The State of Martech 2026: Ambition is Running Ahead of Readiness

by Chris Fowler • 08th July 2026

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The State of Martech 2026: Ambition is Running Ahead of Readiness

This is the seventh year of our State of Martech research, having been the first independent source to do so, and in that time we have seen the industry adapt to changing priorities, shifting budgets and, now, AI. In 2026, marketing technology has never been more central to business growth. It now powers customer engagement, personalisation, reporting, automation and increasingly (or hopefully) AI-led decision-making. Yet the findings from this year's research reveal a market caught between blind ambition and operational reality.

The issue is no longer whether businesses have enough, or even the right technology. In many cases, they do. The real question is whether that technology is connected, trusted, understood and used well enough to deliver the outcomes marketing leaders are being asked to achieve. It might sound obvious, but the numbers tell several important stories.

For years, integration has been one of martech's most persistent challenges. In 2026, it is no longer just a technical inconvenience; it is becoming a strategic constraint.

The research shows that only around a quarter of organisations have most or almost all of their martech stack meaningfully integrated. For the majority, data still does not flow easily between systems, leaving teams reliant on manual workarounds, disconnected reporting and fragmented customer views. Compared to last year, this figure has remained flat, so even with the expected and rapid adoption of AI-native technologies, that's just another silo we're creating.

This matters because integration now underpins almost every major martech ambition. AI needs connected data, personalisation needs connected channels, ROI measurement needs connected systems, and sales and marketing alignment needs connected processes. Most of what I've said should come as no surprise to any of you... hopefully.

But there is a reason why we keep coming back to integration. It is the issue that sits underneath almost every other martech problem: when teams complain that reporting is hard, there is usually an integration issue; when personalisation is underwhelming, there is usually an integration issue; when AI outputs - for those of you using them already - are inconsistent, there is usually an integration issue. And when C-suite cannot see value, there is very often, you guessed it, an integration issue.

And yet, integration is still too often treated as a task to be completed after the platform decision has been made. Buy the tool, implement the tool, then worry about how it connects to everything else. That sequencing is part of the problem. Integration should not be the clean-up job; it should be part of the fundamental design and decision-making process, especially when the vendor does not know the complex, and often regulated, data sets that matter most to your organisation. Without that prior scoping and planning, you'll be relying on a lot of short-term digital duct tape.

The uncomfortable truth is that many organisations are still buying technologies faster than they are connecting it. That was a challenge before AI, and now it becomes a much bigger risk.

AI adoption is accelerating, but readiness is lagging

AI is, unsurprisingly, the headline topic this year. It is shaping boardroom conversations, vendor roadmaps and budget discussions, and depending on who you talk to, it is either the future of marketing or the latest thing we are all being asked to pretend we fully understand.

More than half of respondents say they are actively using AI within their martech platforms, which sounds positive, and in many ways it is. It shows that AI is moving beyond experimentation and into day-to-day marketing technology environments.

But adoption is not the same as readiness, and it is definitely not the same as value. The research shows a clear gap between organisations using AI and organisations having the foundations in place to use it well. Many are deploying AI on top of scattered data, disconnected systems and unclear governance, which is where the story becomes less about innovations and more about operational reality.

AI does not magically fix poor data, automatically connect fragmented systems or solve unclear ownership. In fact, in some cases, it risks making those problems more visible, more quickly.

That is not an argument against AI. Far from it. The opportunity is huge, but organisations that get value from AI will not simply be the ones that turn the features on first. They will be the ones that have done the work underneath: cleaning data, connecting systems, defining governance, training teams and being clear on what AI is actually meant to achieve.

At the moment, much of the market is still using AI for productivity and efficiency. That's fine, because saving time matters, but if AI is only helping teams do the same things slightly faster, then we should be careful about calling that transformation.

The bigger opportunity is in better decision-making, better customer experience, better segmentation, better timing and better commercial outcomes. But again, spoiler alert, that requires having strong foundations in place.

Data exists, but activation is still the problem

Most organisations are not short of data. If anything, they are drowning in it. The issue is whether that data is accurate, accessible, trusted and usable.

The research shows that confidence in data accuracy remains mixed. A lot of organisations have data in multiple places, owned by different teams, governed in different ways and used for different purposes. CRM remains the main data hub for many, which makes sense, but also create challenges when the CRM was never really designed to be the full customer intelligence.

This is where the difference between having data and activating data becomes important. Plenty of organisations collect customer data, but far fewer are using it to deliver consistent, meaningful, personalised experiences across channels. Some are still relying on basic fields such as name, company, industry or location, which is fine, of course, but should be table stakes at this point. Others are using behavioural data, albeit inconsistently, while very few are operating at the level of real-time, automated, highly personalised customer engagement.

And again, this is not because marketers do not want to personalise. They absolutely do. Hyper-personalisation continues to be a major ambition, but ambition is not the blocker. The blockers are familiar: poor data quality, disconnected systems, limited skills, lack of governance and concerns around trust. All of these match the top reasons from last year’s data too, so marketers are still stuck in this familiar-sounding cycle.

So when we talk about data strategy, we need to be honest about what that really means. It is not just buying a CDP, creating a dashboard or agreeing naming conventions, although please, do that too.

It means deciding who owns data quality. It means making data stewardship a proper responsibility, not something that sits vaguely between sales, marketing and operations. It means giving teams the confidence to interpret and act on the data they have, and connecting the systems that data needs to move through. In the next generation of martech platforms, data confidence and understanding will not be a nice-to-have; it will be forced into the day-to-day campaign operations. Acting now will help organisations make more informed decisions about their martech roadmap, rather than scrambling to catch up when the platform demands it.

The skills gap is no longer a side issue

One of the biggest themes in this year’s research is capability. Not just whether organisations have the technology, but whether they have the skills, confidence and operating model to use it properly.

This is where the conversation often gets uncomfortable, because it is easier to talk about platforms than people. Platforms have roadmaps; people need time, training, support, clarity and budget. Annoying, I know.

The report shows that confidence in martech skills is moderate, but not especially strong. Many teams feel they can use their platforms, but far fewer are very confident they are getting maximum value from them, and that distinction matters.

Being able to send an email, build a segment or create a report is not the same as being able to architect a connected customer journey, interpret performance data, optimise lifecycle activity or explain martech’s commercial value to senior leadership. The bar has moved, and AI has only raised it further.

This is why training investment matters. The research shows that organisations with ongoing learning programmes report higher confidence in both data and martech skills. That should not be surprising, but it is still worth saying: capability does not improve by accident, it improves when businesses invest in it.

And yes, external partners have a role to play. Many organisations are outsourcing specialist areas such as integration, configuration, platform management and strategic planning because they cannot source those skills internally, and that can be a smart move. But outsourcing should not become a way to avoid building internal understanding.

The strongest organisations will be the ones that combine external expertise with internal maturity. They will know what to outsource, what to own, and how to make sure knowledge does not leave the building every time a project ends, because martech maturity is not defined by the sophistication of the stack. It is defined by the capability of the people operating it.

You wouldn't invest in a multi-million-pound kitchen, with all of the latest and greatest cooking equipment, then hire chefs who only know how to microwave.

ROI measurement is still weaker than it should be

For all the conversation about growth, efficiency and commercial impact, the research shows that fewer than half of organisations can measure martech ROI in any form. That is a problem, particularly in a market where budgets are under scrutiny, AI investment is being pushed into existing allocations, and senior leaders increasingly want proof that technology is doing more than keeping marketing busy.

To be fair, ROI measurement in martech is not easy. Anyone who says otherwise is probably trying to sell you an attribution model. Customer journeys are complex, buying committees are messy, sales cycles are long, data is imperfect, channels overlap and human beings continue to be deeply inconvenient.

But difficulty is not a reason to avoid the issue. The report shows that many organisations are still measuring at campaign level only, and while campaign reporting is useful, it rarely tells the full story. Clicks, opens, form fills and influenced opportunities all have a place, but martech needs to be connected to broader business outcomes: pipeline, revenue, retention, customer experience, efficiency and growth.

And here again, we come back to integration. Organisations with better-integrated stacks are much more likely to be able to measure ROI, which makes sense. If your systems cannot talk to each other, your reporting will always be partial; you cannot measure a connected customer journey using disconnected data.

So the ROI problem is not just a reporting problem. It is a stack problem, a process problem, a sales and marketing alignment problem and, yes, a skills problem too.

The ability to prove value is becoming a strategic survival skill for marketing teams. Not because every activity needs a perfect ROI calculation, but because martech investment needs a clearer commercial story.

Sales and marketing alignment still has work to do

It would not be a martech report without sales and marketing alignment making an appearance and, sadly, it is still not where it needs to be.

Only a small minority of respondents say sales and marketing are well aligned and working effectively. That is a familiar finding, but it should not be accepted as normal.

Poor alignment limits martech value because martech does not stop at the point of lead generation, or at least, it should not. The value of marketing technology depends on what happens across the full journey: acquisition, nurture, qualification, handover, pipeline progression, conversion, retention and expansion. Gone are the days of marketing launching a qualified lead over the fence and hoping for the best.

If sales and marketing do not agree on definitions, processes, ownership and success measures, martech cannot show its full commercial impact. Marketing may generate engagement, but if that engagement disappears into a poorly managed handoff or an inconsistent sales process, the value becomes difficult to prove.

The research suggests that misalignment is often less about technology and more about process, which is important. A new tool might help, but it will not fix unclear definitions, poor adoption or competing priorities.

This is where organisations need to move beyond saying sales and marketing alignment matters, and start treating it as an operating model: shared lifecycle definitions, agreed handoffs, common reporting, joint accountability and clear feedback loops. All the slightly unglamorous things that make the glamorous dashboards more meaningful.

The market is shifting from expansion to optimisation

One of the more positive signs in this year's research is that martech expansion appears to be slowing. More organisations are now focused on integrating, optimising and improving what they already have, rather than simply adding more tools to the stack. Thank goodness.

For a long time, the answer to many martech problems seemed to be another platform. Need better personalisation? Buy something. Need better reporting? Buy something. Need better data? Buy something. Need AI? Definitely buy something.

But the market is maturing, or at least it is being forced to mature. Budgets are not disappearing, but they are being watched more carefully. AI and data investment are often expected to be absorbed into existing budgets, software licenses are no longer automatically the highest priority, and people, services and system improvements are all becoming more important parts of the conversation.

The next phase of martech value is unlikely to come from simply owning more technology. It will come from making better use of what is already there, which means rationalising where needed, integrating properly, improving data quality, building capability and being much clearer on what success looks like.

This does not mean organisations should stop investing in technology. It means they should stop assuming that more technology automatically creates more value.

Five priorities for martech leaders in 2026

So where does this leave us? This year, the research points to five priorities that should be front of mind for martech leaders in 2026.

First, treat integration as architecture, not admin. It needs to be designed around customer journeys, data flows and business outcomes, not patched together after platform decisions have already been made
Second, resolve data ownership properly. If everyone owns data quality, no one does, so organisations need clear accountability, embedded stewardship and practical governance that teams actually follow.
Third, align AI ambition with AI readiness. AI should absolutely be explored, tested and adopted, but not in a way that ignores the foundations it depends on.
Fourth, invest in people alongside platforms. Training, enablement and capability building are not optional extras; they are what determine whether technology gets used well or simply gets used.
Finally, connect measurement to commercial outcomes. Campaign performance still matters, but marketing needs to tell a bigger story around revenue, efficiency, customer value and growth.

None of these priorities are especially new, and that is kind of the point. The industry does not have a shortage of new ideas; it has a sequencing problem.

The real challenge is not ambition, it is order

The State of Martech 2026 does not show a market lacking ambition. If anything, it shows the opposite. Organisations want to use AI, personalise, prove ROI, drive growth and give martech a more strategic role in the business, but too often they are trying to do the advanced things before fixing the foundational ones.

They are investing in AI before resolving data readiness, chasing personalisation before connecting systems, trying to prove ROI before building the measurement infrastructure, and asking teams to deliver more value from platforms without giving them the training and support to do it. It sounds like a negative spin, but ultimately that is the real story of martech in 2026.

The winners will not necessarily be the organisations with the most expensive stacks, the newest tools or the boldest AI roadmap. They will be the ones that get the basics right, and then build intelligently from there: connected systems, trusted data, skilled teams, clear measurement and shared commercial outcomes.

It might not sound as excting as the next AI announcement, but it is what will separate blind ambition from actual progress.

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