Redefining the customer experience with Agentic AI

Rajeeve Kaul is Corporate Vice President and Global Pricing Officer at McDonald’s, based in Chicago. He has extensive experience in analytics, AI, and general management, driving business transformation and pricing strategy across multiple industries

I remember the good old days when we talked about how convenience and responsiveness would win the day with customers and personalization would be the next frontier. It all seems so long ago that capabilities companies spent all their time and resources building are now increasingly considered to be table stakes. The emerging paradigm on Agentic AI has taken the CX (customer experience) world by storm.

This new capability is dynamic; the agent takes input and reacts to it and responds; it can even take initiative and operate relatively independently to achieve goals while at the same time learning from feedback and becoming smarter with each interaction. At least that’s the story.

Remember, in the last decade plus companies have spent a lot of money building AI capabilities into their solutions. All kinds of customer platforms are now supposedly AI enabled. But the AI that companies were deploying so far was mostly defensive and reactive. It was confined by a set of mostly myopic actions restricted by the purpose of the AI involved to deliver process efficiency. But the shift to agentic throws out the old paradigm. Intelligent AI Agents promise to be proactive, not just solving problems but anticipating them, and initiate action to often resolve issues before the customer reaches out.

Consider the case of flight bookings, Traditional AI is able to find the shortest flight or the cheapest flight and even identify preferred airline partners and stopovers. Intelligent AI has the capability to “recognize” frequent delays on the chosen routes,

offer alternative flights and proactively notify hotel and airport pickups of changes in plans without being prompted – all work that is today done by a human agent. AI that can account for factors like upcoming weather interruptions or other non-systematic disruptions is way different and powerful.

This is the kind of anticipatory service model that customer service can evolve to quite quickly with Intelligent Agents, driving hyper personalization and delivering more nuanced responses as they orchestrate end-to-end customer journeys. The Ai agent thus quickly becomes a silent but powerful personal concierge at a scale we have not experienced before. And this is just one illustrative use case. Another could be around Agents replacing traditional role driven UI systems that are used in transaction processing work with a natural language query and interactive response capability freeing humas from cumbersome linear workflows.

The future of CX lies in this kind of anticipatory service model. Agentic systems will drive hyper-personalization, evolving with each interaction to deliver more nuanced responses, adapt communication styles, and even choose optimal channels—whether email, SMS, or virtual assistant. By orchestrating end-to-end customer journeys, agentic AI becomes a silent yet powerful concierge that not only meets expectations, but exceeds them.

For business leaders and CIOs the implications are profound as Agentic AI evolves from a set of CX features to a true strategic asset. As any CIO will attest, the biggest hurdle with large legacy systems is siloed communications and limited coordination – be it billing, customer support or marketing or supply chain. Intelligent agents hold the promise that they can create a unified and seamless customer experience across the customer journey without being constrained by the boundary and rules of a particular system or protocol to drive end to end KPI delivery and create lifetime customer value.

If businesses are to hand over or share decision making with machines, there must be robust ethical frameworks, governance structures and protocols and fail safes in place to prevent failure

No matter the level of autonomy, there will need to be increased accountability with these systems for them to work. If businesses are to hand over or share decision making with machines, there must be robust ethical frameworks, governance structures and protocols and fail safes in place to prevent failure. This is not about the agents being 100% right all the time - nothing is. There will always be “errors” – unexpected and unanticipated outcomes. The moment there is autonomy, there needs to be very strong protocols and enablers to prevent adverse outcomes. And businesses must get comfortable with the risk of letting customers know they are interacting with an intelligent agent and why – so a bias towards transparency and explainability goes hand in hand with any move towards autonomy for us to begin to get customer acceptance for this paradigm shift.

Ultimately these agents hold the promise to go beyond the scripted to acting increasingly autonomously in a dynamic environment where businesses are comfortable with having the agent represent them in boundary spanning roles when interacting with customers. This level of agency cannot just create a competitive edge for the business but even redefine it.