# What's working for businesses adopting AI

- **Episode:** [169](https://agents.truthliesandwork.com/episodes/169.md) (2025-02-06)
- **Type:** interview
- **Tags:** ai, business, adoption
- **Time:** 9:04–14:29 (start 544s)
- **Listen:** [https://truthliesandwork.com/169?t=544](https://truthliesandwork.com/169?t=544)

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[9:04] Leanne Elliott: Do you mind sharing maybe some of the things that are working particularly well for the businesses that are starting to implement some of these new features?
[9:12] Crevan O'Malley: We had some senior executives from teamwork.com, for Swapfiets, and for Pleo across a various group of industries were represented as some incredible speakers. You know, a couple of things that stand out for me in Swapfiets. Chris talked about the level of tickets that their service offering, which is part of the HubSpot platform, their Service Hub is managing. Yeah. And what they specialize in is effectively bicycles as a service. Unsurprisingly, it comes from the Netherlands, but they're in many, many European cities now. And over the course of 12 months, they process anything up to half a million tickets where people who use that service, who rent the bicycles for a period of time, have an issue. And it can be anything from a breakdown to a use of the app. And to be able to do that in an efficient way where individuals are getting the information that they need in a much faster way doesn't always require them to speak to a human. But when it does, they can elevate that conversation quite quickly. But through the use of chatbots, through the use of knowledge bases, people can get the answers that they need in a very, very efficient way for the business, but also for, for the customer themselves. And I think chatbots have been around for a while, and effectively things like predictive analytics and smart knowledge bases, you know, we probably didn't brand those as AI when they came on the scene first. But again, it's about the AI tools and software fueling those tools and systems to work in a more even efficient way. And we have examples now where instead of actually drafting your knowledge base and selecting frequently asked questions and editing it in terms of that being somebody's job, because your AI can scan your entire content set and actually learns in real time from the experiences of customers, you can actually watch the AI develop and build your knowledge base as they engage with more and more customers. So serving up customers information and data that they need in a timely fashion and that evolving as your business grows. That was quite interesting. On the teamwork.com team, I thought it was really interesting. Lubna talked about— she's the CMO— and she talked about approaching customer experience application investment from the entire go-to-market team. So I think traditionally what we've had is where if the marketing function or the sales function or the service customer support function needed automation or needed an application to help them do their job, they operated in terms of contracting with different vendors in a silo way. And then suddenly you've got that fragmented experience of customer data not being available to everybody who touches the customer. That causes frustration. Being able to set out their requirements across the entire customer journey and agreeing that if the customer is central to the outcomes that they want, well then, in certain decisions, maybe marketing don't get exactly the tool that they want, or sales perhaps have to compromise in a particular piece of functionality, but that the overall investment is a prioritization of what the customer pains and the customer problems are. I think that was a really interesting conversation. And where that leads back to AI is you will find that while you can buy best-of-breed solutions for particular functions, the idea of a platform architecture is really interesting because AI is only as good as the data that you feed it. And if you feed it a disparate amount, a departmental-level amount of data, then you miss the richness of the customer experience across their entire journey. Whereas if you have a platform experience and effectively Leanne or Crevan, whoever the prospect or customer might be, wherever they show up in that journey, they're effectively on the same database. So marketing, sales, and service see the same information, feeds a more speedy and easy relationship with that customer or prospect. And when you layer on AI functionality and value on top of that engagement, you get some of the speeds and efficiency that I talked about. So I think some really interesting use cases, some really interesting problem solving going on at our customer level to tackle, again, as I said at the top of the chat, problems that have been faced with go-to-market teams, sales teams for years. And it's really exciting where the technology is bringing them in terms of being able to service those customers going forward