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.

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.

The hard part was never the connection. It's making AI work reliably inside your specific operations - with your conventions, your processes, your QA standards, and the institutional knowledge that no API exposes.

For our luxury holiday client, this wasn’t a “nice-to-have” optimisation project. It was an existential one. Email performance was flat. Marketo was under scrutiny. And the CEO had made it very clear: Prove the value of the platform and the team behind it in 2025, or both were gone.

Off-the-shelf LLMs make a great demo. Enterprise Marketing Ops needs something more deliberate.

SEO isn't dead. But it's no longer the only game that matters for discovery, and it's no longer where the most consequential first impressions are being formed. The shift from SEO to GEO isn't something to prepare for. It's something to respond to.

Claude connecting to Marketo may well become useful. In the right environment, with the right controls, it could genuinely help capable teams move faster without losing discipline. But that outcome will not belong to the teams who got excited first. It will belong to the teams who treated permissions seriously, kept approvals intact, demanded proper auditability, strengthened QA, respected production risk, and resisted the now very fashionable urge to mistake easy access for

When highly capable Marketing Operations teams are spending huge chunks of their week doing repetitive campaign checks, something has gone wrong in the operating model.

AI agents are multiplying inside the platforms MOPs teams already operate whether you know it or not, and nobody has built the operational layer to manage them.

At Sojourn Solutions, we implement, manage, and optimise marketing automation platforms for enterprise B2B organisations. This guide covers what marketing automation is, how it works in practice, what the major platforms are, and what it takes to run it well.