Speaker 1It's not your fault that your agentic AI systems are acting outside of compliance. There are just too many data sets to guardrail them all. But with Denodo, you can now organize your hundreds of data sources into one layer and govern your agents with a single approach. Try it now with Denodo by visiting denodo.com to learn more. What separates an AI experiment from a production system that can be trusted with regulated financial work? Well, my guest today is Tom Carey, president of Broadridge's global technology and operations business. Let's just say my guest Tom was ahead of his time. He was studying AI in the 1990s when finding a commercial use for it was considerably harder than finding another reason to use a mainframe. But today, he's helping financial institutions connect AI with the platforms, data, and operational workflows that keep markets moving. So we will discuss why firms should simplify a process before automating it, how human review fits in production workflows, and why smaller agents might be easier to control than one agent that is responsible for everything. And Tom will also share lessons from Broadridge's, work on everything from email traffic, token costs, and agent governance. So what does it take to move AI from impressive demo into dependable work? These are just a few of the things we're going to explore today. So enough from me. Let me introduce you to Tom right now. So thank you for joining me on the show today. Can you tell everyone listening a little about who you are and what you do?
Speaker 2Well, first of all, Neil, thanks very much. Thank you for the invite. Great to be here. I'm Tom Carey. I'm head of our products and technology at Broadridge. I probably should introduce Broadridge a little bit as well. So Broadridge is the leading fintech. We are the infrastructure around governance, capital markets, and wealth. So for any of you working with us, our clients and the market, we are that back end of the infrastructure. We do sort of things at scale, and we try and help the industry modernize, electronify, and be as efficient as possible. And what we do. So my role, I actually have two caps. I lead product and technology, and I also run one of our divisions as well. So I look after our capital markets and wealth management groups. So I sort of have a good sort of what I call sort of intersection between what's really happening in the markets and what's happening in sort of the technology evolution of what we do as well. Interesting fact for you. I'm obviously over 50. I have actually a degree in AI. So back in the 90s. In a bizarre scenario, I decided I'd like to do AI as a degree. It was absolutely no use coming out of university to find a job in AI. I almost got a job at a very large automobile company, but they shut down the AI division because they couldn't find a use case for it. So it's been parked for like years and years and years. And it's been really interesting to see the sort of the evolution of compute power plus as well, sort of the, obviously the large language models and the new stuff coming out now that's suddenly like reborn AI because. You know, I'm sure most of our readers know, you know, the coining of AI as a phrase goes back to 1956 and the Dartmouth College Conference, which McCarthy sort of coined the phrase and it it's lived in the wilderness for 40, 50 years as unusable technology. But now the hardware side of things, the compute power plus the new models is like creating a fascinating sort of world. So I look forward to it and we sit in this world of innovation now where Broadridge is powering forward innovation for clients really through a number of models. A number of aspects, platform with number one in our viewpoint of creating a ubiquitous platform for our clients to work against. AI number two, and then there's a big topic in our market around tokenization, digital assets, which we are leading in as well. So there you go, a quick little tour of me and Broadridge.
Speaker 1What an incredible story. I've got to ask, did you get any cool dad points from your kids there? Because having a degree in AI before, and they probably thought it's just a thing that appeared three years ago. That's got to give you some kudos.
Speaker 2You know them, you obviously don't know my kids. No, I wish, but they were, they were like, yeah, whatever. And they, they like, yeah, but you, I mean, they did joke like, yes, but you used to use a big mainframe. We aren't, my little iPhone now is better than your mainframe. So they're, they're kind of condescending on that. But I do, I do, I explain to them, well, I mean, it's a, it's a very interesting degree, just to, I've got a BA, I haven't got a BSc because exactly in psychology, philosophy, linguistics, and computing. Because you were learning about how machines allegedly think at that point in time. Yeah. So there's a very famous book, computers, computers and thought, I think it's called, which was sort of the Bible back then as well. But yeah, no, I'm afraid, I'm afraid I wish, I wish I got credit notes, but I don't, I just get a request from my bank account details.
Speaker 1Incredibly cool. I've been to what, 15 tech conferences this year from Egypt to Vegas, and the theme across every single one of them is agentic AI and agents in just about every industry. And we will have many podcasts. Many people listening, attending these same conferences, but curious about, how's it actually going to work in their world? And one of the reasons I was excited to get you on here today is Broadridge says agentic AI is live across post-trade and wealth workflow. So I'd love to bring this to life a little today. What does production ready mean when the software touches regulated financial operations and millions of transactions? I'd love to try and bring this to life today.
Speaker 2Yeah, let's try and do that. I mean, it's. Definitely beyond prototype, because obviously you talk to a lot of firms and they're sort of embedding either prototypes or basic models, or they've, you know, not, again, this is table stakes, but they've embedded a chat agent or some kind of, you know, accelerator into their product. We've really been looking at sort of the workflows of financial services and when you can actually apply agents and when you shouldn't. And we've, we've been lucky in this because we're lucky or strategically. Sensible in this, we have, we're a technology company at heart. So, you know, the, the, the major part of our business line is, you know, SaaS based technology that's driving, you know, multiple clients every day of the week. So we see a lot of flows and we see a lot of patterns and obviously, you know, AI loves patterns and flows, but we, we don't, we don't take client data and use it at all, but we see the floats. We see the patterns we're fortunate. We also have an operations group, so that's people who are. Actually touch keyboards and actually do operational work. And we do that for about 50 clients. So we can see the operational flows in capital markets and wealth, and actually fingers on keyboards of what people do. So we can marry the technology with our operations and sit back and go, what are these real flows and how can we actually look at those and automate those? And what is the benefit net for ourselves and for our clients as well? So we've been looking at that, you know, and we'll talk about it a little bit later. So like email automation, for example, it's a classic use case that many firms have jumped on effectively. Yeah. But then where do you take it from there into the next step of taking that output from an email and automating the next level of response? And how do you trigger that effectively? So we've got agents embedded in our, in our operational workflows already, and we've been able to test those ourselves. So that's why we're very confident that we've actually got, you know, production grade workflows worked out. We're increasing the number we've got as well. So we keep actually building out the portfolio and looking at those and we've been looking at productivity on scale. Maybe I step back and sort of give you the view of how we think about sort of productivity within Broadridge. Maybe that helps a little bit in terms of setting context. I've sort of heard it as four levels. One, you have this personal productivity. So you've got, you know, your favorite age, your favorite tool, be it, you know, open AI or Claude or some other model where you're, you know, personally going in each day and saying like, oh, I need help on researching this. And it comes back. I can give you some great insights more for you. If you take that and just publish it, because obviously it makes mistakes and it's got its own viewpoints, but that's level one. And that's sort of basic productivity. Level two is where we start to patterns in personal productivity that then we can actually start to actually replicate to other people and say, this is actually a great tool. Start using it. And we have a great innovation function. Our firm that says here is now a tool or service or plugin. If you want to call it that. Or skill that everyone can use. And again, that sort of starts to power and generate sort of productivity across the enterprise. You then get into the production flows. And you've got my first use cases where you've got production flows where the human stays in the loop. Okay. And we're a big believer in the human's going to stay in the loop. We're not going to have the complete autonomous machines yet. But you've got operational flows where the agent does something for the human in the operations. It does the work, brings the results back. And then the human verifies or checks effectively. That's my third level of sort of automation. And that's where you've kind of focused our agent policy at the moment in that regard. Then the final level is where you've got agents and you can just let them loose and you can actually, that's a bad word, let them loose. But in a controlled way, put them into your production environment in a controlled framework. And the human is not in the loop because they're running every day. And just like you would run as code, they're running as code effectively. Yeah. But within a platform. one framework that gives you all the controls and service you need to make sure that what they do is accurate doesn't drift effectively and is in place so that's kind of what we've been doing and i say we we've been working with our clients on these use cases as well and then push them into our own bpo and operations group to prove they actually work and for many
Speaker 1people listening they'll have operational processes that still depend on inboxes documents and manual handoffs so the question i've got to ask on their behalf is how do you convert all that work into an intelligent workflow without removing the controls that those handoffs are carrying yeah that's a great
Speaker 2question and obviously it's people's concern a little bit around this whole evolution of control and obviously most of our clients are regulated we have regulated pieces ourselves so you want to be able to demonstrate from an audit control viewpoint that whatever you're deploying is accurate and correct and i think you know i'll talk about a little bit later maybe the obviously the concern on you know intelligent ai is there's drift there's different responses etc but i think one of the things that i see is that ai is a catalyst for change and what i mean by that it's actually prompting people to really look at what they do and in my experience to date if we take a process that we do take email for example it's your first step there when you truly step back as an organization you're not going to be able to do that because you're not going to be able to do that you're not going to be able to do that you're not going to be able to do that you're not going to be able to do that you're not going to be able to do that you're not going to be able to do that and look at what you're doing you start to actually realize that what you're doing isn't the most logical thing and it applies to all firms globally i think yeah so the very first step actually and i'll call it to lean the process out i don't mean going down a whole two-year lean project but to actually step back and say why in the world do i get 15 000 emails a day and you will find i think most friends will find that actually there's a lot of waste or wonder in the in the term in what they do yeah so in our use case you know we eliminated 30 of our email traffic into one of our groups simply by looking at what it was and realizing it was duplicates it was non-response items it was things that really we should have not been doing effectively and so you get an immediate process benefit from that and in my raw mass and this is my mass rather than broad you know when you step back and you really commit yourself to look at a iifying a process you'll get about half your benefit your true benefit from actually leaning out what you do today you'll step back and realize like we have this four eyes process in this step but then we do it again another step later so you can actually get a really simplified flow from looking at it and actually looking at really what you're doing the next step is there's a lot of automation that is classical ai always classical automation is not the final layer of intelligent ai and you'll get maybe 30 from that effectively you're of your benefit and then the last 20 is sort of the you know the large language model these sort of non-deterministic models in what you do and that's where you get you know experiment was a really cool tech etc but it's that it my my theory on this is actually that cool tech is forcing us to look back at what we do and forcing us to really rethink what we do and what we're doing and what we're doing and what we want to do and realize that yeah there's a lot more i can actually automate now with technology there's a lot more i can lean out and then you can apply you know your smart stuff at the very end for the really clever stuff and then you can really dig into it and say what do i really want our industry is mostly made out of rules it's not made out of like you know human mind deep thought around things so it does lend itself to like more classical sort of agents that are sort of deterministic rather than probabilistic in my in my viewpoint so that's kind of what we're learning as we go and again we're you know no one can claim to have the answer because we're what 18 months into this journey so it's it's kind of like everyone will learn a lot more and there'll be different models that come out that change our viewpoint but that's what i'm seeing in the market
Speaker 1and i think for many people listening they'll be scrolling down their news feed or doom scrolling and seeing so many horror stories around token spend and roi from ai projects etc so i'd love to try and restore the balance in the universe here because i was reading before you joined me today that at broadridge you've had immediate operating cost reductions from your managed model so where did those benefits appear first and how do you test the the savings of not being shifted i've not shifted risk elsewhere yeah
Speaker 2and i don't think this is unusual in a new technology yeah you know i think i think i have a parallel to this in sort of the you know the cloud movement people have deployed workloads in the cloud and just shifted workloads from a distributed infrastructure or a mainframe shift into the cloud and then were shocked three months later that their then you know cloud bill was x multiples higher than their traditional run and then what's happened and the real the real reality is they hadn't architected for a cloud environment so they were posting you know they're pushing terabytes of data back and forth between one frame and a cloud that's not how you architect a cloud so i think there's a lot of step back into like how do you actually then do agentic ai and ai services in a way that's actually cost effective and as you said don't you don't get the i would say the you know the operational saving and then you contribute it towards your you know token bill at the back end we think people have been defined as well as of what it's happening there so first off we have confidence because we have the group and we test it in the operational group in a lot of cases so we can really go in there and say you take this model or you take this new new product line and you go and test it and we can actually see what happens with that model and see what happens. Baselining is always important and it's been true for every business opportunity since I've been in business you need to baseline your costs you need to baseline what you do and have an accurate view of that effectively. If you don't have that then yes you'll wake up in 12 months time and you'll be facing like hmm it doesn't actually provide economic return on what we do. We can accurately forecast that in our models and I say more models to model basic like you're in our sort of business case templates we know what to put into those models and see how they come out and look we for our token usage particularly for ourselves we've got really maniacal on looking at token usage. As well because it's a it's so easy to suddenly have people you know putting the whole workflow through you know an agent and finding out the the bill for that is higher than their salary and you've had horror stories I think you heard them on sort of you know developers running out of tokens you know on day two of the month and then be like okay what do we do now? The answer is you can't you can't send all your source code to what you should to you know this and expect it not to come with a huge cost. So we're very deliberate in that in terms of measuring our token usage. We're not perfect by the way so we are dealing with the same issue but we're actively going through that and saying okay you know one of my groups last month and this is technology you know you could see that token spend was escalating. The reason give you a good example the default configuration in that group was to use the latest model from this vendor this latest model is more expensive. It might be better but it's more expensive. So we had to realize okay we've got to change the configuration in that group so that it stays fixed to the current model and we agree when to upgrade them or change them. And it's nothing no fault of the developers or the technicians in that group simply that our configuration allowed the latest model to be adopted as default. So there's a lot of learnings come from this but I say I don't have it any different to the cloud environment or other innovations we've done in the past. We've done a lot of work or mainframe to distributed but it's the same by the way of you just have to get religion on managing your costs looking at the patterns understanding it once you're there you'll manage it. And I say we can talk about other things as well because obviously there's going to be you know the open weight models and differences there too but I think it'll evolve the cost base as well going forward.
Speaker 1Yeah great point I think so much has changed so much has made that stayed the same and we will have people listening in financial institutions that could be interested in the differences between a managed service and a platform it operates itself when they're exploring agentic workflows. Any tips or advice or things to look out for when making a decision like that?
Speaker 2Now very good question. I think I think it's moved away from not it's not about control. A lot of the conversations are about I need to control this. I don't think that's the sort of the language we're facing. It's also true that every company will end up with an agentic framework. Yeah so I don't think there's any any thought of any firm that I will outsource my whole agentic control framework to a third party. Everyone will have their own. So the interoperability between these agentic frameworks is going to be really key. Yeah so you know you've heard about MCP services but then there was A2A, ADK etc. There's loads of things coming out and our mission we've got our platform. So we have a let me step back a little bit. We have a core platforms in orderage that is our data model, that is our APIs, that is our UX layout, that is our control framework. we made the decision to embed our ai framework into that platform and that's really important for us because suddenly ai ai services become part of our core infrastructure and not a separate utility on the side effectively so it means that anyone coming in they can come into our apis or they could come in via mtp services the control of that and how it works is all in the same environment and we'll get the benefits and scale from that so number one i think everyone will have an agentic framework so there's no point competing or please come to my agentic framework but there is going to be how do our agentic frameworks collaborate and work you then get into sort of what are your benefits what are you trying to do and a lot of cases if you're doing work that every other firm in your sector is going to do then that is kind of a bad news case in my book and this is what i talk to our clients about because why do you want to apply a lot of intellect a lot of intelligence to your business and why do you want to apply a lot of intelligence to your business and why do you want to apply a lot of intelligence to your business and why do you want to apply a lot of intelligence to your business and why do you want to apply a lot of intelligence to your business knowledge a lot of time for something that your competitor or your peer is going to do themselves on the same basis so we we sort of encourage the use case where where you where you have something that's going to be at scale and industry-based you should probably outsource that or use a managed service where you where you create alpha revenue or where you think it's truly differentiated or where you think you will do a ton of different changes and you're unsure of the model that is where you probably should build yourself effectively and that's kind of the proposition that i generally talk to our clients about of you know use use the third parties and broadish is a great example of that and a trusted partner in doing that and then think about where you want to really differentiate what you do and deploy your valuable it resources in doing that you know we thrive on being trusted i'd say that's where our value prop is for broader issues we do things in a very trusted way and we're going to do that in a very trusted way we've been pushing on a transformation agenda as well but you know people come to us because they know that we're accurate and reliable in what we do and that's who you're looking for in a third party the final point i make is none of this is cheap while there's you know while there's the 10x developer cost is outside the 10x sort of efficiency cost of like i can be 10x more efficient the reality is it still costs a lot to innovate we touched on token costs earlier infrastructure costs have been going up repeatedly you know that's a been a really interesting phenomenon in this market that the cost throughout compute is actually getting more expensive effectively when you add it all up in terms of all the service costs behind it so as you think about it there's a whole roi effect on this and you've got balance choosing to develop and support for the long term versus trusting a partner to do it for you
Speaker 1and i'm curious from your experience and the journey that you guys have been on what is the first workflow that typically gives a financial institution the best chance of proving value while also preserving human exception handling accountability and regulatory evidence because i think that low-hanging fruit if they could get something off the ground quite quickly improve value then the buy-in and adoption and everything will follow but which is the best workflow to begin with though well
Speaker 2it's interesting i mean you need to my vision on this stuff is you need to pick something that's relatively high bargain yeah because you need up data i'll be really boring and so you need to pick something that's highly rules-based which kind of goes against against the theory of like i'm going to use intelligent ai to do all my stuff and think i don't believe in that i believe you actually want to experiment with something that's really tightly controlled and repeatable yeah you need to learn and you need to actually experience how your agentic model will work and how your framework will work and there's nothing better than having a deterministic agent in my book eventually i'll talk about this maybe coming up later as well about how smaller agents are better than how small agents are better than how small agents are better than how small agents are better than big ubiquitous agents in in the sort of the world i think do you choose something that's not got a complex decision model in terms of how it works you look at something where the human can verify what the outcome is or or you can write more code to verify what the outcome is so do you know what the input is and do you know from the deterministic flow what the output is if you know that you can build a test harness around it and actually verify your results it's so complex that the outputs are like so varied that you can't actually understand all the all the potential outputs and the humans can't like really deeply think about outputs really bad use case at the moment because you just don't you then you can't actually prove to yourself how it works so that's that's how we think about it that's where we focused on like high volume deterministic or rules-based traffic known outputs put it to work demonstrate it work then you can start to deep dive into the more nuanced cases where you think there's you know really opportunities for thinking models and other things so you know post tray great resolution validation of exceptions account maintenance top level customer inquiry and i mean that's the top level in terms of you know oh we get i keep a very simple use case here you know we get a thousand password resets a day okay well some of that is has to be done in a very simple way and i think we go for a human because the person's actually caused a cyber exception i can cyber exception but you know what i mean a security exception they need to be re-verified but a vast majority of it is the person just really can't get through the current manual automated process for resetting so we're dealing with that so those kind of ones are great because you can actually cut your teeth on those and then really prove your framework works or what a lot of people have to do at the moment is prove their agentic framework works okay so it's less about those that it's about do i have a kill switch do i have an ability to actually monitor do i have audit and control do i have risk exceptions do i have policies and this whole sort of industry around sort of your framework is actually probably the harder piece coming out than actually doing some of these agents in their own right and the last thing i'd say is i am finding somewhat bizarrely that some people are putting a higher standard to ai than to humans which i think is interesting so i've had lots of conversations not internally but with some of my dialogues in the industry around how do i know the agent that's 100 right and my response back is how did you know the human was 100 right so there's sort of that interesting dialogue going on around that and again i agree with the hypothesis you need to control the agent you need to be audited and see it but i think there's a different standard been set to a certain degree in some places around that again i'm not suggesting we should let agents loose but there's it's an interesting dichotomy of human human proof versus agent proof and when
Speaker 1moving from not just one agent but having several agents all collectively contributing to one operational process how do you preserve that clear chain of responsibility for decisions exceptions and customer outcomes i suspect this is a another question you get a lot too yeah
Speaker 2and it's back it's back to the genetic framework again yeah i'm afraid it really is having your framework in place i touched on it earlier we we've got a big belief in creating relatively small agents so we don't believe in the sort of someone taking a big workflow and codifying it into a big agent if you like yeah that does multiple steps we think that's very very difficult to administer it probably doesn't have a long shelf life and your audibility of it gets very very difficult particularly if you are using llm in the middle of it to make determinations etc so we're much more into building smaller agents that we train together in their operations and if you think about it let's let's go i'm going to go for a bizarre scenario here with you you know if you trained an agent to make your coffee in the morning okay don't write an agent that's going to do every step in the process and go through boiling the kettle yeah filling the filling the cup with a bit of coffee pouring the coffee in adding milk etc build each step with an agent and the reason why i say that is because you then can have control over what happened so if suddenly you're suddenly let's get to keep it to coffee or example bizarre is your coffee comes out lukewarm rather than hot you know it's boiling the kettle that's probably the root cause yep versus i don't know what happened now somehow it's come out that way and then you have to start introspecting with the agent and what happens so we we like that then i'm going to bizarre copy examples now but i've for this one maybe you can maybe when you get a bit more advanced you can put a large language model in to learn what coffee type you like in the mornings so on a weekday you want a really strong double espresso effectively yeah because you're you want to be wired for your start of your day at the weekend you want the more mellow thing it can probably learn that over time and actually have the intelligence in it but then there could be drift in that or you may change your personal preference preferences as well so the ability to sort of codify the day you're at a sort of a granular level where you can actually see what they're doing and inspect them i think is the big thing that we're looking at ourselves and then of course you've got controls behind all that as well and your ability and that's that's going to be the huge thing for this marketplace is that when we do have which is undoubtedly going to happen in in the global industry not just financial services issues with agents and you've seen you know the hugging face dialogues around what happened there you're going to be able to inspect a lot of the things that a lot better than you were in the past and you'll be able to shut things down so or change as well so that's our that's our theory on how you get agent flow and how you build sort of smaller more component based agents
Speaker 1brilliant and we started our conversation today talking about when you got your degree in the 90s in ai and we're now coming full circle so i'm going to ask you i'm going to pull out my virtual crystal ball and ask you to look forward five years now and i realize when i say that out loud just how crazy that is because the amount of technological change we've seen in the last three to five years is insane but if you did look into the future where do you see ai in financial services
Speaker 2well it's very difficult as you know because if you'd asked about agentic ai in let's call it september last year yes i'd have given you a very different answer yeah uh you know the way the models evolved since november i'll call it you you'll know what happened in november in terms of certain third-party release it's certainly changed the mindset of what goes on but let's jump forward i think a few things on my mind one is that you know the classical use cases of chatbots search routines report agents they'll be ubiquitous in every application going okay because it's just going to be embedded in and you'll you'll just it'll be much more like your experience you're used to on your iphone in terms of yep it's standard why wouldn't i expect that so the differentiation that was created maybe two years ago is people launching a load of services around sort of that kind of service will disappear entirely there was a conversation around the future of ux as well so the user interface experience and also a lot of firms advertising a headless experience now in terms of their converting their services headless and i see that as really something that will evolve because one of the things that ai has become very very good at is actually taking ux from prototype to production really quickly we've seen that ourselves so we the ux used to be the mammoth job of the firm to like if they wanted to reskin it or rechange it was always a big big exercise we're seeing that really been a power enabler in sort of ai so we think there'll be much more choice in the ux experience that people choose or it'll be agent for agent conversations rather than ux to ux conversations as well so i think that that you'll see evolve a lot in that space the big question probably for me is where regulations will go so you know there's a you might theorize there's a lot more happening on ai behind the scenes with the big players than we know yeah the models we're the models we're using you know they have other models and they have more advanced models so you've got a real question around government sort of regulations be that in the us be that in europe etc and what that all means constraint on what we can and can't do and it's probably going to take some issue to actually really push that agenda as well so you may see over the next five years of sort of like a acceleration in some markets and a step back in some markets too as they realize that there's we have to have more constraints on what's happening and that will come properly i think if there's an issue you know if there's suddenly a you know a financial issue with the use of ai in what it does i think you know it's probably going to be a big issue for the rest of the world so it's probably going to be a big issue for the rest of the world so i think you know it's probably going to be a big issue for the rest of the world so i think you know it's probably going to be a big issue for the rest of the world this this needs more regulation or this needs more control and what happens effectively i think the the last piece we'll see in my view is the models themselves there'll be a democratization of the models so models the models won't be the competitive advantage they've been sort of like in an arms race of late if you like in terms of who's best at what when you actually look at the stats they're getting very very competitive advantage so i think the last piece we'll see in my view is the models very close in their behaviors it's going to be about how you actually use the model in in your business and what you actually have as specialist data in your knowledge framework if you like and how you've actually exploited that data for the best advantage for your clients and for the industry as a whole so we're really thinking about putting ai to work within the enterprise sort of connecting it for our platforms protecting data at all costs and that's got a real key thing in terms of how we think about that and delivering that responsibly and i think that responsible delivery the evolution of the models regulations and a much more move potentially towards agent to agent conversations between firms is kind of the way it will shift five years is sounds like a long time it's obviously not a long time in the markets in reality but we've seen a rapid acceleration in the technology you said over you know 18 months let
Speaker 1alone three years yeah and i think that is a key moment to end on and i cannot thank you enough for joining me today we especially for walking people listening in financial institutions how they can move from ai beyond experimentation into meaningful enterprise scale deployment so many big takeaways but for anyone wanting to learn more about you about broadridge continue the conversation you started today where should they go
Speaker 2well great question thank you for that as well and by the way thank you for hosting as well i think broadridge.com has our corporate website and gives you a lot of information around that it provides a good source for that we are available to talk as well so we're having a lot of dialogues with our clients and the market around that so please reach out to us you can reach out to me directly or to or to our links as well our linkedin site has a lot of information too and we're here to help power the industry our role here as we see it is to help firms get more efficient more resilient in what they do and to share the knowledge about what we're doing and i said before you know we do see a world of agentic frameworks sat in every firm you know and it's how we collaborate on what the best models are the best approaches are so we're here to help and we're here to serve
Speaker 1also and we have covered a lot today and i think the way that you've brought the topic of moving from ai pilots to ai at work making a real difference and what agentic ai looks like in production some of those big savings that you had on your journey as well will really bring this to life for people listening resonate with a lot of people and we're here to help people so i will add links to everything that you mentioned here i encourage people listening to check that out but again thank you for bringing all this to life in a language everyone can understand appreciate you
Speaker 2been great thank you very much pleasure
Speaker 1i think tom's four levels of ai productivity offer leaders a useful way to assess where they are today shared tools can spread these gains and controlled agents can begin handling repeatable workflow steps while people can verify the outcome full automation all that can come later once the controls monitoring and evidence can finally support it and another lesson i think is that the cleverest model is rarely the whole answer because firms need to understand the process establish a cost baseline protect their data and decide where building something themselves can create a real advantage and sometimes that first win comes from removing duplicate work before an agent even touches it you can learn more about broadridge at broadridge.com find tom on linkedin big thank you to him for joining me but where would you place your organization on those four levels of ai productivity let me know at techtalksnetwork.com you'll also find links to everything that my guest left there let's continue this conversation but i'm afraid we're out of time now time for me to go i'll be back again real soon with another guest thanks for listening as always bye for now
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