Lorikeet: You don’t need years to deploy an AI in CX. You need a plan

Revolut launched its AI financial assistant in April this year. Starling launched one three weeks before that. An in-app AI financial assistant has become table stakes for FinTechs overnight.

The good news is you don’t need a year to get there. All you need is a plan and the right data.

Eight Years or Eight Days                                                                  

Revolut’s AI assistant took a year from announcement to launch, and Starling describes their assistant as the culmination of eight years of work. Both were building the infrastructure before it existed off-the-shelf. 

FinTechs we work with deploy an AI assistant in weeks, not months. They don’t build from scratch. They buy infrastructure that’s already solved the hard parts… Knowing when to escalate, handling ambiguity without hallucinating, navigating the tone of a conversation where someone has had their card blocked and is not in the mood for corporate language.

Sound familiar? It should. Every technology shift in financial services has looked like this for a period. Early movers build bespoke, the rest wait for the case studies, and then someone works out you don’t need to build when you can buy much faster. Then it simply comes down to having the right data in place, connecting your systems, and putting in place a clear implementation plan. Financial services knows the playbook. The difference is that the loop has simply gotten a lot faster with AI.

Why Not Wait Longer Then?

Customer expectations have moved, shaped by AI interactions people have outside financial services (support queries answered in seconds, refunds processed mid-conversation), and the gap between ChatGPT responses and waiting for a queued ticket to a human agent is becoming visible in churn data. The FinTechs that move first will set the bar for what customers expect from anyone in the same category.

There is also a regulatory dimension. In the UK, the FCA moved Consumer Duty into more active supervision this year. The review published in March 2025 found that vulnerable customers continue to receive worse outcomes than other customers, particularly where firms have primarily digital customer journeys, and that most firms have underestimated the depth of outcomes monitoring required.      

The reaction to AI in many compliance teams looks a lot like the reaction to autonomous vehicles. Every time a Waymo crashes, the genuine safety improvements (fewer accidents overall, faster hazard detection, no fatigue) get less coverage than the single incident. 

Yet AI is going to help Consumer Duty over time, not hinder it. Autonomy with the right guardrails produces more consistent outcomes than human handling at scale. An AI system can understand a customer’s problem immediately, resolve it on the spot where it can, and route to the right internal team where it can’t, without queue times, without agent fatigue, without twenty different people interpreting the same policy differently. This ensures the core customer care team remains focused and can prioritise the cases with the most need. And this isn’t a far off future for consumers. The infrastructure to build it already exists off the shelf.   

The European Dimension

There are additional points to consider if you operate in Europe. The EU AI Act’s full compliance requirements for high-risk AI sees financial services explicitly on the list. 

Being compliant means documented risk management, transparency mechanisms, human oversight baked into the architecture and a traceable record for every decision that the system makes. The cost of getting this wrong is high: up to €35 million or 7% of global revenue for the most serious breaches.

Most organisations aren’t close to achieving requirements. More than likely they’ve rolled out AI as an experiment (that’s good!) but without a plan to get it scaled in production (not so good). Systems built purely to retrieve and respond weren’t designed with auditability or explainability in mind and it isn’t something you can bolt on later. 

If you operate in Europe and have not mapped your current customer experience AI stack against these requirements, the time is now.

What You Actually Need                                                                                                

You don’t need a year, a team of twenty engineers, or a bespoke model trained on your data. You need focused use cases, infrastructure that handles the compliance layer, and a team willing to run a tight pilot with clear success criteria and a plan to scale.

You can move now. The companies that don’t are still lacing up.

Learn more at lorikeetcx.ai