When the Open Semantic Interchange (OSI) initiative launched last year, it set out to solve a problem every data leader recognizes: the same business metric gets defined a dozen different ways across a company's BI tools, warehouses, and now, AI agents. "Monthly active users" in the CRM rarely matches "monthly active users" in the warehouse, and every new AI copilot added to the stack makes the gap more visible, not less. That initiative has just taken its most consequential step yet.

The pipeline is in the CRM. The forecast isn’t.

Companies everywhere are rushing to deploy AI models to outpace the competition, but they are running headfirst into a brutal reality check: an AI model is only as brilliant as the data feeding it, and most data simply isn't AI-ready. The high-value, real-time data required to power these models doesn’t live in a pristine, pre-formatted cloud data warehouse. It is generated in the physical world on factory floors, inside hospital rooms, and at point-of-sale terminals.

In volatile markets, a KPI dashboard that only tells you what happened last week is already too late to guide confident action. Many companies have dashboards. Fewer have KPI systems that help them spot revenue shifts, retention risk, margin pressure, or supply-chain trouble before those issues hit the business hard. That gap is where augmented analytics comes in.

Implementing a PXM solution provides numerous benefits to your organization, from improving efficiency to increasing sales, reducing returns, and promoting customer loyalty. Today, we’re going to explore these benefits in more detail. Interested in the distinctions between PIM and PXM? See our breakdown of how they differ (but are also similar) here.

Shopify’s MCP servers give Claude real command of your store, from storefront conversations to bulk product updates. Analytics is the one job they were never built for, and pairing them with Databox MCP closes that gap.

Ask a Marketing Lead how the quarterly plan actually gets built. Not the strategy, the mechanics. The answer, in most teams, is a spreadsheet: spend pulled from five ad platforms with five different backends, pipeline exported from the CRM, last quarter’s numbers copied from a deck, targets negotiated in a separate thread. One customer described their version of it to us in a sentence that needs no editing.

Almost every infrastructure team running modern AI is wrestling with the same question. The orchestrator their AI workloads want, Kubernetes, and the orchestrator their HPC environment was built on, Slurm, pull in different directions. This piece, drawn from the HPCKP 2026 session of the same name, looks at why that tension exists, what the workloads actually look like, how teams are bridging the two today, and where the pattern is heading.