3015: Real-World AI Solutions: Appian's Path to Enterprise Innovation

[00:00:03] How can Generative AI reshape the way that enterprises handle everything from data management

[00:00:10] to customer service?

[00:00:12] Well, today we're going to be diving deep into this topic with Adam Glazer from Appian.

[00:00:19] They're a company that's leading the charge in low-code platforms and AI-powered automation

[00:00:25] as the driving force behind Appian's innovative approach.

[00:00:29] Adam brings a wealth of knowledge on how Generative AI is transforming enterprise solutions.

[00:00:36] From enhancing developer productivity to streamlining unstructured data processing, Appian's platform

[00:00:43] is proving to be somewhat of a game changer for businesses across multiple industries.

[00:00:48] So in today's episode of Tech Talks Daily, Adam will share how Appian's data fabric

[00:00:53] and AI tools are not only simplifying complex tasks but also democratizing AI usage and

[00:01:01] powering organizations to deploy sophisticated applications without needing an extensive data

[00:01:08] science team.

[00:01:10] And we'll also explore today real-world examples of how Appian's AI solutions are leading

[00:01:17] to significant productivity gains, cost savings and more efficient document processing, all

[00:01:23] while addressing the challenges of AI implementation.

[00:01:28] So what does it take to make AI a true force multiplier in the enterprise space and how

[00:01:34] can businesses leverage these technologies for success?

[00:01:38] Before we welcome our guest onto the podcast today, delivering daily content to 140,000

[00:01:45] of you wonderful monthly listeners across the globe is no small feat.

[00:01:49] I don't want to take all the credit here because it wouldn't be possible without the backing

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[00:02:43] That's kiteworks.com to get started today.

[00:02:46] But now it's time to dive into today's fascinating conversation with my guest.

[00:02:52] Well buckle up and hold on tight because no matter where you're listening in the

[00:02:56] world, it's time for me to beam your ears all the way to the US.

[00:02:59] Where Adam's waiting to join us today.

[00:03:02] So a massive welcome to the show.

[00:03:05] Can you tell everyone listening a little about who you are and what you do?

[00:03:09] Yeah, hi Neil.

[00:03:10] Thanks for having me.

[00:03:11] It's nice to meet you.

[00:03:12] My name is Adam Glazer and I'm the senior vice president of product management at Appian.

[00:03:19] And for those listeners who might not have heard of us, I won't fault you.

[00:03:23] Appian is a process automation platform and a Gardner recognised leader in the

[00:03:28] low code application platform space.

[00:03:30] In fact we were the first company to ever go public as a low code company.

[00:03:34] So that's where we spend a lot of our focus.

[00:03:36] And fast forward to 2024.

[00:03:38] Everyone's talking about AI, generative AI and what does this mean for me?

[00:03:43] What does this mean for our business?

[00:03:45] And right now on this podcast every day I try and get as many examples on here

[00:03:49] of how an organisation is leveraging it and the kind of value they're getting

[00:03:53] as a result.

[00:03:54] So to begin with to set the scene for our conversation today,

[00:03:57] how are you at Appian leveraging generative AI to address some of those

[00:04:02] productivity challenges that have let's be honest plagued businesses over the

[00:04:06] last decade and beyond?

[00:04:07] First of all no answer would would be complete without talking about data.

[00:04:11] Unless you're trying to get help naming your kids football team or plan a summer

[00:04:15] vacation, foundational models that are trained on the internet but know nothing

[00:04:19] about your business, its rules or the data are not going to actually help

[00:04:24] you achieve the promise of real productivity that that gen AI has the

[00:04:28] potential for.

[00:04:29] And that's why the first thing we did is we invested a lot in the data story.

[00:04:34] Enterprise data notoriously fragmented, difficult to bring together,

[00:04:39] difficult to make performant secure according to all the different rules.

[00:04:44] There's a lot of data sovereignty even within a large organisation.

[00:04:47] So Appian built a virtual virtualized data layer.

[00:04:51] We call it a data fabric.

[00:04:52] We got it patented and what that allows us to do is bring together

[00:04:56] data from disparate sources but address it as if it's cohesive, as if it's one

[00:05:01] data object in the system.

[00:05:04] And so with data fabric as the foundation AI can start to be really

[00:05:08] useful.

[00:05:09] So to more directly address your question, the way that I think about

[00:05:14] it, I don't mean to oversimplify the potential for AI and enterprise.

[00:05:18] One way to look at it is that it's a force multiplier.

[00:05:21] So it's a force multiplier for developers of applications.

[00:05:26] It's a force multiplier for the applications themselves and it's a force

[00:05:29] multiplier for users of applications.

[00:05:32] And Appian is investing in all three at the same time.

[00:05:35] We're investing in capabilities that help low code developers build and

[00:05:41] test and deploy their applications more quickly.

[00:05:43] We have invested in capabilities that those developers put inside of

[00:05:48] applications to do discrete tasks like structured and unstructured

[00:05:51] document extraction, personally identifiable information,

[00:05:55] detection, summarization, sentiment analysis, things like that.

[00:05:58] That can be part of a workflow.

[00:06:00] And we've invested in capabilities that are delivered directly to users

[00:06:04] of applications through chat or similar interfaces.

[00:06:07] Does that make sense?

[00:06:08] Yeah, 100%.

[00:06:09] And just to bring this to life and understand how everything fits in.

[00:06:13] I mean, ultimately Appian also sells solutions.

[00:06:17] So I'm curious, how do you decide what to sell?

[00:06:20] How to build it?

[00:06:21] And so ensure that you're providing the best solution for your client too.

[00:06:26] Yeah, that's right.

[00:06:27] In addition to being a platform, we have a number of AI-powered solutions

[00:06:32] in spaces where we have expertise, where we believe that we have

[00:06:36] a differentiated take on what's out there, where the combination

[00:06:40] of kind of pre-built capabilities, AI-powered workflows, rules,

[00:06:46] interfaces, integrations can be combined with the flexibility

[00:06:49] of the underlying platform to be extended and changed as necessary.

[00:06:53] Provides a real advantage to our customers.

[00:06:56] So in areas like government acquisition management, financial services,

[00:07:00] insurance, case management, we have seen a lot of traction

[00:07:05] at starting with a solution that gets you 80%, 90% of the way

[00:07:10] to the application you want.

[00:07:13] And then you have the power of the platform to make it bespoke,

[00:07:16] to make it unique.

[00:07:17] And as someone who is talking with customers all around the world

[00:07:20] across so many different sectors at a time where so many enterprises

[00:07:24] are struggling to bridge the gap between anticipation

[00:07:27] and implementation of Gen AI, I'm curious, you've put all that

[00:07:31] into one big melting pot.

[00:07:33] What are the most common barriers that you've observed

[00:07:35] and how can they be overcome?

[00:07:37] So I love this question actually because it gets at the heart

[00:07:40] of what I'm seeing, which is the interesting dichotomy between

[00:07:45] both unbridled expectations about AI and unbridled fear of AI at the same time.

[00:07:51] So on the expectation side, look, I think, you know,

[00:07:55] chat GPT kind of came onto the stage in late 2022

[00:07:59] and kind of set the world on fire and people's imaginations went off and racing.

[00:08:04] It's not a perfect technology.

[00:08:05] It's not going to be right all the time.

[00:08:08] It should not be given kind of the keys to the castle, so to speak.

[00:08:12] It has a lot of disruptive potential and it has a lot of productivity possibilities.

[00:08:19] But not harness properly, not provided with the right data,

[00:08:23] not secured properly, not perform enough.

[00:08:26] It's not going to necessarily lead to better outcomes that has to be carefully managed.

[00:08:31] And on the fear side, there was a lot of talk of AI

[00:08:35] taking jobs away of AI, exploiting your data, of AI,

[00:08:41] learning too much of becoming Sky Net and coming online.

[00:08:44] And I think those need to be managed as well.

[00:08:46] So when we talk to customers, we try to do both at the same time.

[00:08:49] We try to level set on expectations of what I can do

[00:08:54] and what it shouldn't be used to do.

[00:08:56] And we try to address some of the fears,

[00:08:59] some of the uncertainty around how you can use AI in an enterprise

[00:09:03] without running afoul of things that enterprises would be right to worry about.

[00:09:08] The privacy of their data,

[00:09:10] the uniqueness of their intellectual property and things like that.

[00:09:14] And a big question here.

[00:09:15] I mean, how does Gen AI also enhance the capabilities of internal chatbots?

[00:09:21] We've all had good experiences and bad experiences with them,

[00:09:23] but how is it enhancing their capabilities

[00:09:26] and what kind of impact have you seen on things like employee productivity as a result?

[00:09:31] It has been a game changer.

[00:09:33] Obviously, I mean, anyone who's ever gone to a customer support website knows

[00:09:37] chatbots are not new, but they have become so much more capable

[00:09:42] thanks to GenRiv AI.

[00:09:45] Is it OK if I give an example?

[00:09:46] Yeah, absolutely.

[00:09:47] Yeah, I think bringing it down to concrete really helped.

[00:09:49] So one of our customers is a large university here on the East Coast.

[00:09:54] And they built a generative AI powered chatbot to help their academic

[00:09:59] student academic advisors with case management tasks like

[00:10:03] understanding the students' history, creating action plans,

[00:10:07] suggesting agendas for student meetings, generating draft messages

[00:10:11] to send to students.

[00:10:12] And if you think about it, this was pivotal because they have like

[00:10:15] they have 50,000 students and only hundreds of advisors.

[00:10:18] And so the difficulty of tracking students and creating a real connection

[00:10:22] with them left them feeling overworked and the students feeling underserved.

[00:10:27] Just imagine trying to prep for a student conversation in five minutes,

[00:10:30] you know, between one meeting and the next.

[00:10:32] So we had a real productivity challenge.

[00:10:34] So it took them two months to build and deploy the solution.

[00:10:37] And as a result, they're able to leverage GenAI both to save

[00:10:41] the advisor's time and to improve the quality of student support.

[00:10:45] So they have a person there.

[00:10:47] It's a vice president of digital transformation, which apparently is a

[00:10:50] real role in academia these days.

[00:10:52] And she asserts that this will actually improve graduation rates.

[00:10:57] So a lot of people talk about GenAI and the enterprise as, you know,

[00:11:00] increasing the bottom line or things like that.

[00:11:02] But here's an example of GenAI really doing some social good.

[00:11:06] And although it's AI getting all the headlines in our news feeds,

[00:11:09] as you said a few months ago, it's actually all about data.

[00:11:12] So in what ways are you happy and helping enterprises convert

[00:11:17] unstructured data into actionable insights?

[00:11:20] And are there any other real world examples of success that you've seen in this area?

[00:11:25] Yeah, yeah, I've got I've got tons of examples.

[00:11:27] I think it's the best way to understand it.

[00:11:29] So prior to GenAI, your unstructured data was kind of the scourge

[00:11:33] of achieving the benefits of automation.

[00:11:36] Because if you think about things like emails or contracts or photographs,

[00:11:41] the variability was too much for traditional OCR methods to handle.

[00:11:46] So organizations resorted to using human capital to power those processes.

[00:11:51] Then then GenAI comes along and it turns out that's very good in this space.

[00:11:55] So by making these AI capabilities readily available to developers

[00:11:59] of enterprise applications, we're really unlocking the potential

[00:12:03] for the tremendous business value in terms of insight and efficiency.

[00:12:07] So I'll give you an example.

[00:12:09] One of our customers, a very large financial services company

[00:12:12] was grappling with a very manual and time consuming process

[00:12:15] for handling case creation issues, failures and whatnot.

[00:12:20] So emails would come flooding in, alerting users to issues

[00:12:23] and the team had to manually triage each one.

[00:12:27] So this is a bottleneck that obviously hindered the efficiency

[00:12:31] of these case workers, but also customer satisfaction,

[00:12:35] which we know is critical to any service oriented company.

[00:12:38] So to address this, they implemented a process using Appian's AI powered

[00:12:44] email classification skill, which automatically identifies the reason

[00:12:48] for case creation failure or challenge and eliminates them.

[00:12:52] The need for manual review.

[00:12:54] And so now they can act much more quickly on these issues, resolving them faster.

[00:12:58] They can handle a bigger case load and they get better customer experience

[00:13:02] results to boot.

[00:13:03] And the cool part about this story is that we actually track

[00:13:06] the performance of these skills.

[00:13:09] They were able to achieve 98% accuracy on classification

[00:13:12] with only providing 220 or 230 sought training examples.

[00:13:17] So a little bit of effort by a non data scientist, a low code developer

[00:13:22] was able to achieve this kind of revolution in productivity.

[00:13:28] And as a result, AI is rapidly transforming customer service

[00:13:32] operations all around the world right now.

[00:13:34] So how is Appian's approach, would you say, be different from

[00:13:38] conventional methods and what kind of benefits are you seeing here?

[00:13:43] So the way that I like to think about it, Appian is kind of a next

[00:13:47] generation development platform.

[00:13:49] Appian and others like us for the modern cloud native enterprise with

[00:13:53] AI at its core, which is to say AI is not a feature.

[00:13:57] It's a cross functional capability.

[00:13:58] So one way of several that our approach is different is that,

[00:14:04] by being a low code platform, we're lowering the barrier to entry

[00:14:07] of adopting AI because low code is faster and more accessible

[00:14:12] to less technical audiences than traditional software development approaches.

[00:14:17] So effectively we're opening the doors for more AI powered applications.

[00:14:21] We've seen large enterprises, Fortune 2000 enterprises

[00:14:25] deploy AI powered applications without having a data science team

[00:14:29] or using a single data scientist.

[00:14:32] And that's part of the promise of low code is that it abstracts away

[00:14:35] and it elevates the capability so you don't have to worry about

[00:14:38] the underlying complexity but you can still avail yourself of the benefit.

[00:14:42] So by embedding the LLM capability into the platform,

[00:14:46] a lot of the details that would plague a development team if they tried

[00:14:49] to build such an application from the ground up like,

[00:14:52] which foundational model should I use or how do I connect to it

[00:14:55] or how I secure the data is taken care of by the platform itself.

[00:15:00] So it has the benefit, in my opinion, of really democratizing

[00:15:03] GenAI usage for a whole new generation of application developers.

[00:15:07] And as I mentioned at the start, we're also extremely focused

[00:15:11] on making it fast and easy to connect to all that latent enterprise data,

[00:15:15] bring it together, rationalize it and then unleash the value

[00:15:19] of generative AI on top of it.

[00:15:21] And I think one of the most exciting aspects of GenAI for me

[00:15:25] is removing some of those pain points from corporate life

[00:15:27] and anyone who is still trying to find an email hidden in a subfolder

[00:15:31] somewhere or a document that they don't know where it's saved

[00:15:36] is one of those pain points that we'd all love to see the back of.

[00:15:38] So can you share how Appian's GenAI tools are actually turbo charging

[00:15:43] things like document extraction and classification processes,

[00:15:47] all those pain points and what this means for data management

[00:15:50] and analysis and all those great things that we can take for granted.

[00:15:55] Yeah, yeah, absolutely.

[00:15:56] I mean, we've been doing document processing as part of our platform

[00:16:00] for years using more traditional OCR services,

[00:16:03] but now with the advent of GenAI,

[00:16:06] it's being given even more flexibility and more power.

[00:16:09] We have one customer who's automated insurance quotes

[00:16:12] and the range of document formats that they get from their network

[00:16:15] of brokers is just so diverse that traditional OCR methods didn't work.

[00:16:20] So GenAI lets them handle all those one-off variations

[00:16:23] so they could get a standard output and makes the downstream processing

[00:16:26] of those quotes much more efficient.

[00:16:30] You know, the way I like to think about it,

[00:16:32] as I mentioned earlier, I don't think GenAI is a feature.

[00:16:34] It's a capability and the best solution

[00:16:37] if you really want to turbo charge these types of use cases

[00:16:41] is to have a mix of tools, document processing tools

[00:16:44] so you can apply the right method to each situation,

[00:16:46] whether it's structured or semi-structured or unstructured.

[00:16:50] And GenAI makes it possible to handle even more of those use cases.

[00:16:54] So another example, a really good customer of ours

[00:16:57] is a US-based fire protection manufacturing company.

[00:17:02] And before using GenAI, their accounts payable team

[00:17:07] processed what it was highly manual and inefficient.

[00:17:10] So they had 11, 12 people handling over 10,000 invoices

[00:17:14] on a given year and order acknowledgement

[00:17:18] in email-based slow error-prone processes.

[00:17:21] So then they brought in document classification

[00:17:23] to classify the documents based on vendor priority.

[00:17:26] Then they apply document extraction on top of that

[00:17:30] to get the account payable information out.

[00:17:33] Then they use other capabilities in the platform

[00:17:35] to automatically search their accounting system,

[00:17:37] which happened to be Microsoft Dynamics

[00:17:38] and compare the data against all the different records

[00:17:42] and automatically update it when they find matches

[00:17:43] or, as is important with some of these AI-powered processes,

[00:17:47] bringing the human into the loop

[00:17:48] where they actually didn't find a match.

[00:17:50] And when we talked to them, they estimated direct savings

[00:17:53] in terms of time spent on their manual document processing

[00:17:57] of something like seven or 8,000 hours per year

[00:18:01] and they're going to get additional discounts

[00:18:03] by paying on time and ingesting invoices more quickly

[00:18:07] to the tune of something like half a million dollars a year,

[00:18:09] again, to that bottom line argument.

[00:18:11] So they've really seen the benefit

[00:18:13] of having multiple types of AI

[00:18:15] that they can bring to bear on a diverse set of documents

[00:18:19] in a specific use case.

[00:18:21] And there are many business leaders

[00:18:23] and people listening to our conversation today

[00:18:25] currently trying to make sense of what GenAI means

[00:18:28] to their organization, how they can use it, et cetera.

[00:18:32] So with the vast potential of GenAI

[00:18:34] that we're all talking about here,

[00:18:36] what would your top recommendations be

[00:18:39] for a business leader from an organization

[00:18:41] looking to unlock its value

[00:18:43] and drive meaningful transformation?

[00:18:45] Any tips or advice that you would offer there?

[00:18:48] Well, the first thing I would say is

[00:18:51] get educated about the strengths

[00:18:53] and practical applications of generative AI.

[00:18:57] It's really useful to have a goal,

[00:18:59] a business outcome that you're hoping to achieve.

[00:19:02] If you roll the clock back to early last year,

[00:19:06] a couple months after ChatGPT launched

[00:19:10] and blew everyone's mind,

[00:19:12] I can't tell you how many discussions I had

[00:19:14] with enterprise leaders that went something like,

[00:19:17] I need to do something, anything with generative AI.

[00:19:21] This is a board level initiative, right?

[00:19:22] So they were looking at it as a solution

[00:19:25] in search of a problem.

[00:19:27] Now, if you roll the clock forward,

[00:19:29] the conversation has changed.

[00:19:30] People are coming to us saying,

[00:19:31] we need to reduce processing time

[00:19:33] or we need to increase customer satisfaction.

[00:19:35] How can Gen AI help?

[00:19:37] And I think that's a really positive step

[00:19:38] in a very short amount of time,

[00:19:40] probably faster, a faster evolution

[00:19:42] of people's understanding

[00:19:43] about how to grapple with a new technology

[00:19:45] than I've seen in many different technology waves.

[00:19:48] Organizational leaders have largely moved past the fear

[00:19:52] that Gen AI is gonna replace their workers,

[00:19:54] which as I mentioned earlier, it won't.

[00:19:56] The way I like to talk about it is,

[00:19:59] Gen AI isn't gonna be the hero of the story,

[00:20:01] but it definitely can be one of the heroes

[00:20:03] of the search for powers.

[00:20:04] And so being able to recognize it

[00:20:06] for what role it has in a transformation,

[00:20:10] in a process, in application

[00:20:11] and what role it doesn't is critical

[00:20:14] to getting off and running with the potential for Gen AI.

[00:20:18] 100% with you.

[00:20:19] I'd also say, don't believe everything it says.

[00:20:21] I was doing a little research for an article earlier

[00:20:24] and I was trying to find a stat

[00:20:25] on exactly how many users a platform had

[00:20:28] and my Google results were saying

[00:20:29] anywhere between eight and 15 million.

[00:20:32] So I went to Claude AI and said,

[00:20:34] can you confirm how many users his platform has?

[00:20:36] And it said 50 million.

[00:20:38] And I said, okay, can you give me a reliable source

[00:20:41] for this 50 million stat you just find?

[00:20:44] And he actually replied with,

[00:20:46] I've just realized I don't have a reliable source

[00:20:49] for the stat I give you as I mentioned,

[00:20:52] which, honestly, it just lied to me there.

[00:20:56] It doesn't even hide it.

[00:20:57] But I'm curious, looking back at your experiences here,

[00:21:01] what are the funniest or most interesting stories

[00:21:03] that you've come across?

[00:21:06] Oh gosh, I don't know that I have a great,

[00:21:10] a funny AI story just yet.

[00:21:14] I mean, I'll tell you an interesting story.

[00:21:17] Something that made me appreciate

[00:21:19] the kind of work we get to do.

[00:21:21] We, I can't say what customer it was,

[00:21:23] but we had promised a customer a pretty important feature,

[00:21:26] something I try not to do as a platform company

[00:21:30] except when it's really critical to that customer.

[00:21:32] So I promised them a feature that they needed

[00:21:34] as part of their critical business processes.

[00:21:37] I promised it on a certain date.

[00:21:38] You know, I had all the estimates in hand

[00:21:40] and we blew it big time.

[00:21:42] I mean, if you know software,

[00:21:44] you know that estimates are only ever wrong

[00:21:45] in one direction.

[00:21:46] It's the unfortunate direction.

[00:21:48] And so this customer,

[00:21:49] they were obviously quite concerned,

[00:21:51] but we shifted into working very closely with them.

[00:21:54] I gave them weekly status updates.

[00:21:56] We were on calls with them all the time.

[00:21:58] And we're worried the customer's not gonna renew.

[00:22:01] They're really upset with us.

[00:22:02] When we finally delivered the feature,

[00:22:04] they flew up to our headquarters

[00:22:06] and they threw my team a pizza party

[00:22:08] for delivering that feature.

[00:22:10] And what they said was, nobody's perfect.

[00:22:12] We struggle with this as well.

[00:22:14] And it taught me the difference between being

[00:22:17] a software vendor and a technology partner.

[00:22:20] And I'll never forget that.

[00:22:22] Absolutely lovely.

[00:22:23] And for anyone listening,

[00:22:24] let's just enjoy our conversation today.

[00:22:26] Maybe they wanna dig a little bit deeper

[00:22:28] and feel a little bit relieved that there is help out there.

[00:22:31] What's the best place for anyone to find you,

[00:22:34] your team online or just find out more information

[00:22:36] about anything we talked about?

[00:22:38] Yeah, thanks.

[00:22:39] So you can learn about Appian.

[00:22:41] I've mentioned some of our capabilities

[00:22:43] throughout this conversation

[00:22:44] and our platform and solutions on our website,

[00:22:47] which is www.appian with two P's,

[00:22:50] a-p-p-i-n dot com.

[00:22:52] And you can find me and connect with me on LinkedIn

[00:22:54] by searching for Adam Glazer, G-L-A-S-E-R, and Appian.

[00:22:59] And I'll come right up.

[00:23:00] Well, as I said at the very beginning of our conversation,

[00:23:03] stay a lot of hype around this technology at the moment.

[00:23:06] But hearing your stories about boosting employee productivity,

[00:23:10] converting unstructured data into actionable insights,

[00:23:14] streamlining and personalizing customer service operations.

[00:23:17] But more than anything,

[00:23:18] I think it was those real world use cases

[00:23:20] that brought everything to life today.

[00:23:22] So big thank you for sharing your insights.

[00:23:25] No, my pleasure. It was great talking to you.

[00:23:27] So as we heard from Adam today,

[00:23:29] the potential for generative AI in the enterprise space

[00:23:32] is immense.

[00:23:34] And Appian's approach to low-code platforms

[00:23:36] and AI enhanced automation

[00:23:39] is opening new doors for businesses,

[00:23:42] ultimately making it easier for them

[00:23:44] to harness the power of AI

[00:23:46] without getting bogged down in complexity.

[00:23:49] So from transforming document processing

[00:23:51] to improving customer service operations,

[00:23:55] Appian's real world implementations,

[00:23:57] I think demonstrate that AI can be a powerful tool

[00:24:00] for both efficiency and innovation.

[00:24:04] And that's where the magic happens.

[00:24:05] It's so much more valuable than the hype

[00:24:08] that we often see in here.

[00:24:09] And a big thank you to Adam for highlighting the importance

[00:24:12] on focusing on specific business outcomes

[00:24:15] when implementing AI,

[00:24:16] reminding us that yes, technology is powerful,

[00:24:19] but it is the strategic application

[00:24:22] that truly makes the difference.

[00:24:24] And as you and your organization look to the future,

[00:24:28] integrating AI into your processes is going to be crucial.

[00:24:32] And I'd love to hear your thoughts

[00:24:34] from today's discussion.

[00:24:35] Will you implement anything that you've learned today?

[00:24:38] How can you unlock the potential of AI

[00:24:40] to drive your business forward?

[00:24:42] Maybe you're doing something different already.

[00:24:44] Maybe you'd like to share that with me.

[00:24:46] As always, email me, techblogwriteroutlook.com,

[00:24:49] Twitter, LinkedIn, Instagram, just at Neil's CQs.

[00:24:53] But please stay tuned for more insights

[00:24:55] on how technology will continue to shape

[00:24:58] the future of work and beyond by tuning in tomorrow.

[00:25:01] I'm going to be here with another guest

[00:25:03] hoping to speak with you.

[00:25:05] But thanks for listening as always

[00:25:07] and until next time, don't be a stranger.