Collaboration, transparency, courage, and creativity are principles I believe will matter. I have also become obsessed with silos. More specifically why they have grown over the last few years and why they risk getting worse as AI agents take more space. Before I go into the risks of AI agents and silos, I need a short recap. Many in‑house teams are very competent, but working in the wrong silos is often a blocker. My view is that siloing has increased over the last ten years. There are several reasons. One is that in‑house teams have grown. They grew because more agency work was brought in‑house and the fragmented digital landscape increased the need for specialists. Teams grow and competence increases, but old structures remain, so many functions, brand, creative, SEO, revenue, CRM, channel owners, e‑com, sales, CX, still have too little interaction and insight into each other’s work. The paradox is that as the in‑house team grows, agency collaborations often grow too. Why? Because each in‑house function wants its mirror image on the agency side. That is how the structure has looked, and still looks. The consequence is that marketing, sales, and CRM departments, including sub‑teams, not only work in silos, they also create their own silo with the agencies they work with. That adds another layer of complexity and a disproportionate share of time goes to communication. AI agents can create more and deeper silos AI agents risk creating even more silos if the organizational perspective is not brought in early and you are not proactive. Today there is almost always a lack of transparency around which agents exist, how they work together, and how collaboration between humans and agents actually looks. AI implementations often happen at the individual level, or by buying a full system for a specific function, for example SEO AI Employees, with no connection to the whole. It can also be a more enterprise‑adapted implementation in proof‑of‑concept mode that tries to solve an isolated problem. So how should you organize for 2026+? First, there are differences between industries and verticals, just like today. My view is that verticalization will increase somewhat. That applies both to the specific competence companies benefit from and to how you should organize for maximum effect. Some principles will repeat across verticals. The drivers come from two directions. One is the opportunities AI creates to make marketing work more efficient. The other is agentic commerce, which will put pressure on companies, and CMOs in particular, to rethink their operating model more drastically. 1) Start from the maturity of your tech stack and data quality Organizing ways of working must start from the current state of the stack and data quality. If maturity is low and data quality is weak, you must implement AI agents and organizational change differently, and vice versa. You need two tracks running at once, improving the stack and data quality while implementing AI agents and organizational change. The critical part is that those two tracks stay very tightly aligned. 2) Remember agentic commerce affects all companies, not just e‑commerce Agentic commerce means AI agents research, compare, and sometimes purchase on behalf of users and companies. Discovery, research, and sometimes checkout move somewhere else. I believe this drives a few important organizational shifts: - Trust and brand ownership across channels. A cross‑channel function is needed to own brand, PR, reviews, policies, customer promises, product claims, and proof. That means teams that historically worked in silos, PR, brand, social, product, insight, must come together. In a world where small companies can compete with big ones and customers are less loyal, this becomes one of the most important functions to drive revenue, both short and long term. - CRO and the funnel change. If choices, or at least recommendations, are increasingly made by an AI agent rather than on site or at checkout, then CRO, e‑com, and web teams must own what actually drives a recommendation. If you take it further, you might ask whether the team and skill mix as we know it should be rebuilt from scratch. - CRM and acquisition, break the walls. What is a new customer and what is a loyal customer when the agent does the research? It is no longer black and white. What historically sat under separate departmental ownership must be seen as one system, not two machines. A new customer may build preference long before the first purchase appears in your data, and a loyal customer can return without ever visiting your site because an agent keeps choosing you based on trust, delivery, price logic, return terms, and product data. That means acquisition, find new, and CRM, nurture existing, can no longer be optimized separately without losing effect. The walls need to come down for real. 3) Agency and consulting partnerships, the operating model and collaboration need to be challenged Agencies will also use AI. That does not mean you must use agencies less. But it does mean the collaboration must be redesigned. Otherwise you end up with two parallel systems, you optimize human‑agent flows internally, and the agency optimizes its human‑agent flows externally, without the systems talking to each other. That is a big risk, duplicated definitions, weaker traceability, more friction, and eventually lower speed. Two things to be clear about when you set up the collaboration: - Transparency and compatibility. Same data sources, same tracking, same definitions, same QA, and a way to connect the agent layer so you do not build two separate systems. - Right value model. If you get exactly the same delivery and pay exactly the same when production is made more efficient by AI, it is skewed. At the same time, an enormous amount of time goes to communication today, and if the operating model is not right, that time will not shrink, it will grow. And finally, some functions are more business‑critical than others. In some cases outsourcing a whole function can work, in others it must stay internal. What can be highly relevant to buy in, advanced analysis, measurement, automation, creative edge, deep channel expertise, and the ability to actually see through platforms, hello Google. 4) Measurement, attribution, and MMM become more important than ever Yes, you have heard it before. But it is true, for several reasons. AI makes differences between companies smaller. It becomes harder to create sustainable competitive advantage. A tight grip on holistic evaluation, and making it part of the entire operating model, is a competitive advantage that is very hard to build and copy. Another reason is a further fragmentation of the buying journey, where an even larger share of research will happen off site, by agents. 5) Do not automate everything, optimize for quality This is not just about content, it is about almost everything. Automation is great, especially as methods like MMM become more accessible, but when everyone can do everything, quality drops fast if no one owns the craft, the bar, and the checkpoints. Think in three levels: - Real experts. Hard decisions, big money, high risk. - Oversight required. Automation can run, but someone must review and steer. - Release control. Low risk, easy to roll back, clear guardrails.