Why growing businesses hit a system wall!

Hi everyone, welcome in and I'm glad to have you here. I'm Kristine Anne Lagarza and I'm hosting today's session for CloudMotiv. Before we get going, quick house rules: this is a live session, so please drop your questions in the chat box as they come up and we're saving time at the end for a live Q&A.

Today we're talking about something almost every growing business runs into, whether or not they have a name for it yet: the moment your systems stop keeping up with your growth, and more specifically why AI—which is supposed to be the fix—ends up making a lot of leaders more nervous, not less.

Quick shape of the 45 minutes: we'll spend some time on the problem and the trust deficit around AI adoption. Then we will hear three different views of the same problem from Nakul Joshi, Akshay Pol, and Akhil Malik. From there, we'll get into the real cost of AI and the CloudMotiv approach to fixing it, go one layer deeper on data and trust with Akhil, and close with live Q&A. I want to introduce our moderator for today, Deanna Grady, CRO of CloudMotiv.

Wonderful, thanks Kristine and hi everyone. I wanted to start with a bit of a confession: most of the businesses I talk to don't actually have an AI problem. They have what's called a trust problem. And it's not irrational. If you're running a growing business right now, you're being told AI is absolutely everything—the productivity unlock, the competitive moat, the thing your competitors are already doing that you're not.

And yet, when you ask people to point you to the results—to the actual dollar impact—the room gets quiet. What we hear over and over is that apprehension isn't one thing; it's manifold: blackbox costs where nobody can tell you what a tool will cost once usage scales, unclear ROI where confidence and expectations are low, and fragmented ownership where nobody in the org actually owns whether AI adoption is working.

Here are the numbers that make this concrete: 70% to 75% of enterprise AI initiatives fail to realize their intended ROI. In our experience, it's rarely the model underperforming. It's three things: 1) Success metrics that were unrealistic from the start, scoped against hype instead of a real baseline; 2) Orchestration that breaks down—AI works fine in a demo, then falls apart the moment it has to hand off between tools and teams; and 3) Outcomes that never get mapped back to KPIs leadership is actually tracking.

From a CEO's chair, it's always a bird's-eye view of how the problem shows up for whoever is signing the check. Every dollar that a business spends on AI adoption, implementing a new tool, or redesigning a workflow is competing with every other dollar which that business could otherwise spend on growing its revenue in the near term.

Most vendors sell AI as an 'outcome'—transformation, efficiency, competitive advantage. But what actually lands on a CFO's desk is a subscription line item with no attached metric to it. That's not a technology problem; that's essentially a capital allocation problem. Without a substantial cost model, artificial intelligence becomes the first line item that gets cut when budgets tighten.

Whenever there is economic or geopolitical uncertainty, it impacts the technology landscape globally. Nobody can defend the spend at the board level when initiatives don't land where intended. The easiest thing to kill is something that might give you results in the future compared to core operations giving you results today.