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 Contact Center Is Dead: Long Live the Operations Layer
We have been lying to ourselves since, well, basically since forever. We placed customer support agents into a padded room called the “contact center,” handed them a ticketing system, and told them to keep the angry people away from the rest of the business. We tracked average handle times; we cheered when a routing algorithm saved a fraction of a second; and we pretended that managing an interaction was the same thing as solving a problem. Deflecting an issue was the holy grail. That era is over. The walls of the contact center have been blown wide open, and the debris is currently raining down on the CRM and operations landscapes. The market is shifting from asking the question “who can capture the ticket best?” to “who can actually resolve the problem fastest?” Which is an entirely different category of question. And far more meaningful. And as Cameron Marsh from Nucleus Research so accurately pointed out in our recent CRMKonvo, that is a much nastier, much more complex place to compete. 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 feel free to do both … The Illusion of the “Smart Ticket” Let’s just be absolutely clear from the start: nobody wants a ticket. A ticket is simply a formalized receipt of failure. It is documented proof that a product broke, a service failed, or a user interface was too clunky to navigate. For years, many vendors, including specialists like Zendesk, Freshworks, and others have built success around...
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...