SAP’s Double Acquisition: How Dremio and Prior Labs Complete a Data Strategy the Competition Can’t Easily Match
On May 4, 2026, SAP announced two acquisitions in the same breath: Dremio, an Apache Iceberg-native agentic data lakehouse, and Prior Labs, a pioneer of Tabular Foundation Models. Neither acquisition is exotic. Together, they are contributing to the most coherent enterprise AI platform strategy any major vendor has shown this year. Let me unravel what each company actually brings, why the combination matters, what it means for the competitive field, and — most importantly — what buyers and SAP customers should be doing right now. The Problem SAP Is Solving Before diving into the deals, let’s formulate the problem addressed. SAP’s CTO Philipp Herzig said it clearly: “Enterprise AI doesn’t stall because the models aren’t good enough; it stalls because the data isn’t ready for AI agents“. That is not a marketing line. It describes a pattern analysts and practitioners see constantly: AI pilots perform in a sandbox and fail when they hit production. The reasons are familiar: data is locked in proprietary formats across a dozen systems, there’s no consistent business context, ETL pipelines take months to build, and governance gaps make audit-ready AI decisions nearly impossible. SAP has also faced an additional problem. The narrative about SAP is and always was that it works brilliantly if everything lives inside SAP and required considerable engineering if you want to connect it to anything else. In an enterprise world where the average organization uses dozens of SaaS applications, that story is a liability. Both acquisitions address these problems directly from different angles. Acquisition One: Dremio and the Data Layer Dremio is an open-data lakehouse built on Apache Iceberg. That...
SAP Draws a Perimeter around Agentic AI and What That Means for the Rest of US
The most consequential enterprise AI governance document published this year arrived in late April with surprisingly little fanfare. SAP’s updated API Policy, version 4/2026, is a short document in plain English. The clause that is most interesting is Section 2.2.2. It restricts how autonomous and generative AI systems are permitted to interact with SAP APIs. Read literally, it has the potential to change the architecture of agentic AI projects across every SAP customer landscape. Read carefully, it is also more interesting than the lock-in headlines suggest. The policy targets a specific category of AI behavior, not AI as such. It connects to commercial mechanics that go well beyond API stability. And the literal text, in its current form, will probably not survive the next two policy revisions intact. There is a lot to unpack. I will walk through what the policy actually says, how the SAP-watching community is reading it, what the rest of the major enterprise vendors are doing in comparison, what counts as an “endorsed architecture”, and what customers and partners should be doing about it now. I’ll close with a view on whether the policy can stand the test of time. What Section 2.2.2 actually says The operative sentence is direct. “Except through and within the limits of SAP-endorsed architectures, data services, or service-specific pathways expressly identified and intended for such purposes, SAP prohibits API use for interaction or integration with semi-autonomous or generative AI systems that plan, select, or execute sequences of API calls”. The same paragraph also prohibits scraping, harvesting, or systematic large-scale data extraction. Three things flow from that. First, only Published APIs,...
The Orchestration Layer in Enterprise AI Just Got Named. It Has a Gemini Logo on It.
What Google Cloud Next 2026 actually told us about the titan pecking order Google Cloud Next 2026 wrapped last week. The official version of the story is the one Google wanted you to read: 260 announcements, 1,302 customer use cases, the Gemini Enterprise Agent Platform, eighth-generation TPUs, a $750 million partner fund, an $240 billion Marketplace backlog. Big numbers. On-message keynote. Tidy “agentic era” framing. The more interesting story is who showed up to validate it, and what Google actually built underneath. Five of the seven enterprise titans I track walked into Las Vegas and announced expanded partnerships that all rest on the same architecture: Gemini Enterprise as the agent control plane, with the titan’s product playing the role of premium ingredient. Salesforce. SAP. ServiceNow. Oracle. Adobe. Add Workday and Palantir Technologies to the picture, both adjacent to my titan list but visibly aligned in the same direction. Two titans were not in the picture. Microsoft, because Copilot is the direct counter-position and Cloud Next is not Microsoft’s stage. Zoho, because Zoho’s stack does not need a Google motion and Zoho’s buyer is not the same buyer. Both absences matter. More about them a little later. What Google actually built Let’s start with the framing. Google did not just ship a model platform with new features. It repositioned Google Cloud from “AI development environment” to enterprise agent control plane. Vertex AI services and roadmap evolutions are now delivered through the new Agent Platform rather than as a standalone product. That is not a naming change, it’s an entirely different playground. The Agent Platform stack now visibly includes: Agent Identity...
Beyond the Buzzword: Sugar’s Bet on Precision Selling and the ERP-CRM Bridge
There is a moment in every technology cycle where a vendor decides the best way to signal relevance is to put the current buzzword in its name. We seem to be in that moment. SugarCRM, the mid-market CRM vendor backed by Accel-KKR, just rebranded to SugarAI. The company declared that CRM as a category has failed to deliver on its 30-year-old promise and that AI makes a fundamental reset possible. CEO David Roberts frames it as moving from “AI as a feature” to “intelligence as the system.” That is a strong claim. And a good claim! Let us see what is behind it. What Sugar Is Actually Saying Strip away the rebrand fanfare and there are three substantial moves here. First, Sugar is narrowing its identity around what it calls “precision selling.” The concept: CRM should stop being a passive system of record that sellers resent updating and start actively telling them where to focus, what accounts are at risk, and what to do next. This is not a new aspiration in the CRM industry. What makes Sugar’s version more interesting than the usual hand-waving is the second move. Second, Sugar is leaning hard into the ERP-CRM bridge. The 2024 acquisition of sales-i gave Sugar the ability to ingest transactional data from over 180 ERP systems and surface revenue signals that traditional CRM cannot see. When a distributor’s reorder volume drops 30% or a manufacturing customer shifts purchasing patterns, that signal lives in the ERP, not in the CRM. Sugar is betting that connecting these dots is where real value sits. Cameron Marsh at Nucleus Research called this “a...