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 to. So the conversation starts: what are they using? Should we switch platforms? Do we need to buy something new? Almost always, the answer is no. They're not using better technology. They're using the same technology - or something very similar - with better processes underneath it. The platform is the same. The difference is how they run it. Same tools, different outcomes Most B2B marketing teams in any given industry are running variations of the same stack. The same handful of MAPs. The same CRM. Similar enrichment tools, analytics platforms, and advertising channels. The technology landscape has consolidated enough that the tools available to a mid-market company are functionally similar to the tools available to an enterprise. The differentiation isn't in the tools. It's in how they're configured, maintained, and operated. One team builds campaigns in two hours because they have a brief template that arrives complete, a template system that works reliably, an approval process with defined turnaround times, and a QA checklist that catches problems before send. Another team builds the same campaign in two weeks because the brief arrives vague, the templates are broken, approvals get stuck in email chains, and QA is someone squinting at a test send on their phone. Both teams are using the same platform. The output gap has nothing to do with technology. It's entirely about the process surrounding it. Process isn't exciting. That's why it works. Nobody gets promoted for building a campaign brief template. Nobody presents a QA checklist at the company all-hands. Nobody writes a LinkedIn post about how they redesigned the approval workflow. Process work is invisible when it works and only noticed when it's absent. Which is exactly why most teams don't prioritize it. The team is rewarded for campaigns launched, pipeline generated, content produced - visible outputs that show up in reports and reviews. The operational infrastructure that makes those outputs possible doesn't get measured, doesn't get celebrated, and doesn't get resourced. This creates a cycle where the team is always busy but never efficient. Every campaign takes longer than it should because the process hasn't been fixed. But nobody fixes the process because the team is too busy building campaigns. The urgent always wins over the important, and the process debt compounds quarter after quarter. Meanwhile, the competitor who invested a week in fixing their brief template, another week in rebuilding their email templates, and another week in structuring their approval workflow is now building campaigns in a fraction of the time. They didn't buy anything new. They fixed the machine they already had. Where process failures actually live The process problems that cost the most time aren't dramatic. They're mundane. They're so routine that the team has stopped seeing them as problems and started accepting them as "just how things work." The brief that arrives incomplete. Every missing field on a campaign brief turns into a conversation - a Slack message, an email, a meeting to clarify what should have been specified upfront. Multiply that by every campaign and the wasted hours are staggering. A brief template with required fields and a "this goes back if it's incomplete" rule eliminates most of it. The template that requires workarounds. If the person building the campaign spends 30 minutes per build working around template limitations, that's 30 minutes multiplied by every campaign for the rest of the year. Rebuilding the templates is a one-time cost that pays back permanently. The approval chain with no deadlines. An approval workflow where the expected turnaround is "whenever they get to it" is an approval workflow that adds days to every build. Setting a 24-hour review window for each stage - with escalation if it's missed - compresses weeks into days. The QA process that depends on memory. If QA is "the builder checks everything they can think of," different builders will check different things and something will eventually get missed. A shared QA checklist takes an hour to build and ensures consistent quality on every send. The handoff between teams that nobody designed. Marketing produces the MQL. Sales receives it. But how? Through what mechanism? With what context? On what timeline? If the handoff isn't defined - if it's just a notification in the CRM that sales may or may not see - the entire upstream process loses value at the exact point where it should be generating it. Each of these is a small problem. Combined, they're the reason one team operates at twice the speed of another using the same tools. The process audit most teams skip Technology audits are common. Someone reviews the stack annually, evaluates new tools, and makes recommendations. Process audits almost never happen - which is strange, because process problems cost more time than technology problems in most marketing operations. A process audit looks at how work actually flows through the team, not how it's supposed to flow. Map the journey of a campaign from request to send. How many handoffs are there? How many of those handoffs involve waiting? Where do things get stuck? What are the most common reasons a campaign gets delayed? Then do the same for lead management. When a lead becomes an MQL, what happens? How long does it take to reach sales? What context arrives with it? What percentage get followed up within 24 hours, 48 hours, a week? Where does the process break down? The answers are almost always embarrassing. Not because the team is incompetent - because the process was never designed. It evolved through individual decisions made under time pressure, and nobody stepped back to look at the whole picture. Technology is a ceiling. Process is the floor. Your marketing automation platform defines what's possible. Your process defines what actually happens. Most teams are operating well below what their platform can do - not because they need more features, but because the operational infrastructure around the platform hasn't been built to the same standard as the technology. The competitor who looks like they're running a better operation probably is. But they're not running better technology. They're running better processes - clearer briefs, faster approvals, reliable templates, consistent QA, designed handoffs. None of that cost them a new licence fee. All of it cost them a few weeks of operational work that nobody wanted to do but everyone benefits from. The gap between your team and the one you're envying isn't in the tools. It's in the operational discipline underneath them. And unlike technology, process improvements don't require a budget approval, a vendor evaluation, or a six-month implementation. They require someone deciding that how the team works matters as much as what the team produces. That decision is usually the hardest part. Everything after it is straightforward.

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 because of the licence fee, but because of what it represents. A problem that was identified, a purchase that was approved, an implementation that never happened, and a decision nobody wants to revisit because cancelling it means admitting the investment was a mistake. Every stack has at least one This isn't an edge case. Talk to anyone who's run a martech audit on an enterprise marketing department and they'll tell you the same thing: every stack has tools that nobody can justify. Not one or two - usually several. The pattern is always the same. Someone identified a gap. A vendor appeared with a compelling demo. The purchase got approved based on a use case that sounded reasonable at the time. Then reality set in. The implementation was harder than expected. The integration with existing tools required development resources that weren't available. The team that was supposed to use it was already stretched thin. The vendor's customer success team checked in for the first month and then disappeared. So the tool sat there. Not producing value. Not integrated. Not cancelled. Just existing - a line item on a budget that nobody reviews closely enough to question. The real cost isn't the licence The licence fee is the obvious cost, but it's rarely the biggest one. The bigger cost is the problem that's still unsolved. The tool was purchased to fix something - a gap in reporting, a missing integration, a capability the team needed. When the tool went unused, the gap didn't close. It just stopped being talked about. The team found workarounds, or they accepted the limitation, or they forgot the gap existed because the purchase had created the illusion of progress. Then there's the opportunity cost. Every pound spent renewing a tool nobody uses is a pound not spent on something that would actually help - better configuration of existing platforms, training for the team, consulting to fix the processes that are actually broken. And there's the complexity cost. Even unused tools add to the stack's overhead. They show up in security audits. They hold data that may need to be accounted for under privacy regulations. They create confusion when a new team member sees them in the tool inventory and asks what they're for - and nobody can answer. Why nobody cancels The psychology of unused tools is straightforward: cancelling is harder than renewing. Cancelling requires someone to acknowledge that the purchase was a mistake - or at minimum, that the implementation didn't work. That's uncomfortable. The person who approved the budget might still be in the room. The vendor relationship might feel awkward to end. The original use case might still technically be valid, even if nobody's going to act on it. Renewing requires nothing. The payment processes automatically. Nobody has to make a decision, have a conversation, or write an email. Inaction is the path of least resistance, and in most organisations, the systems are set up to make inaction the default. This is how tools stay in the stack for years after they stopped being relevant. Not because anyone decided to keep them, but because nobody decided to remove them. One unused tool becomes three The problem compounds. An unused tool doesn't just sit there costing money - it creates conditions for more unused tools to arrive. When the original tool fails to deliver, the gap it was supposed to close remains open. The team still has the problem. So they start looking for another solution - a different tool, a different vendor, a different approach to the same issue. Sometimes that new tool works. Often it joins the first one in the stack, partially implemented, partially adopted, partially solving the problem. Meanwhile, the platform the team already owns - the MAP, the CRM, the analytics tool - may have added the exact capability the unused tool was supposed to provide. Platform vendors ship new features constantly. The enrichment tool purchased two years ago might now be redundant because the MAP added native enrichment. The reporting dashboard bought last year might be unnecessary because the CRM's built-in analytics improved significantly. But nobody checks, because nobody's tracking the overlap between new platform features and existing point solutions. After a few cycles of this, the stack has multiple tools doing overlapping things, none of them fully integrated, and the team is spending more time managing tool complexity than solving the problem any of them were supposed to address. The vendor renewal conversation Most tool renewals happen without a conversation. An invoice arrives, finance processes it, and the subscription continues. The vendor doesn't check whether you're using the tool - they're happy to keep billing. Your team doesn't flag it because nobody is tracking utilisation against cost. Building one step into the renewal process changes this entirely. Thirty days before any tool renews, someone - the tool owner, the ops lead, whoever manages the stack - should answer four questions and document the answers. Is the tool actively used? Not "does someone log in occasionally" - is it part of a regular workflow that would break if the tool disappeared? If the last meaningful usage was three months ago, that's not active. Is the tool integrated with the rest of the stack? A standalone tool that doesn't connect to the CRM, the MAP, or the reporting infrastructure is a tool operating in isolation. Isolated tools produce isolated data. That's rarely worth the licence fee. Has the original use case been addressed by another tool since purchase? Platform capabilities evolve. Check whether the MAP, the CRM, or another tool in the stack has added functionality that makes this tool redundant. Does the cost justify the value? Not in theory - in practice. What specific business outcome did this tool contribute to in the last 12 months? If the answer requires creative interpretation, the tool probably isn't worth the renewal. The audit nobody wants to do The fix is simple and uncomfortable: inventory every tool in your stack, identify who uses each one, and cancel what can't be justified. For each tool, ask three questions. Who on the team used this in the last 90 days? If nobody, that's your answer. What business outcome does this tool directly support? If the answer is vague or theoretical, the tool isn't supporting anything. If we cancelled this tomorrow, what would break? If the answer is "nothing," cancel it tomorrow. Most teams that run this exercise for the first time are surprised by how much they're spending on tools that deliver no measurable value. The savings are usually significant - not life-changing, but meaningful enough that the budget could be redirected somewhere useful. The harder part is building the habit. Tool audits should happen at least annually, timed to renewal cycles. Before any tool renews, someone should confirm it's still being used, still integrated, and still solving the problem it was purchased to solve. If the answer to any of those is no, the renewal should be a conscious decision - not an automatic one. The stack should shrink before it grows Every time someone proposes adding a new tool, the first question should be: have we fully used what we already have? The second question should be: is there anything in the current stack that could be removed to make room? A lean stack that's properly configured, fully integrated, and actively used by the team will outperform a bloated one every time. The tools that create the most value aren't the newest or most feature-rich. They're the ones that someone actually owns, maintains, and uses every day. The most expensive tool in your stack isn't the one with the biggest licence fee. It's the one nobody would miss if it disappeared - and the fact that it's still there tells you more about your procurement process than it does about your marketing technology strategy.

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 "influenced" used loosely. The meeting ends. Everyone goes back to their desk knowing that nothing in that presentation answered the question leadership was actually asking: is marketing generating revenue? The metrics weren't wrong. They were real numbers from real campaigns. But they were the wrong numbers - activity and engagement metrics dressed up as performance metrics, presented with enough confidence that nobody in the room wanted to be the one to say "but what did any of this actually produce?" The gap between what gets reported and what gets asked Leadership asks one question about marketing: is it working? Specifically, is the money we're spending on marketing producing a return? Is the pipeline growing? Are deals closing faster? Is marketing contributing to revenue in a way that justifies the investment? The metrics most marketing teams report don't answer that question. They answer a different one: is the marketing team busy? Emails sent tells you the team is active. It doesn't tell you whether those emails produced anything. Open rates tell you subject lines are working. They don't tell you whether anyone who opened the email went on to become a customer. MQLs tell you leads are crossing a threshold. They don't tell you whether those leads converted to opportunities, entered the pipeline, or generated a single pound of revenue. These metrics aren't useless. They're operational indicators - useful for the marketing team to diagnose and optimize their own campaigns. But they're not performance metrics. They don't connect to the business outcomes that leadership cares about. Presenting them as if they do is where the credibility gap starts. Why teams report this way It's not because marketers are dishonest. It's because revenue-connected reporting is hard to build and most teams don't have the infrastructure to do it properly. Connecting marketing activity to pipeline and revenue requires clean data flowing between the MAP and the CRM. It requires attribution models that are configured correctly and maintained over time. It requires lifecycle stages that are consistently defined and applied. It requires closed-loop reporting that tracks a lead from first touch through to closed-won deal. Most teams don't have all of that in place. The CRM integration has gaps. The attribution model was set up once and never reviewed. Lifecycle stages mean different things to different people. The data that would connect email click to pipeline contribution doesn't exist because the handoff between marketing and sales isn't tracked cleanly. So the team reports what they can measure - activity and engagement - because those numbers are available, they're always positive (you can always send more emails), and they fill a slide deck without requiring anyone to confront the harder question of whether any of it drove revenue. Over time, this becomes the norm. Leadership stops expecting revenue metrics from marketing because they never get them. Marketing stops trying to build them because leadership stopped asking. Both sides settle into a comfortable arrangement where marketing reports activity, leadership acknowledges it, and the actual impact question goes unasked and unanswered. The damage is slow and structural Vanity metrics don't cause an immediate crisis. They cause a slow erosion of marketing's credibility and strategic influence within the organization. When marketing can't connect its activity to revenue, it gets treated as a cost centre - a department that spends money rather than one that generates it. Budget conversations become adversarial. Every investment requires justification, and the justification can never be "this will generate pipeline" because marketing can't prove the last investment did. When times get tight and cuts need to happen, the departments that can't demonstrate revenue contribution get cut first. Marketing teams that report vanity metrics are perpetually vulnerable to budget reductions because they've never built the evidence base that protects them. And the strategic seat at the table disappears. Leadership doesn't invite marketing into revenue conversations because marketing has never shown it belongs there. The CMO gets left out of planning discussions, pipeline reviews, and forecasting meetings - not out of malice, but because marketing's reporting has never demonstrated a connection to the numbers being discussed in those rooms. What revenue-connected reporting actually looks like The shift from vanity metrics to revenue metrics isn't a technology problem. It's a definition problem followed by a configuration problem. Start by defining what you're going to measure. The metrics that connect marketing to revenue are straightforward: marketing-sourced pipeline (deals where the first meaningful touch came from marketing), marketing-influenced pipeline (deals where marketing touched one or more contacts during the sales cycle), MQL-to-opportunity conversion rate, and average deal velocity for marketing-sourced vs non-marketing-sourced deals. None of these require exotic tools. They require clean data, a properly configured CRM integration, and agreement between marketing and sales on what counts as "sourced" and "influenced." Build the attribution model and maintain it. First-touch, last-touch, multi-touch - the specific model matters less than having one that's configured correctly and reviewed regularly. The model should reflect your actual buyer journey, not a theoretical one. If most of your deals involve 8-10 marketing touches before the first sales conversation, a first-touch model is going to undercount marketing's contribution. If your sales cycle is short and driven by a single conversion event, multi-touch may be overcomplicating things. Close the loop between the MAP and the CRM. When a lead becomes an opportunity and when that opportunity closes, the data should flow back to marketing so the original campaign, channel, and touchpoints get credited. Without this closed loop, marketing can report on what it sent but never on what it produced. Report at the level leadership cares about. The QBR slide deck should lead with pipeline and revenue metrics. How much pipeline did marketing source this quarter? How much did it influence? What's the conversion rate from MQL to opportunity? How does deal velocity compare for marketing-sourced vs other deals? Activity and engagement metrics can follow as supporting detail - but they're the footnotes, not the headline. The conversation nobody wants to have The first time you present revenue-connected metrics to leadership, the numbers might not look great. Marketing-sourced pipeline might be smaller than expected. Conversion rates might reveal that the MQL definition needs work. Attribution might show that some of the campaigns leadership loves aren't actually producing results. That's uncomfortable. It's also the beginning of marketing being taken seriously as a revenue function. The teams that make this shift - from reporting what they did to reporting what it produced - earn a fundamentally different relationship with leadership. Budget conversations become investment conversations. Marketing gets pulled into pipeline reviews. The CMO gets a seat in the rooms where revenue is discussed. The teams that don't make the shift keep presenting slide decks full of open rates and MQL counts. Leadership keeps nodding. And everyone keeps leaving the room knowing the actual question wasn't answered. The metrics are available. The infrastructure is buildable. The only thing stopping most teams is the willingness to report honestly - even when the honest numbers are harder to celebrate than the vanity ones.

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 the approach actually changed. It's the same demand generation programme with a filter applied. The strategy is identical. The content is identical. The measurement is identical. Only the audience narrowed. That's not account-based marketing. That's demand gen wearing a name badge. What ABM is supposed to be The entire premise of account-based marketing is that you treat individual accounts as markets of one. You research the account. You understand their specific challenges, their organizational structure, their buying committee. You build campaigns tailored to that account's reality - not your generic messaging repackaged with their logo on it. That means different content for different stakeholders within the same account. The CFO gets messaging about financial impact. The IT director gets messaging about integration and security. The end-user team gets messaging about workflow improvements. Each stakeholder receives something relevant to their role in the buying decision, timed to where the account is in its evaluation process. It also means sales and marketing working the account together - not marketing generating leads and throwing them over the wall. In real ABM, the sales team and marketing team agree on the account plan, coordinate outreach, share intelligence about what's happening inside the account, and adjust the approach based on what they're learning in real time. That level of coordination is hard. It takes planning, resources, content, and genuine collaboration between teams that in most organizations operate independently. Which is why most teams skip it and just filter their existing campaigns by an account list. The spreadsheet is not a strategy The target account list is where ABM starts. It's not the strategy itself. But in most programmes, building the list is the only genuinely account-based activity that happens. Everything after it is generic. A good target account list is built on data - closed-won analysis, firmographic fit, intent signals, strategic value, sales input. That part usually gets done reasonably well because it's a finite, one-time exercise that produces a deliverable everyone can point to. The strategy is what happens after the list exists. How are you engaging each tier of accounts differently? What content exists for each persona in the buying committee? How does marketing activity coordinate with sales outreach? What signals indicate an account is progressing, and what actions do those signals trigger? How are you measuring engagement at the account level, not just the lead level? If the answers to those questions are vague - or identical to how you'd answer them for your demand generation programme - the ABM label isn't earned. The content problem Real ABM requires content that most marketing teams don't have and aren't set up to produce. Demand gen content is built for scale - one ebook serves the entire addressable market, one webinar targets a broad audience, one email template gets sent to thousands of contacts with light personalization. That's efficient and it works for demand gen. It doesn't work for ABM. ABM content needs to be relevant at the account level or at minimum the industry and persona level. That means a case study that speaks to the specific challenges of financial services companies, not a generic customer story. A whitepaper that addresses the regulatory environment the target account operates in, not a broad trends piece. An email that references something specific about the account's situation, not a merge field with their company name dropped in. Producing this content takes significantly more effort per account than producing demand gen content per segment. Most teams underestimate this when they launch ABM. They commit to the strategy, build the account list, and then discover they don't have the content to support account-specific engagement. So they fall back on the generic content they already have - and the programme becomes demand gen with a filter again. Sales alignment isn't optional - it's the whole point The most common structural failure in ABM programmes isn't bad targeting or weak content. It's that sales isn't involved. Marketing builds the account list. Marketing runs the campaigns. Marketing tracks the engagement scores. Sales gets a notification that an account is "engaged" and does whatever they were going to do anyway - which is usually calling the one contact they already know and ignoring the rest of the buying committee. That's not alignment. That's parallel play. Marketing and sales are both active on the same accounts, but they're not coordinating. The messaging isn't consistent. The timing isn't coordinated. The intelligence isn't shared. Marketing doesn't know what sales is hearing in conversations. Sales doesn't know which stakeholders marketing has engaged. ABM without sales alignment is marketing talking to itself about accounts. The investment in targeting, content, and technology gets wasted because the last mile - the human relationship between the sales team and the buying committee - never connects to the marketing activity that's supposed to support it. The measurement theatre Measurement is where the illusion gets maintained. Most ABM programmes report metrics that sound account-based but are actually demand gen metrics with a filter. "We generated 150 MQLs from target accounts this quarter." That's a demand gen metric applied to an account list. It doesn't tell you anything about account penetration, buying committee coverage, or whether the accounts are actually progressing toward a deal. "Our target account engagement score increased by 30%." Engagement scoring at the account level is a step in the right direction - but what does the score actually measure? If it's aggregating email opens and content downloads, it's measuring marketing activity, not buying intent. An account where one person downloaded three ebooks isn't more engaged than an account where five decision-makers each visited the pricing page once. But most engagement models would score the first account higher. "We influenced pipeline worth £2M from ABM accounts." Influenced is doing a lot of heavy lifting in that sentence. Was the account already in pipeline before ABM started? Would sales have closed it anyway? Did the ABM activity actually change anything about the deal, or did it just happen to touch an account that was already progressing? Real ABM measurement is harder and more honest. It tracks how many stakeholders in the buying committee have been engaged, whether engagement is progressing across the account over time, whether ABM-targeted accounts enter pipeline at a higher rate than non-targeted accounts, and whether they close faster or at higher values. If your ABM reporting can't answer those questions, it's reporting on demand gen and calling it ABM. What the first 90 days of real ABM look like If your honest self-assessment revealed that your programme is closer to demand gen with a filter, here's what actually shifting to ABM looks like in practice. Not the full transformation - just the first 90 days. Days 1-30: Shrink the list and deepen the research. Take your target account list and cut it by at least half. The accounts that remain should be ones you can genuinely research and build tailored approaches for. For each one, map the buying committee - not just the contact you already have, but the full set of stakeholders who would be involved in a purchase decision. Use your ABM platform, LinkedIn, and your sales team's relationships to build that map. Days 30-60: Build account-specific content for one tier. Pick your top 10-20 accounts and build content that speaks to their specific industry, challenges, or situation. This doesn't mean creating a custom ebook for each account. It means adapting your best existing content to address the specific concerns of each buying committee persona within that industry. The CFO version. The IT version. The practitioner version. Three versions of one asset is more valuable than one generic version sent to everyone. Days 60-90: Coordinate one joint campaign with sales. Pick five accounts and run a coordinated play where marketing and sales are actively collaborating. Marketing warms the account with targeted content and ads. Sales follows up with personalized outreach that references the same themes. Both teams share what they're seeing - which stakeholders are engaging, what topics are resonating, where the gaps are. Run this for 30 days and measure what happens compared to your standard approach. That's not a complete ABM programme. It's a proof of concept that demonstrates whether genuine account-based activity produces different results from filtered demand gen. If it does - and it usually does - you have the evidence to invest further. If it doesn't, either the execution needs adjusting or the accounts weren't the right ones. How to know if your ABM is actually ABM Honest self-assessment. Four questions. Can you describe a different approach for your top 10 accounts vs your top 100? If everything gets the same treatment, you're running one-to-many demand gen, not tiered ABM. Does sales co-own the account plan, or just receive the leads? If sales isn't involved in deciding which accounts to target, what messaging to use, and how to coordinate outreach, ABM is a marketing-only initiative - and marketing-only ABM doesn't close deals. Do you have content tailored to specific industries, personas, or accounts? If every account receives the same content, the personalization is cosmetic. Real ABM requires real content investment. Are you measuring account engagement, or just lead engagement? If your primary metric is MQLs from target accounts, you're still measuring demand gen. ABM measures how deeply you've penetrated the buying committee, how engagement is progressing at the account level, and how that engagement connects to pipeline. If you answered honestly and the answers were uncomfortable, you have two choices. Either invest in building a real ABM programme - with the content, the sales coordination, and the measurement to match - or acknowledge that what you're running is demand gen with better targeting and stop calling it ABM. Both are valid strategies. Only one of them is account-based marketing.

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 what it looks like when a marketing ops team is actually governing their AI properly - and how it's different from what most teams are doing. What most teams are doing Most B2B marketing teams have some version of the following in place: an AI policy document, a general awareness that AI features exist in their platform, and a vague understanding that someone should probably be paying attention to what those features are doing. In practice, AI features get activated and forgotten. Nobody maintains a list of what's running. Nobody reviews outputs. Nobody checks whether the data feeding AI features is still current. Nobody owns the AI layer as a distinct operational responsibility. The team treats AI features the same way they treat any other platform capability - configure it, trust it, move on. This works until it doesn't. When it stops working, the failures are hard to detect and harder to diagnose, because nobody built the infrastructure to monitor what the AI is doing. What a well-governed team actually does differently The difference isn't dramatic. There are no dedicated AI governance departments or six-figure compliance platforms. The difference is a small set of operational habits that take maybe 2-3 hours per month and produce a level of visibility and control that most teams don't have. They maintain a live inventory. Every AI feature running inside the platform is listed in a shared document - not a one-time audit, but a living register that gets updated whenever something changes. The register is simple: feature name, what it does, what data it consumes, when it was activated, who owns it, when it was last reviewed. When a platform upgrade introduces new AI capabilities, someone checks what changed and updates the register. When a team member activates a new feature, they add it. When a feature gets deactivated, it's noted. This takes minutes per update and creates something invaluable: a single source of truth for what AI is doing inside the platform. Most teams can't produce this list even after hours of investigation. A well-governed team can produce it in 30 seconds. They assign owners, not committees. Each AI feature in the register has a named person next to it. Not "the marketing ops team" - a specific person who can answer questions about that feature. When the scoring model drifts, that person is accountable. When a consent management feature processes data in a way that needs investigating, that person handles it. When the quarterly review comes around, that person reports on whether the feature is still performing as intended. Ownership doesn't mean that one person does everything. It means one person is responsible for knowing what's happening with that specific feature and escalating when something isn't right. The difference between "someone should look at this" and "Sarah owns this and she's looking at it" is the difference between governance that works and governance that doesn't. They review outputs, not just inputs. The most common governance approach is to check the data going into AI features - is the data clean, is consent current, are the fields populated correctly. That's necessary but insufficient. A well-governed team also reviews what comes out. They pull a monthly sample of AI-scored leads and check whether the scores correlate with actual conversion. They review AI-driven suppression decisions to verify contacts are being excluded for valid reasons. They spot-check AI-generated content recommendations to ensure they're relevant and on-brand. They compare AI-assisted campaign performance against a baseline to see whether the AI is actually improving outcomes or just adding complexity. Output review is where you catch drift - the slow degradation in AI performance that happens as data changes, business conditions shift, and models age without recalibration. Input governance keeps the AI fed properly. Output governance keeps the AI honest. They tie review to the calendar, not to problems. Most teams only look at AI features when something goes wrong - when sales complains about lead quality, when deliverability drops unexpectedly, when someone notices an automation doing something it shouldn't. By then, the damage has been accumulating for weeks or months. A well-governed team reviews on a fixed cadence. The AI register gets reviewed quarterly. Scoring model performance gets checked monthly. Consent data gets reconciled annually at minimum and after any regulatory change. Platform upgrades get reviewed within a week of release to check for new AI features that may have been activated automatically. The cadence doesn't have to be aggressive. It has to be consistent. The difference between "we review when we remember" and "we review on the first Monday of every quarter" is enormous in practice. They document decisions, not just features. The register captures what AI features exist. Documentation captures why they were activated, what they're expected to achieve, and what criteria would trigger a review or deactivation. This matters because people leave. The person who activated a feature six months ago may not be on the team when questions arise. If the only record is "predictive scoring is on," nobody knows why it was turned on, what it was supposed to improve, or how to evaluate whether it's working. If the record says "predictive scoring activated in March to improve MQL-to-opportunity conversion, baseline conversion rate was 18%, target is 25%, review after 90 days," anyone on the team can evaluate the feature's performance and decide whether it should continue. This documentation takes five minutes per feature and saves hours of investigation later. It's the governance equivalent of code comments - nobody wants to write them, everyone is grateful when they exist. The real-world difference The practical difference between a governed and ungoverned AI environment shows up in specific moments. When a platform upgrade ships new AI features, the ungoverned team discovers them months later by accident. The governed team reviews the release notes within a week and documents any changes. When lead quality declines, the ungoverned team spends weeks investigating campaign creative, messaging, and targeting before someone thinks to check the scoring model. The governed team checks scoring model output as a first step because it's on the monthly review calendar. When a regulator or enterprise customer asks "what automated decisions does your marketing platform make?" the ungoverned team spends days trying to reconstruct an answer. The governed team opens the register and provides it immediately. When a team member leaves, the ungoverned team loses institutional knowledge about what AI features are running and why. The governed team has documentation that survives personnel changes. None of these scenarios are hypothetical. They're the moments where governance either earns its keep or reveals its absence. It's less work than you think The most common objection to operational AI governance is that it's too much work for an already-stretched team. In reality, the ongoing maintenance is minimal: Updating the register when features change - minutes per update. Monthly output review for scoring and key automations - one to two hours. Quarterly register review - one hour. Annual consent reconciliation - half a day. Platform upgrade review - one hour per release. Total: roughly 2-3 hours per month plus a half-day annually. That's the cost of knowing what your AI is doing. The cost of not knowing is measured in compliance incidents, degraded performance, and the hours spent investigating problems that proper monitoring would have caught weeks earlier. The teams doing this well aren't spending more time on governance. They're spending less time on firefighting. That's the trade-off - and it's one that every marketing ops team should be making.

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, nobody checked what data it's feeding on, and nobody was asked to own it. The policy on the intranet has no idea it exists. That gap - between the policy and what's actually happening - is the difference between having AI governance and having AI discipline. One is a document. The other is a daily practice. Almost every company has the document. Almost none have the practice. The policy-to-practice gap There's a reason AI policies don't translate into AI governance, and it's not laziness or incompetence. It's structural. AI policies are written by legal, compliance, or senior leadership teams. They're written at the principle level - "use AI responsibly," "ensure transparency," "maintain human oversight." These principles are correct and necessary. They're also too abstract to guide the person in marketing ops who just got asked to activate an AI feature in the MAP. What does "ensure transparency" mean when you're configuring a predictive scoring model? Document it? Tell someone? Write it down somewhere? Where? In what format? Who needs to know? The policy doesn't say, because the policy was written for the board deck, not for the platform administrator. The result is a two-tier system. At the top, the policy describes how AI should be governed. At the operational level, AI gets deployed however the team sees fit - because nobody translated the principles into procedures that apply to the actual work. The policy says "human oversight." In practice, nobody is overseeing anything because nobody was told that oversight was their job. Five things organizations get wrong These patterns show up repeatedly. They're not edge cases - they're the norm. Writing the policy and calling it done. The most common failure. The policy gets published and the organization treats governance as complete. Nobody builds the operational layer underneath - the inventory, the ownership assignments, the monitoring, the review cadence. The policy becomes a compliance artifact rather than an operational tool. When something goes wrong, the organization can point to the policy and say "we had governance." But the policy didn't prevent anything because it was never connected to the systems it was supposed to govern. Governing the AI they chose but not the AI that arrived. Most governance frameworks cover deliberate AI deployments - the chatbot the team decided to build, the model the data science team trained. They don't cover the AI that showed up without anyone asking for it: the predictive features that came with a platform upgrade, the AI-assisted tools that a vendor enabled during onboarding, the smart capabilities that an individual team member activated because they saw them in a release note. In most enterprise marketing automation environments, more AI features are running by accident than by design. Governance that only covers intentional deployments misses most of the AI that's actually operating. Assigning governance to a committee instead of an owner. Committees review, discuss, and advise. They don't operate. When AI governance is owned by a committee that meets monthly, the AI features running inside the MAP get reviewed 12 times a year at most - and only if someone remembers to add them to the agenda. Operational governance needs named individuals with specific accountability: this person owns this AI feature, this person reviews its output, this person gets notified when something changes. A committee can oversee the programme. It can't run it. Monitoring inputs but not outputs. Many organizations focus their governance on what goes into AI - data quality, training data provenance, consent records. That's important. But it's only half the picture. The outputs matter just as much: what decisions is the AI making? Are the leads it scores converting? Are the contacts it suppresses the right ones? Is the content it recommends relevant? Output monitoring is where you catch drift, degradation, and failure. Without it, you're trusting the AI because you trust the inputs - and as we've covered elsewhere, those inputs may not be as reliable as you think. Treating governance as a one-time exercise. AI doesn't stay static. Models drift as data changes. Platform features get updated. Business conditions evolve. A governance framework built for the AI environment that existed six months ago may not cover the AI environment that exists today. Governance needs a review cadence - quarterly at minimum - that checks whether the inventory is current, the owners are still in role, the monitoring is still catching what it should, and the AI features are still producing the outcomes they were activated for. What discipline looks like in practice AI discipline isn't a framework or a programme. It's a set of habits built into how the team operates. When someone activates an AI feature, it gets logged - what it does, what data it uses, who turned it on, who owns it. That takes five minutes and it's the difference between a governed environment and a mystery. When the platform ships an update that includes new AI capabilities, someone reviews what changed and documents whether any new features were activated. That's a quarterly task, timed to release cycles. When AI-scored leads are handed to sales, someone checks monthly whether the scores correlate with actual conversion. If they don't, the model gets reviewed. That's not a project - it's a standing agenda item. When consent data changes - new regulations, updated processing purposes, revised preference categories - someone checks whether the AI features that consume consent data are still operating within the updated boundaries. That's an annual task at minimum. None of this requires new technology. None of it requires a dedicated governance team. It requires the same operational discipline that the best marketing ops teams already apply to their platforms - extended to cover the AI features running inside them. The discipline gap is the real governance gap The industry has reached a point where most organizations have policies and most organizations have AI running in production. The gap between those two facts is discipline - the operational habits that connect what the policy says to what the AI actually does. Closing that gap isn't expensive, complicated, or time-consuming. It's the kind of work that gets skipped because it's not urgent, not visible, and not rewarded - until something goes wrong and everyone wishes it had been done. The organizations that build AI discipline now will operate with a level of confidence and control that their competitors - the ones relying on policy documents and hope - simply won't have. And when the regulatory questions arrive, and the compliance audits happen, and the customer incidents occur, the disciplined organizations won't need to scramble. They'll already have the answers. The policy is the starting line. Discipline is the race. Most organisations are still standing at the start.

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 routes the wrong leads to sales for three months before anyone notices. An AI feature activated during a platform upgrade starts making decisions nobody approved. That's when organizations discover whether they have governance or just a document. In most cases, it's the document. Because the document describes what AI should do. Governance is what happens when AI doesn't do it - and most organizations have no process for detecting, escalating, or fixing AI failures in their marketing operations. The failures are already happening This isn't theoretical. Real organizations have already learned what ungoverned AI costs. Air Canada's customer service chatbot told a passenger he was eligible for a bereavement discount that didn't exist. The passenger booked the fare based on the chatbot's advice. When Air Canada refused to honour the discount, the case went to tribunal. Air Canada's defence was that the chatbot was "a separate legal entity responsible for its own actions." The tribunal rejected that argument and ruled that Air Canada is responsible for everything its AI tells customers. The precedent is clear: your AI's mistakes are your mistakes. DPD, the delivery company, had to shut down its AI chatbot after a system update caused it to swear at customers, write poems mocking the company, and describe itself as "the worst delivery firm in the world." A customer's screenshot of the conversation went viral - 800,000 views in 24 hours. The chatbot couldn't even perform its basic function of tracking a parcel, but it was very good at damaging the brand. And Salesforce's Agentforce had serious security disclosures in 2025, with hackers claiming over one billion Salesforce records were stolen across coordinated attacks. Major brands including Google, Adidas, Workday, and Coca-Cola were affected. These are the visible failures - the ones that made headlines. The invisible ones are happening inside marketing automation platforms every day. Scoring models drifting without anyone checking. Consent records being acted on by AI features that were never audited. Suppression rules being applied by agents that nobody monitors. The damage from these failures doesn't make the news. It shows up as declining performance, compliance exposure, and pipeline problems that nobody can diagnose. Why policies fail when AI fails The gap between AI policy and AI governance is the gap between intention and operations. A policy says "AI should be used responsibly." Governance says "when the scoring model produces leads that sales rejects at a rate above 40%, this person gets notified, this process kicks in, and this review happens within 48 hours." A policy says "AI outputs should be accurate." Governance says "every AI-generated customer communication is logged, a random sample of 5% is reviewed weekly, and any inaccuracy triggers a root cause analysis." A policy says "AI should be transparent." Governance says "every AI feature active in our MAP is listed in a register with a named owner, a documented purpose, and a review date." Most organizations have the first version. Almost none have the second. The policy tells people what to believe. Governance tells the organisation what to do. When the AI fails, belief doesn't help. Process does. What operational AI governance actually looks like Governance that works in practice - not just in a document - has four components. An AI inventory. You can't govern what you can't see. Every AI feature, automation, and agent running inside your marketing stack needs to be catalogued: what it does, what data it uses, who activated it, and who's responsible for it. Most organisations can't produce this list because nobody's tried. AI features get activated during platform upgrades, by individual team members experimenting, or by vendors enabling capabilities during onboarding. Without an inventory, governance has no foundation. Named ownership. Every AI feature needs a person - not a committee - who can answer two questions: "what is this doing?" and "is it still doing what we intended?" When a scoring model drifts, someone needs to notice. When a consent record gets acted on incorrectly, someone needs to be accountable. When an agent makes a decision that doesn't make sense, someone needs to investigate. Ownership without a name is no ownership at all. Monitoring and alerting. AI doesn't fail loudly. It fails by making confident decisions that are subtly wrong. A scoring model that's drifting won't throw an error - it'll just route progressively worse leads to sales over weeks. A consent management issue won't crash the system - it'll just process contacts in ways that aren't compliant. Governance needs monitoring that catches these slow failures: conversion rate tracking on AI-scored leads, regular consent audits, output sampling on AI-generated content, and engagement pattern analysis on AI-driven campaigns. An incident response process. When the monitoring catches something - or when a customer complains, sales raises an alarm, or a regulator asks questions - what happens next? Who gets notified? What's the escalation path? How quickly does the AI feature get paused or reviewed? Most organisations have incident response for security breaches. Almost none have it for AI failures, even though the regulatory and reputational risk is increasingly comparable. The 76% problem According to the Association of National Advertisers, 76.6% of marketers now have AI policies in place - up from 55.3% a year earlier. Investment is surging. Nearly 89% plan to increase AI spending. But over 70% of marketers have encountered an AI-related incident - hallucinations, bias, or off-brand content - according to the IAB. And less than 35% plan to increase investment in AI governance. The numbers tell a clear story: organisations are writing policies, spending money, encountering problems, and not investing in the governance that would prevent those problems from recurring. The policy creates the illusion of control. The incidents prove the illusion is exactly that. Governance isn't overhead. It's operational infrastructure. The resistance to building operational AI governance usually comes from the same place: it feels like bureaucracy. Another process, another checklist, another layer between the team and the work. But governance isn't about slowing things down. It's about knowing what's running, who owns it, whether it's working, and what to do when it isn't. That's not bureaucracy. That's basic operational hygiene - the same standard every organisation applies to its financial systems, its security infrastructure, and its legal compliance. AI in marketing operations is making decisions that affect real people, real data, and real revenue. The organisations that govern it properly will move faster than the ones that don't - because they'll catch problems early, fix them quickly, and maintain the trust of their customers, their regulators, and their own leadership. The ones that don't will keep writing policy documents and hoping for the best. Until the next incident proves that hope isn't a governance strategy.

By Andrew Poole - Specialist Marketing Consultant - Sojourn Solutions Before attending The MarTech Summit Madrid, I wrote about what I was hoping to take away from the event. I wanted practical conversations about Marketing Operations, AI governance, customer data, sales and marketing alignment, and the reality of making MarTech work inside complex organisations. Having now attended, the short version is this: The event itself was excellent. The organisation was slick, the venue was fantastic, the agenda was relevant, and the quality of the non-vendor speakers was genuinely strong. But the biggest takeaway for me was not what I expected. It was not that AI has come for Marketing Operations. It is that AI is being heavily pushed into Marketing Operations teams 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 - then panic. First, credit where it’s due The MarTech Summit Madrid was very well put together. The location was brilliant, right in the heart of Madrid, and the venue at VP Plaza España Design worked extremely well for the format. The room setup made sense, with the main stage close to the vendor area, enough space to move around, and a flow that made the day easy to navigate. The agenda itself covered a strong mix of topics, from customer experience and digital experience through to AI, accountability, social strategy, revenue alignment, brand, and the evolution of the marketing function. The official session slides were shared after the event under the theme “Redefining MarTech: Autonomous, Intelligent, Human-Centred”, which is a fairly accurate summary of where the day’s conversation seemed to sit. The non-vendor speakers were knowledgeable, credible, and from a good mix of organisations. That mattered. There was enough breadth in the sessions to reflect the day-to-day reality most marketing teams are dealing with: Data, customer journeys, automation, alignment, brand consistency, AI adoption, team structures, and the ongoing battle to make technology actually do what the brochure said it would do. So no complaints there. As an event, it delivered. As a snapshot of where MarTech is heading, it was even more interesting. And, in places, more worrying. The AI agent gold rush has arrived The most noticeable thing on the vendor side was not the variety of tools. There was variety, at least on paper. Vendors covered everything from mobile security and web page creation to automation platforms, brand systems, font management, and customer engagement technology. But scratch the surface and a lot of them were really there to talk about the same thing. AI agents. Not always in exactly the same language, but close enough. AI agents that could build campaigns. AI agents that could generate content. AI agents that could orchestrate end-to-end marketing activity. AI agents that could help marketing teams move faster, do more, reduce effort, and automate huge chunks of the process. At times, it felt as though you could have swapped the logos on a few of the decks and the message would barely have changed. Everyone had arrived at the same party wearing the same outfit, then acted surprised when the room looked familiar. That does not mean the technology was bad. Some of it was genuinely impressive. There were interesting ideas, useful capabilities, and plenty of examples of how AI could remove friction from marketing workflows. But the repetition was hard to ignore. It felt like a lot of companies had looked at their existing product set, looked at the AI agent conversation happening around them, and decided they needed a version of it immediately. Not necessarily because the market had fully worked out the operational problem. More because nobody wants to be the vendor left saying, “we do not have an AI agent yet.” The missing conversation: Governance This is where the day became most interesting from a Marketing Operations point of view. There was a lot of talk about what AI agents could do. There was far less discussion about what they should be allowed to do. That distinction matters. Because an AI agent that can create a campaign is not just a content tool. It is potentially interacting with data, systems, approvals, audiences, assets, customer segments, consent rules, sales processes, reporting structures, and brand controls. In an enterprise environment, campaigns do not simply appear because someone in marketing had a nice idea and a prompt window. They involve multiple teams. Marketing. Sales. Legal. IT. Data. Security. Brand. Compliance. Regional teams. Sometimes procurement. Sometimes finance. Sometimes the person who knows why one specific field in the CRM absolutely must not be touched because of something that happened in 2019. That is the reality. And yet, too often, the AI agent story seemed to glide straight past it. The narrative was often: Prompt goes in, campaign comes out. Lovely. But where is the approval process? Where is the legal review? Where is the data access control? Where is the PII risk assessment? Where is the audit trail? Where is the disaster recovery plan? Where is the human-in-the-loop checkpoint? Where is the rollback process if something goes wrong? Where is the definition of what the agent can change, what it can recommend, and what it must never touch? Those questions are not boring admin. They are the difference between useful AI adoption and a very expensive incident report. Everyone can make the sausage now One line from the State of MarTech 2026 stuck with me: “Everyone’s learned to make the sausage with AI. Almost nobody’s bought a labelling machine.” That was exactly the feeling. A lot of the market has learned how to produce AI-powered outputs. Content, campaigns, workflows, recommendations, journeys, summaries, segments, dashboards. The sausage machine is running. But the labelling machine is governance. It tells you what happened. Who approved it. What data was used. What system was touched. What risk was introduced. What decision was automated. What should be reviewed. What should be blocked. What can be trusted. And right now, that part feels dangerously underdeveloped. This is especially true when AI agents move from “help me write a subject line” to “help me build and activate this campaign.” Those are not the same thing. One is assistance. The other is operational control. And the closer AI gets to operational control, the more important governance becomes. Dark AI is already here Another issue that did not get enough attention is the reality of dark AI use. By that, I mean the AI activity already happening inside organisations without formal approval, visibility, documentation, or control. People are using AI tools whether businesses have a strategy or not. They are using them to summarise customer information, draft emails, rewrite content, analyse spreadsheets, create campaign logic, build presentations, generate code, and speed up all the awkward little tasks that sit between strategy and execution. Some of that usage will be harmless. Some of it absolutely will not be. The risk is not just that someone uses AI badly. It is that the organisation has no idea where AI is being used, what data is being pasted into it, what outputs are being trusted, or how those outputs are making their way into customer-facing activity. That is before you even introduce vendor-built AI agents that connect more deeply into marketing systems. So when AI agent adoption is discussed as though the main challenge is excitement, speed, or productivity, it misses the bigger operational issue. Many companies are not starting from a clean, governed AI environment. They are starting from hidden usage, unclear ownership, inconsistent policies, and a growing pressure to “do something with AI.” and that is not a foundation, that is a wobbling table with a very expensive vase on top. The networking felt different too A smaller observation, but still worth mentioning: Networking felt harder than it used to. That is not a criticism of the organisers. The event setup gave people plenty of opportunity to speak to each other. But the behaviour in the room felt different. A lot of companies seemed to have sent pairs or small teams. People were friendly, but many naturally stayed close to the colleagues they came with. It felt, in some cases, like attendees were enjoying a rare chance to spend time with their own teams away from the office or home-working routine. Completely understandable. But it does change the dynamic. By the later sessions, a noticeable number of people had already left, and the post-event drinks felt heavily vendor-led. That made the networking less open than I remember from pre-Covid events, where people often seemed more prepared to walk up to strangers and start a conversation without first performing a full emotional risk assessment. Again, not a complaint. More an observation. The event was strong. The people were lovely. But the shape of networking has changed. The real takeaway: AI agents expose the operating model The conclusion I came away with is not that AI agents are bad. Far from it. AI agents have a very real role to play in Marketing Operations. They can support campaign QA, workflow creation, content checks, data review, journey planning, reporting, documentation, governance monitoring, and plenty of other tasks that currently consume too much human time. Used well, they could be genuinely transformative. But the phrase doing a lot of work there is "used well." Because the problem is not whether companies can build AI agents. Clearly, they can. The problem is whether they understand the Marketing Operations environment those agents need to work inside. That environment is messy. Cross-functional. Political. Regulated. Data-heavy. Process-heavy. System-dependent. Full of edge cases, exceptions, legacy decisions, regional differences, and commercial pressure. An AI agent that ignores that complexity is not a solution. It is a demo. And demos are easy. Operational maturity is not. This is where Marketing Operations matters more than ever For me, the event reinforced something I already believed, but with a bit more force. AI adoption in marketing is not mainly a technology challenge. It is a Marketing Operations challenge. The businesses that get this right will not be the ones that simply buy or build the flashiest AI agent. They will be the ones that understand how to introduce AI into the real machinery of marketing without breaking trust, compliance, data integrity, customer experience, team confidence, or brand reputation. That means asking harder questions upfront: What should AI be allowed to do? Which systems can it access? Which data is off limits? Where does human approval sit? How are decisions logged? What happens when something fails? Who owns the agent once it is live? How is ROI measured? How does legal get visibility? How does IT stay comfortable? How does sales trust the outputs? How does marketing avoid creating a faster version of the same broken process? These are not questions to answer after implementation. They are the implementation. Why experience still matters This is why breadth of Marketing Operations experience matters so much. It is easy to talk about AI agents in isolation. It is much harder to understand how they fit across marketing automation, CRM, customer data, campaign operations, consent, sales handoff, reporting, governance, and organisational process. That is the bit companies cannot afford to skip - yet seem to be doing so... Because introducing AI agents into Marketing Operations is not just about building something clever. It is about building something safe, useful, measurable, and commercially defensible. It needs governance from the start. It needs a human-in-the-loop approach. It needs clear controls around what AI can touch, what it can recommend, and what it can activate. It needs involvement from the teams that will be affected, not just the team that got excited in the vendor demo. And, yes, it needs enough red tape to stop the business from accidentally burning through budget, creating legal risk, exposing customer data, damaging the brand, or giving the CFO yet another reason to ask why marketing bought a thing. I’m not being dramatic - the risk deserves a bit of theatre. Final thought The MarTech Summit Madrid was a strong event. Well organised, well located, and full of relevant conversations. But the thing I will remember most is not a single session or slide. It is the wider signal from the room. The AI agent race is on. Vendors are moving quickly. Marketing teams are under pressure to keep up. The technology is becoming more capable. The promises are getting bigger. But governance, operational reality, and cross-functional ownership are still lagging behind... massively! That gap is where the danger sits. It is also where the opportunity sits. Because companies that take AI agents seriously, not as a novelty but as part of their Marketing Operations infrastructure, will have a huge advantage. They will move faster, yes. But more importantly, they will move safely, with control, visibility, and a clearer link to business value. That is the difference between adopting AI and simply adding to the chaos. And after Madrid, that difference feels more important than ever. Andrew Poole is a Specialist Marketing Consultant at Sojourn Solutions, where he spends much of his time thinking about Marketing Operations, MarTech, AI governance, and why perfectly good marketing teams are still being held hostage by broken processes and suspicious spreadsheets. Discover our AI Governance Risk Review

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 programme keeps running as if it's doing something useful. It isn't. It's hurting your deliverability, degrading your sender reputation, and teaching your contacts to ignore everything you send. Most nurture programmes were built once and abandoned This is the uncomfortable truth about nurture in B2B marketing: the concept is sound but the execution is almost universally neglected. The idea behind nurture is good. Not every lead is ready to buy. Some need time, education, and continued engagement before they're ready for a sales conversation. A well-designed nurture programme keeps your brand relevant during that period and delivers the right content at the right time based on what the contact actually needs. That's the theory. In practice, most nurture programmes were built to meet a deadline, populated with whatever content was available, and launched with the intention of "optimizing later." Later never came. The team moved on and the nurture kept running in the background, sending the same emails in the same order to everyone who matched the enrollment criteria - whether those emails made sense anymore or not. The result is a programme that technically functions and practically fails. It sends emails. People receive them. Almost nobody reads them. And the team reports "nurture is running" as if that's the same thing as "nurture is working." The damage is worse than low engagement Low engagement is the visible symptom. The real damage is happening underneath. Every email you send to someone who doesn't want it teaches their email client to deprioritize you. Gmail, Outlook, and every major email provider track engagement at the sender level. When a significant portion of your sends go unopened, your sender reputation degrades. That degradation doesn't just affect the nurture - it affects every email you send from that domain, including the ones people actually want to receive. You're also training your contacts to ignore you. Every irrelevant email that lands in someone's inbox reinforces the pattern: this company sends things I don't need. That pattern is hard to break. By the time you do have something relevant to say - a product launch, an event invitation, a genuinely useful piece of content - the contact has already filed you under "noise" and your email gets scrolled past without a thought. And you're polluting your own data. A contact who's been enrolled in a nurture for six months without engaging looks like a cold lead. But they might not be cold - they might just be ignoring your emails because the nurture was irrelevant. Your scoring model can't tell the difference between "not interested" and "interested but tuned out your messaging." The nurture is creating a data quality problem that makes every other marketing decision less reliable. What a nurture programme should actually do A nurture programme needs to do more than send emails on a schedule. It needs to deliver content the contact actually needs, change course when their behaviour changes, and know when to stop. Deliver relevant content. This means the emails in the sequence should be based on what the contact has shown interest in - not what the team had available when the nurture was built. If someone downloaded a guide about platform migration, they should receive content about migration, not a generic company newsletter. Relevance isn't a nice-to-have in nurture. It's the entire point. Adapt to behaviour. A nurture that sends the same sequence regardless of what the contact does is a drip campaign pretending to be a nurture. Real nurture responds to engagement. If a contact clicks on a specific topic, the next email should go deeper on that topic. If they visit the pricing page, the nurture should accelerate - or hand them to sales. If they stop engaging entirely, the cadence should slow or pause rather than continuing to send into the void. Know when to stop. This is the part most programmes get wrong. There's no exit criteria. Contacts enter the nurture and stay in it indefinitely, receiving emails until they either convert, unsubscribe, or simply stop opening anything. A nurture without a clear exit - based on engagement threshold, time elapsed, or lifecycle stage change - is a programme that will degrade over time by design. The quarterly nurture review If you do nothing else, do this: every quarter, pull the performance data on every active nurture programme and ask five questions. What's the open rate trend over the last three months? If it's declining, the content is losing relevance or the audience is tuning out. Both require action. What percentage of enrolled contacts have engaged with at least one email in the last 60 days? If more than half haven't, the nurture isn't nurturing - it's broadcasting to people who aren't listening. Is the content in the sequence still current? If any email references a product feature, event, or offer that no longer exists, it needs to be updated or removed. Sending outdated content is worse than sending nothing. Are the enrollment criteria still right? The rules that made sense when the nurture was built may not make sense now. If your segments have changed, your products have evolved, or your buyer personas have shifted, the nurture is enrolling the wrong people. Is there a clear exit? If contacts can only leave the nurture by converting or unsubscribing, add a time-based or engagement-based exit. Nobody should be in a nurture for 12 months receiving emails they never open. Build fewer nurtures. Build them properly. The instinct is always to build more. Another nurture for another segment, another sequence for another stage. That instinct produces a tangle of overlapping programmes that nobody maintains and nobody can explain. The better approach is fewer nurtures, built with care, reviewed regularly, and retired when they stop performing. Three well-maintained nurture programmes that adapt to behaviour and deliver relevant content will outperform fifteen abandoned ones every time. The emails your contacts receive are your brand's most frequent touchpoint. Every nurture email that gets ignored is a small withdrawal from the trust you've built. Enough small withdrawals and there's nothing left in the account when you actually need it. Build better nurtures or stop sending them. Your contacts - and your deliverability - will thank you for either one.

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 - a new dashboard, a new workflow, a new capability the team didn't have before. Then it joins the stack. And the stack is already full of tools that were supposed to solve problems too. The stack keeps growing. The results don't. The average B2B marketing team is running more tools than at any point in history. The martech landscape has thousands of products and most enterprise marketing departments are using dozens of them. CRM, MAP, analytics, enrichment, intent data, ABM, content management, social scheduling, project management, reporting dashboards, webinar platforms, event tools. Every one of them was purchased to solve a specific problem. The question nobody asks often enough is whether the problem was actually solved - or whether the tool just got added and the problem quietly remained. In most organisations, the answer is somewhere in between. The tool works for the use case it was bought for. But it's running at maybe 20% of its capability because nobody had time to configure it properly, nobody was trained on the advanced features, and nobody integrated it deeply enough with the rest of the stack to unlock the value that justified the purchase. So the team buys another tool to fill the gap the first tool was supposed to close. And the cycle continues. The problem isn't the technology Walk into any enterprise marketing automation instance that's been running for three years and you'll find the same pattern. The platform can do far more than the team is using it for. Features that were included in the licence have never been activated. Capabilities that would eliminate the need for a separate tool are sitting there untouched because nobody knew they existed or nobody had time to set them up. Most marketing automation platforms are genuinely powerful. They can handle complex scoring, sophisticated nurture logic, advanced segmentation, multi-channel campaign orchestration, detailed reporting, and deep CRM integration. The teams using them are typically running basic email campaigns and simple list segmentation - not because that's all they need, but because that's all anyone had time to build. The gap between what the platform can do and what the team actually uses it for is enormous. And that gap is where most martech spending goes to waste - not on bad tools, but on new tools bought to compensate for underused existing ones. People, not platforms This is where the conversation usually gets uncomfortable, because the real constraint isn't budget for tools. It's investment in the people who run them. A marketing automation platform configured by someone who understands it deeply - who knows the scoring engine, the campaign architecture, the data model, the integration layer, the reporting capabilities - will outperform a stack of five tools managed by a team that's stretched too thin to learn any of them properly. But most organizations don't invest that way. They'll spend six figures on platform licences and then assign platform management to someone who's also running campaigns, managing events, building reports, and handling ad hoc requests from sales. That person does their best with the time they have, which means the platform gets configured to a functional baseline and stays there. When performance plateaus, the instinct is to look for a tool that fills the gap. The actual gap isn't technology - it's expertise and capacity. Another tool won't fix it. It'll just add another login, another integration to maintain, and another thing nobody has time to learn properly. The real cost of a bloated stack Every tool in your stack has a cost beyond the licence fee. There's the time to maintain it, the time to integrate it, the time to train people on it, and the time to troubleshoot it when something breaks. There's the data that lives in it that may or may not sync with your other systems. There's the reporting that comes from it that may or may not match what your other tools say. A stack with 15 tools and no clear integration strategy produces 15 partial views of the truth. The team spends more time reconciling data between systems than actually using the data to make decisions. Meetings devolve into arguments about which dashboard is right. Nobody trusts the numbers because the numbers depend on which tool you're looking at. Consolidation isn't about cutting costs, although it does that too. It's about reducing complexity to the point where the team can actually operate effectively. Fewer tools, better configured, properly integrated, with people who know how to use them - that's the stack that performs. Before you buy, ask three questions Next time someone proposes adding a new tool, ask these before signing anything. Can our existing platform already do this? Not theoretically - practically. Get someone who knows the platform well to evaluate whether the capability exists and what it would take to activate it. You might be surprised how often the answer is "yes, we just never set it up." If we bought this, who's going to own it? Not who's going to approve the purchase - who's going to configure it, integrate it, maintain it, and actually use it every day? If the answer is "the same person who's already managing four other tools," the tool will end up underused just like the last one. What happens to our data? Every new tool is another place where data lives. How does it connect to the CRM? How does it sync with the MAP? What happens when the data in the new tool says something different from the data in the existing tools? If there's no clear answer, the tool is adding complexity, not capability. Invest in depth, not breadth The companies getting the most from their martech aren't the ones with the biggest stacks. They're the ones who picked the right platforms, invested in people who understand them, and configured them to do what the business actually needs - rather than buying a new tool every time they hit a wall. The wall is usually a knowledge gap, not a technology gap. And the fix is usually expertise, not another subscription. Before you buy the next tool, make sure you've used the one you already have.

By Andrew Poole - Specialist Marketing Consultant - Sojourn Solutions 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. Because let’s be honest, MarTech has reached that slightly awkward stage where everyone is talking about transformation, but a lot of teams are still wrestling with the basics: Fragmented data, bloated stacks, manual campaign processes, unclear ownership, and the terrifying sentence, “we’ve started using AI in the workflow.” Lovely. What could possibly go wrong? That’s why I’m looking forward to the event. Not just for the sessions themselves, but for the conversations around them. The best takeaways from events like this are rarely just what appears on a slide. They’re the hallway chats, the raised eyebrows, the “we’re seeing that too” moments, and the occasional brutal truth from someone who has already made the mistake you were about to make. AI is everywhere now. The question is whether it’s useful, safe, and governed. Unsurprisingly, AI runs through a lot of the Madrid agenda. There are sessions on agentic AI, AI-powered customer engagement, AI in sales enablement, AI-driven creativity, and AI accountability. That in itself says something important: AI has moved beyond the experimental corner of marketing. It is now being discussed as part of the operating model. That shift matters. For Marketing Operations teams, AI is not just a shiny productivity tool. It is starting to touch campaign planning, content production, personalisation, sales enablement, data activation, customer journeys, and decision-making. The upside is obvious: Faster delivery, better use of customer data, more scalable execution, and fewer humans spending their lives checking whether someone used the correct UTM parameter for the 947th time. But the risk is just as obvious, at least to anyone who has ever worked near a MAP, CRM, or customer database. AI connected to marketing systems is not harmless. It can make poor recommendations. It can expose sensitive data. It can reinforce broken processes. It can automate bad decisions at scale. And, without the right guardrails, it can turn “we saved time” into “who approved that?” very quickly. So one of the big things I’ll be listening for is how seriously brands are treating AI governance. Not in the vague “we need a policy” sense, but in the practical sense: Who owns AI use inside Marketing Operations? What can AI access? What can it change? What needs approval? What gets logged? What happens when it gets something wrong? The event’s session on AI & Accountability, focused on building trust while innovating with AI, looks particularly relevant here, especially as it includes governance, ethical standards, transparency, and evolving regulation as discussion points. That’s where the conversation needs to go. Not “should we use AI?” That ship has sailed, docked, unloaded, and is now selling merchandise. The better question is: How do we use AI without quietly building a risk machine inside the marketing stack? The MarTech stack is becoming an operating system, not a toolbox. Another theme I’m interested in is the growing overlap between customer experience, digital experience, data, and Marketing Operations. The agenda opens with a panel on leveraging MarTech to deliver unified, personalised customer journeys, with discussion points around real-time, data-driven personalisation, seamless omnichannel interactions, and synchronising digital and customer experience efforts. That sounds like a customer experience conversation. But really, it’s an operating model conversation. Because you cannot deliver joined-up customer journeys with disconnected teams, unclear data ownership, and platforms held together with duct tape, hope, and one person called Sandra who knows where the campaign naming convention document lives. Personalisation at scale sounds glamorous. The reality behind it is much less glamorous: Data structure, field governance, consent management, segmentation logic, lifecycle definitions, platform integration, QA, reporting, and documentation. In other words: Marketing Operations. That’s why I’ll be looking for how different brands are approaching the mechanics behind the experience. It’s easy to talk about seamless journeys. It’s harder to build the operational conditions that make them possible. The strongest marketing teams now understand that MarTech is not a collection of tools. It is infrastructure. And infrastructure needs ownership, maintenance, standards, and a clear link to commercial outcomes. Customer data is still the foundation. AI has just made the cracks more visible. There is also a session on orchestrating customer engagement at scale, focused on unifying customer data, activating real-time insights, and balancing automation with human strategy. That balance is going to be increasingly important. AI does not magically fix weak data. If anything, it exposes it faster. Poor data quality, duplicate records, inconsistent lifecycle stages, vague consent rules, and incomplete customer views all become more dangerous when AI starts using them to generate recommendations, trigger actions, or personalise experiences. This is one of the biggest misconceptions around AI in marketing: That you can bolt it onto a messy operation and somehow leapfrog the hard work. You can’t. Well, technically you can. But then you have simply created faster chaos. Congratulations, your disaster now has automation. The companies that get the most from AI are the ones with the cleanest foundations: Aligned processes, trusted data, properly integrated systems, and clear decision rights. That may sound boring, but boring is often where the money is. Especially in Marketing Operations. I’ll be interested to hear how mature teams are approaching this: Whether they are investing in data readiness before AI adoption, how they are measuring data quality, and whether they are treating AI as a strategic layer rather than another isolated tool. The human side of MarTech might be the most important bit. One session that stands out is the fireside chat on building digitally-ready, future-fit teams for the next era of work, which includes digital upskilling, critical competencies, and how teams can embrace AI meaningfully. This is a conversation marketing leaders need to have more honestly. The future of MarTech is not just about better platforms. It is about whether teams are structured, skilled, and supported enough to use them well. Most Marketing Operations teams are already stretched. They are expected to manage platforms, support campaigns, fix reporting, troubleshoot integrations, keep data clean, support sales, explain attribution, govern consent, handle new tech, and now somehow become AI adoption specialists on top. That is not a skills gap. That is an expectations problem. So I’ll be listening for how organisations are actually preparing their teams. Are they creating new roles? Upskilling existing teams? Redefining Marketing Operations as a strategic function? Or just telling everyone to “embrace AI” and hoping nobody asks for budget? Because AI adoption without organisational change is just theatre. Potentially expensive theatre. With a vendor-branded lanyard. Sales and Marketing alignment still matters. Sorry. There is also a late-afternoon panel on integrating Sales and Marketing across the customer lifecycle, with discussion points around financial return, pipeline acceleration, collaboration, training, and what foundational steps leaders would prioritise if starting again. This is another area where I’m hoping for practical insight. Sales and Marketing alignment has been discussed for years, often with the emotional intensity of two departments trapped in a group project neither of them asked for. But the arrival of AI, more complex buying journeys, and more integrated customer data makes alignment even more important. If marketing is using AI to prioritise accounts, personalise campaigns, generate insights, or shape nurture journeys, sales needs to trust the logic behind those decisions. If sales does not trust the data, the scoring, the segmentation, or the handoff process, AI will not solve alignment. It will just create a more sophisticated argument. The real opportunity is to use MarTech, data, and AI to create better shared visibility across the customer lifecycle. Not more dashboards nobody opens. Not another MQL definition that collapses under scrutiny. Actual operational alignment: Shared signals, shared definitions, shared accountability, and shared understanding of what is working. That is where Marketing Operations can play a major strategic role. What I’m really hoping to bring back For me, the value of attending The MarTech Summit Madrid is not just hearing what vendors, brands, and leaders think is coming next. It is understanding what is already happening inside marketing teams now. I’m hoping to come away with a clearer view of: How brands are moving from AI experimentation to AI governance. How marketing teams are connecting customer experience, data, automation, and operations. Where organisations are investing in skills, structure, and operating models. How mature teams are measuring MarTech’s contribution to growth. And, perhaps most importantly, where the gap still exists between what marketing technology promises and what teams are actually able to deliver. Because that gap is where the real work sits. The MarTech conversation has become bigger than platforms. It is now about how marketing functions operate, how decisions are made, how customer data is trusted, how AI is controlled, and how teams turn technology into measurable business value. That is exactly the kind of conversation Marketing Operations should be leading. So yes, I’m looking forward to the event. The agenda is packed, the themes are timely, and the conversations should be useful. And if I come back with a notebook full of ideas, a few uncomfortable truths, and at least one example of someone pretending their data is “almost there,” then frankly, it will have been time well spent. Andrew Poole is a Specialist Marketing Consultant at Sojourn Solutions, where he spends much of his time thinking about Marketing Operations, MarTech, AI governance, and why perfectly good marketing teams are still being held hostage by broken processes and suspicious spreadsheets.

AI is already inside your Marketing Operations function. Maybe officially. Maybe quietly. Maybe through a sanctioned pilot with a steering committee, governance framework, and the kind of slide deck that makes everyone feel briefly responsible. Or maybe, more realistically, through a browser tab. Someone is asking an AI tool to summarise campaign performance. Someone is pasting audience criteria into a chatbot. Someone is using AI to draft nurture logic, build segmentation rules, QA emails, rewrite form copy, analyse CRM exports, or “just quickly check something.” And that is before we get to the next stage: AI agents connected directly to your CRM, MAP, data warehouse, enrichment platform, sales engagement tools, customer records, campaign assets, reporting dashboards, and approval workflows. At that point, AI stops being a productivity helper. It becomes an operational actor. And if you do not know what it can access, what it can change, what it can trigger, who approved it, or how it is being monitored, you do not have an innovation programme. You have a very confident intern with system permissions. Lovely. Marketing Operations is where AI risk gets real Most AI conversations still happen at the wrong level. They focus on content creation, meeting notes, research, productivity, or whether AI can help someone write a marginally less tragic subject line. Fine. Useful enough. But not where the serious risk lives. The real risk starts when AI gets anywhere near your Marketing Operations stack. Because MOPs is not just a back-office function. It is the operating system behind customer journeys, segmentation, consent, lead scoring, lifecycle movement, attribution, campaign execution, data quality, compliance processes, handoffs to sales, and the increasingly delicate machinery that keeps revenue teams moving. When AI interacts with that environment, the consequences are not theoretical. A bad prompt does not just produce a clumsy paragraph. It could generate the wrong audience logic. Recommend a flawed suppression rule. Misread consent criteria. Expose sensitive customer data. Change a scoring model. Trigger the wrong nurture. Push poor data into the CRM. Create campaign assets that pass the eye test but fail compliance. Make a recommendation nobody can explain three weeks later. That is why AI governance in Marketing Operations cannot be treated as a nice-to-have policy document. It needs to be operational. It needs to be practical. It needs to account for the very specific ways marketing technology actually works. Because once AI is connected to your stack, the question is no longer, “Can we use AI?” The question is: What could AI accidentally do here, and would we know before it became a problem? The biggest risk is not rogue AI. It is vague ownership. The popular version of AI risk is dramatic. The machine goes rogue. The agent makes wild decisions. The system spirals out of control. Someone says “Skynet” in a meeting and thinks they are the first person ever to make that joke. But in Marketing Operations, the more likely risk is much more ordinary. Nobody owns it properly. That is where the trouble starts. The AI tool is approved by one team, tested by another, used by a third, integrated by someone technical, relied on by campaign managers, and questioned by legal only after something awkward happens. So when something goes wrong, everyone has a partial answer. IT says the platform was approved. Marketing says the use case was sensible. Operations says they did not configure the access. Legal asks where the data went. Sales asks why the leads changed. Procurement asks whether the vendor was reviewed. The CMO asks why nobody saw this coming. And MOPs, as usual, is standing in the middle holding the mop. This is why an AI governance risk review matters. Not because your team is careless. Usually the opposite. Marketing Operations teams are often the ones trying to bring order to the chaos. But AI creates new grey areas. It crosses boundaries between content, data, automation, decisioning, and execution. It sits awkwardly between tools, teams, and accountability structures. Without a clear governance model, you do not get innovation at scale. You get scattered experimentation, hidden risk, duplicated effort, and a growing list of things nobody wants to be responsible for. AI governance is not an “AI policy” Let’s clear this up. An AI policy is not the same as AI governance. A policy says what people should and should not do. Governance makes it possible to prove what is actually happening. That distinction matters. A policy might say, “Do not upload sensitive customer data into public AI tools.” Useful. Sensible. Also very easy to ignore, misunderstand, or work around when someone is under pressure and needs an answer by 4pm. Governance goes further. It asks: Who can use AI in Marketing Operations? Which tools are approved? Which use cases are allowed? What data can AI access? What data is off limits? Can AI create, recommend, or execute changes? Where is human approval required? How are prompts, outputs, and decisions logged? Who reviews AI-generated campaign logic? How are exceptions handled? Who owns the risk when AI touches a live system? That is the difference between having rules and having control. And in Marketing Operations, control matters because the stack is full of interconnected systems. One small change in one place can quietly cause problems somewhere else. A tweak to segmentation can affect campaign performance. A change to lifecycle rules can affect sales follow-up. A scoring adjustment can alter pipeline reporting. A consent error can create compliance exposure. A field mapping issue can pollute reporting for months. AI can help with all of this. It can also make the mess faster. Speed is only helpful when the direction is right. The risk is not just data privacy Data privacy matters. Obviously. But too many AI governance conversations stop there, as if the only danger is someone pasting customer records into the wrong chatbot. That is a serious risk, but it is not the whole picture. In Marketing Operations, AI risk shows up in several ways. Data access risk What systems can AI reach? What fields can it read? Can it see customer records, account data, behavioural data, campaign history, opportunity data, consent status, or internal notes? Many teams do not have a clear map of what AI tools can access once they are connected through plugins, APIs, browser extensions, workflow tools, or user-level permissions. That is a problem. Because if you cannot map the access, you cannot govern the risk. Decisioning risk AI does not need to press “send” to create risk. It can influence decisions long before anything goes live. If a team uses AI to recommend audience selection, lead scoring logic, nurture paths, channel mix, suppression criteria, or campaign prioritisation, AI is shaping operational decisions. Even when a human technically approves the output, the human may not fully understand the logic behind the recommendation. That creates a new kind of risk: decisions that look reviewed but are not really understood. A rubber stamp is not governance. It is theatre with a login. Execution risk Once AI can take action inside a platform, the stakes change. Creating lists. Updating fields. Building campaign flows. Changing records. Triggering workflows. Editing assets. Sending alerts. Moving leads between stages. These are not harmless actions. They affect customers, reporting, sales processes, compliance obligations, and revenue outcomes. If AI can execute, then you need clear limits, approval gates, rollback procedures, monitoring, and accountability. “Trust the model” is not a control framework. It is a cry for help wearing a hoodie. Brand and compliance risk AI-generated content can sound polished while being wrong, off-brand, misleading, non-compliant, or just aggressively beige. But the bigger issue is not copy quality. It is context. AI may not understand sector-specific claims, regional legal requirements, consent language, product limitations, customer sensitivities, or the difference between a bold message and a lawsuit wearing lipstick. Marketing Operations teams often sit close to these approval processes. If AI is generating or modifying campaign assets, governance needs to cover not just who can create content, but who is responsible for reviewing it before it enters production. Operational dependency risk The more useful AI becomes, the more teams rely on it. That can be good. But if prompts, workflows, review processes, and outputs are not documented, you create dependency without resilience. What happens when the person who built the AI-assisted QA process leaves? What happens when the vendor changes the model? What happens when an integration breaks? What happens when nobody remembers why a recommendation was accepted?What happens when the AI workflow becomes business-critical but was never formally owned? This is how shadow operations are born. And shadow operations always send an invoice eventually. The stack was already messy. AI just makes it louder. Most enterprise Marketing Operations environments were not exactly pristine before AI arrived. There are old workflows nobody wants to touch. Fields with mysterious origins. Reports people trust because questioning them would ruin the quarter. Suppression lists with names like “DO NOT DELETE FINAL v3”. Campaign templates built by someone who left in 2021. Documentation that exists in theory, in a folder, somewhere, probably. Now add AI. AI can be extremely powerful in this environment. It can help identify issues, accelerate QA, support documentation, analyse performance, suggest campaign improvements, flag anomalies, and reduce manual effort. But only if the foundations are understood. If your data model is unclear, AI can misinterpret it. If your processes are undocumented, AI can automate the wrong thing. If ownership is vague, AI can make accountability worse. If permissions are loose, AI can access more than it should. If campaign QA is inconsistent, AI can scale inconsistency with great enthusiasm. This is why AI readiness and AI governance are so closely linked. You cannot govern what you do not understand. And you cannot safely scale AI across Marketing Operations if you have not reviewed the systems, processes, permissions, data flows, and decision points it might touch. What an AI governance risk review should actually look at An AI governance risk review does not need to be a six-month transformation programme. In fact, the best starting point is usually focused, practical, and fast enough to create momentum. The goal is simple: Find out where AI is creating, increasing, or exposing risk inside your Marketing Operations stack. That means looking at the areas where AI is already being used, where it is likely to be introduced, and where the operational consequences would be highest if something went wrong. A proper review should cover several areas. 1. Current AI usage Start with reality, not the official version of reality. Where are teams already using AI? Which tools are approved? Which tools are being used unofficially? What tasks are people using AI for? Are they using it for content, reporting, segmentation, QA, data analysis, campaign planning, workflow recommendations, or system configuration? This often reveals a gap between what leadership thinks is happening and what teams are actually doing. That gap is where risk likes to rent office space. 2. Access and permissions Review which AI tools, agents, connectors, browser extensions, and integrations can access marketing systems or data. That includes direct integrations, API connections, user-level access, exported files, shared reports, and manual copy-paste behaviour. The key question is not just “Is this tool secure?” It is: What can this tool see, infer, store, change, or trigger? Different question. Much better question. 3. Data handling Look at the types of data being used with AI. Customer data. Prospect data. Account data. Behavioural data. Campaign data. Opportunity data. Consent data. Internal performance data. Vendor data. Commercially sensitive plans. Then assess how that data is being handled. Can it be uploaded? Can it be retained? Can it be used for training? Is it anonymised? Is it regionally restricted? Is consent status respected? Are there rules for sensitive fields? Do teams know what not to share? This is where generic AI policies often fall apart. They say “do not share sensitive data” without defining what that means in the actual marketing stack. 4. Use case risk Not all AI use cases carry the same level of risk. Using AI to draft internal campaign notes is one thing. Using AI to recommend segmentation criteria for a regulated audience is another. A good review should classify use cases by risk level. Low-risk use cases might include brainstorming, summarising public information, or drafting internal documentation. Medium-risk use cases might include campaign QA support, performance analysis, or content recommendations. High-risk use cases might include segmentation, consent logic, scoring models, automated decisioning, customer data analysis, or direct system execution. The point is not to block AI. The point is to stop treating every use case as if it carries the same consequence. That is how grown-ups do innovation. Painful, but effective. 5. Human oversight Human-in-the-loop sounds reassuring. But it only works if the human knows what they are reviewing. A governance review should identify where human approval is required, who provides it, what they check, and whether they have enough context to challenge the AI output. Because “a human approved it” is not especially comforting if the human was exhausted, undertrained, staring at a black-box recommendation, and trying to get a campaign out the door before EMEA logged off. Oversight needs standards. What must be checked? What evidence is required? What gets escalated? What cannot be approved without a second review? What must be documented? That is how oversight becomes meaningful instead of decorative. 6. Logging, monitoring, and auditability If AI contributes to a campaign, workflow, data change, or operational decision, can you prove what happened? Can you see what was requested? What output was generated? Who reviewed it? What was accepted? What was rejected? What changed in the system? When did it happen? Who owned the decision? This matters because Marketing Operations lives in the land of consequences. When something breaks, people need answers quickly. Not vibes. Not “we think maybe the AI suggested it.” Actual traceability. Auditability is not glamorous. Neither is plumbing. But you notice when it fails. 7. Vendor and integration governance Every AI-enabled vendor will tell you their product is secure, responsible, enterprise-ready, and possibly sprinkled with unicorn compliance dust. That is not enough. You need to understand how each vendor handles data, access, retention, permissions, model behaviour, integrations, logs, admin controls, regional requirements, and contractual obligations. You also need to know who approved the vendor for use in Marketing Operations, and whether that approval covered the actual use case being deployed. Because “we approved the tool” and “we approved this tool to interact with live MAP data” are not the same sentence. One is procurement. The other is risk management. The outcome: a clear map of where risk sits The value of an AI governance risk review is not a 48-page PDF that makes everyone nod gravely and then return to chaos. The value is clarity. You should come away knowing: Where AI is already being used Where unofficial usage may be creating risk Which systems and data are most exposed Which use cases are safe to scale Which use cases need controls Which integrations need review Where human approval is required Where logging and monitoring are missingWhere governance ownership is unclearWhat to fix first That last point matters. The goal is not to boil the ocean. The ocean has enough problems. The goal is to prioritise the risks that matter most and create a practical path forward. Some things may need immediate attention. Some may need clearer documentation. Some may need access restrictions. Some may need approval workflows. Some may simply need better training. Some may be perfectly fine. Governance is not about saying no to everything. It is about knowing where to say yes without being reckless. AI should not be slower. It should be safer at speed. There is a lazy argument that governance slows innovation. It can, if done badly. If governance means endless committees, vague policies, disconnected legal reviews, and twelve people debating whether a chatbot can summarise a webinar transcript, then yes, it will slow things down. But that is not good governance. That is corporate fog. Good governance makes AI easier to adopt because teams know the rules, the boundaries, the approved tools, the review points, and the escalation routes. It removes uncertainty. It gives Marketing Operations teams the confidence to use AI where it helps, avoid it where it creates risk, and scale it where the business can actually benefit. That is the point. Not fear. Not bureaucracy. Not pretending AI can be ignored until Q4. Practical control. Because AI in Marketing Operations is not going away. It is moving from experimentation into execution. From prompts into platforms. From “help me think” into “help me do.” That shift needs governance. Not later. Now. Why this needs Marketing Operations expertise AI governance cannot sit entirely outside Marketing Operations. Legal needs to be involved. IT needs to be involved. Security needs to be involved. Procurement needs to be involved. Leadership needs to be involved. But none of those teams fully understand the day-to-day reality of how campaigns, data, automation, scoring, routing, reporting, and platform governance actually work inside the marketing engine. That is the missing layer. A generic AI governance framework may cover broad organisational risk. But Marketing Operations needs something more specific. It needs to account for MAP logic, CRM dependencies, consent flows, campaign operations, lead management, integrations, data quality, approval processes, vendor ecosystems, regional differences, and the glorious haunted mansion that is most enterprise MarTech architecture. This is where specialist support matters. Because the risk does not live in a policy document. It lives in the workflow. It lives in the field mapping. It lives in the campaign build. It lives in the audience criteria. It lives in the API connection nobody has reviewed since the last rebrand. It lives in the operational details. And that is exactly where Marketing Operations consultants should be looking. The question is not whether AI belongs in Marketing Operations It does. AI has huge potential in Marketing Operations. It can help teams move faster, reduce manual QA, improve consistency, identify errors, surface insights, support documentation, streamline campaign production, and make better use of the platforms companies have already paid far too much money for. The opportunity is real. But so is the risk. The organisations that get this right will not be the ones that throw AI at every process and hope nobody asks difficult questions. They will be the ones that build the right guardrails early. They will know which use cases are safe. They will know which need review. They will know what data AI can touch. They will know where approval is required.They will know how to monitor outputs. They will know who owns what. They will be able to prove what happened. That is how AI becomes useful in Marketing Operations. Not as a shiny experiment. As a governed, trusted, operational capability. Before you scale AI, review the risk If AI is already being used inside your Marketing Operations function, now is the time to understand where the risk sits. Not after the first incident. Not after procurement asks a question nobody can answer. Not after an AI-assisted campaign goes live with logic nobody fully reviewed. Not after customer data has been pasted, uploaded, connected, processed, or quietly exposed. Before. An AI governance risk review gives you a focused, practical view of where AI could create risk across your Marketing Operations stack, and what needs to be put in place before you scale further. It is not about slowing AI down. It is about making sure AI does not accelerate the wrong thing. Because once AI starts touching your MAP, CRM, data, campaigns, and customer journeys, “we’ll figure it out later” is not a strategy. It is a liability with a calendar invite. Find out where AI is creating risk inside your Marketing Operations stack Sojourn Solutions helps enterprise Marketing Operations teams introduce AI with the right governance, guardrails, and operational controls in place. Our AI governance risk review gives you a clear view of where AI is already being used, what systems and data may be exposed, which use cases need controls, and what to fix first. Before AI gets deeper into your stack, find out what it can access, influence, and break.