Who Can Keep Up? Making Sense of AI in Customer Experience

In the space of 5 months, Gartner headlines and predictions for AI and customer service could leave even the industry's best analysts “wondering what is next for contact centres and automation?”

• March 5: “Gartner Predicts Agentic AI Will Autonomously Resolve 80% of Common Customer Service Issues Without Human Intervention by 2029”

• June 10: “Gartner Predicts 50% of Organizations Will Abandon Plans to Reduce Customer Service Workforce Due to AI”

• June 25: “Gartner Predicts Over 40% of Agentic AI Projects Will Be Canceled by End of 2027”

So, if you're reading these articles, like me, here’s the scoop: AI is reducing headcount, but also saving it. It's accelerating, but also being cancelled. It’s revolutionising customer operations and being walked back due to complexity, cost, and customer resistance.

It’s no wonder people have whiplash from the headlines and are confused about the future of contact centres and AI. No Jitter recently reported that contact centres are "confronting competing priorities and the AI wolf at the door," caught between the pressure to modernise and the reality of legacy systems, siloed data, and unclear leadership accountability.

So, who can keep up?

The truth is, few can and, perhaps more importantly, few should even try to follow the hype cycle blow by blow, especially because we’re seeing more bold claims about AI than actual working implementations. A little doubt goes a long way right now, as AI predictions are multiplying faster than most companies can debug their latest chatbots. 

It’s no wonder people are sceptical.

However, scepticism isn’t a strategy. So here’s what I hope is a more grounded and helpful perspective.

Foundations Before Futurism

According to experts like Laurent Philonenko in the No Jitter piece, many organisations are trying to leap into AI while still struggling with basic CX hygiene: legacy contact centre platforms, inconsistent self-service flows, gaps in agent knowledge bases. This aligns with what I am seeing in the contact centre space. AI might promise transformation, but it can’t deliver value on top of weak foundations.

Leaders need to resist the urge to bolt AI onto broken systems. Instead, they should prioritise infrastructure, clean data, and well-designed customer journeys. AI can amplify what works, but it will also expose what doesn’t. I know this isn’t the sexy answer, but it’s true. We all want to watch the game and not the turf being laid for it, right? Imagine your favourite sport being played in the dirt, no lines, no grass, dust everywhere making it hard to see and breathe. Think Dust Bowl, instead of Super Bowl or Mad Max instead of F1. 

Human-AI Partnership, Not Replacement

Despite headlines suggesting AI is either coming for jobs or saving them, I believe the reality is less dramatic and more practical. The near-term gains from AI in customer service will come from hybrid use cases: tools that assist agents in real time, summarise conversations, recommend next steps, or automate repetitive post-call work.

That’s not about replacing people. I believe that's about elevating them, and it's far more achievable than building fully autonomous, agentic AI systems that can manage end-to-end customer issues on their own. I’m not saying they are not coming, but the contact centre industry needs to walk before it can run. 

Stronger Leadership, Clearer Outcomes

One of the clearest insights from the No Jitter piece is the growing need for unified CX leadership, which I wholeheartedly agree with. There's no shortage of vendors, priorities, and tools in play, making it easy to fall into the trap of tech-first experimentation without clear goals. However, this leads to focusing on ensuring the tool or platform is implemented well, rather than focusing on the problem it is trying to solve. 

Organisations that want AI to work will need more than the AI itself. They’ll need governance, insight, and observability, as well as cross-functional alignment and accountability. That might take the form of a Chief Customer Officer, or simply stronger coordination between operations, technology, and experience teams. Either way, outcomes need to lead the roadmap.

A Measured Prediction

So if we move beyond the headline whiplash and look at where AI is actually delivering value, a clearer picture starts to form.

By 2027, the most effective customer-facing teams will likely be the same size they are today. What will be different is their capacity. With well-integrated AI, those teams will be able to handle more volume, deliver faster resolutions, and personalise at scale. The real impact will come from the compounding gains of human-AI symbiosis, not wholesale automation. Where automation and voice AI are present, they will be underpinned by good foundations, governance, and human-in-the-loop approaches. 

The Wrap Up

The contradiction in AI predictions isn’t a failure of forecasting. It’s a reflection of the fact that AI is not one thing. It’s a collection of evolving capabilities, tools, and experiments, moving at different speeds in different contexts. Some projects will succeed. Others will fail. Most will certainly be more complicated than ever anticipated.

The organisations that succeed will not be the ones chasing every trend. They’ll be the ones focused on clear goals, strong foundations, and practical and adaptive deployments.

Until next time, and as always

Hooroo