What if the biggest AI challenge facing organizations has nothing to do with technology at all? In this episode of AI at Work, I sit down with Lee Senderov, Chief Transformation Officer at Travelport, to discuss why AI should be viewed as a workforce transformation rather than a technology project, and why many organizations are still framing the opportunity in entirely the wrong way.
While many businesses continue to focus on AI pilots, innovation labs, and isolated technical use cases, Lee argues that the real opportunity lies in empowering every employee. Drawing on Travelport's own AI journey, he shares how teams across the organization are using AI to eliminate repetitive work, create time for higher-value thinking, and solve problems that would never make it onto a traditional technology roadmap.
We explore the practical framework Travelport has developed to drive adoption, covering capability building, creating the right operating environment, and fostering a culture that encourages employees to openly share ideas and AI-powered innovations. Lee explains why successful AI adoption requires far more than deploying tools, and how organizations can create an environment where experimentation becomes part of everyday work.
The conversation also looks at the future of hiring, talent, and workplace culture. Lee predicts that AI proficiency will soon become as commonplace as email skills, shifting hiring conversations away from whether someone uses AI and toward how they use it to improve outcomes. At the same time, he warns against both ignoring AI and becoming overly dependent on it, arguing that the most successful employees will combine AI capabilities with human judgment, creativity, and critical thinking.
We also discuss how AI is transforming the travel industry itself. From changing the way travelers search and book trips to supporting travel professionals during disruptions and complex itineraries, Lee explains how AI and human expertise are increasingly working together to create better customer experiences.
Looking ahead, Lee believes the organizations that thrive will be those that build cultures capable of adapting quickly to whatever comes next. AI may be today's disruption, but the larger challenge is creating a workforce ready to embrace continuous change. Is your organization treating AI as another software tool, or is it rethinking how work itself gets done? Share your thoughts with me.
00:00:00 --> 00:00:02 If you are listening and you're responsible for
00:00:02 --> 00:00:05 security or IT, you will know the reality that
00:00:05 --> 00:00:08 most of your risk now sits inside SaaS apps and
00:00:08 --> 00:00:12 browser activity. That gap is exactly what Nord
00:00:12 --> 00:00:15 layer is addressing with its new business browser.
00:00:15 --> 00:00:18 So instead of bolting security on from the outside,
00:00:18 --> 00:00:22 it builds it directly into the browser itself.
00:00:22 --> 00:00:25 This means you can control access, monitor activity,
00:00:25 --> 00:00:29 enforce policies and reduce shadow IT all from
00:00:29 --> 00:00:33 one single place. And most importantly, it does
00:00:33 --> 00:00:36 it without adding deployment headaches or complex
00:00:36 --> 00:00:38 onboarding. You get things like browser -based
00:00:38 --> 00:00:42 data loss prevention, SAS access control and
00:00:42 --> 00:00:45 zero trust browsing, but delivered in a way that
00:00:45 --> 00:00:47 your team can actually use. So if you've been
00:00:47 --> 00:00:50 trying to simplify your stack while improving
00:00:50 --> 00:00:53 visibility, please check it out at Nordlayer
00:00:53 --> 00:00:56 .com slash browser. But now it's time for me
00:00:56 --> 00:01:04 to introduce you to today's guest. Welcome back
00:01:04 --> 00:01:08 to the AI at Work podcast. Today I'm joined by
00:01:08 --> 00:01:11 the wonderful Lee Senderoff, Chief Transformation
00:01:11 --> 00:01:15 Officer at Travelport. Now Travelport is one
00:01:15 --> 00:01:17 of those companies that many travellers rely
00:01:17 --> 00:01:21 on without ever seeing its name. It sits behind
00:01:21 --> 00:01:24 the scenes as part of the infrastructure that
00:01:24 --> 00:01:27 is powering travel booking across more than 160
00:01:27 --> 00:01:30 countries. And it is also connecting travel buyers,
00:01:31 --> 00:01:35 suppliers, agencies and platforms. And that role
00:01:35 --> 00:01:38 is becoming even more important as AI changes
00:01:38 --> 00:01:43 how people search, plan and book travel. because
00:01:43 --> 00:01:45 we're moving from simple searches like London
00:01:45 --> 00:01:49 to New York on fixed days to more natural requests
00:01:49 --> 00:01:51 such as find me somewhere warm for my family
00:01:51 --> 00:01:55 in the next six months and throw in activities
00:01:55 --> 00:01:57 for the kids and somewhere relaxing for the adults
00:01:57 --> 00:02:00 in the evening but this conversation today is
00:02:00 --> 00:02:03 about much much more than just travel because
00:02:03 --> 00:02:06 my guests will argue that AI should be understood
00:02:06 --> 00:02:10 as a workforce transformation not just another
00:02:10 --> 00:02:13 software tool And the real opportunity here is
00:02:13 --> 00:02:16 not confined to innovation labs or technical
00:02:16 --> 00:02:18 teams. It's actually about giving people across
00:02:18 --> 00:02:21 the business, the confidence, the tools and space,
00:02:22 --> 00:02:24 all these things that they need to change how
00:02:24 --> 00:02:27 work gets done. So we will cover everything today
00:02:27 --> 00:02:31 from enterprise -wide AI enablement, why small
00:02:31 --> 00:02:34 productivity gains can become major financial
00:02:34 --> 00:02:38 impacts at scale and how AI literacy is changing
00:02:38 --> 00:02:43 hiring and culture. and why human judgment still
00:02:43 --> 00:02:46 matters more than ever in an AI enabled workplace.
00:02:47 --> 00:02:49 But enough from me. Let me introduce you to Lee
00:02:49 --> 00:02:56 right now. So a massive warm welcome to the show.
00:02:56 --> 00:02:58 Can you tell everyone listening a little about
00:02:58 --> 00:03:01 who you are and what you do? Sure. Thanks for
00:03:01 --> 00:03:04 having me, Neil. Really excited to be here to
00:03:04 --> 00:03:06 talk everything about AI. So my name is Lee Senderoff
00:03:06 --> 00:03:09 and I'm the Chief Transformation Officer at Travelport.
00:03:09 --> 00:03:11 We are one of the largest travel companies you've
00:03:11 --> 00:03:13 probably never heard of. So think about when
00:03:13 --> 00:03:16 you go on American Express Travel and you book
00:03:16 --> 00:03:19 travel, we are actually the connection layer
00:03:19 --> 00:03:21 between that interface and actually getting you
00:03:21 --> 00:03:26 a ticket. And so we work in 165 countries and
00:03:26 --> 00:03:29 have employees all over the world and have just
00:03:29 --> 00:03:31 embarked on a really interesting AI journey.
00:03:31 --> 00:03:33 So I'm looking forward to sharing a little bit
00:03:33 --> 00:03:35 about it with you. I'd love to hear more about
00:03:35 --> 00:03:37 that journey. And it's incredible, isn't it?
00:03:38 --> 00:03:40 So you're almost the unsung heroes of travel
00:03:40 --> 00:03:42 and many people listening would have used you
00:03:42 --> 00:03:45 without even realizing, right? Correct correct
00:03:45 --> 00:03:47 that's exactly right where the infrastructure
00:03:47 --> 00:03:50 layer that powers travel Unbeknownst to the end
00:03:50 --> 00:03:52 user and it's becoming more and more important
00:03:52 --> 00:03:55 actually because the way people are searching
00:03:55 --> 00:03:58 today is changing dramatically I just actually
00:03:58 --> 00:03:59 heard a stat the other day. That was astonishing.
00:03:59 --> 00:04:03 So 42 % of people are now starting their travel
00:04:03 --> 00:04:06 journeys with some sort of LLM, whether it be
00:04:06 --> 00:04:10 Anthropic or Chatubti. And that search is fundamentally
00:04:10 --> 00:04:12 changing from, remember like, I don't want to
00:04:12 --> 00:04:15 date myself, but we used to search, right? New
00:04:15 --> 00:04:18 York to London, May 15th, coming back on the
00:04:18 --> 00:04:22 20th, right? And we used to receive, there were
00:04:22 --> 00:04:25 first -class tickets and there were economy tickets.
00:04:25 --> 00:04:28 That's it. Your bags came along with it, right?
00:04:28 --> 00:04:31 And now folks are going into now LLM and saying,
00:04:31 --> 00:04:35 I want to take my family of four on vacation
00:04:35 --> 00:04:37 somewhere warm. And what does that even mean,
00:04:37 --> 00:04:39 Neil? Because I was speaking to Canadians yesterday
00:04:39 --> 00:04:41 and warm for them is definitely not warm for
00:04:41 --> 00:04:44 me. Right. And so they're saying, and I want
00:04:44 --> 00:04:46 to go in the next six months and I want to make
00:04:46 --> 00:04:48 sure there's enough activities for my 10 year
00:04:48 --> 00:04:50 old, my 12 year old to do and for my wife to
00:04:50 --> 00:04:54 relax at a spa. Right. All of a sudden, like
00:04:54 --> 00:04:56 that whole world is just shifting fundamentally,
00:04:57 --> 00:04:59 right? Consumers want a completely different
00:04:59 --> 00:05:02 experience with the way they search. It's really
00:05:02 --> 00:05:04 hard for the travel industry to deliver on that.
00:05:04 --> 00:05:06 And at the end of the day, there's gotta be an
00:05:06 --> 00:05:08 infrastructure that powers all of that to actually
00:05:08 --> 00:05:11 get the booking to work, right? So beyond the
00:05:11 --> 00:05:13 search, actually getting to a point where you
00:05:13 --> 00:05:16 book the right ticket, you book the right hotel
00:05:16 --> 00:05:18 room. So we're doing all that behind the scenes.
00:05:18 --> 00:05:20 It's incredible. It really is. I would imagine
00:05:20 --> 00:05:21 that gets even more important when we're looking
00:05:21 --> 00:05:24 at plugging agents into things as well. And I
00:05:24 --> 00:05:27 must admit, my own habits have changed completely.
00:05:27 --> 00:05:29 I go to the US a lot for tech conferences and
00:05:29 --> 00:05:32 things like that. And usually when I land at
00:05:32 --> 00:05:34 an airport in the middle of nowhere, the first,
00:05:34 --> 00:05:35 the old way of doing things, I would Google it
00:05:35 --> 00:05:38 and end up with a page of sponsored results that
00:05:38 --> 00:05:40 don't really tell me anything. But now, of course,
00:05:40 --> 00:05:43 I just say, go to an AI agent and just say, what
00:05:43 --> 00:05:45 is the quickest, the cheapest options of getting
00:05:45 --> 00:05:47 there? And I'll get all the options straight
00:05:47 --> 00:05:50 away. and I'm not wandering around looking completely
00:05:50 --> 00:05:52 lost as I leave the airport. But I mean, you've
00:05:52 --> 00:05:56 argued that AI is actually a workforce transformation
00:05:56 --> 00:05:59 too. So what do businesses fundamentally misunderstand
00:05:59 --> 00:06:02 about the scale of change that we're talking
00:06:02 --> 00:06:05 about here? You know, I think it's interesting.
00:06:05 --> 00:06:07 I was at a PE conference yesterday and a lot
00:06:07 --> 00:06:10 of folks were talking about AI. And some of the
00:06:10 --> 00:06:12 conversations were around, well, we're going
00:06:12 --> 00:06:15 to use AI to solve this really big problem. I
00:06:15 --> 00:06:17 actually think that that's a really dangerous
00:06:17 --> 00:06:20 place to start because AI is going to solve many
00:06:20 --> 00:06:23 big problems. But inside of organizations, a
00:06:23 --> 00:06:25 lot of times the ROI on getting that big problem
00:06:25 --> 00:06:28 solved with just AI could take years to come
00:06:28 --> 00:06:30 to fruition. And let's be honest, the private
00:06:30 --> 00:06:32 equity industry does not have the patience for
00:06:32 --> 00:06:35 years. So I actually think, and from what we've
00:06:35 --> 00:06:39 seen, empowering people to do their jobs day
00:06:39 --> 00:06:42 to day with AI and taking it out of a tech team,
00:06:42 --> 00:06:45 just like you do with email, right? Email is
00:06:45 --> 00:06:47 a technical tool, but could you imagine if only
00:06:47 --> 00:06:50 the engineers were allowed to use it? Or if every
00:06:50 --> 00:06:53 time you wrote an email, it had to go through
00:06:53 --> 00:06:55 some form of governance before you sent it out?
00:06:56 --> 00:06:58 It would not empower you at all. And so we think
00:06:58 --> 00:07:01 about AI in that realm. How do we actually remove
00:07:01 --> 00:07:04 the drudgery, the annoying work from people to
00:07:04 --> 00:07:07 create more time and bandwidth for the creative
00:07:07 --> 00:07:09 thinking to solve the stuff you and I just talked
00:07:09 --> 00:07:12 about, right? How to present those better results
00:07:12 --> 00:07:15 to the traveler. So I think part of it is taking
00:07:15 --> 00:07:18 it out of just the technology team, even though
00:07:18 --> 00:07:20 there's of course a place for it there, but it's
00:07:20 --> 00:07:24 not the only place. And despite everything you
00:07:24 --> 00:07:26 said there and what we're talking about, many
00:07:26 --> 00:07:28 companies and many people listening in organizations
00:07:28 --> 00:07:31 are still in an environment that is treating
00:07:31 --> 00:07:34 AI adoption as something driven by the technical
00:07:34 --> 00:07:37 teams or innovation labs and those multicolored
00:07:37 --> 00:07:40 beanbags and foosball tables etc. So tell me
00:07:40 --> 00:07:43 a bit more why you believe that the real opportunity
00:07:43 --> 00:07:46 there actually comes from enterprise -wide enablement
00:07:46 --> 00:07:50 rather than those isolated AI initiatives. Let
00:07:50 --> 00:07:53 me give you an example. So we launched this training
00:07:53 --> 00:07:56 course for 75 people in our organization. We're
00:07:56 --> 00:07:57 an organization of about 2 employees, just
00:07:57 --> 00:08:01 to put it in perspective. We launched a training
00:08:01 --> 00:08:04 for 75 employees of all different levels, senior,
00:08:04 --> 00:08:07 senior level, junior, junior level. And we're
00:08:07 --> 00:08:09 broken out into cohorts and everyone experiments
00:08:09 --> 00:08:12 in a different way and shows off their work on
00:08:12 --> 00:08:14 this weekly call we have. And I had a middle
00:08:14 --> 00:08:17 level manager who came and said, I built an agent
00:08:17 --> 00:08:21 that saves me three hours a week. Okay. Wow.
00:08:21 --> 00:08:23 Three hours a week. That's great. This guy's
00:08:23 --> 00:08:25 super excited because now it's like it helped
00:08:25 --> 00:08:28 him do something with reporting that now he doesn't
00:08:28 --> 00:08:30 have to do manually. So I went back and I really
00:08:30 --> 00:08:32 like processed this and thought about it. So
00:08:32 --> 00:08:34 if the average employee, let's just say cost
00:08:34 --> 00:08:37 $80 an hour, right? So he saved three hours a
00:08:37 --> 00:08:40 week. Let's say he works 45 weeks a year. That's
00:08:40 --> 00:08:43 $10 . Okay. Nothing to write home about,
00:08:43 --> 00:08:45 right? That project would never make it onto
00:08:45 --> 00:08:48 a tech roadmap. But now multiply by a thousand
00:08:48 --> 00:08:53 employees. like just saved $11 million. And even
00:08:53 --> 00:08:55 if that's aggressive, I'll take a $5 million
00:08:55 --> 00:08:59 savings any day. I can put that money back into
00:08:59 --> 00:09:02 growth, hiring more people, supporting our customers
00:09:02 --> 00:09:05 better. That would never make it to a tech roadmap.
00:09:06 --> 00:09:09 And so that's how I look at this. If you empower
00:09:09 --> 00:09:12 people with the right tools and we can talk about
00:09:12 --> 00:09:15 sort of a framework that we're using to actually
00:09:15 --> 00:09:17 bring this to life. But now you're going to have
00:09:17 --> 00:09:20 these savings occurring throughout the organization
00:09:20 --> 00:09:22 and never having to even go through approval
00:09:22 --> 00:09:25 processes. So the time to decision, this isn't
00:09:25 --> 00:09:28 something that hits a product roadmap wish list,
00:09:28 --> 00:09:31 goes to a product team, is actually getting in
00:09:31 --> 00:09:34 the way of doing customer facing work because
00:09:34 --> 00:09:37 it's internal support work. So now that a lot
00:09:37 --> 00:09:39 of the internal support work can actually happen
00:09:39 --> 00:09:41 from the teams doing it, they're feeling the
00:09:41 --> 00:09:43 pain points and they can correct those pain points
00:09:43 --> 00:09:46 in real time versus having to wait for a technologist.
00:09:47 --> 00:09:50 That I think is where the magic unlock is. And
00:09:50 --> 00:09:52 before you join me on the podcast today, I was
00:09:52 --> 00:09:54 doing a little research on you and I love how
00:09:54 --> 00:09:57 you said that the biggest risk isn't under investing
00:09:57 --> 00:10:00 in AI tools. It's actually. failing to change
00:10:00 --> 00:10:02 how people work. And we've said for many years
00:10:02 --> 00:10:05 that there's nothing more damaging to an organization
00:10:05 --> 00:10:07 than, hey, we've always worked this way. Things
00:10:07 --> 00:10:09 have got to change. And I know you've shifted
00:10:09 --> 00:10:11 your own working culture. You mentioned you've
00:10:11 --> 00:10:14 got your own framework there. And just to bring
00:10:14 --> 00:10:16 to life, maybe share that. Because I'd love to
00:10:16 --> 00:10:19 inspire people listening on where they, if they
00:10:19 --> 00:10:20 followed in your footsteps, where they could
00:10:20 --> 00:10:24 be. Sure. And I want to caveat that, Neil, with
00:10:24 --> 00:10:26 one thing. I'm not an AI expert. I don't think
00:10:26 --> 00:10:29 many people are. This is so new. So I'm learning
00:10:29 --> 00:10:34 as I go and with just speaking to smart people,
00:10:34 --> 00:10:36 thinking about things in a little bit of a different
00:10:36 --> 00:10:38 way, and then adapting really quickly. So when
00:10:38 --> 00:10:40 this framework started, actually, it was pretty
00:10:40 --> 00:10:43 robust and really heavy. And I said, it's just
00:10:43 --> 00:10:45 not going to work. Like, let's just keep refining
00:10:45 --> 00:10:47 it, keep refining it. And we brought it down
00:10:47 --> 00:10:50 to really like three core parts. So one is building
00:10:50 --> 00:10:53 capability. So how do you actually impart the
00:10:53 --> 00:10:56 knowledge and the tooling on folks, right? So
00:10:56 --> 00:10:58 if you think about it, organizations pick today
00:10:58 --> 00:11:00 between being a Microsoft shop or being a Google
00:11:00 --> 00:11:03 Gmail shop, right? No one uses Gmail and Outlook
00:11:03 --> 00:11:06 in the same organization. And so sort of deciding
00:11:06 --> 00:11:08 what's that toolkit that your employees are going
00:11:08 --> 00:11:11 to have. And whatever is right for your organization
00:11:11 --> 00:11:12 and your needs, there are a variety of different
00:11:12 --> 00:11:15 tools out there. But figuring out what is that
00:11:15 --> 00:11:17 toolkit, who's the administrator, and treating
00:11:17 --> 00:11:20 it just like you would a Microsoft Office or
00:11:20 --> 00:11:23 a Google Mail. then it's actually teaching people
00:11:23 --> 00:11:26 how to use that tool. We take that for granted
00:11:26 --> 00:11:28 because email's been around for so long, but
00:11:28 --> 00:11:30 when it first came out, people didn't know how
00:11:30 --> 00:11:33 to use it. I still remember stories of CEOs that
00:11:33 --> 00:11:35 would have their assistants print out each one
00:11:35 --> 00:11:37 of their emails so they could read it. Then they
00:11:37 --> 00:11:39 would dictate back and the assistant would write
00:11:39 --> 00:11:41 the emails back like that doesn't happen anymore
00:11:41 --> 00:11:43 because everyone's working on their phones So
00:11:43 --> 00:11:45 teaching people how to use it is critical So
00:11:45 --> 00:11:48 that's sort of step one is to building the capability
00:11:48 --> 00:11:51 Step two is creating the right environment and
00:11:51 --> 00:11:55 this is this goes be to connecting the data correctly
00:11:55 --> 00:11:57 in a safe, secure way so that you're not putting
00:11:57 --> 00:12:00 your data, your customer's data, information
00:12:00 --> 00:12:03 at risk, but also figuring out how are you going
00:12:03 --> 00:12:05 to deploy these agents? Because what we're finding
00:12:05 --> 00:12:08 is now that we've empowered so many people, we
00:12:08 --> 00:12:10 have hundreds of agents. Which one should someone
00:12:10 --> 00:12:13 use? Which ones are the official agents of the
00:12:13 --> 00:12:15 company versus something that Neil uses to get
00:12:15 --> 00:12:19 his job done? And then supporting those folks.
00:12:19 --> 00:12:22 So I think one of the One of the things we've
00:12:22 --> 00:12:25 learned is when you build an agent that's powerful
00:12:25 --> 00:12:30 enough to support a pretty robust workflow, it's
00:12:30 --> 00:12:32 very difficult for 700 people in your organization
00:12:32 --> 00:12:35 to use that tool because it needs more infrastructure
00:12:35 --> 00:12:37 in order to be able to work correctly. This is
00:12:37 --> 00:12:40 where my tech team comes in. So I have a dedicated
00:12:40 --> 00:12:43 small tech team of folks who look at these. key
00:12:43 --> 00:12:45 projects and say, okay, this one needs a bit
00:12:45 --> 00:12:47 more technical support or we really want to make
00:12:47 --> 00:12:50 sure the prompts are right. And so they go in
00:12:50 --> 00:12:52 and work with the functional teams to support
00:12:52 --> 00:12:54 them launching these agents. So that sort of
00:12:54 --> 00:12:57 part two is creating the right environment for
00:12:57 --> 00:13:00 people to be able to thrive. And then part three
00:13:00 --> 00:13:03 is sparking the innovation and motivating and
00:13:03 --> 00:13:05 you'd be shocked like it how much fun people
00:13:05 --> 00:13:08 are having just with things like office hours
00:13:08 --> 00:13:10 just coming and showing what they build we're
00:13:10 --> 00:13:13 doing we have like a slack channel where people
00:13:13 --> 00:13:16 can just share like this is what i did today
00:13:16 --> 00:13:18 so it doesn't have to be some it could be as
00:13:18 --> 00:13:22 simple as i used it i built an agent to. save
00:13:22 --> 00:13:24 me three hours of work a week, but they're sharing
00:13:24 --> 00:13:28 and it's really peer -led versus top -down led.
00:13:29 --> 00:13:32 So those would be, that's my three -prong framework
00:13:32 --> 00:13:35 that we're implementing right now. I will, the
00:13:35 --> 00:13:37 other caveat to that is it'll probably keep changing,
00:13:38 --> 00:13:41 but right now this is working. So a special thank
00:13:41 --> 00:13:43 you to Danodo for supporting the Teptalks Network
00:13:43 --> 00:13:46 and helping us keep these conversations going
00:13:46 --> 00:13:50 because moving beyond AI pilots, all starts with
00:13:50 --> 00:13:53 connecting your models to trusted enterprise
00:13:53 --> 00:13:56 data. So if you're ready to move beyond AI pilots,
00:13:56 --> 00:13:59 Denodo can help you connect your AI models to
00:13:59 --> 00:14:03 trusted enterprise data in real time. So you
00:14:03 --> 00:14:07 can scale faster and reduce risk. So if you're
00:14:07 --> 00:14:09 interested in turning AI into business value,
00:14:10 --> 00:14:14 simply visit denodo .com. We are talking today
00:14:14 --> 00:14:17 in a period where AI proficiency is becoming
00:14:17 --> 00:14:20 both a baseline expectation but also somewhat
00:14:20 --> 00:14:24 of a cultural signal in the workplace. How do
00:14:24 --> 00:14:26 you see hiring practices and talent evaluation,
00:14:27 --> 00:14:29 how do you see these things changing over the
00:14:29 --> 00:14:32 years in the in the workplace as AI literacy
00:14:32 --> 00:14:36 and prompting engineering becoming essential
00:14:36 --> 00:14:39 skills? You know, I think it's interesting because
00:14:39 --> 00:14:41 it's gonna happen on both the recruitment side
00:14:41 --> 00:14:43 and on the candidate selection side, right? So
00:14:43 --> 00:14:45 on the recruitment side, could you imagine today
00:14:45 --> 00:14:48 if someone got sent a resume and said proficient
00:14:48 --> 00:14:51 in email? Email example because it's very obvious
00:14:51 --> 00:14:53 to people you're laughing Neil, right? Because
00:14:53 --> 00:14:56 if I saw an it I mean I laugh when I see proficient
00:14:56 --> 00:14:59 in Microsoft Word because if you're not right
00:14:59 --> 00:15:03 So, you know like right now we're still seeing
00:15:03 --> 00:15:05 like proficient in AI that's gonna go away Or
00:15:05 --> 00:15:07 you're gonna go to an interview and the recruiter
00:15:07 --> 00:15:09 is gonna ask, do you know how to use AI? That's
00:15:09 --> 00:15:12 what they're doing today. That's gonna go away,
00:15:12 --> 00:15:14 right? Because it's going to just be a part of
00:15:14 --> 00:15:17 how we work. And the magic is gonna be, how did
00:15:17 --> 00:15:21 you use it to drive results, right? So it's going
00:15:21 --> 00:15:25 to go from, oh, if you ask a salesperson today,
00:15:26 --> 00:15:28 how did you hit your numbers? And they said,
00:15:28 --> 00:15:31 I wrote a bunch of emails. You'd say this. This
00:15:31 --> 00:15:33 person doesn't know what they're doing. However,
00:15:33 --> 00:15:36 that person said, I wrote a drip campaign that
00:15:36 --> 00:15:38 helped me get front of mind of all of my target
00:15:38 --> 00:15:40 audiences. And I did this by myself with the
00:15:40 --> 00:15:43 marketing. That's smart, right? So the AI conversation
00:15:43 --> 00:15:46 is going to progress to that. How did you use
00:15:46 --> 00:15:49 an agent to improve your work? How did you use
00:15:49 --> 00:15:52 an agent to improve the KPI that you're responsible
00:15:52 --> 00:15:54 for in the company? And I think that we're just
00:15:54 --> 00:15:57 going to start to see it roll into your normal
00:15:57 --> 00:16:00 way of doing work. And it's not going to be necessarily
00:16:00 --> 00:16:02 called out as AI. It's going to be how you put
00:16:02 --> 00:16:05 it into practice. And then on the candidate side,
00:16:06 --> 00:16:07 it's going to be the same kind of evaluation.
00:16:07 --> 00:16:11 Is this a place that embraces change? Is this
00:16:11 --> 00:16:13 a place? And this is something I even saw 10
00:16:13 --> 00:16:15 years ago when I was doing digital transformations
00:16:15 --> 00:16:18 for companies. Is this a place that's going to
00:16:18 --> 00:16:21 embrace change or run away from it? And the top
00:16:21 --> 00:16:23 echelon of candidates are going to say, they're
00:16:23 --> 00:16:24 going to have their choice. And they're going
00:16:24 --> 00:16:26 to say, I want to work at a place that empowers
00:16:26 --> 00:16:29 me to be a better version of myself. And that
00:16:29 --> 00:16:32 includes being able to use tools that make me
00:16:32 --> 00:16:35 better at my job. And so I think it's going to
00:16:35 --> 00:16:39 be an interesting shift in both directions. Yeah,
00:16:39 --> 00:16:41 I completely agree. And it feels like there's
00:16:41 --> 00:16:43 an interesting balance emerging as well, where
00:16:43 --> 00:16:47 not using AI can be seen as a red flag, but overusing
00:16:47 --> 00:16:50 AI, not questioning what you're given can also
00:16:50 --> 00:16:53 raise concerns too. So how do organizations encourage
00:16:53 --> 00:16:57 healthy AI adoption without creating maybe dependency,
00:16:57 --> 00:17:00 a lack of critical thinking or losing that human
00:17:00 --> 00:17:02 judgment and creativity? Because again, bit of
00:17:02 --> 00:17:05 a balancing act. Complete balancing act and I'm
00:17:05 --> 00:17:08 definitely not somebody who advocates no humans
00:17:08 --> 00:17:11 at work by any any stretch of the imagination
00:17:11 --> 00:17:14 I mean look I think that it's it's it's a journey
00:17:14 --> 00:17:17 right and I think that it's also how do we remove
00:17:17 --> 00:17:20 the drudgery of the work and Create more space
00:17:20 --> 00:17:25 headspace and time for that creative Interesting
00:17:25 --> 00:17:29 innovative power that humans have I think so
00:17:29 --> 00:17:32 much work has for better or worse become rinse
00:17:32 --> 00:17:36 repeat work and work that doesn't necessarily
00:17:36 --> 00:17:38 challenge us. And so if we can create space by
00:17:38 --> 00:17:42 using AI for some of that, imagine what we could
00:17:42 --> 00:17:46 create. I think a lot about this because people
00:17:46 --> 00:17:48 ask me, we don't really know how to implement
00:17:48 --> 00:17:51 AI or we're gonna hire this consulting company.
00:17:51 --> 00:17:53 They're gonna come in and tell us how to use
00:17:53 --> 00:17:55 AI. Like save your money. Just go to a middle
00:17:55 --> 00:17:58 manager at your organization, a director level,
00:17:58 --> 00:18:01 and say, if you could hire one person, what would
00:18:01 --> 00:18:05 they do? And I bet you 50 % of what that person
00:18:05 --> 00:18:09 does, we could probably automate with AI. So
00:18:09 --> 00:18:12 it's not about, again, it's not necessarily about
00:18:12 --> 00:18:14 solving all the big, big company problems with
00:18:14 --> 00:18:18 AI. It's about thinking about, okay, there is
00:18:18 --> 00:18:22 work that folks should probably stop doing in
00:18:22 --> 00:18:24 order to, like I said before, create space for
00:18:24 --> 00:18:26 the work they could be doing. And that's how
00:18:26 --> 00:18:31 I view AI. And back to yourselves, I was reading
00:18:31 --> 00:18:34 that Travelport recently announced a new phase
00:18:34 --> 00:18:38 of accelerated growth as AI continues to reshape
00:18:38 --> 00:18:41 travel distribution. So on that side of things,
00:18:41 --> 00:18:43 how are you seeing AI changing the travel industry
00:18:43 --> 00:18:46 specifically? Both behind the scenes, operationally,
00:18:47 --> 00:18:49 and some of the things that myself and people
00:18:49 --> 00:18:51 listening won't get to see, and also in that
00:18:51 --> 00:18:55 customer experience itself that we do. Yep. So
00:18:55 --> 00:18:57 there's so many different ways, but if we go
00:18:57 --> 00:19:00 back to the search conversation we had before,
00:19:00 --> 00:19:03 that's shifting the infrastructure need significantly
00:19:03 --> 00:19:06 because the amount, think about the amount of
00:19:06 --> 00:19:09 search, that search queries that are going through
00:19:09 --> 00:19:12 to an airline today. So if I used to search London
00:19:12 --> 00:19:17 to New York, May 15th to the 20th, that was a
00:19:17 --> 00:19:20 pretty constrained search, right? Now I'm searching,
00:19:21 --> 00:19:23 I want to go somewhere warm in the next four
00:19:23 --> 00:19:28 months. Think about how many times that LLM needs
00:19:28 --> 00:19:32 to ping Delta, United, all these airlines in
00:19:32 --> 00:19:35 order to actually be able to get that needle
00:19:35 --> 00:19:37 in that haystack, which is the needle is the
00:19:37 --> 00:19:40 right flight for me. And every time they ping
00:19:40 --> 00:19:44 these networks, it costs them money. So how do
00:19:44 --> 00:19:46 we create an infrastructure layer that reduces
00:19:46 --> 00:19:49 that burden for all involved? And that's what
00:19:49 --> 00:19:53 travel port is working on. Exciting times and
00:19:53 --> 00:19:55 I think travel has always been an industry powered
00:19:55 --> 00:19:59 by huge amounts of fragmented data, suppliers,
00:19:59 --> 00:20:02 pricing models and indeed customer expectations.
00:20:03 --> 00:20:05 So from your work, how is AI helping simplify
00:20:05 --> 00:20:08 some of that complexity while also creating those
00:20:08 --> 00:20:12 smarter and faster retailing experiences? Yep.
00:20:12 --> 00:20:15 So I mean, I think, look, it's if you just take
00:20:15 --> 00:20:18 a very simple journey, go on a customer journey.
00:20:18 --> 00:20:20 I want to go to Dubai and I don't know what to
00:20:20 --> 00:20:23 do. I have three days in Dubai. What should I
00:20:23 --> 00:20:26 do? Amazing, right? Go into ChatGPT or Anthropic
00:20:26 --> 00:20:29 or any of these models and you'll get an amazing,
00:20:29 --> 00:20:31 I mean, I used to use these books, Lonely Planet.
00:20:31 --> 00:20:33 I don't know if you remember those. Yes, cool.
00:20:33 --> 00:20:35 I mean, just think about that and it used to
00:20:35 --> 00:20:37 be, they had to print a new version every year
00:20:37 --> 00:20:38 because the restaurants would change or new one
00:20:38 --> 00:20:41 would open or hotel would burned out, whatever
00:20:41 --> 00:20:44 it is, right? But now it literally, in a few
00:20:44 --> 00:20:46 seconds, I can get an itinerary. I can even say
00:20:46 --> 00:20:48 I want to go to a spa, I want to do shopping,
00:20:48 --> 00:20:50 I want to eat at the best restaurants, give it
00:20:50 --> 00:20:52 some parameters and you get the best itinerary
00:20:52 --> 00:20:54 you could have ever imagined three days in Dubai.
00:20:55 --> 00:20:57 Okay, but now you've got to book the ticket.
00:20:57 --> 00:20:59 Well, that gets more complex, right? That gets
00:20:59 --> 00:21:01 a lot more complex, especially if you're going
00:21:01 --> 00:21:04 across carriers. So let's say you're flying from
00:21:04 --> 00:21:07 Paris, you're going to Istanbul first, and then
00:21:07 --> 00:21:09 from Istanbul, you're gonna go to Dubai and not
00:21:09 --> 00:21:11 on the same carrier. That gets really complex.
00:21:12 --> 00:21:14 Our systems can't really support that. That's
00:21:14 --> 00:21:16 where travel port and the infrastructure systems
00:21:16 --> 00:21:21 come in. Now you're in Dubai and There's a war
00:21:21 --> 00:21:25 and you want to leave. What happens? The LLM
00:21:25 --> 00:21:28 can't actually help you leave Dubai. It can give
00:21:28 --> 00:21:30 you options. It can tell you, call the embassy,
00:21:30 --> 00:21:34 call your travel agent, call the airline, but
00:21:34 --> 00:21:36 it can't actually get you out of Dubai, right?
00:21:36 --> 00:21:41 And this is where the need for travel agencies
00:21:41 --> 00:21:44 and travel professionals really comes in. Now,
00:21:44 --> 00:21:47 the AI will make it easier for them to serve
00:21:47 --> 00:21:50 you as a customer because they can answer the
00:21:50 --> 00:21:53 phone faster or they can get information faster
00:21:53 --> 00:21:55 at their fingertips, but you still need that
00:21:55 --> 00:21:59 human layer in conjunction with the agentic layer.
00:21:59 --> 00:22:01 I think what we're going to see, just like we
00:22:01 --> 00:22:04 did when Google search came out, this convergence
00:22:04 --> 00:22:10 of human -led and machine -led information that
00:22:10 --> 00:22:12 together will make a much more powerful consumer
00:22:12 --> 00:22:16 experience. And I'd love to pull out a virtual
00:22:16 --> 00:22:19 crystal ball here. And if we were to look further
00:22:19 --> 00:22:21 ahead into the future, what do you think will
00:22:21 --> 00:22:24 separate those organizations that successfully
00:22:24 --> 00:22:27 build AI -enabled workforces from those that
00:22:27 --> 00:22:30 struggle to adapt both technically, culturally,
00:22:30 --> 00:22:32 operationally, and even competitively? Do you
00:22:32 --> 00:22:35 think that gap will quickly widen? I think it's
00:22:35 --> 00:22:38 widening today. So I think there's the financial
00:22:38 --> 00:22:42 aspect of it. So as I think about Writing a five
00:22:42 --> 00:22:45 -year financial plan for an organization 20 years
00:22:45 --> 00:22:48 ago I would grow my expense my operating expense
00:22:48 --> 00:22:51 line at a simpler rate that I would grow revenue
00:22:51 --> 00:22:53 That's going out the window for AI empowered
00:22:53 --> 00:22:55 organizations because of what we talked about
00:22:55 --> 00:22:58 earlier Can have one person with a few agents
00:22:58 --> 00:23:01 do the work of maybe what five people used to
00:23:01 --> 00:23:04 do So growth can come without the added overhead
00:23:04 --> 00:23:08 that he used to need Years ago and the organizations
00:23:08 --> 00:23:11 that get it right financially speaking Are going
00:23:11 --> 00:23:13 to be healthier in the sense that they're going
00:23:13 --> 00:23:16 to have more capital created by these efficiencies
00:23:16 --> 00:23:20 for marketing for innovation For paying higher
00:23:20 --> 00:23:23 salaries to the folks that are in tune with this
00:23:23 --> 00:23:25 world So financially speaking they're going to
00:23:25 --> 00:23:27 set themselves apart and that I think is going
00:23:27 --> 00:23:30 to happen actually relatively quickly I think
00:23:30 --> 00:23:32 the other thing is just talent recruitment like
00:23:32 --> 00:23:36 we talked about before the top echelon of talent
00:23:36 --> 00:23:38 is going to want to work at an organization that
00:23:38 --> 00:23:41 empowers them to be the best they can be. And
00:23:41 --> 00:23:43 so they're going to be able to attract better
00:23:43 --> 00:23:47 talent. That's another piece of it. And I think
00:23:47 --> 00:23:49 the last piece is that we don't know what's next,
00:23:50 --> 00:23:53 right? You said crystal ball. No one even imagined
00:23:53 --> 00:23:57 AI in what it is today, 20 years ago. I mean,
00:23:57 --> 00:23:59 AI was being used, machine learning was being
00:23:59 --> 00:24:03 used, but how it is packaged today for you and
00:24:03 --> 00:24:05 I to use, We didn't know and imagine this 20
00:24:05 --> 00:24:07 years ago. And so we don't know what's coming
00:24:07 --> 00:24:11 next, but it's organizations that are actually
00:24:11 --> 00:24:13 building this into the ethos of who they are
00:24:13 --> 00:24:16 that are gonna win because they're gonna be equipped
00:24:16 --> 00:24:18 to adapt to whatever's next. So it's not gonna
00:24:18 --> 00:24:21 be about I bought the most AI tools or I'm running
00:24:21 --> 00:24:23 the most experiments. It's going to be, did I
00:24:23 --> 00:24:27 build a culture of quick adaptation so that whatever
00:24:27 --> 00:24:31 comes at us next, we can take advantage of. So
00:24:31 --> 00:24:33 that's what I think is going to set the winners
00:24:33 --> 00:24:36 apart from the losers. I think that's a powerful
00:24:36 --> 00:24:38 moment to end on. But before I let you go for
00:24:38 --> 00:24:41 anybody listening, I think the key message here
00:24:41 --> 00:24:44 is organizations must stop thinking about AI
00:24:44 --> 00:24:46 as a technology tool and start thinking it as
00:24:46 --> 00:24:49 an enablement layer for the entire workforce.
00:24:49 --> 00:24:51 And anyone that wants to dig a little bit deeper
00:24:51 --> 00:24:53 on that, find out more information about the
00:24:53 --> 00:24:55 journey that you've been on travel port, keep
00:24:55 --> 00:24:58 a lookout for announcements and things you're
00:24:58 --> 00:25:00 working on there, or connect with you or your
00:25:00 --> 00:25:01 team. Where would you like me to point everyone
00:25:01 --> 00:25:04 listening? Oh, yeah, they can find me on LinkedIn.
00:25:04 --> 00:25:08 or at travelport .com. And yes, I'd welcome any
00:25:08 --> 00:25:11 questions. Awesome. I'll add links to everything.
00:25:11 --> 00:25:14 And as we spoke about multiple times today, many
00:25:14 --> 00:25:16 companies are still mis -framing AI as a tool
00:25:16 --> 00:25:19 for technical teams, but that real unlock is
00:25:19 --> 00:25:22 enterprise -wide enablement. And the risk isn't
00:25:22 --> 00:25:25 under -investing in AI tools. It's failing to
00:25:25 --> 00:25:27 change how people work. It's that beautiful mix
00:25:27 --> 00:25:29 of technology and people. And just a massive
00:25:29 --> 00:25:33 thank you for bringing that home today and really
00:25:33 --> 00:25:35 putting it in a language everyone can understand.
00:25:35 --> 00:25:37 Really appreciate your time. Thank you, Neil.
00:25:37 --> 00:25:39 It was so fun to be here with you and to talk
00:25:39 --> 00:25:42 about this. Although this was a technical conversation
00:25:42 --> 00:25:44 today, one of the things I love was just how
00:25:44 --> 00:25:47 practical and human it felt. Because AI at work
00:25:47 --> 00:25:50 is often discussed through the lens of a large
00:25:50 --> 00:25:54 transformation program, a big platform or expensive
00:25:54 --> 00:25:56 consulting project. But Lee brought it back to
00:25:56 --> 00:25:58 something far more relatable. What if someone
00:25:58 --> 00:26:01 in the business could use AI to remove three
00:26:01 --> 00:26:04 hours of repetitive work every week? On its own.
00:26:05 --> 00:26:08 That may never make a roadmap, but across the
00:26:08 --> 00:26:11 workforce, it could change the economics of the
00:26:11 --> 00:26:15 entire organization. And I loved exploring how
00:26:15 --> 00:26:18 AI is reshaping travel too, because as consumers
00:26:18 --> 00:26:21 continue to move towards natural language search
00:26:21 --> 00:26:25 and intelligent agents, the industry also needs
00:26:25 --> 00:26:28 infrastructure that can turn inspiration into
00:26:28 --> 00:26:32 actual bookings. And this is where travel ports...
00:26:32 --> 00:26:35 platform transformation and API strategy becomes
00:26:35 --> 00:26:38 especially important. Especially as travel retail
00:26:38 --> 00:26:42 becomes faster, more contextual and more complex.
00:26:42 --> 00:26:44 I think that message was clear. The companies
00:26:44 --> 00:26:46 that win with AI will be the ones that build
00:26:46 --> 00:26:49 a culture of adaptation. The ones that train
00:26:49 --> 00:26:53 people, create safe environments, encourage experimentation
00:26:53 --> 00:26:57 and still preserve that human judgement. So a
00:26:57 --> 00:27:00 big thank you to Lee from Travelport for joining
00:27:00 --> 00:27:03 me here on AI at Work today. Remember, you can
00:27:03 --> 00:27:06 find more at travelport .com. I'll also include
00:27:06 --> 00:27:09 the links for LinkedIn too. You can find me at
00:27:09 --> 00:27:12 techtalksnetwork .com. Remember, if you're attending
00:27:12 --> 00:27:14 any tech conferences, go to events. There's many
00:27:14 --> 00:27:17 that you can meet me there on too. But that is
00:27:17 --> 00:27:20 it for today. So thank you for listening as always,
00:27:20 --> 00:27:22 and I will speak to you all again very soon.
00:27:22 --> 00:27:23 Bye for now.