At Sojourn, we’re regularly asked by clients to help define roles and responsibilities within the Marketing Operations function - especially as the field continues to evolve and blur traditional boundaries. One of the most requested areas? Guidance on the MarTech Manager  role. The MarTech Manager isn’t just a technologist. Nor are they “just” an ops lead, vendor wrangler, or part-time firefighter. The modern MarTech Manager sits at the center of marketing’s ability to perform - part strategist, part systems architect, part diplomat, and part fixer-in-chief. It’s one of the most misunderstood - and most critical - roles in any B2B organization with a serious marketing function. So what exactly is  the role today? What does it look like on the ground, what kind of mindset does it demand, and how should organizations onboard and support someone stepping into it? The soul of the role: Value, not just velocity The MarTech Manager is the custodian of marketing’s most powerful (and expensive) tools. That means their primary responsibility is not  just to keep the lights on or make sure the latest platform is integrated - it’s to ensure MarTech is delivering tangible value  to the business. This is about enhancing performance, not just enabling it: Is the tech stack contributing to pipeline or revenue? Is it accelerating campaigns or clogging them up with process debt? Is each platform used to its potential — or just a shiny checkbox? It’s the MarTech Manager’s job to connect the dots between tool and outcome, investment and return. It’s where strategy meets accountability. The day-to-day reality: No two days the same Despite the high-level vision, the day job of a MarTech Manager often ranges from the deeply technical to the highly relational. It’s a role of constant translation - between IT and marketing, vendors and users, vision and feasibility. Some of the day-to-day and near-term responsibilities include: Auditing the ecosystem : Mapping tools, integrations, gaps, duplications, and risks. Stakeholder interviews : Understanding needs and pain points across marketing, sales, data, IT, and leadership. Vendor management : Handling renewals, roadmaps, and relationship health. Documentation : Use cases, adoption data, product usage levels, success metrics. Measurement : Defining KPIs across the stack - adoption, usage, ROI, contribution to business outcomes (e.g., leads, AQLs). Roadmap development : Short-term wins and longer-term vision, aligned to business goals. Enablement : Supporting users with training plans, onboarding guides, and practical tools. Governance : Supporting change management, data quality standards, integration protocols. It’s fast-paced, cross-functional, and at times chaotic. But it’s also high-impact - especially when managed proactively rather than reactively. The mindset that makes it work You can’t run this role on a checklist. The best MarTech Managers combine a rare mix of traits - strategic clarity, technical curiosity, and a diplomat’s finesse. Here’s what that mindset looks like: Strategic yet scrappy : They need a long-term plan - but they also know how to ship quick wins that move the needle now. Challengers of the status quo : Not afraid to ask, “Why are we doing it this way?” - and then change it. Data-minded and business-aware : They watch KPIs like a hawk but can translate numbers into business stories. Relationship builders : Able to connect with stakeholders from junior marketers to exec sponsors to grumpy sysadmins. Detail-oriented : Yes, the whole stack should be documented down to version number and integration flow. Technically fluent : They don’t need to code - but they need to speak the language of platforms, APIs, and increasingly, AI. Future-focused, but grounded : They stay on top of trends without chasing every shiny object. This isn't just a “platform owner” role - it's someone who blends art and science, helping marketing become more effective and  more efficient. What good onboarding looks like MarTech Managers aren’t plug-and-play. They need structured onboarding that sets them up for success - and helps them map a complicated landscape fast. Here’s what a smart 90-day onboarding plan can look like: Days 0–30: Discovery and diagnosis Conduct full audits (stack, stakeholders, vendors, documentation) Prepare an initial roadmap with quick wins and longer-term ideas Build a risks/issues log for the current state Days 31–60: Alignment and planning Socialize findings with stakeholders Prioritize roadmap items and finalize the plan Develop budget requests for the upcoming fiscal year Deepen audits into key priority markets or teams Days 61–90: Expansion and KPI definition Complete full ecosystem audits Define what “MarTech success” looks like - adoption, productivity, ROI Set and socialize clear KPIs Month 4 onwards: Execution and enablement Create a training and enablement plan by audience Establish governance standards with architecture/data leads Start AI pilots or automation experiments Create dashboards to track ROI and stack health Develop and maintain: Platform scorecards Stakeholder maps Vendor evaluations Risk/issues log Roadmaps with project timelines Enablement materials (cheat sheets, videos, playbooks) Final thoughts: Why this role matters more than ever In 2025, marketing teams are under pressure like never before. Budgets are tight. AI is exploding. Tech stacks are bloated. And yet, the demand for impact hasn’t slowed. That’s where the MarTech Manager proves their worth - not just keeping tools running, but transforming MarTech into a measurable driver of marketing performance. It’s not an easy role. But with the right mindset, support, and onboarding, it’s one of the most rewarding - and business-critical - roles in the modern marketing org. Discover our Services Discover our Podcast

We’re delighted to share some well-earned recognition for one of our key partners: Demandbase has been ranked #1 on G2’s Enterprise Grid® for Account-Based Advertising.  🎉 This isn’t just a marketing trophy - it’s a customer-powered endorsement. G2’s rankings are built on real user reviews, which makes this recognition especially meaningful. It reflects what actual marketing teams are experiencing day to day: that Demandbase delivers measurable results at scale . At Sojourn Solutions, we’ve seen firsthand the value Demandbase brings to enterprise Marketing Operations. In a space where targeting, personalization, and performance tracking need to be seamless and scalable, Demandbase continues to set the standard. Their platform empowers marketers to move beyond traditional lead-gen and fully embrace Account-Based Marketing as a driver of revenue . From tighter alignment between sales and marketing, to more efficient budget use and smarter campaign execution, our joint clients are unlocking serious value - and this G2 ranking just confirms what the data’s been saying all along. We’re proud to collaborate with technology partners like Demandbase who don’t just follow the industry - they push it forward. Congratulations to the entire team on this well-deserved honor. Here’s to even more impact ahead! Discover our Services

Let’s start with the obvious: Marketing Operations today is not what it was even three years ago. The tech stack is fatter. The acronyms are more confusing. And now - because the gods clearly thought we weren’t juggling enough - AI is in the mix, rewriting the rules faster than most of us can update our dashboards. So what does this mean for Marketing Ops (MOPs) leaders? Short answer: It’s complicated. Long answer: It’s complicated, evolving, full of opportunity - but only if we stop trying to play yesterday’s game with tomorrow’s tools. Let’s unpack the state of MOPs in 2025, how AI is shifting the center of gravity, and what it means for the folks responsible for keeping the whole circus running smoothly. The MOPs role: From backstage tech wrangler to strategic linchpin Historically, MOPs has been the unsung hero of B2B marketing. The team that made the campaigns run, the leads flow, and the attribution dashboards look vaguely believable. MOPs pros were the Swiss Army knives of the org - masters of systems, workflows, integrations, and polite-but-firm “no, you can’t just add a field to the form and expect it to work.” But as the MarTech stack exploded and AI swept in, something fundamental changed: MOPs stopped being just operational. It started becoming existential. Why? Because the business finally realized that without a properly configured, AI-ready, data-clean ecosystem, none of the flashy stuff - personalization, predictive journeys, generative content - actually works. MOPs is no longer just supporting marketing; it’s enabling modern marketing. Scratch that - it’s defining it. And with great relevance comes great responsibility. AI has entered the chat… and it’s not just another tool Let’s clear this up right now: AI isn’t “just another shiny object.” It’s not another platform you can duct-tape onto Eloqua or Marketo and pretend everything’s fine. AI is fundamentally altering the way marketing works - from how content is created, to how audiences are segmented, to how results are measured. For MOPs, that means a few things: 1. Your data plumbing matters more than ever AI is greedy. It wants clean, structured, up-to-date data - and lots of it. If your CRM is full of dead leads, duplicates, and free-text job titles like “marketing wizard,” your AI output is going to be… creatively awful. MOPs teams now need to play data steward, ensuring their org’s data hygiene is tight enough to make AI usable, not laughable. 2. Workflows are getting smarter - but more fragile AI can help you build dynamic campaigns that adjust messaging and channels in real time. Great, right? Sure - until someone updates a tagging taxonomy and breaks five downstream workflows because no one thought to loop in MOPs. As AI automates more, the margin for error shrinks. MOPs must evolve from workflow builder to workflow architect, designing systems that are resilient, explainable, and (ideally) future-proof. 3. Measurement is moving beyond attribution Goodbye, linear attribution. Hello, probabilistic models, propensity scoring, and hallucinated insights if you’re not careful. MOPs teams are being asked to validate AI-generated insights, explain where predictions are coming from, and ground marketing performance in reality - not just pretty charts. This means MOPs needs to understand enough about AI models to ask smart questions, spot nonsense, and bridge the gap between “model says” and “what actually happened.” Shifting roles: What MOPs leaders are now expected to own The AI era hasn’t just added new responsibilities - it’s also blurred a few old ones. Here’s how the MOPs leader role is expanding, whether you signed up for it or not: Strategic advisor : You’re not just configuring tools - you’re shaping how marketing works. AI initiatives need your buy-in, because they depend on your data and processes. Tech translator : You need to explain AI projects to CMOs without a data science degree, and explain campaign needs to data scientists without causing eye-rolls. You’re the human Rosetta Stone. Risk manager : AI decisions carry reputational and compliance risk. Is that chatbot hallucinating GDPR violations? Is that AI-generated lead score missing key context? MOPs is now part of risk mitigation. Talent enabler : As AI automates tasks, it also widens the skill gap. Your team will need retraining, upskilling, and frankly, some reassurance that they’re not about to be replaced by a large language model that doesn’t complain about Jira tickets. What this means for MOPs leaders (and your sanity) First, let’s address the elephant in the room: this is a lot. You’re not imagining it. The pace of change is insane, and the expectations placed on MOPs leaders are rising faster than most orgs are prepared to support. Here’s the truth: You can’t do everything - and you shouldn’t try. Part of your job now is triage: what matters today, what can wait, and what needs to be killed with fire. You need executive backing . AI projects that skip over MOPs inevitably fail. Be vocal. Demand a seat at the table. You're not “the tech person”—you’re the backbone of modern marketing. You should upskill strategically . You don’t need to become a machine learning engineer. But you do need to understand enough about AI to vet vendors, guide implementation, and sanity-check the outputs. You have to build cross-functional trust . AI doesn’t live in a silo. Data, IT, legal, content, sales - they’re all stakeholders now. MOPs is the natural integrator, the one team that sees the full picture. Use that to your advantage. The bottom line: Your value is only increasing - but so is the complexity AI isn’t coming for your job - it’s just making it harder to fake competence. The good news? If you’ve been quietly running the show from behind the curtain for years, this is your moment. Marketing Ops is no longer “operations support.” It’s strategy execution. It’s risk mitigation. It’s the engine behind real marketing transformation. So embrace the chaos. Own your new role. Laugh when the AI writes terrible subject lines. And when the CMO asks, “Can we make the chatbot sound more human?” - you can smile knowingly and say: “Only if you give me budget.” Discover our Services Discover our Podcast

The AI paywall just came down Oracle has just done something rare in enterprise software: It gave something valuable away for free. As of June 2025, every Eloqua customer can access its Advanced Intelligence suite - including both Classic and Generative AI features - at no additional cost . No “premium add-on.” No “AI license uplift.” Just submit a service request (SR), and you're in. In a MarTech world where new AI features often come with hidden fees and bloated sales decks, this move is a big deal. And if you're in Marketing Ops or campaign management, it's not just welcome - it's strategic ammo. The big reveal: Free AI for all Eloqua users Here’s what Oracle announced: All Eloqua customers - regardless of package - can now access Advanced Intelligence features for free . This includes: Classic AI : Predictive modelling, lead scoring, send-time optimisation. Generative AI : Content suggestions, email drafting, dynamic personalisation. To activate, you submit a simple support request  (SR) via My Oracle Support. That’s it. The rollout started on June 12, 2025 . Oracle will proactively reach out to customers throughout June and July , pod by pod, to help them enable and adopt the new functionality. Bottom line: if you're an Eloqua customer and not using AI yet, you just ran out of excuses. Why Oracle did this: Competitive pressure + AI arms race Let’s call it like it is: this isn’t just generosity. Oracle’s move is a strategic response to: Salesforce’s GPT push  across Marketing Cloud HubSpot’s relentless AI positioning  for SMBs Marketo/Adobe’s own AI personalization and journey building tools Oracle knows that if Eloqua doesn’t keep up - or better yet, outflank - marketers will jump to platforms that are cheaper, smarter, and easier to automate. Giving away AI isn’t Oracle going soft. It’s Oracle going to war. By removing the cost barrier, Oracle is forcing the conversation back to value realisation - which AI features are actually making marketers faster, smarter, and more efficient? Spoiler alert: If your team is still hand-scoring leads in Excel, the answer’s obvious. Your marketing advantage: What AI unlocks in Eloqua Here’s what you’re now sitting on: Classic AI (predictive, rules-based) Lead scoring with predictive modeling : Move beyond static rules and let the model learn from conversion history. Segmentation insights : Discover new high-converting segments based on behavioral data. Send-time optimization : Hit inboxes when your audience is actually reading them. Generative AI (LLM-powered) Email writing suggestions : Draft subject lines or full emails with contextual cues from past performance. Form and landing page copy : Get AI to propose on-brand, conversion-optimized content. Dynamic personalization : Tailor messaging at scale without manually creating dozens of versions. This isn’t toy-level AI - it’s strategic enablement for any marketing team that wants to operate like it’s 2025, not 2015. How to get started Oracle made this part simple: Log in to My Oracle Support Submit a Service Request  asking to enable Advanced Intelligence Wait for confirmation  (and possibly onboarding instructions) Start testing and exploring Oracle will also reach out proactively throughout June and July - but don’t wait. Be the team that’s already live when the rest are still stuck in meetings about “AI readiness.” Best-practice tips: Make AI work for you Getting access is the easy part. Getting results? That takes some intention. ✅ Start small, scale fast Don’t roll AI out across everything. Start with one high-impact campaign or program and use it as a proving ground. 📊 Measure everything Run A/B tests comparing AI-generated content vs. human-only versions. Benchmark conversion rates, engagement, time saved. 🧠 Train your team Fear of the unknown will stall adoption. Run short enablement sessions, demo features, and frame AI as augmentation - not replacement. 🔁 Tune and iterate Classic AI models improve with data. Generative AI gets better with prompts. Don’t set it and forget it. FAQ: What marketers are asking “Do I really get this for free?” Yes. No cost. No strings. Just file an SR and activate it. “What’s the catch?” No catch - but there is some effort required : You need to request it You may need to train your team You’ll want to adjust workflows  to make use of the new tools “When do I have to act?” Now. Rollout started in June, and Oracle plans to reach all customers by July. But you can beat the queue by submitting your SR today. Strategic implications: This changes the MarTech table stakes This isn’t just about Eloqua - it’s about what we now expect from enterprise MarTech vendors. AI is no longer a paid premium. It’s the baseline. If Oracle can offer predictive and generative AI across its entire user base, every other platform is on notice. Licensing models will shift. Adoption curves will flatten. And the excuses for not using AI will vanish. For B2B teams especially, this opens the door to faster campaign production, better segmentation, and higher ROI - without needing to double headcount . Final call-to-action: Don’t wait for the AI memo If you’re running Eloqua, this is your moment to: File the SR Test the tools Become the internal AI success story Treat this like an opportunity to lead - not just another tech update. Marketing teams that embrace this early will look smarter, ship faster, and spend less time reinventing the wheel. The Eloqua AI paywall is gone. What you do next is entirely on you. Need help setting this all up? We are here to help. Let's chat. Discover our Services

Why Marketing Operations teams need to think differently about AI - and how to do it without the noise, panic, or perfectionism. The AI buzz has officially reached “louder than a toddler with a drum kit” levels. Depending on who you ask, it’s either revolutionising marketing or just a shiny distraction. Teams are either racing to build entire AI strategies or hesitating at the starting line, unsure what’s real and what’s just another clever automation dressed up in buzzwords. Here’s the truth: Most B2B marketing teams are somewhere in the messy middle. Curious, cautiously optimistic, but quietly overwhelmed. This article is for them. First, let’s be honest about what AI really is (and isn’t) Now we've already written other articles on this - we even have an in depth whitepaper on the topic - but it helps to strip away the smoke and mirrors. AI, at its core, is software that can perceive, decide, and act - ideally learning and improving as it goes. But that definition gets stretched beyond recognition in marketing circles. Some systems are barely more than fancy “if this, then that” logic trees - rule-based engines that look clever but never actually learn . Others use machine learning to find patterns and make predictions, which feels smarter… but still has guardrails. Then there’s generative AI - the kind that writes your subject lines, drafts landing pages, and churns out halfway-decent blog intros. It mimics creativity using statistical patterns from its training data. It sounds human. Sometimes it even fools humans. But don’t confuse mimicry with intelligence. And finally, there’s the new kid on the block: Agentic AI . This is where things get interesting. Agentic systems aren’t just reactive - they’re proactive. They don’t just wait for prompts; they flag problems, take action, and even course-correct without needing a human to point the way. If you’re wondering whether something is actually  AI or just marketing fluff, here’s the test: Can it change its behaviour without you telling it to? If yes, it’s probably real AI. If not, well… it might just be a clever trick in a shiny box. Download our Whitepaper So where does that leave us? Right now, agentic AI is starting to take real shape in Marketing Operations - and not just in the “labs and keynote slides” kind of way. Think about your campaign workflows. How much time gets eaten up by briefing, building, QA’ing, adjusting, launching, and monitoring? MOPs teams are often stuck juggling 100 spinning plates, just trying to keep things from falling over. Now imagine an AI agent that can take a campaign brief, build the campaign itself, monitor performance, and suggest improvements - all while you’re in your 1:1s or trying to eat lunch without another Slack notification. That’s not science fiction. That’s where this is headed. And the early adopters are already reaping the benefits. Same goes for analytics. We’ve all lost days to pulling reports, tidying up data, and trying to extract insights from dashboards that seem designed to hide them. Agentic AI flips that. Instead of just answering “what happened?”, these systems start to answer “what should I do next?” And they do it while scanning your goals, your targets, and your previous performance - then recommending actions that actually move the needle. This isn’t about replacing humans. It’s about finally giving humans the breathing room to think, create, and lead instead of constantly cleaning up after the machine. Meanwhile, the software landscape is shifting under our feet. One of the most under-reported shifts in AI is that it’s accelerating software development itself. Thanks to low-code tools, no-code platforms, and AI assistants like ChatGPT or Claude quietly writing software behind the scenes, we’re entering what some are calling the “Hypertail” era. In short: there are going to be billions - maybe trillions - of tiny apps, agents, and automations, many of them custom-built by non-technical users. What does this mean for Marketing Ops? It means the gatekeeping around software is crumbling. You no longer need a dev team to build a tool that helps you work smarter. And when AI is added to that mix, the cost and complexity drop even further. Great. So how do you actually start? Here’s where most companies get stuck. They want to “do AI,” but haven’t figured out what that actually means in practice. So let’s break it down. Start by asking: Why are we doing this? Is it about productivity? Process improvement? Changing how the business works? Are we racing to build AI, or are we adopting it to achieve outcomes? What pace are we comfortable with - steady, or accelerated? How mature is our data environment? Are we training our teams to use AI confidently? These sound like basic questions, but skipping them leads to half-baked strategies and a lot of wasted budget. Then get specific - and small You don’t need a 12-month roadmap to get started. What you need is a well-framed experiment. Pick a use case with visible impact but low risk. Write a performance summary with a generative AI tool. Try a prompt-based briefing workflow. Use AI to surface anomalies in your campaign data. Just don’t try to change the entire engine at once. Keep it small, move quickly, and document everything. Share wins. Share failures. Share what you learned and how you’d do it differently. The teams that build this reflex - experiment, learn, repeat - will outpace the ones that try to build the perfect AI plan before touching a single tool. A final word of caution: AI isn’t just a technology problem It’s a strategy problem. It’s a people problem. It’s a leadership problem. The biggest risks aren’t rogue bots or hallucinating models. They’re vague goals, misaligned teams, and the slow erosion of trust when experiments aren’t communicated clearly. The companies that thrive in this next era won’t be the ones with the most tools. They’ll be the ones with the clearest intent - and the courage to start small, move fast, and keep learning. In closing: AI won’t save your Marketing Ops. But it might just unlock them. Used well, AI can free up your people, clean up your processes, and speed up your execution. Used poorly, it’ll just give you another tech headache to manage. Start where you are. Ask better questions. Run smarter experiments. And don’t wait for perfection. Your future campaigns will thank you for it. Discover our Services

(And what it really says about the state of marketing) According to a recent Gartner’s MarTech Survey , average MarTech utilisation has dropped to just 33% , down from 58% in 2020 . That means two-thirds of the platforms and tools marketing teams are investing in are sitting idle or underused. Let that sink in: despite the explosion of marketing technology over the last decade - and all the promises of automation, personalisation, and data-driven brilliance - CMOs and Marketing Ops teams are using only a third of what they’re paying for. So, what’s going on here? The obvious answer is complexity. But the real reasons go deeper. And they reveal something uncomfortable about the way marketing is structured, resourced, and led today. Let’s unpack it. The MarTech market has outpaced human capability MarTech vendors have been shipping software like there’s a race to cover every pixel of the customer journey. With every product release, a new dashboard, AI feature, or “next-gen” integration promises to fix some broken piece of the funnel. But here's the problem: humans are still required to implement, integrate, and operate these tools - and there simply aren’t enough skilled people (with time!) to do it all. Most organisations don’t have the bandwidth or expertise to squeeze ROI out of more than a handful of systems. And while the CMO may have a shiny MarTech stack slide for the boardroom, most of it gathers dust in the shadows of Excel and PowerPoint. Tool buying has become political, not practical Let’s be honest: a lot of MarTech gets bought not because teams need it, but because someone senior wants to be seen as “transformative.” You know the story. A vendor pitches a slick demo. A few buzzwords get dropped (“real-time orchestration,” “AI-powered personalisation,” “composable CX”). A few competitors are name-checked. And suddenly, you’ve got a new platform that doesn’t integrate properly, doesn’t fit your processes, and now needs five new headcount to operate. What’s left? A team that uses 10% of it while trying not to get fired. The integration tax is real - and it's killing adoption You don't just buy  a MarTech platform. You marry it. And like any marriage, integration is where the real work begins. But unlike actual marriages, no one wants to do the hard parts here. Getting different systems to talk to each other - cleanly, securely, and usefully - is still one of the biggest barriers to utilisation. When data isn’t flowing, automations break, dashboards lie, and everyone defaults back to manual workarounds. This is where so much MarTech dies: not in ambition, but in execution. Change fatigue is throttling even the best platforms In theory, new platforms should unlock new value. In practice, they often trigger process chaos , retraining burdens, and morale decay. The average marketing team is on its fourth or fifth "must-have" platform this decade. And after a while, every new implementation feels like a threat, not a win. Ops teams grow jaded. Users disengage. Features go unexplored. Projects stall out halfway. The result? Tools get blamed. Adoption drops. Utilisation follows. (Spoiler alert: the next shiny platform won’t fix this either .) There’s a lack of strategy - and even less orchestration One of the most damning insights from the Gartner report wasn’t just the drop in usage. It was that only 24% of marketers said they had the capabilities to fully utilise their stack . That’s not a MarTech problem. That’s a marketing leadership  problem. Too many orgs treat their stack like a list of features instead of an interconnected system of capabilities. There’s no strategic orchestration. No roadmap. No measurement beyond basic campaign metrics. And definitely no real process for rationalising what gets added (or removed) from the stack. In short: We’ve bought the tools without defining the jobs. So what now? If your MarTech utilisation is under 40%, you're not alone - but you are  wasting budget, time, and opportunity. The fix isn't more tools. It’s more focus. Here’s what leading orgs are starting to do differently: Audit ruthlessly  – Identify what actually gets used, what’s delivering value, and what’s just legacy tech debt. Kill or consolidate the rest. Design around outcomes  – Build the stack backward  from business goals, not vendor hype or peer pressure. Empower Marketing Ops  – Stop treating MOPs as IT’s sidekick. Let them lead on architecture, governance, and adoption. Invest in enablement  – Platforms don't drive value. People do. Budget for training, change management, and adoption as much  as licenses. Slow down to speed up  – Fewer tools, better used, always beats a bloated stack full of features nobody knows how to turn on. Final thought: Maybe it's not MarTech that’s failing. Maybe it’s us. MarTech isn’t going away. If anything, AI and data ecosystems are only making it more powerful - and more complex. But technology alone doesn’t transform marketing. People do. Until we stop treating MarTech like a silver bullet and start treating it like a discipline, the utilisation rate will stay low - and the real cost of underperformance will stay high. It’s time to stop buying tools we can’t use, and start building strategies we can actually execute. Discover our Services Discover our Podcast

In the not-so-distant past, Marketing Operations (MOPS) teams were seen as the mechanics in the marketing engine room - configuring platforms, wrangling data, and manually executing campaigns. But as AI moves from passive tool to active teammate, a new era is emerging: one powered by agentic AI . Agentic AI is more than just automation. It's AI that can think ahead, act on its own , and adapt as things change. These aren’t just smarter tools; they’re digital coworkers with initiative. And for MOPS, that changes the game entirely. What is "Agentic AI" (and why should you care)? Traditional AI is like a vending machine: you punch in what you want, it spits something out. Useful, but not exactly proactive. Agentic AI is more like a trusted colleague who knows your goals, figures out how to get there, and starts working - often before you've even asked. It plans, it decides, it learns, and it acts. Think of the difference between a chatbot that answers FAQs and an AI marketing assistant that audits your tech stack, flags underused tools, and books a meeting with your vendor to sort it out. This isn't sci-fi. It's already here. Where Agentic AI fits in Marketing Operations MOPS pros juggle a lot. Broadly speaking, they handle: Campaign operations Data and analytics Tech stack management Strategic enablement and governance Agentic AI has something to offer in every single one  of these areas. Let's unpack it. 1. Campaign Operations: From Manual Execution to Autonomous Launch The current reality: Campaigns require tons of setup: building workflows, tweaking segmentation, testing subject lines, managing approvals. MOPS teams are often the bottleneck, not because they want to be - they’re just overwhelmed. With agentic AI: An AI agent could take a campaign brief and run with it - generate emails, build landing pages, set up automations, double-check compliance, and hit "go." Then it watches the results in real time, adjusts subject lines, tweaks CTAs, and reallocates budget if needed. Picture this: your AI notices Subject Line A is tanking in Germany. It runs sentiment analysis, tests a new variation, and deploys it - all before your coffee's gone cold. Bottom line:  Agentic AI turns your campaign engine from manual shift to autopilot. 2. Data and analytics: From reporting to real-time action Today: MOPS folks spend hours wrangling data, running reports, and building dashboards that often answer yesterday’s questions. Actionable insight? That still takes time, context, and follow-through. With agentic AI: Your AI proactively flags unusual trends, surfaces new opportunities, and recommends next steps. It doesn’t just show you that webinar leads are converting better—it reallocates budget to double down and pings the events team with the news. Example: your AI sees a spike in high-quality leads from webinars in EMEA, pauses some low-performing paid search ads, and proposes a regional content plan. Bottom line:  MOPS doesn’t just report on what happened. With agentic AI, it helps decide what to do next . 3. Tech Stack Management: From Chaos to Clarity Reality check: Today’s MarTech stacks are sprawling. Dozens of platforms, hundreds of integrations, endless opportunities for things to break. Most teams don’t have time to fully optimize every tool. Enter agentic AI: Your AI agent continuously monitors tools and workflows, spots inefficiencies, and suggests fixes. It flags unused features, identifies redundant spend, and even implements improvements (with your sign-off). Scenario: it notices your lead scoring model is out of date. It rebuilds a new one based on recent buyer behaviour, tests it in a sandbox, and recommends rollout. Bottom line:  Agentic AI keeps your stack lean, mean, and in fighting shape. 4. Strategic enablement: From process police to growth partner What it looks like today: Governance is vital but often reactive. Playbooks get ignored, naming conventions go off the rails, and new team members unknowingly break things. With agentic AI: AI enforces governance gently but firmly. It catches inconsistencies, prompts users to correct them, and even coaches new hires. It audits usage patterns and suggests where your playbooks need updates or simplification. Picture this: an AI that sees inconsistent campaign tagging, auto-corrects it, and then messages the marketer with a friendly guide and a quick quiz. Bottom line:  Your ops team becomes a growth engine, not the "no" department. What Marketing Ops Leaders should do next Agentic AI isn’t plug-and-play magic - not yet. It thrives in environments where goals are clear, data is accessible, and there’s room to learn and improve. But here’s the thing: ignoring this shift would be like ignoring mobile, social, or CRM. It’s happening. The smart move? Start small, learn fast. Try this: Spot the patterns:  What repeatable, rules-based tasks could an agent handle? Get your house in order:  Integrate your systems, clean your data, and open up APIs. Upskill the team:  Teach your folks how to collaborate with AI - not fear it. Experiment:  Pick one area (like campaign QA or lead scoring) and run a pilot. Final thought: MOPS just got a seat at the table For years, Marketing Ops has quietly kept the machine humming. But agentic AI changes the game. When your ops team can launch campaigns, fix processes, optimize spend, and scale governance - without needing a task list from above - they become strategic power players . This isn't about replacing people. It's about giving your best people the best teammate they’ve ever had. So no, the future of MOPS isn’t just automated. It's agentic . Curious how to bring agentic AI into your MOPS world? Sojourn Solutions is already helping teams turn vision into action. Let’s talk . Discover our Services

Change fatigue is the new normal in Marketing Operations - here’s how to survive it without becoming the office cynic. Let’s be blunt: Marketing tech is moving faster than sanity There was a time - not that long ago - when adding a new tool to your tech stack felt like progress. Something to be proud of. Maybe even a LinkedIn post. Now? It feels like adopting a puppy every other Tuesday while the old ones are still chewing your furniture. From CDPs to DMPs to AI-powered hyper-mega-personalisation platforms, the MarTech world has become a dizzying alphabet soup of possibility. And while vendors pitch the promise of effortless scale  and seamless automation , what Marketing Ops teams are left holding is the mop and bucket of yet another “strategic pivot.” Change fatigue is real. It’s happening. And if you’re a B2B leader responsible for Marketing Operations, integration, or strategy - you probably feel it in your bones. Let’s dig into why this is happening, what it’s doing to your team, and what you can actually do about it (short of moving to a goat farm in Portugal). What is change fatigue, exactly? Change fatigue is the exhaustion that sets in when people are asked to change - constantly, rapidly, and without adequate time to adapt or reflect. In the MarTech space, this shows up as: Yet another platform rollout when the last one is still bedding in Constant re-orgs around “customer experience” or “data-centricity” Competing tech priorities from marketing, IT, and sales leadership Strategic ambiguity: “Why are we even doing this again?” The result? People disengage. Projects stall. Shortcuts become survival tactics. Innovation - the very thing we’re supposedly chasing - gets quietly buried under a pile of Jira tickets. Why MarTech is a perfect breeding ground for change fatigue 1. Innovation cycles are outpacing adoption cycles Vendors now push feature updates monthly (or weekly), and internal teams are expected to just “keep up.” But every new capability means a learning curve, process shift, or a dashboard that looks nothing like last week’s. It’s exhausting. 2. AI is the new FOMO drug Leadership reads one McKinsey report and suddenly your team is piloting three AI tools, building an internal task force, and rewriting your personalisation strategy - all while still dealing with last quarter’s Salesforce sync issue. 3. No one owns the change MarTech changes usually sit in a no-man’s-land between marketing, IT, and ops. That means no clear ownership, vague accountability, and a lot of “we’ll circle back on that.” Change fatigue thrives in the cracks between teams. 4. ‘Strategic’ often means ‘reactive’ Too many companies buy new tools to fix the last mess, not to drive a long-term plan. That knee-jerk buying behaviour (often dressed up as “bold leadership”) leads to a graveyard of half-implemented tech, misaligned teams, and a sense of déjà vu every time a new acronym lands on the roadmap. Discover our Podcast The hidden cost: What it’s really doing to your teams Beyond the missed deadlines and budget overruns, change fatigue in MarTech has a deeper, more insidious cost. Talent loss : Skilled people leave not because they can’t do the work - but because they’re sick of being ping-ponged between priorities that contradict each other. Cultural cynicism : When every tech initiative is pitched as a “game changer,” nothing is. People stop caring, even about the good ideas. Shadow systems : Teams build workaround tools and processes in spreadsheets or Notion docs because they’ve lost faith in the stack. Brand risk : Fragmented data, disconnected journeys, and half-baked campaigns don’t just waste money - they corrode customer trust. How to spot the signs of change fatigue (before it eats your strategy) Team enthusiasm has flatlined  – New initiative? Cue the blank stares. Low tool adoption  – You’re paying for licenses no one uses (and everyone resents). Increased internal friction  – Ops vs marketing vs sales vs IT: everyone blames everyone else. Micromanagement creeping in  – Because no one trusts the process anymore. Over-reliance on external vendors  – Not because it’s strategic, but because the team is tapped out. So what the hell do you do about it? Let’s get real: you can’t stop change. But you can manage the velocity , the volume , and most importantly, the human impact . Here’s how: 1. Audit before adding Before introducing any new tech, ask: what’s the business problem?  If it’s not clearly articulated - and agreed upon cross-functionally - park it. Tech for tech’s sake is the enemy. 2. Create a MarTech change council No, not another committee that meets twice then disappears. A real task force with teeth - cross-functional, empowered, and focused solely on evaluating, sequencing, and communicating MarTech changes. 3. Align change with capacity Roadmaps are only useful if they reflect reality. If your team is already maxed out integrating that CDP, don’t layer on a new personalisation engine . Prioritise ruthlessly. Sequence sensibly. 4. Communicate like it’s your job Because it is. Change fatigue is often a result of poor communication. Treat every MarTech shift like a mini product launch - with messaging, enablement, training, and a damn good reason “why.” 5. Measure adoption, not just deployment Stop patting yourself on the back for “going live.” Track actual usage, team sentiment, and business outcomes. Celebrate meaningful adoption - not checkbox delivery. 6. Give people space to recover If you just finished a big platform rollout, don’t start the next one immediately. Let your team digest, reflect, and stabilize. Recovery is part of the process. Final thought: not all change is progress In B2B Marketing Operations, we’re supposed to be the adults in the room - the calm heads who bridge ambition and execution. But we’re also human. And if your team is burned out, you don’t need another innovation. You need a pause. Change fatigue isn’t a weakness. It’s a signal. Listen to it, and you’ll lead more effectively than any AI tool ever could. Discover our Services

Introduction: When the champagne goes flat So, the deal’s done. Execs are smiling. The press release is out. And you, the Marketing Ops pro, are suddenly holding a bag filled with duplicate CRMs, mismatched databases, and twenty tools all claiming to be “the single source of truth.” Welcome to post-M&A MarTech integration. If you’re feeling overwhelmed, you’re not alone. This guide is built for you - the ones in the trenches, the MOPs leaders, the stack whisperers. Because while M&A headlines talk about financials and synergies, what often gets lost in the merge  is the very engine that drives go-to-market success: the marketing tech stack. Let’s unpack the chaos, find the common pitfalls, and give you a survival framework to not just clean up - but come out stronger. Chapter 1: The real cost of MarTech duplication Mergers are famous for redundancy. Sales teams. HR policies. But nothing breeds more duplication than the MarTech stack. Here’s what you typically inherit: Two or more CRMs with no shared schema Multiple email marketing platforms, each with active nurtures A mix of CDPs, DMPs, and ESPs, all fighting for the “truth” Redundant data warehouses - one cloud-based, one on-prem Overlapping ABM, intent, and analytics tools Multiply that by global regions and multiple business units, and you’ve got a stack that’s more Frankenstack than functional. ⚠️ What gets lost: Data fidelity (field mismatches, mapping errors, skewed reports) Campaign continuity (paused, duplicated, or misfiring automations) Operational clarity (no one knows what’s live or critical anymore) Internal trust (teams start going rogue to protect “their” platforms) Chapter 2: why MarTech gets ignored in M&A planning Let’s not sugar-coat it: MarTech is often an afterthought in M&A discussions. During due diligence, the focus is on: Financials Legal risk Product overlap Customer concentration Tech? Lumped under “IT.” And Marketing tech? Buried beneath that. Common (and dangerous) assumptions: “We’ll just pick the better platform.” “IT can handle the integration.” “We’ll sort it out after close.” “A vendor will take care of it.” Spoiler: They won’t. And without a plan, you inherit a stack that slows down everything from segmentation to reporting. Chapter 3: The MOPs integration checklist You can’t fix what you can’t see. So here’s what to focus on first: 🔍 1. Audit everything Inventory tools by function, owner, cost, and renewal date Map current integrations, data flows, and automation dependencies Identify active vs. dormant platforms 🧠 2. Define your ‘future state’ What will be the system of record for each function? Which tools are strategic vs. tactical? What gaps will need new investment? 🤝 3. Stakeholder alignment Bring in Sales Ops, IT, RevOps, and Finance early Assign owners for platform consolidation decisions Create shared KPIs for integration success 🔄 4. Plan for phased integration Don’t try to “big bang” it Prioritise platforms that affect customer experience first Build interim bridges for data sync and continuity Chapter 4: The data danger zone Let’s talk data - the lifeblood of every MarTech system. Post-M&A, it’s also a minefield. What typically goes wrong: Conflicting schemas and field definitions (e.g. “Region” means different things) Duplicated records across systems with no clear dedupe logic Misaligned consent and compliance flags (especially in global orgs) Broken attribution and funnel reporting If data isn’t mapped and migrated carefully, it creates chaos that lasts years. ✅ Pro move: Create a data governance playbook  early. Define naming conventions, field-level mapping, ownership, and hygiene standards. Without this, your new stack becomes a source of endless noise. Chapter 5: Campaigns in limbo Marketing automation platforms often house the most delicate machinery - triggered nurtures, scoring models, lead routing, etc. And most of it isn’t documented anywhere. So when you merge two MAPs, you risk: Breaking nurture sequences mid-flow Duplicating emails to the same leads Scoring models clashing or resetting Ops teams operating in parallel, unaware of each other 🧩 Fix this first: Freeze unnecessary new campaign builds during transition Document existing automations and their business logic Prioritize critical journeys (e.g. hand-raisers, opp-stage buyers) Build interim workflows that can handle hybrid data inputs Chapter 6: Managing the humans (yes, even them) Let’s not forget the emotional side of integration. People are protective of their platforms, their data, and their ways of working. Expect: Political fights over tool ownership Teams resisting platform sunsets Shadow ops continuing in parallel Attrition of key people with stack knowledge 🎯 Your job: Over-communicate the plan and why it matters Recognize team input and involve them in decision-making Create a roadmap with clear wins to build trust Design training and transition plans for shared tools Chapter 7: Tech is the tactic, strategy is the unlock The MarTech stack is not just a pile of tools. It’s the engine  behind go-to-market. So use this forced re-evaluation to ask bigger questions: Are we building a best-of-breed stack or simplifying to a platform approach? Can we consolidate vendors and leverage better pricing? Where can we improve CX by aligning systems (e.g. sales + marketing handoffs)? What does AI unlock once our data is actually unified? The integration process is painful. But it’s also a chance to get strategic - and fix things that were broken long before the merger. Key takeaways: Survive and thrive ✅ What to remember: Start with an audit.  Guessing = danger. Map future-state architecture.  Don’t just combine for convenience. Fix your data layer first.  Everything else depends on it. Prioritize impact.  Don’t optimize trivia. Involve the right people.  This isn’t just a Marketing problem. Don’t assume AI is a shortcut.  Clean systems first, then smart systems. Final word: You didn’t ask for this - but you’re the one who’ll make it work Mergers can feel like a tidal wave. But they’re also one of the only moments when you get a mandate to change everything  - and build something better. As a MOPs leader, you’re not just keeping the lights on. You’re rebuilding the engine, in real time, while the car is still moving. It’s not glamorous. It’s not in the press release. But it’s where the real value of the deal either happens… or doesn’t. Now go make it happen. Discover our Services Discover The MOPs Brief

When two companies merge, most of the headlines talk about market share, valuations, and synergies. Maybe there’s a stock bump. Maybe there’s a press release with the word “transformational” used at least four times. But behind the scenes - usually just out of earshot from the boardroom - there’s a group of people quietly hyperventilating. They’re the ones staring down two overlapping, bloated, often contradictory MarTech stacks and wondering: how the hell are we going to make this work? Because here’s the truth: mergers and acquisitions are just as much about technology alignment  as they are about financial engineering. And for Marketing Operations and digital strategy teams, the real battle often starts after the deal is done. Two stacks enter, one stack limps out When two organisations come together, their MarTech ecosystems rarely fit like puzzle pieces. More often, it’s like trying to combine a high-performance sports car with a family SUV - both have value, both serve different needs, and neither was built with the other in mind. You’ll usually find: Two CRMs  (with wildly different field definitions and dirty data) Multiple email platforms  (because someone once liked the UI better) Redundant CDPs, CMSs, DMPs, and data warehouses Conflicting customer journeys A dozen tools that no one remembers paying for And guess what? Every one of those tools has passionate defenders who will tell you, with complete confidence, that their platform is the backbone of everything . Welcome to the tug-of-war. The quiet chaos of post-merger integration While the finance team celebrates synergy and the CEO posts a LinkedIn selfie with the “new team,” Marketing Operations is in the trenches. Their job? Rationalise the stack, migrate data, maintain campaigns, and somehow keep revenue flowing. Here’s where the wheels often come off: No clear owner of the integration strategy.  Is it IT? Is it Marketing? Is it both? (Spoiler: it’s neither unless someone gets accountable fast.) Incomplete audits.  The deal went through, but no one actually inventoried the platforms in use. Political battles.  Each team wants to keep their tech, their process, and their preferred vendor contacts. Different maturity levels.  One org might have a deeply automated, AI-enabled stack. The other may still be manually segmenting lists in Excel. Integration becomes a minefield of duplicated functions, stalled migrations, and “temporary” dual systems that somehow persist for years. Where M&A deals go wrong (from a MarTech perspective) Let’s be blunt: many M&A deals fail to realise their full value because no one properly plans for the tech stack fallout. Here’s where the cracks start: MarTech due diligence is an afterthought In most M&A negotiations, tech gets lumped into the “IT” bucket - a checkbox in the due diligence process. But MarTech isn’t just back-office tooling. It’s how you acquire, retain, and grow customers. It’s how you engage in-market buyers. It is  your go-to-market engine. Ignoring it until after the contract is signed is like buying a house without checking the plumbing. There’s no common customer definition This one’s sneaky and deadly. If the two merging companies don’t have a shared taxonomy for accounts, contacts, personas, and stages, then combining their data is going to be a mess. And your CDP, CRM, and reporting dashboards will all end up lying to you. You can’t sunset what you don’t understand Sunsetting tools is essential post-M&A. But you can’t kill what you can’t map. If no one knows exactly what’s connected to which systems, what automations are running, and what campaigns are dependent, you end up keeping everything “just in case.” That’s how tech debt becomes cultural. How to win the tug-of-war (or at least not lose) Let’s be practical. If you’re navigating a merger or acquisition — or see one coming — here’s how to approach your MarTech stack with some sanity. Start with a stack audit You need to know what you're dealing with. Inventory all platforms, licenses, user roles, API integrations, and critical workflows. Map what’s actively used versus what’s shelfware. Look at renewal dates. Look at dependencies. Bring the skeletons out of the closet early. Define the future-state architecture Don’t just reactively merge tools. Design a future-state stack that aligns with the new business goals. That might mean keeping one platform, merging two, or ripping everything out and starting fresh. But make it intentional. Ask: What are our core platforms? Which system becomes the source of truth for customer data? What functionality is duplicated, and what’s missing altogether? How will we handle compliance, consent, and governance post-integration? Prioritise integration by value, not ease It’s tempting to knock out “easy” integrations first, but that’s how you end up optimising trivia. Instead, prioritize based on business impact: Which systems drive revenue? Where are the biggest customer experience gaps? What’s stopping campaign velocity or reporting? Focus there first. Communicate - and then overcommunicate A lot of MarTech integration fails because people assume things. Don’t. Marketing needs to know what’s changing. Sales needs to know what’s working. IT needs to know what not to unplug. Document everything. Share timelines. Set expectations. M&A is a time of uncertainty. Transparency wins. Bonus round: What AI means in a merged world A quick aside - AI has a role to play here, but only if your data’s clean and your systems are talking to each other. AI can’t solve a fragmented customer view or deduplicate 200,000 contacts across two CRMs. But it can  accelerate personalisation, scoring, and segmentation once your stack is rationalised. Just don’t expect ChatGPT to clean up your HubSpot–Marketo hybrid monster. That’s still your problem. Final thought: It’s not a tech stack, it’s a growth stack M&A deals aren’t just about combining P&Ls. They’re about unifying teams, customers, and go-to-market strategies. Your MarTech stack sits at the centre of that. If you treat it as an afterthought - something to “sort out later” - you’ll end up with fragmented experiences, misaligned teams, and wasted investment. But if you lead with strategy, clarity, and ruthless prioritisation, you can turn the chaos into an advantage. You’ll move faster, get more value from your tech, and actually deliver on the growth the M&A was supposed to unlock. Because in the end, it’s not just a tug-of-war. It’s an opportunity to build a stack that’s stronger, smarter, and finally worth the hype. Discover our Podcast Discover our Services

Separating hype from help when integrating systems with AI-driven features Mergers and acquisitions are a special kind of chaos. MarTech teams often find themselves in the eye of the storm, trying to stitch together two (or more) wildly different tech stacks while maintaining business continuity, data integrity, and some semblance of sanity. In recent years, AI has been hailed as a silver bullet that can magically solve the complexity of stack integration. Spoiler: it’s not. That said, AI does bring real capabilities that can genuinely ease the burden - if you know where to look and what to ignore. This article cuts through the noise to show what AI can actually  do during post-merger MarTech integration, where it tends to disappoint, and how to use it as a lever rather than a crutch. The post-merger stack problem: Why this is hard When two companies merge, their MarTech stacks don’t just combine - they collide. You’re likely dealing with: Overlapping platforms (two MAPs, two CRMs, multiple analytics tools, etc.) Conflicting data models that don't speak the same language Inconsistent campaign logic and legacy workflows that don't align Multiple systems of record for the same customer, all claiming to be the truth Different privacy and compliance protocols based on region or industry It’s not just a tech problem - it’s a people and process problem. MOPs teams are expected to somehow align strategy, tools, and execution across all of it, while keeping the lights on. Add in time pressure, budget constraints, and competing internal politics, and you’ve got a recipe for disarray. In this context, AI gets marketed as a quick fix. The reality? It can help - but only if you understand its strengths and limitations. The promise and peril of AI in MarTech AI thrives in environments where there are large amounts of structured or semi-structured data and repeatable tasks. This is exactly what much of Marketing Operations deals with: data normalisation, lead scoring, behavioural predictions, and campaign automation. The promise is real. But AI in MarTech is also oversold. Many tools claim "AI-powered" capabilities that amount to glorified rule engines or simple if/then logic with a shiny front end. Worse, some teams adopt AI tools expecting strategic clarity or stack consolidation decisions to be made for them. AI can: Process enormous volumes of customer and campaign data Identify patterns and trends that humans would miss Predict future behaviors with a degree of statistical accuracy But AI cannot: Understand your unique customer journey nuances Weigh human dynamics, org politics, or customer sentiment Decide which tech stack to keep or sunset post-merger Use AI to accelerate and enhance human judgment—not to replace it. Where AI can help Data deduplication and cleanup One of the biggest headaches post-merger is duplicate and inconsistent data across systems. AI-powered deduplication tools can recognize fuzzy matches across fields and databases. These systems use machine learning to detect patterns such as variations in spelling, abbreviations, or partial entries, which traditional rule-based deduplication tools might miss. Examples: Merging "John Smith, Acme Inc" and "Jon Smyth, Acme Corporation" Matching records where email domains are different but behaviors align AI tools can also flag outliers and inconsistencies, making it easier for teams to cleanse data at scale. This reduces downstream integration errors and avoids broken segmentation or scoring models. Identity resolution Customers interact with brands across channels: email, web, social, in-app, events, etc. Merging two stacks often results in disjointed customer records across platforms. AI can help unify these disparate identities using probabilistic matching and behavioral linkage. Benefits: Build more accurate customer profiles Improve segmentation and targeting Reduce data fragmentation between CRMs, CDPs, and MAPs A solid identity resolution engine enables personalization at scale and sets the stage for compliant data handling. Journey mapping and personalization AI can analyze historical customer journey data across both merging organisations. It can uncover successful pathways, drop-off points, and content preferences. This analysis helps MOPs teams create hybrid nurture programs that reflect the best of both legacy companies. Use case: A newly unified MAP can use AI to suggest new multi-touch campaigns based on cross-company engagement patterns. AI can also help tailor content to personas or lifecycle stages by learning from historic engagement data, enabling smarter, faster personalization. Predictive lead scoring and prioritisation AI-powered scoring models use historical engagement, firmographic, and behavioural data to predict which leads are most likely to convert. In an M&A context, where sales teams are likely being reshuffled and territories redrawn, this can be a lifesaver. Key advantages: Enables faster prioritization of leads from both legacy pipelines Helps newly unified sales teams focus on high-propensity targets Just make sure your data is cleaned and mapped first. Garbage in, garbage out. Workflow automation AI can observe how teams previously used automation platforms and make recommendations to simplify or unify processes. Some platforms even auto-build workflows based on historical logic or offer templates based on best practices. Use case: Creating re-engagement workflows for inactive segments based on campaign history This is particularly useful when your new combined MAP or CRM is overloaded with fragmented or duplicative workflows. Where AI falls flat Tool rationalisation and stack strategy AI doesn’t understand vendor relationships, political dynamics, or budgetary nuances. It can’t tell you whether your team prefers Pardot over Marketo or whether Salesforce's renewal contract includes heavy penalties. This is strategic work.  It requires workshops, interviews, and a long-term vision for marketing and sales alignment. Contextual business decision-making AI doesn’t understand business nuance. It can’t decide whether expanding to a new market or integrating a specific tool supports the new business model. It doesn’t know your board’s risk tolerance, your compliance obligations, or your internal turf battles. These are executive-level decisions, and no machine can substitute for that context. Data model design AI can help suggest field mappings or infer relationships between data points. But designing a unified data model - especially across complex B2B buying cycles - requires deep architectural thinking. AI won’t: Know what your key segmentation drivers are Understand your lifecycle stages Make decisions about attribution model priorities That kind of model-building is foundational, and it takes human collaboration to get it right. Consent and compliance logic AI tools may be able to automate elements of consent management (e.g., surfacing records missing consent flags), but they don’t understand legal nuance. Data privacy regulations like GDPR and CCPA are complex and often require manual interpretation. Risks of relying too much on AI: Sending marketing emails to contacts without compliant opt-in Assuming outdated consent logic is still valid Ignoring jurisdictional data handling requirements You still need a legal team and strong governance protocols. How to vet AI tools during a Merger or Acquisition Here are smart questions to ask any vendor claiming AI capabilities: Is the AI embedded or bolt-on?  Native AI tends to be more reliable and better integrated than third-party add-ons. What data does the model need, and where does it live?  Make sure it can connect to your unified systems and not just your legacy data silos. Can you audit the model’s decisions?  Transparency is key. If a model scores a lead or suggests a workflow, can you see why? How customizable are the outputs?  If the tool is rigid, it may be worse than doing it manually. What happens if the model is wrong?  Have guardrails, overrides, and human review steps in place. The best tools treat AI like a helpful analyst, not an infallible oracle. The human element: Why AI won’t replace judgment Post-merger integration is fundamentally about people: aligning cultures, blending teams, negotiating priorities, and making hard calls about what stays and what goes. No model can do that for you. Your AI tools can: Speed up analysis Eliminate repetitive tasks Suggest optimizations But your MOPs leaders must: Define the integration roadmap Navigate organizational complexity Own the strategic outcomes There’s no replacement for human judgment, especially in moments of uncertainty. Treat AI like an assistant, not a decision-maker. Conclusion: Use AI like a scalpel, not a sledgehammer AI has its place in post-merger MarTech stack integration, but it’s not a one-size-fits-all solution. Used wisely, it can dramatically improve speed, accuracy, and scalability. Misused, it can add complexity, amplify errors, and create a false sense of security. Your job is to: Know where AI adds value Know where it doesn’t Set the right expectations with leadership Stay in control of the strategy Because when someone says, "AI will take care of it," your answer should be: "Great. But only after we know what 'it' actually is, and why it matters." Want some support? Let's chat. Discover our Services Discover our Podcast

Introduction: When data becomes collateral damage Mergers and acquisitions are high-stakes moves designed to unlock growth, expand market share, or streamline operations. But buried under the headlines and handshake photos is the unglamorous reality: data chaos. Marketing Operations (MOPs) professionals often inherit this mess. Disparate systems, duplicate records, mismatched models, missing consent flags - it all creates a perfect storm of bad decisions and broken customer experiences if not handled right. While strategy, branding, and integration get the boardroom attention, it’s the unseen data layer that makes or breaks post-merger success. This article is a deep dive into post-M&A data hygiene: What it is, why it matters, and how to do it right. It covers the four pillars of MOPs-led data recovery and hygiene: Governance Mapping Validation Cleansing And yes, we’ll also walk through the disasters that happen when these are ignored. Data governance: laying down the law Why it matters Post-M&A, data governance isn’t just a compliance checkbox - it’s a risk mitigator, a trust builder, and a sanity saver. Without strong governance, your unified MarTech stack becomes a Frankenstein of conflicting standards, undefined ownership, and liability minefields. What good governance looks like Ownership clarity:  Every dataset needs an owner, especially when multiple teams are involved. Documentation:  Define data types, standards, usage rules, access rights, and lifecycle policies. Policy alignment:  Harmonize policies from both companies to ensure legal compliance across geographies and industries. Steering committees:  Form a cross-functional team to oversee ongoing data decisions and resolve conflicts. Pitfalls to avoid Assuming one company’s policies will automatically take precedence Letting IT own governance without marketing input Neglecting governance for shared systems (CRM, CDP, MAP) Governance isn’t sexy, but it’s the backbone of sustainable integration. Data mapping: Connecting the chaos Why it matters You can’t unify what you don’t understand. Mapping is about creating a clear inventory of fields, formats, relationships, and flows across your systems. It’s the first step toward integrating meaningfully - not just technically. What mapping entails Field matching:  Align first name to first name, job title to job title—and know where they don’t match. Field definition:  Understand how each field is used , not just what it’s called. ("Lead Source" may mean wildly different things across systems.) Data lineage:  Document where data originates, how it flows, and where it’s stored. System roles:  Know which systems are sources of truth, which are derivative, and which are deprecated. Tools and tactics Schema comparison tools (e.g. Talend, Informatica) Field-level audits Data flow diagrams Workshops with key users to uncover tribal knowledge Mapping is tedious—but skipping it guarantees bad integrations. Validation: Trust but verify Why it matters Post-merge systems may look unified on the surface, but they can hide deep inconsistencies. Validation ensures your mapped and migrated data is not just present, but accurate, relevant, and usable. Validation best practices Sample-based QA:  Run audits on subsets of records across systems to confirm consistency. Business rule checks:  Validate that scoring models, segmentation logic, and lifecycle stages still function correctly. User testing:  Bring in marketers and sales reps to test real workflows. Volume monitoring:  Watch for spikes or dips in activity that could indicate pipeline blockages. Key questions to answer Is the right data arriving in the right system at the right time? Are marketing workflows firing as expected? Is sales seeing the same customer truth as marketing? If not validated, even clean data is useless. Cleansing: Removing rot at the root Why it matters Cleansing is where the real hygiene happens. All the governance, mapping, and validation in the world won’t help if you’re sitting on outdated, duplicate, or irrelevant data. Core cleansing activities Deduplication:  Use AI or rules-based tools to consolidate records Normalization:  Standardize field formats (job titles, phone numbers, countries, etc.) Obsolete data removal:  Delete or archive records that no longer meet quality thresholds Consent flag alignment:  Update or remove contacts without compliant opt-in Enrichment:  Add missing firmographic or behavioral data to enhance records Tools and tech Data cleansing software (e.g. Openprise, Ringlead) CRM/MAP dedupe tools Custom scripts for batch cleansing Data brokers for enrichment Cleansing is an ongoing discipline, not a one-time event. Build it into your post-merger roadmap. Real-world data disasters The misaligned country field:  One company used "US" while the other used "United States." Result: segments broke, emails went out to the wrong regions, and privacy violations occurred. Duplicate CRM records:  Sales teams unknowingly worked the same accounts twice. Customers received multiple outreach emails and churned. Consent chaos:  Half a million records lacked GDPR flags after a merger. Emails went out. Fines followed. Final thought: Integration is a hygiene test, not a tech test Technology won’t save you if your data hygiene sucks. What matters is the discipline behind how data is governed, mapped, validated, and cleansed. For MOPs teams, post-M&A is your moment to lead - not just to clean up, but to shape how the new marketing engine runs. Because when the data is clean, compliant, and aligned, everything else becomes easier: campaigns, segmentation, lead flow, reporting, sales handoffs, and customer experience. The tech stack might merge in a few months. But the data battle? That’s yours to win every day. Need help navigating a messy M&A integration? Get in touch for a MarTech assessment built for the chaos. Discover our Services Discover our Podcast