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AI Leader of the Month: Abid Bodla, CEO & Principal Consultant at Novizant 4-1-26
00:22:54
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- [00:00]
A
This is Scott Becker with the Becker Business and the Becker Private Equity podcast. We're thrilled today to be joined by a brilliant leader in artificial intelligence. In fact, we've selected Abed Bodla as our AI Leader of the Month at Becker Business and Becker Private Equity. He's going to talk about his company, NovaSent, a technology consulting firm that focuses on mid market enterprise companies, but does a remarkable job of trying to fill the gap from idea to actually implementation of enterprise level AI solutions. Just a brilliant, brilliant leader. Abed, can you take a moment to introduce yourself and tell us a little bit about novasent?
- [00:44]
B
Sure. Thank you Scott for inviting me. So I'm the founder and CEO of NovaSend, which is a technology consulting firm where we help enterprise companies, think companies into healthcare industry, manufacturing, home builders move from experimenting with AI to actually running AI in production on the Microsoft platform. And our focus mostly is on Microsoft. And a little bit about my background that I've spent about 20 plus years working with Fortune 500 companies and enterprise technologies and I've seen that a lot of technology waves come and go. What's different if you focus purely on AI is that in AI the gap between a compelling demo and a system that actually works in a real world is enormous. And that gap is exactly where we operate and in novizant in our AI projects that we are doing right now we are in the middle of a very exciting AI engagement and this is with a company that's one of the major medical image device manufacturing company where we are doing a very transformational project with their customer service engineer training department. We are using Microsoft AI foundry and agentic AI and to implement a process that fundamentally change how their customer servers engineer access knowledge and how their training department develop and train a training material for their customer engineers beyond consulting work. Just last bit here is that I have also built a voice AI SaaS solution which has helped me actually learn a lot about how a product can be developed especially for AI. And especially by implementing this as one thing to advise client on AI strategy and it's another to actually build and deploy an AI product yourself. So you have gone through that experience of building a product and deploying it out in the real world, which makes a huge difference.
- [02:56]
A
When you're a consultant who's actually done what you sell or done what you work with clients on and they've actually done it, it gives you a whole different level of understanding of it that's almost impossible to replicate, quite frankly. You mentioned a bit this concept of this gap between sort of the demo to an actual solution, an actual practical solution that somebody's using. We often think of that gap between sort of the one off uses of AI versus enterprise solutions. Talk a little bit about how you help people bridge that gap from sort of demo or idea to actual useful enterprise solution. Talk about what that looks like. And we know that's so important, but how is that actually done and how do you work with clients on that?
- [03:43]
B
So demo sometimes usually just picks up a one scenario, right? It doesn't look into overall framework, the governance, security, that those doesn't come into a play. And that scenario usually is very easy to implement because you have a very controlled environment, a use case, networks perfectly fine. But when you are building into actual real time solution then you have to think about how it will scale. What are the guardrails you have to put into place from security point of view, how will you get the data that is needed for the solution? So for this IntelliFlow or the project that we are developing for this particular client for for AI solution we have to put in the data from different environments. They use service manuals, there's a video transcript sitting in different environment and SME knowledge into different email systems, other drives. So building this up, creating a rag layer that agent understand and then making sure that agent doesn't hallucinate. So there are a lot of things you have to do to make it production ready. That takes time, that needs commitment from executives. And then you test it out, make sure it works again and again and eventually it works.
- [05:17]
A
But there's a lot of commitment and drive there. You work a lot with mid market and enterprise companies that are sort of moving from this to fill this gap to get to real productive uses of AI. What are their teams have to look like to do this? Well, is there a certain amount of staff and intelligence and commitment they have to make to it to work with a consultant and make this work really well?
- [05:41]
B
Yes, of course we have seen is that the people because in this particular project that we're working on right now everyone is busy into different projects they are doing over the year. Teams I have is already reduced in multiple departments that I've seen the clients we have worked with. So that commitment and buy in from the executive levels at the highest level is very important. They are giving the time to the team that need to work on and then there's definitely need to be a status update and oversight. I would say on the project that it is progressing well and if their bottlenecks are coming that that is taking the priority and sometimes we have offered to help like for example this whole Azure infrastructure setup that we have to do for this particular project. The client team is busy so though it was not part of our scope to start with, but we have offered to help that okay, we're going to initially at least set it up for you guys and help you while you are busy so that the bottleneck can be avoided. It's just to making sure that it just doesn't drag down and everything is on the top. Executives are monitoring it, status update is happening and what is an end goal. It's very clear and communicated well for the project.
- [07:10]
A
Thank you. I love that clarity about the end goal, clarity about where we want to be. When people are doing AI integrations with sort of cloud and enterprise solutions, where do companies underestimate the complexity of what they're trying to do and how do they get through that complexity to make sure you get to fill this gap from idea or demo to actual performance. Where do people underestimate the complexity of what they have to do and then fill that gap? How do they end up making that work?
- [07:43]
B
So there are two things to it, right where the company stand right now. Are they migrating their stuff over to the cloud as part of this AI exercise or whatever or part of a digital transformation? They're moving to a cloud. So the cloud migration sometimes data readiness, data readiness to move the cloud could become an issue. Right. Sometimes they underestimate the cost of the integration because in the beginning it looks like it's a simple lift and shift. But for the enterprise customers they build solution over years and it's when this actually start to look at the integration it start becoming clear that it is more complex. The workload that have been built over decades, business logic buried in on premise system, custom integration, hard coded processes, undocumented dependencies that someone has built something or that engine is no longer there. So integration definitely becomes a challenge. And access to the data and the data and its access is most important when we talk about AI because that's the foundation layer when we're talking about the rag layer. Now I'll get back to the same example I gave earlier that for this particular project that we are doing IntelliFlow project for this medical imaging device manufacturing company to build a rag layer especially for the service training department, it's the same thing. The service manuals are at different location, videos are getting stored into a different system and they're transcript and their SME knowledge is sometimes it's an article, sometime it's an email. And how do you combine all these together? How this knowledge becomes meaningful to AI to generate the content that they are looking for is definitely become a challenging environment to deal with. But the honest answer is your integration debt is always worse than you think. So the AI projects have a way of exposing it fast because AI agent needs to look at that knowledge and then it has to generate the content that you're looking for. So integration challenges definitely are going to be there. That needs to be resolved going forward
- [10:12]
A
as AI integrates more into the workforce, into what companies are doing and delivering at mid size firms, midsize companies, what will that mean for the engineering teams, for the software teams, for people traditionally in a lot of these roles, we're seeing it sort of the mega companies, Amazon meta platforms looking at some layoffs. Some of that they say is due to AI. Some of it of course is due to them just trying to be a little bit more profitable so the markets respond to them better. But what will this mean for a typical engineering or team technology team at a company, a mid sized company, in terms of the AI impact and
- [11:00]
B
what
- [11:00]
A
impact this might have on a mid sized company's team and group?
- [11:06]
B
So it's a conflicting. Even if you look at media, what people are saying and the podcast other people are saying, for some people it does say that it's gonna change, introduce the jobs very quickly. But when you look into some other people and it isn't a reality as well, that it's not going to happen overnight even if it happens. So it's not the fewer people, it's a different leverage. I would say the best engineers today are the ones who can direct AI, not just write code. And the shift I'm seeing is for individual contributors who can execute to engineers and who orchestrate. So you need people who understand how to design agenting workflows, how to evaluate AI outputs and how to build systems where AI and human hand off to each other intelligently. So that's a fundamentally different skill profile that most engineers teams were hired for is different than what they were hired for. But this has happened over the past engineering or technology advances as well that you have to retool your team. And so for leaders my practical advice is this that don't restructure your team yet upskill first let bring them up to AI skill, let them take advantage of wipe coding we call it identify the engineers who are natural and then you identify your engineers who are naturally curious about give them real projects and let them build the muscle of your organization Then redesign your organization around what you have learned. And the companies that are getting this right aren't the ones who replace their teams, they're the one who retools them. Because that your team has all, especially the companies who have built system over years in the organization. Their teams have all the knowledge. So they definitely have to retool them, they have to train them. I would say they may not need their partners that much. Over time, team may reduce, but right now I feel like that you still need your team to build a solution and build efficiency and even build new way of doing work that can contribute to profitability of the organization.
- [13:38]
A
I think that's so right and so important. At the end of the day it's going to be people plus technology people plus AI. And at the end of the day, to make it really work and customize for your group, for your team, for your company, you're going to need internal people to go with your external resources like Nova Scotia to make this all work. I mean, is that a fair statement? You're going to need sort of both, aren't you?
- [14:03]
B
Yes, I agree. And whenever does such need come up, you definitely have to decide between whether you want to build something, you buy off the shelf something, or you partner with someone. So the framework that I use comes down to three question, like whatever you're trying to do, is this core to your competitive advantage? Do you have the talent to sustain it? And how fast do you need to move? So if the answer to all three points away from building, then you partner with someone. And in the AI world right now, that almost always means partnering around a platform, not building foundational AI from scratch, except on very special circumstances. The real decision is which platform ecosystem to bet on and how deeply you want to integrate it. And what I tell clients is that buy the commodity partner on the platform and build only what makes you unique. So for most of our clients on the Microsoft stack, that means that use Microsoft AI Foundry and Copilot and handle the heavy lifting and we build workflows and get some partner in the beginning like, and if I go back to my previous question where I said, you know, train your team internally on AI, sometimes it takes time. So, and I do agree that if you want to, you know, speed things up, you definitely need to look out for partner who have skills in that, in that particular area and can, can make sure your POC is successful, can make sure that when you're building that ground framework, a partner who has done these things multiple times, who has worked on different industries Have a more broader experience of running into issues and how to resolve them can definitely help build a sound foundation or a framework to building AI solution.
- [16:12]
A
And you've got this great experience of having worked both with and for Fortune 500 companies and also having buil your own stack your own stuff that you work off of, which is, which is remarkable. And you talk about sort of buy what's the commodity and then put on top of it the stuff that really takes domain expertise and true partnership expertise. Is that a fair statement?
- [16:36]
B
Yes, I do agree. Yes it is.
- [16:38]
A
And we talked about this a little bit earlier. When you come into companies to work with companies that are really trying to fill that gap to AI enterprise solutions, when you look at sort of technical due diligence, what do people have to do to be prepared to do that? You mentioned some people have to do a lot of work to get their data in the right order. What are some of the things you see that companies should think about when they're getting ready to really deploy AI? More enterprise type solutions, bigger solutions.
- [17:09]
B
The biggest thing of course is when you bring an AI solution like I mentioned earlier is integration which sometimes goes into a technical debt because AI needs to communicate with this system, right? You have to see that is your organizational systems are up to par, right? Because if you are build, if you have organizational systems that are not open, not easily accessible, then you go into, you know, making those system accessible making so that AI can understand, communicate with that system. And to some extent it goes back to, you know, building a middleware, building an integration framework to communicate with those systems. So that's what I would say, right? You know, that your systems are ready to communicate with AI is definitely very important. And then you know, building a platform that you have. So either you go with a, for us it's always Microsoft framework or Microsoft Foundry, which is the enterprise rag that we use and then use Copilot and other system to integrate with those systems to you know, build it out.
- [18:26]
A
Fantastic. And talk for a second. When you look at sort of the rest of this year, where are you most focused and excited as we, as we work through the rest of 2026, what are you most focused on and excited about?
- [18:40]
B
So I think for, for us who work with to large enterprises, I think the I'm excited about is my focus has been over the past year, past many years has been, you know, custom building custom workflows or business processes for the clients. And I'm seeing more and more clients are now bringing in that AI flavor to it that how do they improve those processes and add through it by using AI and this particular project we are doing for client, for this generative AI, for training material, for knowledge access and this is more and more happening with more organizations where they see a value of it, they see that how it helps their current work staff do more and do quickly. And I will just, I think I'm going to see more and more such projects happening this year and I'm very excited about it. Making the systems go live, go into production is very exciting for this year.
- [19:45]
A
And Abed, let me ask you this question. What advice would you give to sort of a mid sized company, mid market company that's trying to deploy AI at scale, that's trying to really make a difference and make that impact that AI can have? What advice would you give to companies trying to do that? Are there overriding pieces of advice that you would approach a company with?
- [20:12]
B
When we talk about scale, of course you have to select a platform and for us it's Microsoft, it's scalable. Their Microsoft Foundry is scalable. Same is true for if you're working on Amazon. You just have to select a platform that's scalable. You have to select a platform where your data already reside. In our case most of our clients, some of the systems already in Microsoft, they're already into Microsoft Cloud, Azure Cloud. So selecting the right platform is one thing that is scalable. I'm talking about the AI platform. Another is working with a partner who have done a solution at that scale and have experience of doing those solutions. And the third of course is to making sure that those companies see the value of it and they have a enterprise or executive focus, an executive buy in to make sure that those projects get done quickly right and priority is given to them. Last year, if I look in my experience that we have done last year was more into a POC and companies didn't was not much focused on bringing those POC to production. But this year seemed to be it's changing so they should just stay focused on it. Challenge will come in any new technology, new product. So they should just focus, just need to stay focused, work with those companies who have experience of building solutions. Just to rephrase it, select a platform that is scalable and can handle multiple models like Microsoft Foundry can now handle 11,000 plus different models you can use into Microsoft Foundry, Whether it's an OpenAI, a cloud on the other models that can be used. So focus on three, four those things and then it has to be incremental. You have to make something go live which is measurable. That's a value. So build on a success and then scale out of it throughout the organization.
- [22:24]
A
Again. Abed, what a pleasure to visit with you. Abed Podla, CEO, founder of NovaSent, Brilliant AI Leader of the month. We are so thrilled to get to feature you, Abed. What you're doing is remarkable. The mix of intelligence and practicality is unmatchable. Thank you so much for joining us today on the Becker Business and the Becker Private Equity podcast. Thank you very, very much.
- [22:50]
B
Thank you, Scott.