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...
Beyond the Honeymoon: Why Map Communications Bets on Zoho for a Decluttered Tech Stack
Recently, while on the ground in Austin, Texas, attending ZohoDay 2026, I had the pleasure of sitting down with Vaibhav Dani, the CEO of Map Communications. In the enterprise software ecosystem, we talk endlessly about digital transformation, but it is always refreshing to ground those lofty concepts in reality by speaking directly with the leaders navigating these complex implementations. Our conversation touched on a surprisingly common, yet notoriously difficult challenge: harmonizing a homegrown operational tech stack with off-the-shelf enterprise software. Map Communications’ journey with the Zoho ecosystem provides a masterclass in pragmatic architecture, the age-old “buy versus build” dilemma, and the foundational data hygiene required to actually make artificial intelligence work. TL;DR If you do not want to read this, here’s the full length video interview. Everybody else, please read on. The Business Context: Bespoke Service at Scale To understand their technology strategy, you first have to understand their business. Map Communications is a nationwide, employee-owned (ESOP) virtual receptionist and bespoke answering service operating across the US, Canada, and the UK. They serve a wide array of clients, ranging from legal firms and SMBs to large enterprises in various industries. Because their core service is highly specialized, Map relies on its own proprietary, homegrown software lineup to manage day-to-day operations and real-time answering services. However, when it comes to managing the customer lifecycle from the moment a prospect lands on their website to the execution of contracts and ongoing support, they rely on the Zoho suite. The Age-Old Dilemma: Buy vs. Build As businesses grow and their processes add complexity, leadership is inevitably faced with a choice: do we build custom modules...
Navigating the K-Shaped Economy: Zoho’s Enterprise Strategy, AI, and True Value
During ZohoDay 2026 in Austin, I had the opportunity to sit down and chat with Tony Thomas, the head of Zoho US. Tony has been in this role for just over a year, navigating an economic and technological landscape that got ever more complex. Our conversation covered everything from shifting macroeconomic realities and Zoho’s upward trajectory into the mid- and enterprise market, to the fundamental ways generative and agentic artificial intelligence are challenging the traditional economics of software implementation. With software pricing already being under pressure, it is more than likely that implementation costs are next. I said it before, the time-and-material billing that SI’s are still favoring is probably going the way of the Dodos. What became abundantly clear during this conversation was that Zoho continues to forge its own contrarian path, adapting to market pressures by continuing to rethink how software value is delivered. One could even say that Zoho’s thinking gets validated by current developments. TL;DR If you prefer watching the interview to reading, find the full video here. The Macroeconomic Squeeze on the SMB The software market does not exist in a vacuum, and the broader economic climate over the past year or so has been challenging for businesses. Historically, SMB segment has been Zoho’s mainstay. However, Tony noted that especially this demographic is facing severe headwinds. Tony pointed to the K-shaped economy, where large enterprises continue to see gains while smaller businesses and large segments of the populace are struggling or being left behind. Small businesses have “taken it on the chin“, battered by general economic uncertainty and the specter of tariffs, casting doubt on the speed of their recovery....
The Illusion of Value: Why Salesforce’s Agentic Work Unit is the New “Bad Query” of the AI Era
The News On February. 25, 2026, Salesforce announced a pricing and metrics update. During the company’s Q4 FY2026 earnings call, CEO Marc Benioff, together with CMO Patrick Stokes, unveiled the Agentic Work Unit (AWU). Positioned as a metric to quantify the labor performed by autonomous digital systems, Salesforce defines an AWU as one discrete task accomplished by an AI agent. According to Salesforce, this discrete task represents the exact moment “raw intelligence is converted into real work“. It is not a fixed unit but measured as a processed prompt, a completed reasoning chain, or an invoked tool. Salesforce explicitly designed the AWU to move the industry conversation away from the raw consumption of Large Language Model (LLM) tokens. As Benioff noted, tokens only measure “how much an AI talks,” whereas the AWU is intended to measure actual business execution. The scale of this rollout is massive. Salesforce reported that its platform has already processed over 19 trillion AI tokens, translating them into 2.4 billion Agentic Work Units, with 771 million AWUs delivered in the fourth quarter alone. This new metric serves as the underlying foundation for Salesforce’s evolving Agentforce monetization strategy. The bigger picture Following a nearly 18-month period of pricing triangulation, which included a $2.00 per conversation model and a $0.10 per action “Flex Credit” model, Salesforce is leveraging the AWU to track system utilization, even as it wraps enterprise purchasing in familiar, unmetered per-user license agreements starting at $125 per user per month. To understand the significance of the Agentic Work Unit, one must view it through the lens of a broader industry crisis: the so-called “SaaSpocalypse”...