AI in Q1 2026: Less Magic, More Context, and the Death of the Outbound SDR
Welcome to the second quarter of 2026. The dust of the generative AI explosion seems to have finally settled, so actual business realities can be seen. For the last few years, the enterprise software market has been drowning in vendor promises of AI magic. Now, companies are waking up to the hard truth. AI is no longer a futuristic promise; it is a budgetary line item with concrete expectations. As our guest Clint Oram accurately pointed out in our CRMKonvo sit-down, businesses are actively hunting for 20 to 40 percent productivity gains from their knowledge workers. But are these gains real, or just another SaaS vendor hallucination? The market is scrambling to figure out what actually works and what is just expensive hype. 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 … While the underlying LLMs have become core components of daily workflows, the execution at the enterprise level remains often fraught with mediocre strategies. At the same time, we are seeing a profound shift in how work is accomplished with the help of AI. This year will be defined by a massive, societal scramble to understand if, and if so, how, this technology supports the bottom line of the companies using it. Let us see if there is actually any substance there, or if we are just increasing vendor revenues. The focus must shift from adoption at any cost to architectural integrity, and it already does in some areas. Vendors love to sell you...
The Uncomfortable Truth About Enterprise AI in 2026: It’s Not Intelligence, and That’s a Problem
As enterprises scramble to deploy AI, the Great AI Debate’s eighth installment reveals a widening gap between what vendors are selling and what actually works at scale. Dr. Michael Wu and Jon Reed spent this episode cutting through the hype around language models, domain expertise, and the financial reality of building sustainable AI systems; and they didn’t pull punches about where the field is failing. 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 Domain Expertise Imperative: Correlation is Not Causation One of the most dangerous, and frankly lazy, narratives pushed by AI maximalists is the idea that artificial intelligence negates the need for deep domain expertise. This is a fundamental misunderstanding of how these models work. As Dr. Michael Wu frequently points out, almost all machine learning and AI systems today are built using supervised or reinforcement learning. They are, at their core, sophisticated correlation engines. They do not understand causality. They can surface 50 variables that move together, but they cannot tell you whether A causes B, B causes A, or if a hidden confounding variable C is responsible for both. If an LLM correctly states that smoking causes cancer, it is not because it understands the biological mechanisms of cellular mutation; it is because it has been fed enough human-generated text asserting that relationship. It creates the illusion of causal reasoning without the substance. This is precisely why domain experts, whether in healthcare, supply chain logistics, or financial services, are more vital...
Flipping the math: How AI changes Build vs. Buy
For the longest time, companies have been trapped by enterprise software vendors. First by shrink-wrapped software packages. Then by SaaS offerings. Both situations led to what one even in a SaaS world can call shelfware – although these days the shelf is a virtual one instead of a physical one. Buyers still get enticed to purchase more capabilities than they need, which leads to them paying more than necessary while often using software packages that offer overlapping capabilities. One of the promises that SaaS started with, was to end this. Sadly, it looks like this promise was not kept. And this is no wonder; after all vendors want to be sticky. And they need to have increasing revenues. This means that they need to offer an ever-increasing number of capabilities, aka features, to warrant their pricing and eventually regular price increases. Combined with the frequently used strategy of offering related capabilities, i.e., seats for an adjacent software that is not yet needed by a customer, this led to two things: bloat and shelfware. Both go at the expense of the enterprise buyer. Since the dawn of packaged software, the argument to buy, i.e., to voluntarily step into this trap, is the same: Buying is cheaper than building. Which probably was correct. Buying from a specialist was the logical choice. Engineering talent was, and still is, scarce. Building software includes a lengthy process of requirements engineering, years of development and ultimately never-ending maintenance. Just that most of this is true for most implementations of purchased enterprise software, too. And the buying process is arguably broken. Need identification is often done...