In most marketing organizations, saying yes is the path of least resistance. A request comes in from sales - "can we do a campaign for this segment?" Yes. The CEO wants a presence at a new event - yes. Product marketing needs email support for a launch - yes. A partner wants co-branded content - yes. Someone read an article about a new channel and wants to test it - yes.

There's a test you can run to see how your organization thinks about marketing ops. Look at where the team sits in the org chart. Look at who they report to. Look at what they get asked to do on a daily basis. Then look at what gets said about them in leadership meetings. If the answers are "buried under demand gen," "a marketing manager who doesn't understand the platform," "build this email, fix this list, pull this report," and "nothing - they don't come up" - then your or

The deadline is approaching... The first question is not whether your organisation is compliant. It is whether Marketing Operations even knows what it owns.

There's a particular kind of panic that sets in when a competitor launches something impressive. A slick new campaign. A personalized experience that feels like it was built by a team twice your size. A webinar series that runs weekly without missing a beat. Content that shows up everywhere, perfectly timed, perfectly targeted. The instinct is to assume they have better tools. A more powerful platform. A bigger tech budget. Some integration or capability you don't have access

Somewhere in your marketing technology stack, there's a tool that nobody uses. Not "underused." Not "we use it for one thing." Nobody uses it. The login credentials have been lost. The integration was never completed. The person who championed the purchase left the company eight months ago. The annual renewal went through automatically because finance didn't flag it and nobody in marketing remembered to cancel it. That tool is the most expensive thing in your stack - not beca

The quarterly marketing review follows the same script in most organizations. Someone opens a slide deck. The first few slides show activity metrics - emails sent, campaigns launched, webinars hosted, content pieces published. Then engagement metrics - open rates, click rates, form submissions, social impressions. Then the MQL number, presented with a slight uptick and a green arrow. Leadership nods. Someone asks a question about pipeline. The answer involves the word "influe

Here's how most ABM programmes actually work: someone builds a target account list in a spreadsheet. The list gets uploaded into the marketing automation platform or the ABM tool. The same campaigns that were already running - the same nurture emails, the same content offers, the same webinar invitations - get filtered to only hit contacts at those accounts. The team calls it ABM. Leadership reports it as ABM. The ABM platform vendor counts it as adoption. But nothing about t

There's plenty of content about why AI governance matters. The regulatory pressure, the risk of ungoverned automation, the compliance deadlines. We've written about it ourselves. But there's far less content about what good AI governance actually looks like in practice - inside a real marketing operations environment, with a real team, running real campaigns on a real platform. This is that article. Not the principles. Not the framework diagram. The operational reality of wha

Your company has an AI policy. It went through legal, got presented at an all-hands, and it's sitting on the intranet right now saying all the right things about responsible use, data protection, and human oversight. Meanwhile, someone on the marketing team activated a predictive scoring feature during last quarter's platform upgrade because it looked useful. It's been running ever since - deciding which leads get prioritized and which get buried - and nobody logged it, nobod

Every organization with an AI policy thinks they have AI governance. They have the document. It's been signed off by legal, reviewed by the board, published on the intranet. It covers principles - fairness, transparency, accountability, responsible use. It reads well. It says the right things. And then the AI does something wrong. A chatbot makes a promise the company can't keep. An automated workflow suppresses a segment that should have been contacted. A scoring model route

AI is already being pushed into Marketing Operations by companies that, in some cases, do not seem to fully understand the operational environment they are entering. And that should make marketing leaders pause.

Every quarter, someone on the team finds a new tool. It showed up in a LinkedIn ad, or a competitor mentioned it on a webinar, or someone saw a demo at a conference and came back convinced it would solve the problem they've been struggling with for months. So they buy it. Or they start a free trial that quietly becomes a paid subscription. Or they pitch it to leadership with a slide deck that makes it sound like the missing piece. And for a few weeks it feels like progress -

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.