighter, allowing through only complex or high-value issues. This will cause some dissatisfaction, which might be noisy and public in the early stages. What have consumers done in the past when they’ve been forced to change how they interact? Two historical examples shed light. In the mid-2000s, enterprises were keen on moving as much call volume as possible to automated interactive voice response systems and did so aggressively. So aggressively that a robust anti-IVR consumer movement erupted and caused a great deal of reputational damage to the contact center industry. (I urge people not familiar with this time to search “2006 IVR cheat sheet” and “gethuman.”) Then came social media. In the late 2000s and early 2010s, consumers found that Twitter and YouTube provided excellent workarounds in drawing public opprobrium to brands that shortchanged service. Humiliation and retrenchment followed. (Search “United breaks guitars” for more fun reading.) Those famous incidents were the tip of an iceberg that forced enterprises to take a hard look at the balance between human work and automation. But those incidents took place in an era when the tools for automation were manifestly worse than those available today. A 2025 chat interaction is exponentially more productive than a 2010 or 2015 chatbot. So how will consumers react to a widespread change pushing them towards near-universal automated service? As with most things, people will complain but ultimately adapt and conform. Even so, enterprises should be prepared to deal with some very foreseeable negative consequences. While it’s plausible to assume that extended automation and head count reduction lead to significant cost savings in service operations, there will be brand-management effects that affect the business more broadly. In the early phase of the transition, customers with nuanced problems might experience frustration when AI systems fail to fully understand or resolve their issues, potentially leading to repeated contact attempts. And some customer segments, particularly older people or those less comfortable with technology, might resist adapting to AI-driven service models. Downstream of that, reducing the human element may diminish the emotional connection some customers value, potentially decreasing loyalty for customers who prefer human interaction. The customer-company relationship could become starkly transactional, affecting customer loyalty and the likelihood to recommend or advocate for a brand. My fear is that if enterprises do not think through the negative downstream consequences, the benefits of positive actions may be out of reach. One aspect of this AI revolution is that it appears to have overcome the inherent conservatism in contact center technology deployment. Centers typically have waited a very long time before adopting disruptive new technology. As evidence, we are 15 years into the shift to the cloud, and roughly half of the seats are still on-premises. Businesses are typically cautious about disruption to customer-facing processes. But AI is moving into place much faster. And the promise of automation is that it permanently changes the cost structures of running contact centers. With most of the cost bound up in labor, replacing humans with automated agents may generate unbelievable savings. Enterprises are following that drive. I think that’s partly because contact centers are more integrated into back offices and into enterprise CX strategies that have different cost structures, success metrics and decision-makers. The result is fast adoption, leading to rapid head count reduction in the coming years. That head count reduction benefits customers and enterprises, but only if the potential pitfalls are foreseen and pre-managed. Enterprises have to ask: What happens if customers respond poorly, even if only temporarily? Can we tolerate the brand damage that historically has occurred? Will the business respond by quickly reinserting humans into the process, undercutting the benefits all around? There are several steps enterprises should take now to prepare for the uncertainty created by diminished human involvement in service delivery, including: Extensively testing and optimizing the user experience stemming directly from automated interactions. Quality analysis of automated interactions should focus on whether the AI can offer customers (simulated) empathy and flexibility in options and responses. Training the customer base on what they should expect and how to work with automated systems. Customers should be informed about the changes and new options with guidance for navigating among multiple complex channels on offer. Enterprises should suggest ways to communicate with automated systems that deliver better results. Planning for a transitional period in which some portion of the customer base will reject the new options. During that period, tune your key performance indicators to allow some interactions to generate escalations that aren’t really needed and permit certain people or teams to absorb longer calls, lower customer satisfaction scores and poorer customer sentiment. At the same time, train those agents to make use of those challenging interactions to train customers in best practices for finding answers and working with automated systems. Negative outcomes are not guaranteed, but they are possible and maybe even likely. Planning for them will prevent surprises that generate C-level panic and policy whiplash. Regards, Keith Dawson

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