Somewhere in your marketing automation platform, there's a nurture programme that's been running for over a year. It was built for a campaign that made sense at the time - someone wrote the emails, set up the wait steps, configured the enrollment rules, and moved on to the next thing. Nobody has looked at it since. Open rates have been declining for months and click rates are worse. Most of the enrolled contacts haven't engaged with a single email in the sequence. But the pro

Next week I’ll be attending The MarTech Summit Madrid, taking place on 19 May 2026 at VP Plaza España Design. And, judging by the agenda, it looks like exactly the kind of event Marketing Operations needs right now: Less theoretical hand-waving, more practical conversation about how marketing teams are actually dealing with AI, data, customer experience, automation, sales alignment, and trust.

If AI is already being used inside your Marketing Operations function, now is the time to understand where the risk sits.

A marketing qualified lead (MQL) is a prospect who has been evaluated by the marketing team and determined to be more likely to become a customer than other leads in the database. The qualification is based on a combination of who the lead is - their job title, company size, industry, and other firmographic data - and what they've done - their engagement with marketing content, website visits, email interactions, and other behavioural signals. The MQL is the handoff point bet

Account-based marketing (ABM) is a B2B strategy that focuses marketing and sales resources on a defined set of target accounts rather than casting a wide net across the entire market. Instead of generating as many leads as possible and qualifying them down, ABM starts with the accounts most likely to become high-value customers and builds tailored campaigns specifically for them. The fundamental shift is from volume to precision. Traditional demand generation asks "how many l

The real nightmare is what happens when AI stops being a clever assistant and starts becoming an operator. When it can access systems. Query databases. Change records. Trigger workflows. Sync audiences. Update fields. Delete things. Move people between lifecycle stages. Send campaigns. Alter routing. Touch customer data. At that point, AI is no longer just helping your team think. It is acting inside the machinery of your business.

The AI companies aren't incentivised to surface it - their models work by being confident, not by presenting uncertainty. The incumbent platforms aren't going to complain - they're the beneficiaries. So the responsibility falls on the people making the actual decisions.

The goal is to reach the right people, at the right companies, with the right message, at the right moment. That is where Demandbase and LinkedIn become a very useful combination. And, more importantly, that is where ABM starts to look less like a theory and more like a working growth engine.

AI usage is widespread, data is flowing into tools that aren't governed, and nobody has had the conversation yet. It's working fine. Until it isn't.