Tipu Swaran

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For all the talk about AI making customer service faster and more personal, there is still a harder question to answer: Does the customer actually feel better served? An instant answer or perfectly timed recommendation may be useful, but usefulness is not the same as feeling understood. When a customer has a complicated problem, is frustrated or simply wants to talk to someone, efficiency can quickly become the problem.That tension is becoming more important as companies bring AI into more customer interactions. Customers are increasingly comfortable with AI when it saves time or effort, but many still expect a human option when the stakes are higher. The challenge then is figuring out how technology can remove friction without taking away the judgment, reassurance and connection that make good service feel like good service.In this article, members of the Senior Executive AI Think Tank—a curated community of leaders specializing in machine learning, generative AI and enterprise AI applications—explore that challenge from different angles. They discuss personalization and customer memory, where human involvement matters most, how AI can give employees more room for empathy, and how customer signals can shape better products. They also examine what it really means for AI to remember—and how leaders can tell whether an interaction leaves customers feeling understood, rather than simply processed.

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Across industries, executives are investing aggressively in artificial intelligence. Yet despite billions spent on experimentation, relatively few organizations have turned AI pilots into scalable platforms that generate repeatable value. According to PwC’s Global CEO Survey, 56% of CEOs report they’ve seen neither revenue nor cost benefits from investments in AI—a signal that experimentation alone is not enough to create enterprise impact. Members of the Senior Executive AI Think Tank—a curated group of leaders specializing in enterprise AI, machine learning and digital transformation—say the problem is rarely technical. Instead, organizations struggle with leadership alignment, operating models, governance and cultural change. Below, their insights reveal a consistent theme: Scaling AI requires redesigning how companies operate—not simply deploying more technology.

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No longer confined to analytics dashboards and recommendation engines, AI systems are now initiating transactions, approving workflows, flagging anomalies and even orchestrating other software agents. With this sudden increase in autonomy, business leaders are left asking: Where should humans step back—and where must they stay firmly in control? According to a 2025 McKinsey survey on the state of AI, nearly nine out of 10 organizations now report using AI in at least one business function, yet most are still early in scaling these technologies and many lack robust governance and risk controls. As artificial intelligence advances from advisory tools to agentic systems capable of multi-step planning and execution, the leadership challenge shifts: defining not just what AI can do, but what it should do. Members of the Senior Executive AI Think Tank—a curated group of experts in machine learning, generative AI and enterprise-scale transformation—argue that the real issue isn’t capability but accountability. Across their industry expertise, they all converge on one theme: The boundary between human judgment and machine decision-making must be dynamic, evidence-based and anchored in responsibility. Here is how they recommend drawing—and redrawing—that line.