How Enterprise Leaders Should Measure the ROI of AI - with Darko Todorovic of HTEC

**Daniel Faggella** (0:12)
Welcome everyone to the Emerge AI in Business Podcast. Today's guest is Darko Todorovic, Chief Technology Officer at HTEC Group.
HTEC is a global engineering firm focused on AI-centric software and hardware development, working across financial services, medtech, automotive, telecom and enterprise software from more than 20 engineering centers. In this episode, Darko examines why enterprise AI investments frequently produce successful pilots but stall when deployed at scale. A failure rooted not in technology, but in organizational readiness, talent gaps and the absence of defined success metrics. He walks through how leaders can establish cost per unit baselines, build ROI measurement frameworks and treat AI agents as co-workers rather than tools. A mindset shift, he argues, is the critical precondition for scalable, measurable impact. Today's episode is sponsored by HTEC. In this episode, we cover how enterprise leaders can measure and prove AI ROI after deployment. To go deeper on this topic and learn how to identify real AI trends by tracking where venture funding is flowing and by listening to how leading CEOs describe risk and competitive strategy, download our free PDF report, Three Ways to Discover AI Trends in Any Sector, at emerge.com/ait1.
That's enerj.com/ait1 to download your copy. Now, the conversation with Darko.

**Daniel Faggella** (1:57)
Darko, welcome to Emerge's AI in Business Podcast Studio.

**Darko Todorovic** (2:00)
Thank you. Thank you very much for having me, Jolanda.

**Daniel Faggella** (2:02)
That's great, I'm excited to talk to you today. I know that you've been inside engineering and delivery for quite some time, which means that you've been on the receiving end of what clients actually ask for, what they think they're measuring, versus what they actually end up being able to show. And it always feels as if the ROI conversation or question is very straightforward at the start of the project. I always have this direct idea at the beginning, we're going to do X and it's going to deliver Y. But then somewhere in between the commitment and the close up, our clarity kind of disappears and we kind of lose track of what it is that we want from this. From your perspective, what is actually happening in that gap?

**Darko Todorovic** (2:41)
Well, that gap is multifaceted problem at this point of time. First of all, the technology is not mature enough. You don't have the basic components that you can choose from. So from the implementation side, it's always a question of how to go about it. We know that we need data, but nevertheless, we think that we have the right data, right sources, that everything is one data lake, et cetera. At the end of the day, that is maybe not usable in the proper way by the AI agents. So on the technology level, on the technology layer, we need to make sure that we have the right setup for the problem that we are solving, which brings us to the point is we shouldn't start by implementing technology for the sake of technology, and just putting a sticker on top of the process that it's AI process. We should completely rethink all of the processes that we want to augment with AI, and really see how we implement it with making sure that AI agents are not the tools anymore, but they are our coworkers in the process. So when you start from there, there is a higher likelihood that the success of the implementation of the AI-enabled process is going to be successful, and that you can see the tangible ROI. Finding these processes is the first task that every organization actually gets as a task. So you have a task force that is formed inside of a CIO office or a CTO office. It depends on the maturity of the company, that we need to bring up our efficiency with the implementation of the AI tools, and you are asked to do that. So first of all, do they find the process? They say, okay, this is going to be the process that we want to augment with AI. Then we are going to choose the tools that we are going to use in technologies, etc.
Then we are going to implement the perfect process with the help of AI. In demo, this works perfectly. Then we are going to deploy it to a customer or customers, depending on the maturity of the company and what the company actually does.
Once that happens, usually things go south. This actually is a function of not the technology, the advancement of the technology that is implemented, not the function of the wrong process that was selected, is the function of the changes that need to happen inside of the organization, but more importantly, within the people that are using, that are on the receiving end of these AI agents, to help them actually be more efficient in the processes they are delivering or they are working on on day to day. So in a nutshell, I think that having the organizations realize that this is a huge change management problem is very important at the beginning of any AI down.