The Gen AI Imperative: Know-How to Take the Lead – Transcript

Kyle McNabb:                   

What really struck me about that is they shared that they have eliminated millions of hours of work. Where did those hours go? What they shared with us is they’ve reinvested a lot of those hours into product innovation and go-to-market innovation, where they couldn’t get to in the past.

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:                   

Hi, everyone. I’m Kyle McNabb, vice president of Research and IT Advisory here at The Hackett Group. I’m joined today by Vin Kumar and Andy Warzecha, leaders here at Hackett, helping us to drive our Advisory Program. And we are thrilled to have an open conversation with you about observations we are seeing amongst the organizations that we engage with, our members and so on – about their experiences and strategies with generative AI. Let’s get right to it.

Vin, we’ve had hundreds of discussions with clients and prospects and service providers about their AI and generative AI strategies and plans. What have we learned?

Vin Kumar:                         

As we talk to members, we’re talking to hundreds of members daily on how they are looking at Gen AI. There are really three camps coming through it. The first camp is people who are – you know – wait and see. They’ll tell you what they’re waiting for and what they’re doing. The second camp are folks and people who have started doing proof of concepts. They’ve established some sort of pilot, and they’re seeing the results. Then there’s a third camp – that is small – who are kind of seeing some of the breakthrough performances. They have got a use case very well-developed, employed, and getting benefit from that.

 So coming back to the wait and see, what they are waiting is for… you know, there is a lot of noise. They are saying let things settle down in the marketplace, and then we’re going to be approaching on how we want to Gen AI. They have all the enterprise solution vendors knocking on the door and trying to tell them, hey, we are building in Gen AI capabilities. The SAPs, the ServiceNows, the Oracles, the Microsofts – all of them are coming with Gen AI capability enabled that they can just turn on and start using their enterprise application. So they are waiting to see is what capabilities are these, and they’ve done that for probably the last maybe six to eight months. But now, realizing certain nuances there and changing the perspective of saying OK when we wait and see, now we are waiting for SAP to release certain functionalities, but, oh, by the way, we have to be on S/4HANA, we have to be on the cloud, or the same thing with ServiceNow. There are restrictions, and they are realizing that maybe they cannot wait. But that’s one camp that we are seeing.

The second camp, where we have seen the POCs, where they have deployed POCs in areas, they have proof of concepts. They have deployed pilots. They are really seeing that they can get value for it. The challenge they are facing is how do we scale? Great, we use it in this particular POC – a pilot we have put in place. It’s doing regulator reporting for us. We are able to use it. It’s doing the footnotes for us. Fantastic. But now how do I expand it across finance or other functions to do it?

 And then there are some really unique breakthrough examples where we have seen significant benefits. They are doing things that were either cost prohibitive or very difficult for them to do it, or they couldn’t even think of doing it. So those are the three camps that we are seeing today. But fundamentally across all of them, everybody is assessing it and realizing it ­… why they’re going to fall into which kind of direction and which camp that they want to kind of explore.

Kyle McNabb:                   

Well, Vin, the breakthrough idea here sounds intriguing. So can you give us a couple of examples?

Vin Kumar:                         

Some of the big examples for one is… this is a company looking at the supply risk management, right? So they were doing the supply risk management became more frequent after the pandemic and now have two committees. There’s an internal audit committee. There’s a risk and control committee. They come and audit their suppliers once a quarter. They look at anywhere from 1% to maybe about 3% of all their suppliers and do their risk assessment. The audit does it on I think 15 parameters. The risk and control does it across 30 parameters. They were doing once a quarter, and that’s kind of what they were going on.

Today, using Gen AI enables kind of automation, so it’s not just Gen AI by itself. They’ve embedded Gen AI across the intelligent automation fabric, and now are able to look and do real-time risk assessment for suppliers that was fundamentally not possible for them, and they operated in kind of nonsophisticated geographies and markets where they could get supplier credit information. So they had to do it in a much more inorganic way. They had to collect the data and analyze it than now they were able to do, and now that’s giving them significant benefit.

One initial benefit is, hey, they didn’t have to wait to go into the RFP process, and in the RFP process understand and identify clients who were of potential risk and didn’t meet some of their thresholds – that they’re unable to work with them and then to take it off. So now they were able to do it right up front. Every time, every day, anytime they could do a real-time assessment.

We have another client who moved away from reporting. They had over 30,000 reports being produced on a monthly basis, and they bought it down. Using intelligent automation fabric and overlaid by Gen AI were able to eliminate and move to completely zero reports. There they could get significant benefits. They were able to reduce more than 30% of head count in their FP&A. And this was not just creating capacity, but actually realized that savings and impact, and they were able to realize that cost savings to do that – to redeploy the resources – but actually were able to realize that savings to do that.

So those were some extreme examples of where they thought these were never possible or extremely cost prohibitive – were using Gen AI to achieve those types of breakthrough performance.

Kyle McNabb:                   

Yeah. My understanding in both of those examples … for the first one, I did hear from our discussions with them that they were able to reallocate their resources and do more strategic value add, vendor and supplier engagement. And to their point of view, it’s actually helped them gain market share and improve their ability to compete.

The second one you brought up, what really struck me about that is they shared that they have eliminated millions of hours of work like you’ve highlighted. Where did those hours go? What they shared with us is they’ve reinvested a lot of those hours into product innovation and go-to-market innovation that they couldn’t get to in the past. So it’s great to see that for those firms that are thinking of breakthrough. They’re not just thinking of what’s the immediate savings they can get, but how do they then turn around and invest those savings into things that make them more competitive and different.

Andy, so Vin noted earlier that organizations seem to be in that wait-and-see mode, waiting for updates from their preferred app – preferred infrastructure vendors. Help us understand why do we think that approach is flawed.

Andy Warzecha:              

Well, first of all, Kyle, I would say when you look at this it’s part of the answer. You know, if you look across organizations – what they’ve invested in – there are certainly going to be a grounds up from what the vendors that they’ve already invested in is going to provide. But recognize the rate and change that we’re seeing in the market right now is happening at a pace we haven’t seen for many, many years.

I harken back to the days when folks were moving to the cloud with e-business and those types of initiatives. The vendor solution is going to provide a common framework where all boats float, meaning it’s going to be incremental gains for everybody that has those solutions. And, granted, you can do some prompt engineering and create some custom agents on top of those solutions for your own organization, but everybody has the same set of capabilities that are being delivered by that particular vendor in the market.

The flawed element here is that while that’s going on – while you’re waiting for the vendor to provide the solution six/nine months out, while you’re waiting to upgrade that ERP system to be able to incorporate those systems – you’ve got competitors that are really looking at other alternatives. They can provide transformational or even breakthrough – as Vin was just talking about here – performance results.

So those that are getting ahead of the game here, those that are really looking holistically at costs, the opportunities for not just the incremental gains, but those that are looking for transformative and breakthrough opportunities by looking at what are the competitors doing, what are similar companies doing that are in different fields. They’re going to gain an advantage here, and it’s important to note that advantage is not going to be able to sustain what organizations are today in their competitive landscape. There’s going to be winners and losers coming out of this.

Kyle McNabb:                   

I really like the approach there – talking about the holistic approach. Yeah. I think we’re seeing a little bit more of the impact here of if you’re not focused on doing work the old way, and you can reimagine it and do something different, it can give you a little bit more of a different, competitive advantage.

 So, Vin, I know you’ve had a lot of conversations with leaders in particular. What do we mean when we say holistic approach?

Vin Kumar:                         

Before I go into the holistic approach, just to add to what Andy was saying Kyle, is that one of the… when you’re waiting, when companies are waiting for that enterprise solution provider to come and enable this Gen AI capability, the biggest challenge that they’re seeing is that the Gen AI capability, even when they work with a road map, And there are some which are even giving an edge to new capabilities that are being released, however, the significant constraints on how you could do them. You can use them only if you’re on the right version. There is cost prohibition to do it, and some of them are not available for certain industries to do it.

 So there are a lot of constraints, even in the embedded solutions that you have to… As enterprises have to address and to do it, and that’s why there is a realization that maybe the wait and see will not work, even if the wait for it, that’s not going to come to them because of things. They’re not looking to upgrade a version of their enterprise software just to get Gen AI capability. It doesn’t kind of help them from a ROI perspective, so they’re saying, OK, we are not going to get it, or we are going to be in multiple platforms, and we never get to one ERP to do it, so we have to develop [inaudible 00:11:06]. So that’s one of the bigger realizations coming, and coming to this holistic approach.

What are we seeing with the second two categories who have created the POCs and then the ones who are having breakthrough performance is that you have to address a Gen AI solution from a use case perspective. You have a use case. You’ve got to have a solution for the use cases. However, you have to have an enterprise strategy, or at least if you’re a large organization of functional strategy on how we are going to do it.

So there are a couple of things you have to address. One, you have to address how are you going to address the risks and regulatory requirements in your industry and for services that you’re trying to use Gen AI, and how does that impact your function? So that has to be done because that’s going to govern all the use cases within that function.

Second, when you look at the Gen AI solutions, you have to build for a use case. It’s quite simple. It is that there are basically four types of Gen AI solutions you have to do. It’s almost like what they call a modular approach. What I explain to clients, it’s like Lego blocks. There are only four types of Lego blocks you’re going to use to build the solution if it’s either a digital-tuned assistant, a prompt engineering solution, a rag solution, or a fully trained model that you’re going to do.

The fully trained model is not applicable for anything in SG&A. It’s too expensive. It needs a lot of infrastructure, compute time, a lot of data, a lot of SME time to train it. So nobody we are recommending and nobody’s using the fully trained model for SG&A functions perfectly apt for your R&D and product.

So in SG&A, it’s just these three solution blocks. It’s either a rag model, it’s a prompt engineering solution or a digital-tuned assistant, or a combination of that. So the holistic approach is there’s a central organization – COE – within the function or within an enterprise level building these blocks for the enterprise, and all the function and every use case, you put these building blocks together. You may change the color of the Lego brick – you could make it a rectangle one, maybe longer, smaller. Are you going to use a square one or the cylindrical block? You got to use that and put it together.

So you’ve got to have a holistic approach to do this. Otherwise, it’s going to go down a similar to the RPA path, where every use case had to be scripted from scratch, and there’s no way to scale and also to keep the governance of it and adopt the impact it’s going to have on the organization to do that.

And that’s kind of where some of the ones who are a little ahead of finding a challenge is. OK, they did the POC, it’s great, but they’re kind of stopped and say, OK, now what do we do? How do we scale this? It took us all this effort to get a POC done or a pilot done, but now how do we scale this?

Andy Warzecha:              

So, Kyle, let me ask you a question. You and our research team have been digging deeper into what’s at stake here. What do we believe is at risk for organizations that don’t shift gears and start taking a holistic approach?

Kyle McNabb:                   

Great question. I’ll start off by helping to kind of set the stage here. What’s really at stake is missing the opportunity to ask and address a question that I actually heard recently from a couple of chief data officers and CFOs over the past couple of weeks. Now that they’re looking at what’s possible with AI and Gen AI, they’re asking the question what would we do differently if we didn’t have to do this work? They’re looking at this from a standpoint of, hey, we can reimagine the status quo.

Back to the two examples that Vin shared earlier, like what would you do if you didn’t have to spend millions of man hours or person hours yearly assembling reports? How would you reallocate that talent and that skill set? And if you do that and do that effectively, you’re going to be able to build up better competitive advantage. You’re going to be able to improve your ability to hire and retain the right talent. You’re going to be able to put yourself in a better position to grow.

 And so that’s what really is at risk we believe from the research we are seeing is in any organization’s ability to survive – let alone thrive – in today’s environment where organizations are reimagining what’s possible. Now, truth be told, there is a lot of risk in doing this. So any emerging technology and Gen AI is not immune to this.

It does introduce a lot of real risk, security, privacy, and even brand risk associated with how an organization may misuse AI and deliver potentially bad results when engaging with employees and stakeholders and more. But that inaction, we think in particular from our research, introduces just as much risk, if not more, around how you compete, how you can hire and retain talent, and expose yourself to other risks as well, because let’s face it, Apple just announced a couple of weeks ago they’re embedding AI capability into their phone.

You see it all today. Every new Windows-oriented laptop is going to have AI capability built in. People are going to have access to this in their normal course of day-to-day activity. They’re going to use it to get work done. And so without a good holistic approach on this, you are exposing the organization to greater privacy and IP issues as people, and people are very creative, they’re going to find a way to get more productive, leveraging whatever is available to them – be it on their phone, on the laptop, or what’s available to them at work. So inaction we really think is really going to lead to a greater disparity between the haves and the have-nots, as organizations that do act, and act quickly, are going to find themselves in a better position to compete. And those that are laggards, well, they might be the way of the dodo.

So let me ask both of you at this point, so we do believe that there’s a lot at stake for organizations that we speak to, and we highlighted a bit of those that act now have an opportunity to create even greater competitive advantage and those that don’t may soon find themselves struggling to survive, let alone thrive, in today’s ever-changing market. What are three things that we would advise leaders that we engage with? Three things that we believe that their organizations should try to do if they find themselves in more of this wait-and-see mode. Vin?

Vin Kumar:                         

Three things I would suggest, Kyle, is one, education. You’ve got to educate your team on what Gen AI is. There is so much of hype, and that’s one of the most popular kind of learning series that we are offering to our clients, and everybody is taking, is part of the demystifying of Gen AI. How do we look at it from an enterprise function perspective?

Two is irrespective whether you want to wait and see or you want to jump, you’ve got to start dabbling. You’ve got to identify some of your team members who need to go and experiment with this – play around with this. That’s when you’re going to realize what are the capabilities? What are the risks? What are the shortcomings? But you’ve got to do that.

 And three is you may have waited for the last six months, but you can’t wait anymore. You’ve got to start putting together a road map of how you’re going to enable your function – your enterprise – with Gen AI. It’s not a matter of if it is … right now we need to start planning for how do we want to do it. If you’ve not already started engaging and adopting Gen AI, you’ve got to put a plan, and you are going to be expected by the leadership to have a plan ready as you go into your next fiscal year to start executing and adopting.

Andy Warzecha:              

Yeah. I would say start with your own leadership. Focus on what’s hot for your organization and where you can find business value. So ideally what you’re looking at here is that holistic view that we’ve been talking about. Certain amount of this technology is going to come from your strategic partners, but you should be looking beyond that. Where is the breakthrough? Where is the transformation going to come from, looking holistically and prioritizing that across your organization? Where are you going to spend the time to get the breakthrough versus the incremental-type benefits?

We have a tool here at Hackett called AI Explorer that our consulting team is using. We’re offering free demonstrations of that. But whether you go down that path of using outside help or looking at this inside your organization, it needs to be a top-down view. Secondarily, on the technology side, you got to look beyond what you’re doing with the providers that you’ve already aligned with.

Again, look at what your competitors are doing. Look at what like industries are doing. Take a look at the use cases that are becoming available. Look at Amazon. Look at Microsoft. Look at ChatGPT. There’s a lot of materials becoming available out there, and organizations talking about how to leverage their own specific LLMs to address the use cases that you may be identifying.

The third piece here is don’t boil the ocean. There is an awful lot of underpinnings around data to make this technology work – security, privacy, ethical uses. Focus on the use cases that are going to give you differentiation. I’d also encourage you to think not just what’s going on in your organization, but also those of you that are considering outsourcers, there is a significant amount of investment going on in all of the outsourcers to enable this on an outsource basis, so this has gone well beyond your mess for less. It’s about being able to raise the bar in terms of the values that the outsourcers can provide using this technology in their portfolios.

So let me close with from our standpoint generative AI is an organizational imperative. You need to start taking action on this now. You cannot afford to wait given the rate and pace of change that’s taking place. And lastly, you’re not going to get any differentiation from just sticking with what you’re going to get holistically from the vendors that you’re already aligned with. That is an all boats float scenario, and you’re not going to get the transformative and breakthrough results to give you competitive advantage.

Kyle McNabb:                   

Fantastic. Andy, Vin, thank you. And for our audience, I do think we are in an unprecedented time right now, giving us the ability to reimagine what’s possible. With AI capability, the status quo of how we’ve done knowledge work in the past doesn’t have to be the way we take it forward in the future. So thank you, Vin and Andy, and I’m excited to see what’s next.

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