Is Agentic AI the Holy Grail for Customer Experience? (Or are we too busy debating swallows?)

How AI agents can help orchestrate the journey across customer service, sales and marketing to drive new levels of growth.

I will get to ‘debating swallows’ at the end but now for something completely different…

Working in customer experience for many years now, the ‘Holy Grail’ has always been the notion of the ‘segment of one’. This almost mythical ideal that we can engage with the customer as a unique individual, tailoring products, services and messages specifically to their personal preferences and behaviours. For decades data, technology, platforms, processes and people have kept this of reach but today, AI and most specifically Agentic AI has now firmly put this Holy Grail within our grasp.

A lot - and I mean a lot - has and is being written about AI, Generative AI and Agentic AI and its impact on all aspects of business across every industry with maybe the most transformative opportunity to completely reinvent businesses and how they operate today.

For customer experience, the real power specifically for Agentic AI is the potential to radically transform and deliver the next level of personalisation across the entire end to end journey - from early reach and engagement, through the sales journey and into customer service and loyalty. The power of a series of agents acting as dynamic orchestrators across the journey, truly connecting the ‘front office’ (marketing, sales and customer service) capabilities through uniquely understanding and responding to each customers individual needs no matter where they are in their journey is both extraordinarily powerful and hugely lucrative for businesses of all shapes and sizes.

To level set, Agentic AI is autonomous, goal-directed series of ‘agents’ capable of initiating actions, making decisions, and adapting over time with little or no human interaction. In the customer experience it has the ability to anticipate, adapt and orchestrate experiences in real-time across all touchpoints, deeply understanding the individual and personalising their journey to the next level.

This new depth of personalisation with the combined opportunity to connect a business’ front office operations is truly transformative but there are still some significant challenges standing in the way.

First and probably the most significant is data - having a unified view of all your data across your customers interactions still seems unfortunately a long way away. Mainly because, and to the second point, businesses still only ‘see’ their customers through their own operational silos. Specifically in the growth engine of most businesses, the front office - sales, marketing and customer service – business functions are still operating independently, in unconnected teams and processes with only slithers of views of their customer.

While there’s clearly expertise required across the various functions, this siloed approach continues to perpetuate a fragmented data and technology landscape with ever-growing overheads and significant technical debt through legacy, unconnected and disparate systems.

Lastly and maybe this is the challenge, it requires the business to have expansive thinking - to reimagine what their entire business and processes could look like today with an AI-first approach. Then they would look more holistically at their customer, putting their needs first, redefining each interaction with them across the journey, unencumbered by business function and thinking only, how best can I serve them? 

This need for a new type of data, AI enabled ‘systems’ thinking is part of the reason why most organisations are only identifying individual use cases for AI or are stuck in the proof-of-concept stage. They are not elevating the opportunity for AI to more broadly look at how it can impact the wider business and not only reimagine processes but reinvent how they work, breaking down the silos by operating under a unified AI-first framework.  

For the front office, the impact is significant. Deploying a series of agents not only enables goal-driven coordination (the agent can understand the customer goals and not just behaviours) adapting actions to help reach them but with contextual memory, will maintain long-term understanding of interactions and preferences to ensure continuity and personalisation. It will do this with cross-channel fluency operating across all touchpoints without losing context and adapt in real-time, dynamically adjusting based on feedback, sentiment or even external factors (e.g. supply chain delays, pricing changes).

These hyper-personalised journeys will not only drive new levels of efficiency through reducing friction but with more precise targeting deliver higher and quicker conversion to sale as well as longer term customer satisfaction and loyalty through greater anticipatory relevance.

For example, think about the impact in retail where agents orchestrate the journey from product discovery to revenue reconciliation (the ‘lead to cash’ process). Here a series of behavioural AI agents can analyse real-time customer behaviour across browsing patterns, social signals and purchase history to identify high-intent leads, even connecting instore events with computer vision (CV) detecting dwell time and product interaction.

Then multimodal agents adapt messaging based on where they are (the channel), time of day (even the weather) and customer mood via sentiment analysis to make personalise product recommendations with dynamic pricing. Here other agents monitor competitor pricing, their own inventory, seasonal and market demand signals to adjust prices in real time with other agents optimising cart value based on preferences and promotions. Now a fulfilment agent evaluates the fastest and cheapest fulfilment path and sends instructions to the warehouse or store agents. A payment agent handles the payment and validates in real time against fraud engines, while the revenue reconciliation agent ensures that the revenue is accurately booked. You get the picture.

This is a total reinvention of the classic lead to cash process, autonomously delivered by a series of agents who are acting both independently but also orchestrated through a layer that manages the flow of goals, decisions and actions across all the agents (or a multi-agent system coordinator combined with a workflow engine).   

At each stage of the journey it’s delivering significant business value and impact – increased conversion rates and average order value, reduced manual interventions and lead to order cycle times. Increased retention, lifetime value and CSAT scores – to name a few.

So AI and specifically Agentic AI can fundamentally be the new driver and accelerator of growth that all modern businesses demand. The technology is already here – Microsoft, Salesforce and others are leading the way so what’s holding businesses back?

It’s the swallow debate.

If you’ve ever seen Monty Python and the Holy Grail probably one of the most iconic scenes is the ‘swallow debate’. Here’s a quick version of it but if you haven’t seen it, you should (and the whole film).

King Arthur approaches a castle seeking brave knights to join him on his quest for the Holy Grail and form the Knights of the Round Table. Rather than focusing on the ‘quest’, the guards at the castle obsess over how ‘Patsy’ (his trusty servant) has a coconut shell (which makes the sound of a horse trotting). The guard asks: “Where’d you get the coconut?" Arthur: “We found them.” The guard: “Found them? In Mercia? The coconut’s tropical!”

A debate unfolds on how the coconut could have got there with an offhand suggestion a swallow could have flown it over during migration. The debate continues, discussing the physics of bird flight ‘It’s a simple question of weight ratios! A five-ounce bird could not carry a one-pound coconut.” It goes on and on…

This is the challenge today, rather than seeing the opportunity to join a bigger, more noble quest for the Holy Grail, the guards focus too much on how and why the coconut got there. Or in the context of modern businesses, we looking at the wrong stuff, the small things (the incremental 'use case'), trying to fix already broken processes and not grasping the AI opportunity to entirely reinvent both processes but maybe more importantly how we do business.