AI and Enterprise Platforms – Transcript

Joe Nathan:

Gen AI has a ton of potential new capabilities and has a lot of opportunities. If vendors don’t start adopting Gen AI, or start embedding Gen AI capabilities in their platforms, there’s a big risk of losing their competitive edge.

Announcer:

Welcome to The Hackett Group’s “Business Excelleration Podcast.” Week after week, you’ll hear from top experts on how to achieve Digital World Class performance.

Kyle McNabb:

Artificial intelligence, or AI – from machine learning, predictive analytics, and now generative AI – is today’s transformative and disruptive challenge and opportunity. Enterprises can now reimagine how work gets done, how experiences get delivered, and how decisions get made, and even how their workforce needs to be reallocated, thanks to how technology increasingly mimics the way we all make decisions and work.

Now, enterprises have reengineered and standardized their functions on operations – on core technology platforms for decades. Today, we see these core technology platforms supporting finance, HR, procurement, marketing, IT, sales, and more, innovating with AI capabilities and promising new value to their customers.

This podcast explores the impact of AI on these core enterprise platforms. We’ll discuss key trends, benefits, challenges and what the future holds for organizations looking to leverage AI solutions in their operations. I’m Kyle McNabb, vice president of Research at The Hackett Group. Joining me today are Rob Donahue, associate principal in our Transformation Practice and leads our Enterprise Platform Strategy, and Joe Nathan, associate principal in our Transformation practice and leads our work in cloud and AI. Now, to start off with, I think lost in much of the noise and promise of AI is that organizations have billions of dollars invested in skills and operations based on core enterprise platforms. Joe, let’s start with you. What are these platform vendors doing today with AI, and why are they doing it?

Joe Nathan:

First, thanks for having me here, Kyle. As you mentioned, there’s so much that we hear about the promise and wonders of Gen AI solutions. We hear a lot of compelling use cases around Gen AI. Like, for example, Gen AI creates new product designs, it writes articles for you, it does research, it predicts outcomes. So this generates a lot of curiosity and interest as well in the market. And naturally, when this interest comes in – when there’s so much of good use cases around Gen AI – it creates a compelling situation for product vendors to start embedding Gen AI. We actually call this embedded Gen AI where you have Gen AI capabilities embedded in large platforms of products. So we call it embedded Gen AI.

And what these platforms typically help provide is it provides the same benefits that you typically get from native Gen AI solutions, like say you get improved efficiencies. It helps you with customer engagement. It provides better security and compliance. It helps you make key additions as well. We also need to make sure that how do we distinguish this from native Gen AI solutions? In native Gen AI solutions, you start building applications. Here, you have things served to you in a platter. Applications are Gen AI capabilities already built and embedded in your solutions. It’s a lot more easy to integrate this to your enterprise platform. And the best, you don’t require AI development skills.

It also comes with standard security and that’s a big concern today. So when we go into the detail, I’ll speak a little more about how security helps and how embedded Gen AI solutions or what product platforms provide comes with standard security. It’s a lot more easy to use.

And just talk about some of the areas where platform vendors are doing or using Gen AI specifically around say having some advanced chatbots that predicts what you’re trying to say and without having a user in the loop, it tells you what you should be looking at, provides the right results, and it provides excellent data analytics insights. It also optimizes processes for you. Just like a human would do it, it looks at your current state, tells you it provides good recommendations on how we should be optimizing processes and so on. It also provides a lot of personalized customer experiences. When we go into the specific use cases, we’ll walk you through some examples, but probably these are some of the great things that Gen AI does for platform vendors.

Kyle McNabb:

Thanks, Joe. Rob, build on that for us. What are some of the different functional and process areas that you’re seeing becoming more AI-enabled?

Rob Donahue:

It’s a great call up to talk about that because when we say AI, it’s getting a lot of press right now really around the generative AI and the ChatGPTs and OpenAI and Copilot. But AI and machine learning has been within our enterprise platforms for several years now, but they’re becoming much more functionally focused and process focused as they’re getting AI-enabled, and bringing in the embedded generative AI platforms.

So the areas we’re seeing them in our business platforms or enterprise platforms are around powered BI and powered business analytics and intelligence platforms within our CRM applications. Joe already mentioned the chatbots, so customer engagement for marketing and customer service or even within our internal services from an HR services standpoint and IT services where chatbots can become the help desk or the frontline standpoint, interacting with our end users or our customers is a big area that’s being pushed by AI and generative AI.

But also, we’re seeing just within the tool sets out there now in the ERPs and the other enterprise platforms, they’re embedding AI just in the interaction with the applications. Such as recently, we’ve seen Copilot embedded inside of Microsoft Dynamics where a user who’s looking for specific help can chat with Copilot to ask about help that’s specific to their implementation of Dynamics, and that’s just going to expand over time. That was quickly put in, and that’s really moving the needle forward, and the other vendors are all showing generative AI within their tool sets.

So I think what’s really going to happen across functions – such as sales, procurement, finance, and those other SG&A functions – the way we interact with the platforms is really going to change. It no longer is going to be so much more transactional for key activities, especially around analytics and how we look at data and think about data, but it’ll become much more conversational.

Kyle McNabb:

Thanks for that. Now, you mentioned earlier the noise around generative AI may lead people to believe that there’s a lot of new and immaturity out there, but tell us more. Are there specific functional or process areas that you think and see are more mature than others, and why?

Rob Donahue:

From a machine learning and automated process orchestration standpoint, you think about it, supply chain, some of the bleeding edge supply chain tools and supply chain planning has been using machine learning for a long time to really predict buying patterns and needs. Same thing with procurement around category management in those areas. Machine learning has for a long time been helping people understand where they should be spending their time and money when it comes to those very value-added processes. Advancements in supply chain or procurement and improvements in those areas really drive bottom-line results for companies, and vendors have focused for the last few years on really putting machine learning and some automation to drive those areas out there.

The next step is what’s happening with generative AI where it’s now going to learn on the data, and instead of the human having to make some of the decisions based on how the data’s being returned by the application, the application’s going to make direct and actionable recommendations.

I really see in areas like supply chain where a supply chain manager or leader for supply chain planning, instead of asking for a report from their different supply chain analysts, and then getting some data back, and then getting perspectives on the data from the team, they’re going to ask the AI a question about, “Hey, what is the best place to push my supply chain forward? Where am I losing optimization that’s really causing me inventory drain or cost leakage?” They’ll be able to ask those conversational questions, and answers will come back instantly versus having to get data in a report, and then internally doing those analytics.

Kyle McNabb:

We’ve talked a little bit broadly about different categories of supply chain, business intelligence, etc. Rob, do you have examples of particular providers and their AI capabilities?

Rob Donahue:

Yeah. If you think about just from general AI, if you look in times of the supply chain standpoint, if you look at the Kinaxis world of tool sets they have, they’ve always been a leader in that space and what they’re doing with generative AI. But the more packaged vendors – the SAPs, the Microsofts, the Oracles – are catching up quickly and bringing that machine learning into their space. And those vendors that are more of the ecosystem vendors, the bigger vendors – the Oracles, the Microsofts, the SAPs – they’re putting so much energy and money behind developing generative AI tool sets for those processes like supply chain’s a really good one.

I bring up supply chain because it’s such a needle mover for companies in terms of really driving the bottom line. But finance is another huge process that’s going to have a big lift in terms of where AI is going to play in as well in terms of how we interact with our data, and how much more we’re going to let transactions flow through and let the machine do the transacting, and then we’re dealing with exceptions versus dealing with transactions, and that’ll be a key area that’s being hit as well.

And really, I think the big three vendors are certainly hitting the areas hard in their core ERP platforms, and then extending to the edge. And then your key vendors in terms of the Kinaxis vendors, your Blue Yonders and the warehouse management, and those spaces are always pushing those tool sets in. And then from a finance standpoint, if you look at companies like HighRadius, which really sits on top of your finance world, they’ve been using machine learning for almost a decade on top of their tool set to really advance it pretty far. And now, they’ve taken the next step further where it’s becoming much more an interactive model and really driving things forward to really get efficiencies in that area as well.

Kyle McNabb:

Joe, Rob just spent a little bit of time talking about the impact in supply chain, finance and other functional areas. Now you’ve got particular affinity for core IT with cloud and work that you do there. Where are you seeing AI being used in IT?

Joe Nathan:

IT platforms certainly integrate a lot of Gen AI capabilities. Again, it’s not going to be as exciting as supply chain or finance examples that Rob was mentioning, but I’ll try my best to talk about some of the areas where IT is really gaining from Gen AI.

So again, as I mentioned, so AI analytics certainly a big area. Automation is another area, and also the intelligence that Gen AI provides – that’s an area where Gen AI is being used in IT platforms. And specifically, if you look at developer experience, that’s become a huge area within IT organizations. And how are you going to make sure that you provide that compelling experience to developers? How are you going to make it exciting for them? How do you also make it easier and smoother for them to work? Those are things that some of the Gen AI platforms are providing.

IT operations management is another big area. So this is more around how do you, especially, this is going to be a combination of both predictive analytics and Gen AI that comes in. It automates your AI operations. It also predicts what’s going to happen even before it happens and starts looking at how do you self-feed. Those are the areas where it looks at.

Monitoring platforms is also another big area where Gen AI solutions are being helpful. Cloud management platform is another big area where you have a lot of Gen AI capabilities, specifically around how do you make sure that without having the human in the loop to start optimizing our cloud platforms, Gen AI certainly helps in doing some of that work for you.

Rob Donahue:

You bring up a great point on IT isn’t as exciting in terms of what AI is doing because it’s really behind the scenes, but you bring up a great point about how the predictive nature and the proactive nature that the AI tools are bringing to the tool set is really going to change things. Because if you talk to anybody about how they want to improve their ITOM or ITSM, it’s being proactive, finding a problem before it happens or monitoring and management. As these AI tools advance and can predictively or proactively see problems before they happen, and also potentially act on them or alert on them, is going to really, I think, change things and really improve the overall delivery of these platforms around IT operations in those areas. And that’s a key callout.

Kyle McNabb:

Yeah, I think some very good developments that have taken place within IT. So Joe, maybe some examples like we asked Rob earlier, IT platform providers and some of their key API capabilities?

Joe Nathan:

As Rob mentioned, right, so every vendor right now is trying to provide some kind of form of Gen AI capabilities in their platforms, in fact, because otherwise there’s risk of losing out. And if you look at some of the notable ones, right? Again, it’ll be a miss if I don’t start with Microsoft Copilot. So this is something which Microsoft has providing Copilot Gen AI capabilities and different facets of their software solutions suite – be it GitHub – which helps in the developer experience. It looks at code automatically. It helps you with code – with pair programming. It just helps you improve your developer productivity. It looks at, as the developer is working, it provides suggestions on, OK, this is what you’re doing. This is probably a better way to write this framework, or why don’t you use reuse this framework that someone from your organization has already written? So it provides you some of those capabilities, and that’s from a developer standpoint.

The other part of Copilot is more from a workplace experience or employee experience standpoint. So we all probably use the practice suite that Microsoft provides – PowerPoint or Word or any of the Excel spreadsheets. So Copilot has some of these capabilities enhanced, more pronounced in Word and PowerPoint. And from what I’ve heard from Microsoft Spreadsheet, they’re working on that as well. So that will certainly make it a lot more easier.

ServiceNow is another great platform. So they have a lot of chatbots. They have some AI searches. They’re making a lot more intuitive. And because benefit that ServiceNow is doing is their self-service or also the self-heal side of it, so can we predict challenge problems that are going to happen and start working toward closing it out or starting providing alerts? So those are the benefits that it’s providing.

I know I started with Microsoft and ServiceNow, but I would say that every platform is trying to do this. There’s no clear winners or losers established at this point. Every vendor is still trying to reinvent themselves with new Gen AI capabilities. BMC Helix is doing it right now. Any of the ITSM, ITOM platforms are going to be doing it right now.

Kyle McNabb:

Help everybody understand as well, what’s the risk of enterprises not looking to take advantage of some of the embedded AI capabilities from their platform providers?

Joe Nathan:

Gen AI, I think Rob spoke over a lot of good applications of Gen AI, both business applications and some of the enterprise solutions. I spoke a few of them from the IT or technology side. So, as you all know, the platform space is really crowded. There’s a lot of vendors offering solutions. And platform as a country is trying to renovate themselves and start evolving as to start, how do you make sure we can continuously improve – add the next new thing? So Gen AI has a ton of potential new capabilities and has a lot of opportunities. So if vendors don’t start adopting Gen AI or start embedding Gen AI capabilities in their platforms, there’s a big risk of losing the competitive edge. So that’s from a platform vendor standpoint.

I would say the biggest saving is from the teams that are using those applications, building on those applications. If you’re able to leverage an enterprise IT solution or enterprise platform that has Gen AI capabilities, you don’t have to go buy these point solutions, which have specific Gen AI capabilities. You’d just be creating a plethora of technologies within your organization, and you just make your environment complex. So one is, for having Gen AI platforms start offering these, sorry, enterprise platforms offering these Gen AI capabilities, that’s just going to make it a lot more easier – seamless for organizations to integrate.

Rob Donahue:

I think, Joe, the call out there is really, and you highlighted a key issue and a risk of they won’t be able to bring AI into their platform because a lot of companies and firms and enterprises have allowed their core enterprise platforms to become technical debt, and they haven’t kept up, and they haven’t modernized their platforms. So they’re still on old on-premises versions of technology that has long since moved into the cloud because the investment to move your financial ERP or your supply chain world or your warehousing world to these modern platforms seems like it’s too much, and there’s not as much to gain from doing it, which isn’t true from a business case standpoint as our studies have shown within The Hackett Group.

However, if you want to take advantage of the AI capabilities that the enterprise platforms are bringing, and not have to buy some third-party tool to sit on top of your data to create point solutions that are going to be very expensive and very, as Joe said, high IT complexity, modernizing your tech stack around your enterprise platforms to the latest versions getting on the cloud.

If you look at the vendors out there, some of the major vendors still are managing and helping clients in an on-premises or what we call a perpetual license world. What they are not doing is putting their AI capabilities into that platform. They are only putting them into their SaaS or task-based platforms only. So a company really needs to look at their technical stack around their enterprise platforms across their functions, especially what drives their business, finance, supply chain, manufacturing, warehousing, sales – those areas – and look at their technical stack and say, “Do I have technical debt? Have I gotten so far behind that I really need to look at my platform, take advantage of AI?” Because, to Joe’s point, you will be behind your competitors who have been more nimble or more forward thinking and keeping up on their platforms to get to that AI. So really the risk is also not just integrating into it, but the risk of not being able to integrate because you haven’t spent enough time making sure you’re keeping your tech stack modern.

Kyle McNabb:

Rob, that’s a great overview of some of the risks and challenges enterprises need to be aware of. Joe, do you have any others that should be in consideration?

Joe Nathan:

With any new technology, just like Rob mentioned, there’s going to be some risks involved. I won’t call it a risk, but more about you need to get your foundation stronger so that way you can successfully adopt some of these Gen AI solutions.

So the first thing is around data privacy and security. We all hear about situations where when you’re using Gen AI solutions, especially if it’s in the cloud, you don’t want your data out in the public. So that’s something which organizations have to be super careful about, and make sure they can work around it, and enforce stringent policies, and make sure there’s good data labeling – data classification – so you can be able to protect yourself.

And master data is another important thing because when Gen AI is trying to use your data, you don’t want to be confusing it with multiple sources of truth or multiple data. So these are anyway things that you need to work on. It’s not just for Gen AI, but you still have to figure it out and just make sure that’s addressed.

The other area is around skill gap and talent acquisition. So you want to make sure that you have the right skill set to not just implement the solutions and go away, but also how do you sustain the operations through operations?

And the last, but not the least, I want to talk about ethics and regulatory compliance. This is a really important area for AI because from a regulatory standpoint, you can certainly hardwire it. But from an ethical standpoint, there’s some organizations that we’ve heard where you want to make sure that the solution is not just right from a regulatory standpoint, but also going to follow the same ethics, because you as a human, we all go through so much hours of training to make sure you imbibe the culture and ethics of your organization. So how do you make sure that Gen AI solutions also have the same ethical solution?

And just to give an example, so we were recently talking to the CIO of one of those large movie producing firms for production companies. So there’s a lot of capability for Gen AI to start to provide or start creating content. But at the same time, that also puts them at an ethical situation where they don’t want to do it, because the company was founded based on the Actors Guild or the Writers Guild. So they want to certainly protect them and start not something which goes against ethics. So those areas where you have to make sure that you know what solution you’re using, and how do you use it, and also in an ethical way, and something that aligns with your values and your core values.

Rob Donahue:

Joe, as we were talking through this, it made me think a little bit about our clients and really, when they’re starting their journey, one of the questions we’re getting asked is, “How do you know where to start AI first when it comes to the modern AI tool sets?” I know you’ve worked a lot with our clients in this area, and we’ve talked a lot about it. Maybe you could expound upon some of the things that our clients should think about when it comes to enterprise platforms. You mentioned, obviously, looking at the stuff that’s within the package enterprise applications, but even then, understanding what the use case could be or what may be a right starting point that’s the right size for the organization would be. What would be some things that I know you’ve told some clients that they need to think about?

Joe Nathan:

Yeah, so typically when clients are working on their AI journey, they start just going with a use case bingo and say, “Hey, by the way, these are the 30 different AI use cases. Now, let’s start adopting it, and start seeing what makes the most sense.” What we recommend clients is don’t start with a big list – start with something which is going to be, for you, something easy to adopt. I’ve mentioned about all these challenges. So you want to fix all your data before you start adopting Gen AI. You want to get something, which is in a much more closed environment, you can identify a use case or to start trying it out – see what’s the value. Is that something which really makes sense for you? And once you get some benefit or value from this Gen AI platform, that’s when you start exploring other areas as well.

And the typical areas you typically look at for clients, Rob, as you mentioned. So one is certainly from an operation standpoint. How do you make sure you can stay lean, or you can optimize your operations using Gen AI solutions? The other areas are also innovation. That’s another area where companies are trying to use Gen AI platforms – be it from new product development or you want to have creating digital twins. You can bring things to market a lot more faster compared to what you don’t have. And drug manufacturing – drug production – companies are doing this a lot. So they’re using AI – Gen AI – to fast-forward some of their testing, or also in drug development. So those are some unique use cases that we can look at when you’re using through Gen AI.

Kyle McNabb:

Rob, I’m going to ask you to take a look forward into your crystal ball. Where do you think these platform providers are going to make the biggest impact with Gen AI?

Rob Donahue:

I really think the platform providers are really going to change how we as users of these enterprise platforms and our customers who interact maybe with some of them, interact with them – I guess for lack of a better term. It’s really going to change how we get data, interpret data and analyze data, but also how our suppliers and our customers interact with us from a data perspective, and I’ll give a couple examples.

One real-world example is there’s a large global retailer who works with a lot of smaller vendors in terms of getting product on the shelf. And they have moved to a generative AI chatbot to negotiate with suppliers. And the feedback they’re getting is that the suppliers prefer negotiating with the chatbot than the real person. They feel they’re getting fair structured deals, and it’s working so much better for the retailer – for these small retailers that really, it’s not worth it for them to spend the time going back and forth on negotiations for a product mix that they’re going to carry for six months or a year and only in a third of their stores. But they want to have a broad base of products. And so creating this interaction model with generative AI and a chatbot that really negotiates for them has really moved the needle to allow more vendors to get into their system and allowed them to get the merchandising mix larger in that case. So that’s a real-world example that’s happening in the industry now using generative AI.

And then the other examples I see is I really see in the finance platforms in those areas, like I mentioned, we’re going to be much more conversational than transactional. When we talk to clients about modernizing their ERP platforms, we’re always telling them, “Hey, when you move to a cloud-based system and a modern ERP, you’re going to manage by exception – not by transaction.” And that’s what we’re getting to with the highly automated process orchestrated systems of today. But generative AI is really going to change that because no longer is a leader going to go to an analyst and say, “Hey, run me these numbers. Let me know what you see for performance X, Y and Z,” Or “Hey, could you run a scenario where if we changed how we organized my sales group, what the impacts might be and what the benefits might be?” And they go back and they do that. A week later, there’s a presentation. Instead, that leader’s just going to ask the ERP that question in that way and get an answer back instantly with interpretive information and a viewpoint because the system has been learning on their data.

So some of the skill sets we’re going to want to look for people isn’t just business analytics and understanding finance and numbers, but do you understand how to write prompts and how to make sure you’re getting complete information. So it’s really going to change a little bit the skill models that we have when we interact with our platforms as well.

Kyle McNabb:

Yeah, those are fantastic examples for how organizations can start to think about how they reimagine the way work gets done, and to your point, rethink their skill sets that they need to have as well. Joe, what about you? What do you see the future holding?

Joe Nathan:

I align with Rob in terms of how Gen AI provides a lot of capabilities, and a lot of companies are winning from using these solutions. The only one suggestion – one thing I would make – is a lot of companies have made investments in Gen AI, and certainly, the early adopters of Gen AI platforms are gaining, and there’s a lot of acceptance of those products in marketplaces, and I think Gen AI is here to stay, and it’ll certainly create the new path for platform vendors.

Kyle McNabb:

Well, thank you both, Joe and Rob, for joining me today, and sharing your insights on how AI is impacting enterprise platforms, and what enterprises can expect to see as we go forward. And for our listeners, thank you for joining us, and stay tuned for even more insights on how we believe you can reimagine work, and how your operations perform leveraging AI.

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