52 - Financial Clarity in an AI-First World with Kunal Agarwal

That's great advice and a good place for people to start because there's so much involved.

Like we are often here, we have to adopt AI.

You said it earlier, competitors are starting to do this thing.

So being able to do it the right way is key, addressing your cost, your data, everything else you piloted or tested.

In the past, you've talked about kind of A difference, a distinction between cost cutting and cost optimization.

You made some really cool points of that.

I've heard you say in the past.

How would you explain that difference to, let's say, a CFO who just wants the numbers to go down? How do you do that? So,

look.

Doing anything with AI will cost some money.

We've got to be honest about that.

And the difference between cost cutting and cost optimization is really you cannot cut your way, you cannot cut your cost way through innovation.

So innovation is going to cost dollars.

And the difference between the two is, are you using those dollars efficiently or are they going to waste? Or are they just being idled away because of all of these types of reasons that we're doing right now? So...

The way to understand if you are getting the best out of the dollars that you're spending is to ensure that you're able to measure everything end to end from that data source to that AI model and then everything else in between.

So you really understand if the cost going through this entire pipeline is actually useful or not.

So once you do assess this, what we usually see is people get 20 to 40% improvement by just putting enough attention on this problem.

And what really the side effect of that becomes is now you have more headroom open for real AI use cases to go and deploy those save dollars on.

So that's what we mean by cost optimization rather than cost cutting, which is it's not about just slashing your bill, it's about getting more yield for that $1 that you're spending on these platforms.

I think now is a good time for companies to really start doing that because we're in this inflection point between experimenting with AI to running production workloads on AI.

We're also at this inflection point of certain teams and my companies are using AI with everybody in my company is going to start using AI.

So now that you have settled down and understood what your AI posture looks like inside your company, it's good to start doing those measurements and governance on your AI projects right away, because this is only going to explode over the next several months.

And once you open this up to various business units and various other projects, you may see a 20x, 30x, 40x increase in its usage.

And then trying to rein everything back in becomes a nightmare situation.

So you may already have good opportunities to put checkpoints and guardrails in certain places.

That's great advice and a good place for people to start because there's so much involved.

Like we are often here, we have to adopt AI.

You said it earlier, competitors are starting to do this thing.

So being able to do it the right way is key, addressing your cost, your data, everything else you piloted or tested.

In the past, you've talked about kind of A difference, a distinction between cost cutting and cost optimization.

You made some really cool points of that.

I've heard you say in the past.

How would you explain that difference to, let's say, a CFO who just wants the numbers to go down? How do you do that? So,

look.

Doing anything with AI will cost some money.

We've got to be honest about that.

And the difference between cost cutting and cost optimization is really you cannot cut your way, you cannot cut your cost way through innovation.

So innovation is going to cost dollars.

And the difference between the two is, are you using those dollars efficiently or are they going to waste? Or are they just being idled away because of all of these types of reasons that we're doing right now? So...

The way to understand if you are getting the best out of the dollars that you're spending is to ensure that you're able to measure everything end to end from that data source to that AI model and then everything else in between.

So you really understand if the cost going through this entire pipeline is actually useful or not.

So once you do assess this, what we usually see is people get 20 to 40% improvement by just putting enough attention on this problem.

And what really the side effect of that becomes is now you have more headroom open for real AI use cases to go and deploy those save dollars on.

So that's what we mean by cost optimization rather than cost cutting, which is it's not about just slashing your bill, it's about getting more yield for that $1 that you're spending on these platforms.

I think now is a good time for companies to really start doing that because we're in this inflection point between experimenting with AI to running production workloads on AI.

We're also at this inflection point of certain teams and my companies are using AI with everybody in my company is going to start using AI.

So now that you have settled down and understood what your AI posture looks like inside your company, it's good to start doing those measurements and governance on your AI projects right away, because this is only going to explode over the next several months.

And once you open this up to various business units and various other projects, you may see a 20x, 30x, 40x increase in its usage.

And then trying to rein everything back in becomes a nightmare situation.

So you may already have good opportunities to put checkpoints and guardrails in certain places.