Vice President, Field Engagement and Marketing
Welcome to this session of SaaS Metrics Palooza 23. And this session is being conducted by our Titanium sponsor, Oracle NetSuite, and their Vice President of Field Marketing and Engagement, Ranga Bodla. And with that, it's the Finance Guide to AI. And I'm going to hand the stage over to Ranga right now. Thank you so much, Ray, for the invitation. And thanks for this opportunity to speak with all of you. I'm presenting today on the Finance Leader's Guide to AI or Artificial Intelligence. My name is Ranga Bodla, the Vice President of Field Engagement and Marketing over here at Oracle NetSuite. And in my role, I get the opportunity to work with our customers, our prospects, and thought leaders across the industry. And one of the hot topics of today is talking about AI or artificial intelligence. So with that, let me go ahead and dive in. So I thought it'd be useful to start with some basic definitions. And I know you've all had gotten some different definitions from folks. I like this one from the American Psychological Association, natural intelligence, the ability to derive information, learn from experience, adapt to the environment, understand, and correctly utilize thought and reason. And I like this particular definition of AI that comes from Oracle itself, which is, in the simplest terms, AI, which stands for artificial intelligence, refers to systems or machines that mimic human intelligence to perform tasks and can iteratively improve themselves based on the information they collect. Now, AI as a term, of course, is getting thrown around quite a bit. Everybody's talking about AI. And I think it's important to look back and think about our history for a second. The history of AI goes all the way back to 1956, when, I guess, depending on who you ask, is often thought of as really the birth of artificial intelligence. And since that time frame, of course, there have been different waves of innovation and investment that have occurred around artificial intelligence. Within this broader umbrella, though, of artificial intelligence, there are concepts like machine learning. There's concepts like deep learning, natural language processing, robotic process automation. And I think the important thing here is, at least in the way I think about it, is AI is a broader landscape. It is not AI equals chat GPT or AI equals machine learning. AI encompasses all of these different technologies. Now, what is so exciting about AI is November of last year, chat GPT 3.5 got introduced to the world and really exploded the usage of this new technology of generative AI. And we're very much at the early innings of what this has to offer. Now, of course, the development of generative AI, language learning models, these has all accelerated quite quickly. And what will happen with it, what we will see, will continue to rapidly evolve. Now, particularly for finance executives, it's really important to understand that generative AI is one piece of the puzzle. But things like machine learning, RPA, natural language processing, these are also all important elements of AI, particularly as it relates to finance and how finance leaders can and should think about using AI. Here's the thing, while there is a lot of optimism about AI, everyone's excited about it, expectations could not be higher for artificial intelligence, particularly for generative AI. I particularly like the Gartner hype cycle and how it talks about this, which is we're at the peak of inflated expectations, which is people are feeling like, you know what, generative AI is going to solve world hunger. It's going to transform everything. Now, I, for one, am very optimistic about the opportunities that generative AI has and what it can deliver. However, I also do think there is a little bit of a reality. It's going to be interesting to see how it evolves, how it will develop. And of course, it will go into a trough of disillusionment before it goes into that slope of enlightenment and the plateau of productivity as we start to use it more and we start to see it. Now, I also like this statistic from Deloitte CFO Signals, which is that CFOs in general are experimenting with generative AI on a budget. 42% of CFOs say their companies are experimenting with generative AI, and 15% are building into their strategy. Now, this was done in Q3 of 2023. And as we've entered into Q4 of 2023, I think you'll start to see these surveys change. But I think the important thing here is, as it looks today, maybe yesterday, if we're really accurate, is that less than 1% of next year's budget will be spent on generative AI, but one third of CFOs predict one to 5%. CFOs are really dipping their toe in the water when it comes to artificial intelligence and thinking, you know what, I definitely want to be there. I don't want to be left behind, but I'm just getting started here. Now, I particularly, we do a lot of work with the CFO Leadership Council. You know, they're a great partner to CFOs and to finance leaders, and giving them advice about things. You know, they have this slide that I've got from them, which is really about opportunities and risks for CFOs in the age of AI. And I think the opportunities are really good ones, and they go in and are consistent with a lot of the different discussions we've had around automation. AI can bring cost optimization. It can help reduce time and help customers become more efficient. It can enhance decision-making. It can help in terms of talent and skill development, and particularly with talent shortages that we have, particularly in the finance realm. And it can also help with competitive advantage. It can help you as a company develop that moat around your organization to help protect you against some of those competitors. Now, of course, that doesn't come without its risks. There's data security and privacy risks, potential ethical concerns, regulatory compliance, cybersecurity, algorithmic bias, and there are other concerns as well. And in fact, this notion of cybersecurity and AI, those two in particular are tied together because people are really concerned about what AI's impact could be on a company, and particularly their finance function, and what that can look like overall for the company. So finance leaders are approaching this cautiously, not unlike how finance leaders approach the cloud. But I think the opportunities will outweigh the risks, and there's a real opportunity here, no pun intended, for artificial intelligence, particularly for our finance leaders. Now, I think there are a number of different areas in which AI can be applied, and I'm speaking specifically now from the perspective of how can this be applied to the area of finance. First and foremost is automation. There are opportunities for AI in terms of automation, automating data entry, financial analysis, inventory management, and I'll give some examples here in a moment where I see this actually being used today, and in some real areas where it can provide some automation, can help assist people in this regard. Actual insights. I think this is a huge area and opportunity for artificial intelligence, where it's helping finance leaders enhance their decision-making, giving them better insights, and also giving them predictions, so that it's helping make sense of all of this data. I think no one will tell you that they have a shortage of data. The issue is having a shortage of information to be able to make better decisions, and to use that to guide how we move forward. This notion of human-like interaction. Maybe we're finally at that point of that enhanced human-like interaction by integrating machine learning and natural language processing. People being able to talk to these systems in a more natural way than we've been used to in the past. I think that's a huge opportunity. I may be dating myself here, but I think of Star Trek IV and Scotty talking to the computer. We're much closer now than we ever were before in that notion. Lastly, this notion of anomaly detection. I think this is one more on the backend side of things, but it's hugely, hugely important, where there's opportunities around enhancing security, fraud detection, and detection of general unexpected patterns. I think there is that concern with cybersecurity in particular, that you have a lot of bad actors being able to use AI to break into systems and to go and create mischief. I also think there's an opportunity for AI to provide a foil to that, to provide better anomaly detection, to have better fraud detection, and to, frankly, to meet some of those things that are challenges that have been introduced by this artificial intelligence technology. So, as a very specific example of automation, here's an example of where AP automation, where of OCR bill capture. AP automation, it's typically within NetSuite. We're leveraging technology called optical character recognition. This technique allows for the identification of fields and values from PDFs or images and extracting them, and then putting them automatically into the system. So, rather than somebody going and pulling up a bill, going and manually keying that information in by using AP automation and this OCR bill capture, it's going and pulling that information automatically out and then putting it into the system. And this is a really big opportunity to help streamline, helps reduce errors, helps automate, helps reduce the time required. And it can also be used in many other different ways where it can be used to extract information from documents for vendor onboarding or other elements as well. Another great example, and I mentioned before, I had on that slide of some of those benefits, I had automation up there front and center. You know, that second one I had was actionable insights. And I think this is another area where I am excited, particularly around artificial intelligence is leveraging technology of AI to help automatically unveil and visualize different hidden insights that are hidden inside the data. You know, one of the biggest problems that we have, there's no shortage of data. That's all, as I mentioned before, it's more of pulling information out and being able to help provide that guidance. So, here's an example on this slide. You know, we analyzed this data set gap analyzed automatically, and we found the following insights. So, it's pulling information out for you as a CFO, as a leader that you can then share with your colleagues and then to start to have a conversation about it, as opposed to all of that time being spent on actually pulling those insights out. The insights are provided for you. So, now the conversation we have, how can I go and use that information? How can I go and change a process? How can I go and leverage this or build on this? On the same side of it, on the other side is just explaining there might be an anomaly that's pulled out. And those anomalies, it's the notion of exception reporting. Rather than telling me all the good things that are happening, I kind of need to know what are the bad things so I can go and focus on those things and make sure to have an action plan for it. Another example is the notion of leveraging natural language queries. One of the things that we write in some of our analytics products is this notion, excuse me, where you can actually talk to the system in a more natural language way. So, rather than having to figure out, like I need to ask it a question in a certain way, you can actually type in more of a natural text, automatically generate those analytics and those visualizations that you need. So, I need a visualization of the number of cash sales by shipping state and sales channel, for instance, and getting that graphic for yourself. Or taking those analytics and converting it into natural text, giving you the ability to convert those graphs into a narrative. And again, this all comes back to how can finance be a better business partner to the other leaders in the organization? And the tools can help enable finance to be that thought leader, to be that business partner. And the finance executives are using those tools. So, rather than them spending their time on actually putting this together, they're spending that time on, okay, here's what the data's telling us and here's what we should do about it. Here's how we can act upon that. And the conversation becomes more on the action rather than the recording. And I think this is a really important element that I think is critically important right now that the finance function is constantly looking for ways in which it can be a better business partner to the rest of the organization. This is just one of those areas. And you think about pulling in data from multiple places, pulling in your finance data, pulling in your sales data, pulling in all these elements, and then being able to have those insights so that you could have that conversation is really critical. Another area where I think AI can really come into play here and is a particular area is in the area of planning. And this is another area that finance leaders can use today. This is not looking in the future, where you can leverage techniques like machine learning to drive your planning process. Machine learning drives predictive and prescriptive analytics. And you can improve your demand planning, as an example, by taking those insights into your buying behavior, your operational capacity. You can identify patterns and anomalies. These are all elements that I think are really, really important. You can take and mine a dataset to predict a target value or identify classes of records. We've built in a number of predicted models that can be leveraged and use machine learning. And these predictive analytics allow the finance leader to build scenarios. They can figure out which algorithm's gonna produce the most accurate outcome. And you can think about this, not just in terms of financial planning and building a better plan, but also supply chain planning, sales planning, financial planning. I mean, all of these are different ways and can be used. And on the right-hand side, with this screenshot of the statistics, you can think about that as a capability to identify those outliers, those trends, and those clusters. And these are all elements or examples of artificial intelligence that they can use today that can help the average finance leader to be a better business partner to the rest of the team. You know, the other aspects of predictive planning is it enables you to think about how do I better forecast into the future? So again, leveraging your historical data to make sure that you're using your historical data to figure out where you can go in the future. Now, again, garbage in and garbage out. You do have to be very, this is where I think the data maturity and data governance is so critical. You do need to make sure that you've got good data and, you know, unfortunately, or wherever you wanna look at it, AI is not gonna solve. If you've got bad data, you've still got bad data. But if you're thinking about that data maturity, thinking about the right data governance, and you pull that into these systems, it will help you forecast out into the future based on the historical data. You can predict sales units by product and looking at that as an example. You can run a predictive plan. You can change some of those parameters. And really get that predictive plan. And this is, I think, really the, where AI is really gonna become powerful for those finance leaders is the continual planning process. Being able to think about those scenarios and be able to model what those scenarios look like so that we can better be prepared to take action on those to figure out what's gonna actually make sense and how is that going to impact where we go. And just, you know, some examples here where you can leverage that predictive planning for that. The last example I wanna bring up in terms of a visual example is risk prediction in a supply chain. So in this example, this is a supply chain control tower. And the idea here is to use artificial intelligence to run simulations that track inventory levels and predict the effect of changes to say a bill of materials or the production process. These predictive risk tools really let you model your supply and demand in detail, which results in a reduction of those production bottlenecks. And this is a really important aspect of making sure that those supply chain planners, for example, can use this to figure out, okay, do I feel good about this? Do I have an expectation that I'm gonna be able to do this? Do I have an expectation that I'm going to be late? And then it could change either communication, it could change what you're doing and working with that vendor. You know, again, these insights are really important to make sure that you're setting expectations. The biggest, one of the biggest failures is oftentimes is not necessarily the failure to deliver, it's also the failure of setting expectations properly. And so the prediction planning here as an example is a really great example of making sure you set those expectations properly and bringing that across. Now, I wanna go back to the slide that I shared earlier. And, you know, just to recap, you know, we talked about some examples, how you can leverage automation, you know, an example of OCR bill capture. We talked about actionable insights with the example of auto insights, or really that narrative. We talked about that human-like interaction where you can have more of a natural way of asking questions. And then the last one, which we didn't talk about, but I'll just bring up here again, which is that notion of anomaly detection. Anomaly detection is so critical right now where it's really important with us in this notion. In today's age, where you have bad actors, we wanna make sure that we can catch and stop those bad actors and prevent that. And so I think it's really important to use technologies like this anomaly detection as a way to make sure that those bad actors can be stopped. And oftentimes this is on the backend side of things. It's not necessarily visible to the user, which is good, but you should know that your provider is thinking about this and that they're focused on it to make sure that you are protected and your data is protected, which I think is probably the most critical element of that. So just kind of bringing this back here at the end, I think there's some key takeaways that I wanna make sure and leave with you for all of you finance leaders around artificial intelligence. Think first and foremost, you have to have a AI-friendly mindset. AI, while the expectations are high and this will change over time, it is here to stay and it is going to only grow in its importance. And I think it can have huge impacts and opportunities for finance and I couldn't be more excited about that. The second is you should explore and understand artificial intelligence and finance. There are a number of use cases that you can use today and you can leverage, but everyone is developing these capabilities and they're starting to incorporate them into your product. You are gonna see this first phase really being about enhanced applications and finance can be able to leverage those capabilities. Most everybody is going to be pulling these in. I can say NetSuite I know is pulling these capabilities in today and continuing to evolve on bringing artificial intelligence into our applications. This is only gonna continue and evolve and you're gonna continue to see that grow over time. And then lastly, think of AI particularly as a partner for you. Think of it as a partner that can both advise you, give you information to help you do your job better and assist you. It can help take away some of those manual tasks, some of those elements of your job that frankly are manual, computationally intensive and take a lot of time out of your day but don't provide much value at. And that's really where I think some of the opportunities are for AI. So with that, I'm gonna go turn it back over to Ray to close us out. Ranga, thank you so much. We were talking before you came up on stage and you had just attended an AI developers event, I believe conference. It was interesting because we have 50% of our audience here are finance leaders and they're like, oh my God, it might be fine. But I think you heard some things that now is a great and fine time to begin because there was not as much deployment in finance as other departments. Is that accurate? Is that what I heard? Yeah, I think what you're seeing is a lot of the use cases. Well, this is particularly in terms of generative AI. In generative AI cases, in particular you're seeing it in sales, you're seeing it in marketing. Finance is further down the row, down the stack. I mean, I think that will change over time. I do think the important point is, if you're worried that you're behind, you shouldn't worry that you're behind, but what I would encourage everyone to think about is how am I thinking about AI and how it impacts my strategy? How am I thinking about incorporating AI into my organization? And how am I making sure that I'm thinking about AI as an opportunity for my organization? I think that's, you know, as I mentioned, I think that AI-friendly mindset is really what I would encourage the finance leaders in particular to think about because you have an opportunity here with AI to really leverage it much more broadly in your organizations. Well, thank you so much, not only for being our Titanium sponsor, that's Oracle NetSuite, Ranga Bodla, who's the VP of Field Marketing and Engagement. Thank you for a very thought-provoking and insightful presentation. Really appreciate it.