Conversations about AI in customer support often start in the wrong place. It's easy to ask whether you should implement AI and how to do it. The harder question is whether your support operation is ready for it — and that often doesn't get enough attention.
This month’s reading approaches that from a few directions: what AI deployments require in terms of people and expertise, why the interactions that remain after automation are more demanding than the ones being replaced, and why organisations with complex products are finding that this moment strengthens the case for the right outsourcing partner.
What’s worth your attention this month?
1. What an AI deployment requires and where most organisations are underinvesting
In a recent edition of Founder-Led Customer Success, John Gleeson makes a compelling case for what makes an AI deployment work in practice. Every successful deployment depends on two things: (1) someone who can give the model the context it needs to operate in a specific business environment, and (2) someone who can judge whether its output is good enough. The instinct is to solve this by hiring engineers — which works when the underlying work is technical. However, in customer service, the person best placed to verify an AI's output is someone who has handled those interactions by hand, a domain expert who knows where the edge cases are and what good judgment looks like under pressure.
2. What the market is asking of outsourcing providers right now and why the answer is more interesting than the headlines suggest
On the episode of the CX Files podcast: Restoring Investor Confidence In BPO, BPO strategy advisor Jim Farnsworth makes a case worth considering. His view is that investors have oversimplified their read on outsourcing, pricing it as a labour-arbitrage business rather than an operational capability facing reinvention. The knowledge that experienced CX providers carry around compliance, multilingual delivery, workforce management, and complex integrations across multiple markets holds substantial value. The question is: can that value be reframed around outcomes and orchestration rather than seats and hours?
Farnsworth's more interesting observation is about timing. The providers best placed for that restructure are the ones that are already ahead — the ones whose commercial structure, talent approach, and operational design were never built around headcount in the first place. Reinventing a global organisation that employ thousands of people is a fundamentally different challenge from continuing to build something that was designed differently from the start.
3. The outsourcing industry isn't facing an AI crisis. It's facing a leadership and communication crisis.
While technology is reshaping delivery models at pace, too many organisations are avoiding transparent conversations with customers, employees and investors about what the future looks like. The result of that is uncertainty, speculation and a disconnect between perception and reality.
In the article BPO Is Burning - And Silence Is Starting to Look Like Surrender, Mark Hillary argues that the future of outsourcing isn't about protecting traditional delivery models. It's about redefining the role of the BPO as a strategic transformation partner helping organisations adopt AI, redesign customer operations and blend technology with human expertise to achieve better outcomes. Providers that can clearly communicate this shift will be the ones that earn the trust of clients, employees and investors alike.
What do we think about it?
1. AI readiness in customer operations starts well before the technology is deployed
Our team has noticed a pattern with AI implementation. The first question is almost always about automation — how quickly can AI handle more of the volume? That's an understandable place to start, and the technology genuinely can deliver it. The difficulty is that automation built on a poorly understood operation tends to resurface the problems that were already there. A better starting point is understanding how your operation works first — where are your contacts coming from, who owns the knowledge and how is it maintained, what does the internal resolution process look like before AI is asked to improve it. The quality of those foundations will determine whether AI drives continuous improvement or introduces new operational challenges.
"Using AI in your customer operations is as much a change management programme as a technology deployment. The foundations of a better operation need to be in place before you can make major gains from AI." Aidan O'Shea, Co-founder and CEO, Otonomee
2. Why complex products often make the strongest case for the right outsourcing partner
In our latest article, Why complex products are the best for outsourcing, we explore one of the most common concerns we hear from companies with technically complex products: that outsourcing the support function would mean losing the institutional knowledge that makes the experience good. That concern deserves to be taken seriously, and it points toward what to look for in a partner rather than toward ruling outsourcing out. As AI takes on more of the routine interactions, what remains in the human queue carries the highest stakes — edge cases, technically demanding situations, and conversations where the outcome has real consequences. That work requires experienced people with genuine product knowledge, operating with real authority. For companies with complex products, finding a partner built for exactly that tier of work is becoming more pressing.
Where we’ve been
Irish Examiner, Brendan Ring on building a people-first remote business
Otonomee's Chief People Officer, Brendan Ring, was recently featured in the Irish Examiner, reflecting on leadership, growth, and what it takes to sustain culture across a fully distributed organisation. One thread worth pulling on for anyone evaluating outsourcing partners: in a remote-first operation, culture requires deliberate design rather than proximity, and the organisations that get this right tend to show it in retention. An 85 percent staff retention rate against an industry average of 55 to 70 percent is the consequence of how the organisation was built.
Closing thoughts
The thread connecting this month's reading is straightforward: AI is raising the bar for what good customer support looks like, and the operations best placed to meet that bar are the ones built around quality, expertise, and people from the start. For organisations thinking about their support model, the useful question is less about whether to outsource and more about what kind of operation is genuinely equipped to handle what AI leaves behind.
If that's a question you're sitting with, we'd be glad to think through it together.
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