The AI Content Trap: Multiplying Mediocrity at Scale
The AI Content Trap: Multiplying Mediocrity at Scale Marketing has always suffered from a volume addiction; however, the advent of generative AI has turned a bad habit into a terminal condition. In the recent discussion with Volker Hildebrand in our CRMKonvo, we explored the uncomfortable reality that while AI has made marketing faster and cheaper, it has largely failed to make it better. The cynical view, which I happen to hold is that marketers frequently confuse the amount of content produced with the actual impact on the customer. We are now in an era where everyone has the same tools to flood the market with what in the words of Volker just “multiplies mediocrity” – or in mine creates instant mediocrity. The core problem is that generative AI multiplies mediocrity by definition. It ingests existing data and spits out an average of what is already there; consequently, when every startup uses these tools to build their websites and social posts, they all end up saying the same. If you look at the CRM space today, the messaging is often nearly indistinguishable. Everyone promises “revolutionary” efficiency and “seamless” integration. As Volker noted, this is a trap for startups; if they cannot differentiate their story, they simply will not survive the noise. TL;DR If you want to watch the full CRMKonvo, please go ahead here (optimized for smartphones) or here (optimized for tablets/computers). Else, be my guest and continue to read. Or do both … The Productivity Mirage Vendors love to sell AI based on productivity gains. They promise you can save 20 percent of your time on content creation. But...
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...
The Agent Wars Are Over. The Substrate Wars Just Started
Three titan announcements in two weeks reveal what enterprise software vendors are actually fighting over in 2026, and it is not agents. If you have been tracking enterprise AI announcements through 2025, you have been watching a race about agent counts. How many prebuilt agents. How many industry-specific use cases. How many customer stories. Agents were the marketing, the demo, the SKU. A year of the same playbook. Something shifted in April 2026. Inside a two-week window, Salesforce, SAP, and ServiceNow each published an announcement that, at first glance, looks like more of the same agent theater. Salesforce launched Headless 360 at TDX 2026 and the Agentforce Experience Layer. SAP pushed a simplified-architecture argument alongside a persistent agent memory layer on BTP. ServiceNow rolled out Context Engine and, on its SPM community blog, Fred Champlain published an essay reframing governance itself as “strategic decision debt”. Different products. Different audiences. The same structural move. All three titans just walked one layer down the stack. Read individually, each announcement is a product release. Read together, they are a category shift. The competition is no longer about who has the best agent. It is about who owns the substrate those agents operate on. And each titan is staking a different piece of it. The Pattern Nobody Is Naming Strip the vendor branding from all three sets of material and the structural claim is identical: “Your agents are only as good as the layer underneath them. The data they ground on, the logic they inherit, the memory they carry, the permissions they respect, and the decisions they represent. That layer is what we...