Challenges and Opportunities of Deploying Enterprise AI - with Derek Choy of Aktana


This is daniel fidel. And you're listening to a and business podcast into thursday. So this is our making the business case episode. Every tuesday we cover use cases every thursday. We talk about the arwa. They i and the realistic considerations for deploying ai. In the enterprise. We speak this week with derek choy. Who's the co founder and ceo of a company called Mcdonagh offers Sales and marketing. Ai based solution into the pharmaceutical space this is a company that has developed a solution in a part of the farmer ecosystem. That frankly doesn't have that much attention rather complicated use case rather different than the way. The companies are running their marketing and sales operations. Now so how do you make an ai. Product work when you're not just one of many but you really are changing the game. What are some of those hurdles you have to overcome and are the some opportunities you can kind of grab hold of bringing a brand new type of product to market. If you're a vendor you're gonna wanna listen to this episode and if you're an enterprise leader that's may be considering spinning out in a product or even picking what vendor you might wanna work with. These realistic challenges are things. You're gonna want to bear in mind and making those decisions and making the hopefully with a greater degree of context and a greater likelihood of success. I really do appreciate derek. Sharing some of his journey some of his hard lessons learned in making ai. Work for novel new use cases. If you're getting started with the pulling in your business or in your enterprise you wanna help your clients. Deploy artificial intelligence. You can download our beginning with a. I guide it's three critical insights for non technical professionals. If you don't write code but you really want to understand core fundamentals about what it takes to apply in an enterprise context go to emc rj dot com slash biji. One and you can download our free. Pdf brief on exactly that topic. It's beginning with three key insights for non technical professionals and again that's e. m. e. r. j. dot com slash b e g and in the number. One that's being g like beginning and then the number one if you're looking for more context on exactly this topic and taking some of these insights the next level. Hopefully that resources will be helpful for you without further ado. We're gonna fly into this episode with garrick choi of tana. You're on a in business podcast. Derek we're gonna talk about making the business case and you guys are in the life sciences domain obviously a world that's that's certainly not not nude investing in l. Now when you kinda go about explaining to a customer client what's different about deploying in a system like the one that you folks offer versus of traditional it. What are those key distinctions. That are really important to be frank about an honest about that. Make the the adoption deployment considering a little bit different. Yeah that's a great question. I mean i guess the first thing. I think something everyone probably already knows the importance of data and what that means is the ability to. It's not just the importance of having access to data that's available one off but having ready for it to be used for scalable aiops locations in this means having it in a the rightful matt and kind of unified but also having Updates it's updated frequently enough to be able to be used to update models that you create so i think that's a really big thing and the readiness of accompanying. Does i need to understand that. That's a heavy list one of the things that you know. We've been focused on company. Help customers move. This is understanding that. There's a possibility that the more you try to have your data the ready to not only build machine learning on but actually have that same data set be the same data and structure. You're going to test deploy this model in production on the easier it gets. I think something that we really help customers with and we we try to push them towards which thinking about rather than taking dada that might lead you to Theoretically optimal betta prediction but is harder to integrate in full hodeida to a maintain trying to focus on getting the subset of data that you can actually pull into a data warehouse and be able to run your eye on top of and do that in production that is actually gonna be more valuable than focusing too much on the ideal lonzo just more spots and maybe not easy integrate. That's one aspect. Yeah yet in. That might even be a little bit different customer to customer but certainly as the data considerations are going to be a big part of the mix up. there's so much hands on guidance from vendor companies like yourselves to take that into account. What else is a little bit different. Yeah they gotta have their data house in order and obviously the usually needed out of guidance from you. What are some of these other ingredients. Many definitely need. I'm the right kind of focus on people had change management. This is something that i think we've learned over. The years is so critical that so much complexity and so much going here that you actually need to make sure that the organization is ready for the change. That's happened and so what this means. What we found is the companies on most successful are the ones where all the way from the right kind of from the executive level that dedicated to and they're they're willing to undergo the change that's required in the life. Science industry means a commercial organization that brand strategy to change the way they managed content is going to change the content needs to be more real time needs to be tagged in the right way Greater needs to be willing maybe to move in a different way so they done slowdown kind of what's happening rations in the chinese mocking operations training the sales representatives Trained on different things and the sales reps themselves as well as the management will need to adopt different tools. At if that's gonna happen. You need that biden all the way from the top but also need that buying from the lowest level as well so it at the top down support the local buy-in so that changes not just something that is talked about but it's actually something that people adopt and continue to engage with ties back to another aspect of why think is different with a solution set up once and then done in a what we've found what we do in terms of recommendations is you have the scattered e that you set up within your engine and that's setup phase but then over time you're learning all the time the strategy is changing. The market is changing. You're looking at things out working and you ought to get things done not working. You're adjusting kind of what the engine is doing. And that requires kind of an ongoing kind of Maintenance as well as support evolution which might not be as and you have to plan for that you have to get like the role of the right people involved at it might not be as prevalent. We looking at just standard. It solutions yeah mean. All three of those. I will say are relevant in every single industry. And i'm sure you can imagine that tackling oil and gas to financial services. The data considerations the change management kind of cultural willingness considerations in especially you mentioned regulatory obviously finances plenty of that and then the served the considerations around ongoing maintenance upkeep iteration you know prevention algorithm drift Adjusting to reality. These are new. These are different than i feel four executives who are not aware of this that that sort of presumed that. Ai is just it. We're just gonna plug it in just like we did with salesforce just like we did with hubs just like we did with whatever all of this comes as a surprise and a big unpleasant surprise as opposed to a natural part of kind of beginning to get to the next level when it comes to solutions for their company. Obviously i'm not saying is always the right move to adopt but i think with the right expectations. People would be in a better place to make a smart decision. When you think about how to frame these will we could. We could call new challenges new considerations how do we frame these new challenges and new considerations to make sure that you know we're we're sort of educating these buyers as we're moving in with them because they may not know that this is what i requires but we may need to say look. This is what it is. But here's why it's actually a good thing. We kind of make that argument for folks that don't quite get it yet. Yeah it's really important aspects of the obviously being able to convince all the stakeholders you need to have that buying as i mentioned that by critical away from the top and yet the focus on what they care about. I think what we've learned is we have to focus on what the business outcomes in the impact. You're hoping to have and baking sheet a defining that all the way from the start but then also making sure that you break that down to kind of some different indicators that you can use to help drive towards that and i think you know if in the will of commercial operations in kind of trying to optimize. That was a marketing. The ultimate outcome. You're trying to help his sales you would focus on. How can we show that. It's gonna be sales impact in the area but then also being very clear about why you're going to see it and being cleared some of it is gonna come directly from the use of you know the intelligence itself by providing better decisions that someone's going to act on some of us actually gonna come more indirectly because you're gonna be having decision makers think and act in a different way than they did the full they're gonna be more data driven even if the recommendation that you make someone is to do x but they look at it and they say you know what. I'm gonna do why. That thoughtful decision is actually different than what i might have done. That may lead to them. And by the way if you close the loop may also lead to your recommendations getting beat up because you see someone took a different action you recommend it and then you learn that actually leads a bit outcome because i think when you think about impact from hey i do have the direct impact which is when you have a data driven recommendations be adopted. Obviously that is one one place where you're going to be driving different behavioral type of indirect impact on the fact that people paying attention to the questioning things. They're kind of engaging with systems rather than just having them be something that they input tool off things to actually is indirect benefit that you qualify for someone as you're thinking about executives We think about that. Derek is will we have breakdown. Roi into three ways. There's measurable roi which is often tied to something financial or something that proxies to something maniacal strategic roi and then also capability roi so the new capabilities. We need to build i. E cross functional teams. I e better data infrastructure. Those things. can we want to be able to frame our deployment so that the necessary capability building has spin out benefits and prepares us better for the future in general and it sounds like you're trying to do that with a cross functional team thing and i think that that's it's very smart. I think that you know these are seen as hurdles by executives unless you can frame it in a better way. Are you also able to do that. With data those you folks have to get your hands on data and infrastructure. And make sure that's clean are you also finding. Is it possible in your case. Anyway to find a way to frame that overhauling rethinking of of how are data is sorted cleaned harmonized as having kind of spin out future positive enablement for the company beyond just the expense of getting your your system started absolutely and i think the way that we try to do this is number one. Recognize that when you are making the investment in the data to be able to pal like what we all this the benefit directly get when it comes to using it to drive s disfigures case but then there's also the benefit that you can start using that data. Full father use cases and really. It's recognizing that somebody think about this as a second order data. One of the things that happens is you leverage dada announced into recommendation engine. And then you ends up providing you with a new set of data which is how with data used drive a use case or particular type of a strategy and that strategy effective with that customer. In that second type. Dodd created in terms of the customer responsiveness to a particular tactic or particular hyper strategy that dada is potentially even more valuable than initial if customers not realizing companies not realizing that the initial data it builds on itself inland second order things from it and then they start capturing that any integrating back. There's a whole another set of values that can kind of cool. Yeah and again. We've got to take the things that are that are hurdles that are seen as challenges and frame how they're actually modernizing improving. Nah helping the company moved forward any last closing notes. Derek things for you when it comes to really making the business case you know getting to yes for lack of better terms when it comes to an ai solution. That really for you is important. You've talked about some great points. I love this capability focused. And i think it's awesome that you folks are are really conscious about making that part of your presentation. Because i think it has to be. I think very mature thing. Any other little notes about what's critical for for sort of framing the right way and getting you know getting to a pilot or deployment think setting the expectations. The right way away from the stock that when when you gonna a you're gonna wanna leverage the best when it comes to technology with the best when it comes to human intelligence and on there's always a human factor to it. I think that's been pretty critical for us as a business and how we position ourselves and have credibility without customers. That when you're gonna liberty is solution. It's it's great that you can up with the best algorithms and machine learning models and optimization but you gotta need to associate that as well with the best input for the rules and constraints that you gotta set from the experts that you have but also you're going to need the focus on that change management in adoption from the news and those people that will be managing the program. But also you're gonna need to focus on the element of what the or the play is. What the company needs to State cone is need to be able to do to get a managed to scale think. She's set those expectations upfront. In you also associate that with the impact they get in. You can see the very clear and transparent about the hour ride. Not just being about the cost of the the data kostov the technology but the awry in the cost element needs to include kind of the human aspects. Such managing that change and itching still demonstrate. That win those things. All add up. You still gotta get the impact that you're looking for and you still have a huge win that transparency really helps people have credibility that this can actually transform an organization. I like it. i think that's that's really the only way to to grow one of these. Very hands on survey is solution firms at at scale. I think that companies like yours. Derek whether it be the the bespoke ai consultants who know what they're doing or the vendors that have a lot of experience and learned a lot of very hard lessons as i'm sure you have on. It's it's really you folks in again. The people selling these services to some degree who are going to. I guess i could call it. Your pitch is actually part of what's necessary it's necessary education for the c. Suite two to think about and like you said set the right expectations about what is so. It's sort of part of leveling up their smarts at the same time and i. I think that's a pretty cool win win. I like the way you worded it derek. I know we're up on time for this one. But i sincerely appreciate you jumping on and getting into one of these making the business case episodes with us. It's been great to have you on absolutely thanks for having me. That's all for this episode of the ai. in business. podcast. I appreciate you joining us if you like what. You're hearing here. Be sure to stay tuned on social. You can find us at emc. R. j. on twitter or at emerge official intelligence research on linked in or on facebook. And you can get all of our latest infographics latest articles and our latest episodes of this podcast. The i'm business podcast. And our other show called the a in financial services podcast. If you're interested in banking insurance financial services. Broadly before to stay tuned for that podcast as well. If you're not a resubscribe you can search i in financial services on itunes or apple podcasts or on spotify or soundcloud and you can subscribe to us there as well and stay tuned for that show but otherwise do hope you stay engaged on social or twitter following has really grown since i started mentioning social at the end of the podcast. It's been great to hear some of the comments and see some of the engagement from some of our longtime listeners. On social as well. I really do appreciate you. Great part of the community. We'd love to put into the broader conversation on social to. I'm often creating lengthy threads on twitter and linked in where we have some of our listeners and subscribers chime in with some of their ideas and maybe you could be part of that conversation so follow us on lincoln. Twitter facebook otherwise. You'll catch us here. Next tuesday for our use case episode and the airline business gas and i look forward to catching them.

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