SensibleAI Clustering Analysis Overview

So last year at this conference very similar to this, we announced the general availability of Sensible AI Studio. And since then, it's empowered our developers, our implementers, our partners to build unique and innovative workflows with AI on the platform into the business processes that matter most.

And that's led to what you saw a brief stint of earlier, our narrative analysis capability, and it's even allowed actually our own development teams to build these turnkey solutions. Right? And so one of those turnkey solutions is the narrative analysis capability that you saw earlier.

So what is the narrative analysis capability about? It allows you and your team to seamlessly summarize hundreds to thousands of comments that your teams are generating as part of the month-on-close process and pull out the key nuggets of information that matter most for decisioning.

Number two is AI account reconciliations. And AI account reconciliations is all about reducing cycle time and mitigating risks for those preparers, the approvers, and the auditors that go through that loop every single month.

And then last but not least is the operational data chat, which gives you instantaneous access to operational insight, the operational information that you decide to bring in to OneStream in natural language.

So today we're continuing to double down on our sensible AI studio portfolio. And when we think about that, we are incredibly excited to announce the general availability of our sensible AI clustering analysis solution.

Thank you. And so sensible AI clustering analysis allows you to define what we call intelligent peer groups that allow you to bucket your entities by the actual operational and performance potential. And what's interesting, you know, if you do that, if you're a retailer, that might mean that you're bucketing your entities, your stores, you know, by parking lot size, shelf space, population density, distance to nearest competitor.

If you're a manufacturer, you know, you know, that might mean I'm clustering and grouping and getting my intelligent peer groups by plant capacity, by production line count, skew count, and distance to suppliers.

And what's really interesting is once you truly have that apples to apples comparison for the very first time, it unlocks three incredibly powerful workflows for you within the solution. So number one is benchmarking.

This is all about allowing you to identify actionable spending gaps. So not, hey, this region underperforms. It gets into the nitty-gritty detail to be able to tell you, you know, hey, this store is overspending on SG&A relative to its peer groups.

And here's exactly what we can do about it. And this is what I'd recommend doing. Right? That's the level of detail that we can start to get to with sensible AI clustering analysis. Workflow number two is drift analysis.

Think of this as your early warning performance detection system. So this means that for every single entity that you have inside of your organization, you can find the ones that are trending away from their peer group in the wrong direction from a performance standpoint.

And flag those on a weekly basis so that you can catch and take action not in the quarterly business review and not in the annual review. And then last but not least is the peer group aware analytics.

So this is really about giving you the ability to automate the reviews. So these are peer aware scorecards by entity and allows you to start your reviews with the analysis and not the data wrangling exercise that typically goes on with it.

So at the end of the day, why does this really matter? This is all about smarter capital allocation. So the next time a leader in your organization needs to choose, you know, if you're a retailer, which stores to expand, which to renovate, which to shut down, or which ones to acquire, clustering analysis is there to tell them where the dollars are actually going to move the needle without necessarily hurting growth investment.

Right? That's what we're after.