Artificial Intelligence (AI) has proven to be a disruptive technical tool with promising applications across industries. But, as a CIO, how do craft your AI strategy, identify most promising user cases, build required analytics capabilities, and embed it in the very fabric of your business to maximize business outcome?

Contributors

• • Hem Chari, CIO, Automation & Controls from GE
  • Neil Arnott, SVP IS, and CIO, Traffic Tech

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Transcript

Sanjog Aul [00:00:22]:

Hello, and welcome to this segment on CTN. To learn more, please visit ciotalknetwork.com, and the topic for today is Steps To Maximizing The AI Opportunity, and our guests for today’s show are Neil Arnott, who’s the SVP, IS and the CIO with TrafficTech. Hey, Neil. How are you doing?

Neil Arnott [00:00:41]:

Good. Yourself, Andrew?

Sanjog Aul [00:00:42]:

Very good, sir. Very good. Life is beautiful. God is kind, and I’m enjoying this discussion with you today, and we also have Hem Chari, who’s the CIO automation and controls at GE Power. Hey, Hem. How are you doing, sir?

Hem Chari [00:00:53]:

Hi, Sanjog Aul. How are you doing? Thanks for the invitation.

Sanjog Aul [00:00:57]:

Oh, honor is all mine, and this topic of AI is not new. A lot of people and, in fact, almost every one of us is talking about it, and we also see that AI is proving to a tool to be a tool which can really disrupt whatever we have been doing in a positive way, and, of course, we wanted to make the most of it, but the question comes is how? And that’s what we want to cover today. So Neil, maybe I’ll start with you here today. When you’re looking at AI in an organization, it’s supposed to be a technical tool, but does it automatically lends itself to become the responsibility of a CIO? Or would you rather have this become a business initiative and seen by business, evaluated by business, and let them run what should be done with AI?

Neil Arnott [00:01:50]:

I think it obviously, I’m a little biased, but I personally still think that the CIO takes the lead and shows the business where it can be used within the business, but then I think the business would take the lead on the individual projects. That’s kind of how I see it happening. I mean, I think business leaders are qualified, but it’s a partnership really in my opinion. Any successful project requires, good technical leadership and a good business leader to see the potential business benefits.

Sanjog Aul [00:02:16]:

So when you when you gave this response, are we assuming that AI has that technology or the geek side of it which is not as well understood by the business that’s why CIO has to lead the path, and that question I’m going to ask you, Hem, when you look at AI and suppose GE, right, your organization, GE Power people are in the business side trying to always figure out a way to get certain things automated, get some intelligence in their processes so that it is not requiring hand holding by humans. So in all of those areas, if they knew enough in terms of what AI is capable of, which is coming down to the basics that it can help with some intelligence, do some automation, help with some analytics, is that much information or knowledge enough for business to say, let us identify the use cases. Let us do the due diligence on it, and, mister CIO, why don’t you be with us in this due diligence, but not necessarily be leading the show?

Hem Chari [00:03:22]:

I mean, that’s a great question, and the way I would paraphrase it is the role of a CIO, I mean, I especially where I come from, I would see it more in a bimodal, it’s bimodal because you have to balance between managing the operations and also the aspect of innovation, and that’s often a challenging aspect because in an operations perspective, you’re focusing on a few things. How am I preempting disruptions, which is where you have the convergence between technology and business? How do I ensure that my I’m meeting my basic business requirement, the deployment of my ERP system, my data lake and everything? This is where the challenge has been. The CIOs have been focusing more on the operational aspect of it, and that kinda takes away from the need to focus on innovation, and in in part, at least in where I work, the business has stepped up because they have understood that AI or IoT, this is not a technology. It’s I mean, it cannot provide business value on its own. It is something about how do we adopt this technology and how do we improve the process business process through this power of technology, and this is where they’re able to come in and say, hey, we have some use cases that we want to try it on. Like, I think one example that, has been prevalent in the public knowledge is how GE has been able to leverage the data from the aircraft engines or gas turbines and turn that into insights that now we can provide information to our customers and say, Hey, the main with respect to maintenance or with respect to downtime, here is when you need to take your system down or here is something you need to be doing the maintenance of it. So certainly, the business is taking the lead, but I mean, from a CIO perspective, I think we are lagging behind because of the natural course of action we need to take, which is balancing between operations and innovation. I don’t know, Neil, if that’s the challenge you felt on your side.

Neil Arnott [00:05:28]:

Yeah. I’d have to say right now, the business I don’t know. I would say if the business is stepping up right now. I would say they can identify the business cases for us, and one of the nice things about some of the new tools with AI is it’s more of a power user type of tool rather than a developer type tool. So that’s a nice improvement. That’s kind of where we are right now, but we’re still kind of we’re still exploratory on a lot of our pieces. We’re kind of looking at internal processes right now. We’re not touching anything external to learn. We want to walk before we run type of deal.

Hem Chari [00:06:09]:

So I mean, I think on the IT side, I would agree with it. On the IT side, we are looking more inward because we ourselves don’t know what the capabilities are, but clearly on the business side, the business leaders have been smarter in adopting these technologies, and they’re using this as a competitive edge when we go to our customers, and again, we’re essentially in a platform economy wherein what is additional services we can provide, and just to add to what you both said, if you look at the

Sanjog Aul [00:06:53]:

And just to add to what you both said, if you look at the potential of AI, that’s well understood. Then the ecosystem that’s developing, which is with the SaaS model or analytics as a service or AI powered deployment of certain functionality as a service. Everything is available by outsiders, so those people could be coming and meeting with the business leaders. Say we can suck in the data from whatever you’re making available to us, and then we can run with the rest, and, frankly, on the other side, the traditional corporate IT folks are not qualified yet to say we understand AI enough for us to be front ending it. So isn’t this becoming almost like, okay, mister CIO, go ahead and give me the data. Like, give me or wherever the data is coming from, you just help us get a sense of it or we will patch you with this AI as a service provider, and then rest of it, we don’t need you, and it’s not that we are you’re being, you know, you’re supposed to step aside, but you are not really in the critical path of AI getting adopted. So, Neil, are you are you seeing that that’s where this is gonna go? Is this becoming more of a peripheral initiative which may as needed invite you or you require you but otherwise it can just get provisioned over time by by these third party providers and you will just be required to help with the data.

Neil Arnott [00:08:33]:

I’m not seeing that yet. You know, some of that sounds a little like shadow IT a little bit. I’d have to say with the I mean, all the data for us is all in house. We don’t have much data in the cloud. We’re looking more for some of the, you know, some of the RPAs and some of the machine learning stuff on premise at this point, not much SaaS angles to it. So we’re not seeing that yet, but I mean, it might come, and I’m not saying it won’t, but right now we’re not seeing that.

Hem Chari [00:09:15]:

If I may add, Tania, I think, you’ve seen the industry shift towards certain creation of new roles. So like for instance, the role of a CDO is something that you hear a lot across the industry, and the role of the CDO is essentially to take control of the data and make magic out of it. So that’s where and in my mind, we are seeing the shift in certain areas wherein the CIO role, and it needs to evolve over time, but we are at a phase wherein I mean, AI in itself is in its nascent stage, and it’s creating opportunities. I mean, at the end of the day, data is critical for everything, and what can we make out of this data is what your outputs will be, and 100% of the data is critical for everything.

Neil Arnott [00:10:06]:

In my industry, we’re, I mean, we’re in third party logistics. We’re non asset based, so we are information brokers in the end. So

Hem Chari [00:10:15]:

Exactly.

Sanjog Aul [00:10:17]:

Yep. So so based on what your responses were, which at least tells that there’s there will be some shift. There will there’s not a confusion, but we are still trying to figure out that who should lead the pack and then what support or driving capacity should the CIO and their group should get involved and it’s a little fluid right now and and it is kinda shaping up. So which is understood. It’s understandable. Now next question would be, so suppose whatever that role CIO’s group may play in in AI proliferation, What would we say is the best approach to identifying the most promising use cases? It it it takes me back to discussion about IoT sometime back where they used to say, yeah, IoT is great, but we have a tough time justifying the the complete rampant use of IoT because we do not really see beyond the first few proof of concepts enough use case for us to do a big time investment. What are we doing when it comes to AI and identifying the use cases even though business may drive this whole thing? And what is the best approach or best practices based approach to identifying use cases so that we very quickly know whether we should go head over heels in this AI, you know, this this rush or the madness that we we have started now, but before we get into it, please stay tuned listeners. We’ll be right back after this break and this question is for you, Hem. Okay.

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Sanjog Aul [00:13:44]:

Welcome back. So, Hem, if based on whatever experience you had or you heard your peers talk about, adopting AI or at least starting that process, what are some of the approaches they have used which seem to be effective when it comes to identifying use cases, whether business does on its own or in collaboration with the CIO group?

Hem Chari [00:14:04]:

Yeah. So, Sanjog, in in our case, I mean, we’ve not really honed in on a singular approach if I mean, in in identifying the promising cases. What the what the users and the businesses have been doing has been identifying the the pain points and also opportunities and say, Hey, here is something that we are doing. This is a rather mundane process. So we are actually paying a lot of money to an external company for something that we could probably leverage, AI or machine learning or even our RPA to solve this problem. Let me give you an example. So in GE, we pay we used to be paying an external company about $7,000,000 to $8,000,000 to help classify something we call as harmonized tariff systems. So what that means is when we are buying products or when we are shipping products, we need to understand what at what tariff rates those will be charged and make sure that, the tax associated with it, if any, is accounted in a proper way, and we were actually paying this company about $7,000,000 on a year to year basis.

Sanjog Aul [00:15:14]:

Lake team and understood, say, hey, there’s an

Hem Chari [00:15:14]:

opportunity to leverage new technology, and dependency on the organization and this resulted in about $6,000,000 in sales. We still use it for in some cases, we have a dependency, but we’ve been able to significantly weave off the dependency and leverage what the technology is doing for us, but again, these are use cases that the businesses came in and say, hey, here is something we can really make an impact leveraging technology. So what’s your yeah.

Sanjog Aul [00:15:55]:

Go ahead, Neil. Yeah.

Neil Arnott [00:15:57]:

Sorry. Yeah. For myself, and our team, for us, it’s I mean, it’s looking at what is easy to implement and has business benefit. We don’t always really look for the biggest benefit, especially on a new technology. We’ve kind of found that for us, like, a 4 to 6 week implementation is kind of what we want with maybe like an 8 week on the outside, and then kind of build, we want to build, make some successes and then kind of build on those successes. It was kind of our always been our goal with new technology.

Sanjog Aul [00:16:29]:

And so the the the scope of AI and and Neil during the break you mentioned that AI could be so many things to so many people. Right?

Neil Arnott [00:16:39]:

Yep.

Sanjog Aul [00:16:40]:

So when you look at AI and if you continue to look at that as a technology tool, then there’s one way to look at it or one way to harness, but if you start looking at it as a capability, which could take many forms, then you could be really painting on a white canvas and paint whatever you could want. So has that painting started at the business level? Started thinking that okay I have a a way to not just automate something at a basic level but I could have some cognitive angle that could be attached to it or some intelligent automation could be done or there are some decisions could be made at a warp speed, and what’s possible? How far can we stretch our imagination? Has that part of ideation, already kicked off in as part of the strategy phase in your business yet? Or we are saying, okay. Let’s tinker with this as a technology tool, and we will see where this goes and and after that that ideation will start.

Neil Arnott [00:17:48]:

Sorry. Is that question for me?

Sanjog Aul [00:17:49]:

Yes. Yes. That’s for you sir. You do. Sorry about it.

Neil Arnott [00:17:51]:

Yeah. We’re we’re still very much at the exploratory stage. We definitely have identified a couple different processes. One of the things for us is, as a third party logistics company, we deal with 10,000 to 15,000 carriers a year. So we’re looking at, like, some of the documents, and automatically identifying the documents through OCR technology rather than the old school way where you try and do things with templates. Now with machine learning and recognizing the type of document, we can auto index a lot of that stuff and push the process that much faster through the chain.

Sanjog Aul [00:18:30]:

So, Hem, when you when you look at your organization and when you’re identifying the use cases, are you looking at just the low hanging fruits or is there, some some method to the madness of saying I’m gonna try to through deduction, through analysis, find the promising use cases which mean I may not even try right this minute, but these are some of the set of initiatives which I would line up. So I stretch out what all is possible.

Hem Chari [00:19:01]:

For for us, I mean, we’ve been starting slow. I mean, there are pockets of areas wherein we’ve kind of gone advanced. So like for instance, if I were to take a smaller cases, we’ve been looking at every business process wherein there’s a lot of manual touch points. Like, if I were to give an example on the finance space, every time we’re doing a quarter close, there’s a lot of activity associated with pulling data and being real time in terms of pulling information, updating the records so that the leadership can view how the results are. We’ve been able to leverage the power of RPA and automated, and within Power alone, they’ve been able to reduce about 7,000 hours of effort because of this. If I were to take it to another level on the ERP side, AI has been able to we’ve embedded AI in the SAP implementation, wherein they’re able to automate the labor intensive invoice matching process, some of the key elements. So we’re looking at opportunities wherein we see, hey, this is something there is a manual touch point and maybe we can leverage the power of automation and eliminate that. Now if I were to take it to the other extreme, we have a team which is on the infrastructure side. They are looking at all the locations wherein they have to manage the network capability, and what they are trying to do is they create a predictive analysis model wherein they’re looking at every location and through this predictive analysis, they’re able to identify where a potential problem could arise, and already even before the issue happens, we are able to start logging a ticket or notifying the network operations team saying, hey, This site in Germany, we anticipate it’s going to go down in 2 hours because of these factors, and here is the ticket to go associated with. So there is an infrastructure team that is taking that to the next level, and the whole idea is being able to analyze before something happens and we are able to stop that, from issue from happening. So again, it’s, it’s twofold. I mean, we are starting in, small in some cases, but in in some areas, we’ve gone pretty much mainstream trying to make big things happen.

Sanjog Aul [00:21:17]:

So in short, Hem, shall I say that the approach is more opportunistic versus holistic at this stage?

Hem Chari [00:21:27]:

I would agree with that. Yeah.

Sanjog Aul [00:21:29]:

Okay. So Yep. So, Neil, when you look at your organization and, of course, you’re looking at AI as, you’re tinkering with it. You’re trying to find where it can work. Are you expecting AI to work every time, all the time, or are you by design keeping a human backup or a human control as part of how you planning to use AI going forward?

Neil Arnott [00:21:54]:

Yeah. We’re definitely looking at more of an augmentation, like, augmenting the role of attended processes versus unattended processes at this point, kind of helping the users rather than replacing the users. I, you know, I don’t think it’s a panacea. I mean, it’s a technology like anything else, but we are very encouraged on it, and I find what’s nice about some of the technologies within AI like the RPAs and stuff and machine learning, it’s not expensive to kind of get into it. You know, $15,000-$20,000 and you’re kind of you have your own robots and you have your own tools to start using it and giving it to a power user or giving it to a BA, etcetera.

Sanjog Aul [00:22:44]:

So when when you are looking at this, are you not looking at maximizing because the very show’s topic was steps to maximizing the AI opportunity. So would you say that, yes, while that intention is there, but maximization of the AI opportunity is, a little farther away. Right now you’re trying to evaluate whether we should go all the way with it. Is it where you are?

Neil Arnott [00:23:11]:

Yes. Yeah. For us for us, we’re definitely looking at processes where, it will definitely like maybe cut down some of the obviously cut down the mundane, you know, and get rid of it and have those users kind of work on more value added pieces of the business type of thing, and obviously, there’s some places we see where maybe we do have five guys doing it today and it will go down to two guys, but those two people will still be needed and the other three will move them to other positions, and I think what’s also key for some of these processes is to have, like, a dashboard where you can kind of make sure, you know, things are still going on and that it doesn’t get held up by, maybe a scenario you didn’t think of and work comes to a halt kind of thing.

Sanjog Aul [00:24:07]:

So sometime back, Hem, I was talking to a group of, CXOs, both business and IT, and there was a consensus that if you truly wanted to maximize the AI opportunity, then just looking for the cost cutting endeavors and especially displacing human workers to save x number of thousand hours, that will take us only so far unless otherwise we have a growth centric outcome as the, the intention. Or that’s what if you are not pursuing, then maximizing the AI opportunity will just stop us from converting ourselves from a bean counter to a venture capitalist, if you will.

Hem Chari [00:24:50]:

Yeah. I mean, one thing, I mean, at the end of the day, when we talk about technology, it’s technology is still a product of someone’s imagination. I mean, when we talk about AI, we are we are still at a phase wherein we are not 100% sure how these technologies will work, and we’ve been able to focus on small areas and get the wins, but then, again, in my mind, if I were to take the example of Google, I mean, Google has its own inherent issues with AI and how it operates. I mean, like, for instance, if you search for if you do a search in Google and say, if a woman searches for jobs, typically, Google will highlight the low paying jobs versus if, if someone else is doing so. Again, there are inherent biases that are associated with the technology, and all those need to be sorted out, before we can probably say we’re gonna go 100% into AI. The way I see it is there are I mean, as the technology evolves, as the proliferation happens, be it machine learning, Blockchain, AI, all the elements we’re talking about, this creates more opportunities and that is something that we still have we still don’t even know about it. I mean, I think the recent survey from McKinsey said by 2030, 30% of the jobs that don’t exist today will be created because of the technology. So we’re going through a phase trying to understand what the capabilities are and I think that’s where the hesitation is. Not everybody is gonna jump into it because we don’t know what the pitfalls are and we need to recognize that, understand before we are able to maneuver ourselves into that path.

Sanjog Aul [00:26:30]:

So the mindset of cutting cost, which, of course, is a low hanging fruit wherever you can shave off, that is a natural low hanging fruit which anyone would want to go after, but is there a healthy skepticism or I will not do I will remove the word healthy, but there is pure skepticism just because it’s new, and that’s why we are not going after the growth oriented initiatives for AI. Is that what Hem, GE Power is not a small entity, and you got all the resources anyone can imagine. So are you even at your organization level looking at it in a conservative manner versus making the full use of what’s the potential could be?

Hem Chari [00:27:10]:

Absolutely. I mean, if you look at I mean, you’re spot on. We need to be we’re we’re being careful because we don’t know what the I mean, what the outcome would be with certain technology. I mean, even when you look at privacy aspect of it, when you look at all the technology, the security aspect of it, there are still a lot of unknowns that need to be addressed, and companies are going to be very careful in making sure that all those issues are tackled and we are comfortable before we can go and say, okay, now we are a we have gone full blown into this technology and everything we are doing is associated with that. I mean, which is why we’re we’re doing it in pockets. We’re getting better at it and as we learn from it, we’re gonna continue to invest more and more and get to that end goal, but not today.

Sanjog Aul [00:27:56]:

So Neil, I’ll take a break right now, but then I’ll like to come back and ask you this question, Neil, that what’s your view on the the type of mindset we carry as business and technology leaders, no matter which tool comes our way, that determines how well do we harness the value from it. If you are conservative and you say I’m not sure what’s gonna happen next, then you will hold yourself back. You’ll not go all the way, and that could leave some money and some opportunities on the table. So what is being done at organizational level to not just look at AI in one as one technology instead of blockchain and many other things that were discussed here is to change the mindset so anything new comes. We are not afraid. We are we are not rash, but we should be at least progressive in in looking at it. So what is being done in that regard in your organization? So while I’m touching the culture side, but a question at this stage is is I guess good for us to answer. So please stay tuned, listeners. We’ll be right back, and, Neil, this question will be for you.

Speaker 0 [00:29:11]:

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Speaker 0 [00:29:42]:

Patient centered care requires a connected enterprise. Are you ready? If you’re looking to scale your health care IT efforts, visit redmane.com/health today. Whether it’s to connect data from multiple partner solutions or developing software for unique needs, Redmane can help. To find out how Redmane can help your company deliver on the patient centered care promise, visit redmane.com/help or call (773) 693-3919. Visit today.

Speaker 0 [00:30:15]:

Predict your company’s future by creating it. Is your workforce able to connect, exchange ideas, and share brilliance simply and securely? Create tomorrow, today. Empower your people to innovate anytime and anywhere with secured BlackBerry Enterprise Mobility Management and document sharing solutions. To learn more, visit blackberry.com/enterprise. You are listening to CTN CIO Talk Network with Sanjog Aul. Now back to the show.

Sanjog Aul [00:30:55]:

Welcome back. So we wanted to maximize the AI opportunity or any other opportunity that may come connected to a newer technology or a newer capability. So, Neil, why should we hold back? Why should we be preventing even a full blown experimentation to see what the limits are? And in case we do find an opportunity for growth, why would we not wanna capitalize on it? What are we afraid of? And what is holding back the business and the technology leaders from going all the way, at least, in the experimentation stage as soon as it comes along?

Neil Arnott [00:31:28]:

Well, I mean, I think like any new technology, you’d want to watch out for pitfalls and always start with something small and then kind of build on successes. Whether that’s holding back, I guess, is a liability is a potential issue. I mean, if your RPA starts putting in data that’s wrong to the customer or your voice chat box starts giving wrong answers to your customers, you know, what are the liabilities there type of issues. I think as a CIO, I guess, in some ways, I’m conservative, I guess, and whereas, obviously, the entrepreneur of our or the leader of our company is very, is obviously out there and wanting to push the envelope, and then I think it’s really the balance between the two that makes the company a success.

Sanjog Aul [00:32:25]:

So isn’t progress better than perfection? Right? So we are waiting for everything to line up.

Neil Arnott [00:32:29]:

Agree with that. Yes.

Sanjog Aul [00:32:31]:

Right? So if you had to do that, hey, I’m gonna come back to you because you made that that comment about us us not going and, going all the way. It may not be prudent. So if you had to offer a suggestion to the listeners and who are dealing with similar situation, how would they approach so that they are leaning more towards maximizing the potential versus saving their back?

Hem Chari [00:32:55]:

I mean, I think the comment I made was from a role of a CIO perspective, it is balancing operation versus innovation. Do I and the focus typically comes in saying, do I keep the business, my focus is to make sure that the business is running smoothly so that the leaders can then go sell the product, and again, we are bringing revenue and everything. From an innovation perspective, certainly, that’s the balance I’ve been focusing on. How do I make sure that we are able to take or carve out a component of our team or work with horizontals within GE Power and engage with the innovation team and start to look at opportunities, but then all the things that Neil talked about, how do we ensure that we are from a liability perspective, from a security perspective, from a privacy perspective, we are not bringing more problems for the business versus trying to solve certain things, which is why we’ve been incrementally approaching it. I mean, when we are quick to take an experiment, recognize if it fails and then drop it and go move on to other one, but again, there is an element of circumspection to make sure that we’re doing the right thing and also in the interest of the business, and having said, we do have a CDO who is taking it, who is trying to make this impact in a bigger element. So they are taking the data and making sure that now they could be focusing on bigger and better things, and IT from a CIO perspective, we are there to support them.

Sanjog Aul [00:34:31]:

So is balance word that you used multiple times, is that a synonym for risk aversion? Is that what is the approach we’re taking in organizations? I’m not just saying yours, but overall, that’s what you see?

Hem Chari [00:34:43]:

I would, I mean, yeah, risk aversion, yeah, being careful at this point to minimize. Again, as I said early on, the implications of AI or implications of technology is not fully known. I mean, I’ll give you an example. Recently, you heard, I think, Alexa started ordering stuff for people without them asking for it or sending the information, my information to somebody else without somebody acknowledging or somebody asking for it. Again, these are minor things, but what if happens at a business level? What is the implication? And is there a privacy aspect of it? You’re dealing with countries now. European countries have the GDPR and how do we address all those things? All those need to be factored in to minimize that, and I think the way you put a risk aversion is certainly a one key element to this. So I don’t know, Neil, if you agree with that.

Neil Arnott [00:35:38]:

I do agree. Yes.

Sanjog Aul [00:35:40]:

And, so Neil, coming back to you, when we go about looking at the execution model, so, of course, when you’re experimenting, that’s one thing, but then when it comes to really putting it in production and you made a comment about an outside vendor who provides AI or AI centric capability as a service and directly reaching out to business could be looked at as shadow IT, which is a big no no for a corporate CIO. Yeah? But at the same time, if we are not qualified to scale the operations, if we are not qualified to think through what would it take to build such a platform, and there is something that’s available right there which will give you or reduce time to value and actually get you to where you want to as a business, then would you still be looking at that with that, not rejection, but it’s it’s them against us. Because we want to

Neil Arnott [00:36:37]:

be able

Sanjog Aul [00:36:37]:

to figure out a will buy or rent mode, right, for execution? Sorry? We need to figure out what would be the best mode which could be build or buy or rent, and all of those different cases could make sense in different scenarios, and so we have to be open and be ready to pick any of those. How do you go about selecting when you’re thinking about execution mode beyond experimentation stage when it comes to AI?

Neil Arnott [00:37:10]:

Well, we see I mean, if the business comes to us with a solution, that makes sense. I have no issue there. It’s not really an us versus them. It is definitely a cooperative approach. It’s just we have to watch that the business users historically anyway for us are really just thinking about their department or their issue versus obviously it’s the CIO’s responsibility to make sure corporately it makes sense for the entire enterprise, and if we have a solution, that’s quite similar, obviously, that’s already been corporately endorsed, we go with that. There is a lot of different technology like we talked about before though within AI. So, there like you say, there might be cases where, you know, SaaS model does make sense because maybe this particular piece of software has some piece to it that our corporate one does not or maybe it’s a problem that will go away in 3 months so all we really need it for is a temporary solution.

Sanjog Aul [00:38:20]:

So the approach, the parameters that you will look at, so Neil, you are faced with say a situation where you have to, what would be our basis of figuring out, I’m gonna build a platform in house, or I’m gonna just buy an off the shelf, or I’m gonna subscribe to a service? Because these are not just you put them and then you remove them and then you put them and then remove them. Right? So these are not small decisions. So what would be your due diligence process?

Neil Arnott [00:38:51]:

For us really, cost benefit and how long is this, like are we picking a tool or are we solving a solution are we solving a problem? And then what makes sense in terms of the tool selection based on that? For us, I have to say we do have a more of an approach that we build things or that we have all the technology on premise versus a SaaS model. Especially if it’s pulling data into the cloud. We’re much more conservative in that respect still.

Sanjog Aul [00:39:29]:

And when we talk about like the AI and and most organizations still do not have the talent or the wherewithal or in the subject matter expertise to be able to pull it off in terms of finally utilizing it to create the end results. So is there enough openness that is warranted by all CIOs and all corresponding business leaders to basically shed those those boundaries, you know, remove break those boundaries and say, it doesn’t matter. You wanted a capability, you’re getting it. Are we shifting towards that mindset because these new disruptive technologies are coming and in most of them, we don’t have the in house capabilities?

Neil Arnott [00:40:15]:

I’d have to say for us anyway, we build our own software for our own TMS here. So we do have quite a bit of talent within our organization for business analysis, and so some of that helps us with some of these tools of the AI technologies. Okay.

Sanjog Aul [00:40:36]:

So, Hem, when you are looking at this whole process of building capabilities over time, so while experimentation is going on, what has been done in the past, in the recent past, because this AI is not the only new disruptive technology. What is a typical approach to going about building this, quote unquote capability? That’s what I look at it.

Hem Chari [00:40:58]:

People, process, tools, etcetera.

Hem Chari [00:41:00]:

Multiple approaches. I mean, we’ve recognized upfront that this is the talent that we need to build within GE. So over the last 3 to 4 years, there’s been an influx of data scientists and the lead technologists who are focusing on those areas. So we’ve been building that capabilities, and then more so, we’ve been also open to recognize that there are opportunities outside the company. So like for instance, we’ve been able we’ve approached Kaggle or Topcoder as an avenue to find solutions for some data sets or sometimes the data sets that we couldn’t find or we couldn’t make sense out of. So we haven’t been shy of approaching outside vendors or outside experts in trying to solve something while we’ve been building our own capabilities in house.

Sanjog Aul [00:41:52]:

Let’s take a quick break, listeners. We’ll be right back, and Hem, the question for you will be about the workforce. So while that’s good that you’re not shy of using outside help, but then there is also a natural displacement of worker that’s likely to happen when you try to automate things or you try to do things differently. That means those people suddenly are not useless because they have institutional knowledge.

Hem Chari [00:42:16]:

You have to train them.

Sanjog Aul [00:42:17]:

You have to retrain them. You have to retool them. So how is that being even tackled? Because you’ve not gone in there and in many cases as you mentioned, that some of the jobs which may not even be, known to us will get created. So what is your approach to talent retention, retooling, management, so that the very people who would see different technologies come and go, they are are available as a sustained capability. Again, human resource has a capability, and we are able to maximize the output from them. So, let’s take, a quick break, listeners. We’ll be right back and explore.

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Sanjog Aul [00:44:52]:

Alright. Welcome back. So let’s talk about the displaced workforce and what do we do to retool them. How do you even manage that transition because retooling doesn’t happen just overnight, and these people, you once you started putting AI in place, maybe you’ll use them for some time to babysit the AI process, but after that, they could not be sitting there idle, and without them being retooled, how do you use them in the best best possible manner? So it’s almost like changing tires on a moving car. What is being tried, Hem, at organizations such as GE, and what’s the recommendation that would come from you in that regard?

Hem Chari [00:45:33]:

I think, this is a I think, for us, it’s an evolving process, and there’s no magic bullet in how to address this. When we talked about some of the use cases wherein automation has helped us, simplify certain lives, that has allowed us, especially in the finance space, that has allowed us to have this so called FP&A to focus more on intelligence and more on strategic aspects versus the data gathering exercise that they were doing. Now from a business perspective, they’re looking at this as an opportunity to do two things. One is we’re going to have to rationalize our organization because now we’re able to get more done through automation, and it also means that the role of that person changes. From an IT perspective, there are two aspects of it. We need to go back and retool the employees because now they need to adjust to this evolving technology, and that is going to be a challenge because there will be some people who will adapt accordingly, and then there will always be people who are much more comfortable with the legacy systems, and then there needs to be a path set in for them because we know over the next 5 to 10 years that that’s not something that’ll continue. So we’ll have to work with them to see what makes sense at that time. The other element is the hiring, the new talent that we’re bringing, especially the millennial generation, and that is I mean, this is a phenomenal bunch of team because they are more adept with respect to the technology and there’s a lot of focus on trying to bring them on and kind of letting them loose so that they can take the knowledge for what they’ve gained and try to implement some of these products within the company. So it’s kind of a threefold answer. I don’t know if that makes sense.

Sanjog Aul [00:47:30]:

No. It does make sense. We just have to see how do these transitions happen, and so Neil, to that end, if you had to wear your consultant’s hat, if you will, and you had to solve this problem for others or when it comes and becomes a capability development issue for yourself since you mentioned that you take pride in developing talent over time, but frankly, some of that talent, not just IT, but also then the business side might get replaced or displaced, not replaced. So they’ll be displaced, so that means you will have to do something with that talent. What do you think could be a good strategy that the listeners could utilize?

Neil Arnott [00:48:13]:

Like, for us, a lot of the automation we’re looking at is about getting rid of some of the data gathering positions, and like you said, giving them more, moving them up the value chain to do more analysis, and sometimes, that skill set isn’t within that individual. For us, it would be more looking and we are also targeting the finance area as well to start. For us, it would be we are always hiring in the operations side. So for us, if someone is doing data gathering, we have enough room within the operations to potentially move people over from our finance department to our operations department, doing similar roles. Although, long term, similar to what Hem said, I mean, we’re going to probably eventually automate the data gathering within the operations side as well. So the low end positions or the positions where it is much more of a mundane task will be a thing of the past. I think everybody would agree with that. So it’s really a question of education and retraining and trying to move people up the value chain if the capability is there.

Hem Chari [00:49:45]:

If I may add to me, I mean, based on an element of the employee wanting to adjust to the moving times as well. I mean, we can provide the training as much as possible, but there’s an element of an employee wanting to recognize that digital transformation is happening and that that change needs to happen within themselves as well. So that is also a critical element.

Sanjog Aul [00:50:10]:

So that’s a shared responsibility of sorts. Right? Or someone to stick around and they should think that, yeah, they wanna do something.

Neil Arnott [00:50:20]:

Okay. Yeah. Very quickly, you thought you’d fix quit the company kind of thing.

Sanjog Aul [00:50:21]:

That is true. That is true. So when it comes to the risk side, so we did talk about the privacy risk. We also have some other issues. So, Hem, if you look at the governance models the way they were taught earlier, the pre AI era versus now because a lot of things you’re trying to hand over to a machine or an algorithm, what would be the shift that you recommend should be in the governance models?

Hem Chari [00:50:45]:

And we are I mean, I think data is the center of everything right now, and when you look at the corporate leadership, when I look when I look at my own leadership, for them, every decision making process, which is now which I would say is part of the governance from a corporate governance perspective, is based on data. They’re looking at data as their as an essential tool to figure out how to manage the business and what to focus on from a future perspective. So for me, that is already getting embedded into it, and this is something that is going to evolve over time as we understand the as we start to really understand the full benefits of AI.

Sanjog Aul [00:51:29]:

Risk management for you, Neil, has it gonna change based on these newer technologies and anything new that you’re going to be doing disruptive?

Neil Arnott [00:51:39]:

Well, definitely, as our company has grown, we used to have one lawyer, and now we have five lawyers. So it

Sanjog Aul [00:51:46]:

So it’s like a risk management but from more after the post damage, if you will, versus anything you could do proactively to minimize or that’s not something you feel we can even control?

Neil Arnott [00:52:00]:

I guess, I mean, that brings back to the conservative approach, right, versus the entrepreneur approach. We have to be careful, I mean of potential liability. I mean, I touched on this earlier. You know, if your chatbot gives the wrong information to your sales lead or customer or if one teaches the machine with, you know, unrealized bias, what’s the risk there? I mean, if your bot starts to populate an application or database with incorrect data, you know, all that type of issues. I mean, one of the things I see with the RPA is similar to almost some of the customized reporting in the past is we might end up with 200 bots running and who maintains those and makes sure they’re all still relevant and who wants the data that you’re creating, etcetera, and there’s a lot of maintenance going on there that would have to happen down the road.

Sanjog Aul [00:52:58]:

On behalf of the show and our, yeah. Go ahead, Hem.

Hem Chari [00:53:00]:

Now I was going to say that one emphasis is data. I mean, at the end of the day, data is the most critical thing. So garbage in, garbage out, then that’s where your risk is. As long as you’re managing that data well, I think the outcome will certainly support the business in their growth, but if your core data has problems, then the risk and all the elements that we’ve talked about will become critical in addressing those things.

Sanjog Aul [00:53:27]:

On behalf of the show and our listeners, thanks so much Neil and Hem for sharing your thoughts on how leaders can work together along with business, the CIOs and the business leaders to make sure that the very opportunity of AI can be maximized. So thanks so much again for your thoughts and comments.

Hem Chari [00:53:44]:

Thanks, Sanjog Aul. Thank you.

Sanjog Aul [00:53:46]:

And listeners, hope you enjoyed, got some nuggets out of this. Please like us on Facebook, search for CTN, CIO Talk Network, and be sure to follow us on Twitter and LinkedIn. Thank you again for listening to this segment on CIO Talk Network. This is Sanjog Aul, your talk show host. Till next week, take care, and God bless.