Martech in 2026: What Stays, What Goes, What Matters

The future of martech is not about more tools. It is about fewer, better-connected ones doing more of the work your team currently does manually. The stack has been growing for over a decade, and the question most senior marketers are now asking is not “what should we add?” but “what are we actually getting from what we already have?”

That is a more useful question. And the answer, in most organisations, is uncomfortable.

Why Most Martech Stacks Are Underperforming Right Now

I have worked with marketing teams across thirty industries, and the pattern is consistent. There is a CRM that was set up three years ago and has never been properly configured. There is an email platform that the team knows how to use for basic sends but nothing more. There is a CDP that someone bought because a competitor had one. And there is a reporting dashboard that no one fully trusts because the numbers never quite match the numbers from the other dashboard.

This is not a technology problem. It is a strategy problem that technology has been asked to solve, and it cannot.

The martech industry has grown by selling the promise of what tools can do rather than what they will do inside a specific organisation with specific people, specific data, and specific commercial goals. The result is stacks that are expensive, partially implemented, and producing outputs that no one is confident acting on.

If you want to understand where martech is heading, start by understanding why the current state exists. Then you can see which direction the pressure is coming from.

If you are thinking about how automation fits into this picture more broadly, the marketing automation hub covers the strategic and operational dimensions in more depth.

What Is Actually Changing in Martech Right Now?

Three structural shifts are happening simultaneously, and they are reinforcing each other.

The first is consolidation. The era of the best-of-breed point solution is not over, but it is under serious pressure. Large platforms are acquiring smaller tools and folding them in. Buyers are tired of managing twenty vendor relationships, twenty contracts, and twenty sets of credentials. The vendors who survive the next five years will either be deeply embedded platforms or highly specialised tools that do one thing so well that no platform can replicate it cheaply enough to bother.

The second shift is the move from third-party to first-party data. This has been coming for years, but the pace has accelerated. Teams that built their targeting and measurement on third-party cookies are rebuilding from scratch, and the ones doing it well are discovering that first-party data, when properly collected and structured, is significantly more valuable than what they were renting from data brokers. The teams doing it badly are discovering that they did not actually understand their customers as well as they thought.

The third shift is AI, and it is worth being precise about what that means in a martech context. AI is not primarily a content generation tool, though that is how most marketing teams have encountered it so far. At the infrastructure level, AI is changing how data is processed, how audiences are segmented, how bids are set, and how personalisation is delivered at scale. The surface-level applications are the ones getting the attention. The infrastructure-level applications are the ones that will matter.

Where Will AI Have the Most Impact on Martech?

When I was at lastminute.com, I ran a paid search campaign for a music festival that generated six figures of revenue in roughly a day. The campaign itself was not complicated. What made it work was the combination of the right audience, the right moment, and the right message. We did not need AI to do that. We needed commercial instinct and a basic understanding of how the platform worked.

That kind of campaign still works. But the environment around it has changed completely. The platforms themselves now use machine learning to set bids, optimise delivery, and determine who sees what. The marketer’s job has shifted from managing the mechanics to setting the objectives correctly and feeding the system good data. If you set the wrong objective or give the system bad data, it will optimise toward the wrong outcome with impressive efficiency.

This is where AI will have the most impact on martech: in the systems that sit underneath campaign execution. Predictive lead scoring, dynamic content assembly, real-time personalisation, churn prediction, and attribution modelling are all areas where AI-native tools are already producing results that rule-based systems cannot match. The Semrush breakdown of martech tools gives a useful overview of where the category boundaries are currently sitting, though the landscape is shifting fast enough that any snapshot dates quickly.

What AI will not do is replace the need for clear commercial thinking. The teams that will get the most from AI-powered martech are the ones that can articulate precisely what outcome they are trying to drive, which customer segments matter most, and what data they actually have versus what they think they have. That is not a technology question. It is a strategy question.

What Happens to the Point Solution Market?

Early in my career, I taught myself to code because the business would not give me budget for a new website. I built it myself. The tools available then were limited, and doing something technically sophisticated meant doing it from scratch. The proliferation of point solutions over the last fifteen years was, in part, a response to that kind of constraint. Suddenly, a marketing team could buy a tool that did exactly what they needed without involving IT, without a long procurement process, and without writing a line of code.

That era produced genuine innovation. It also produced stacks that nobody can fully map, with data sitting in silos and integrations that break every time a vendor updates their API.

The point solution market is not disappearing, but it is contracting. The middle tier, tools that are reasonably good at a few things but not exceptional at any of them, is being squeezed from both directions. The large platforms are absorbing their functionality. The genuinely specialist tools are holding their ground because they do something specific well enough that the platforms have not caught up.

For buyers, this means the evaluation question is changing. It used to be “does this tool do what we need?” Now it is “does this tool do what we need well enough that it justifies the integration overhead, the additional contract, and the ongoing management cost?” That is a harder question to answer, and it is one that vendors are not always keen to help you ask honestly.

What Does First-Party Data Strategy Actually Require?

The phrase “first-party data strategy” has been in every martech conversation for the last three years. Most of the time, it is used as a destination rather than a description of a specific set of decisions that need to be made.

In practice, a first-party data strategy requires four things. You need a clear picture of what data you are collecting, where it lives, and how clean it is. You need consent architecture that is legally compliant and commercially sensible, which means not asking for permissions you do not need and not burying the ones you do. You need the technical infrastructure to activate that data across your channels, which is where CDPs and data warehouses come in. And you need a team that understands what the data means and can make decisions based on it.

Most organisations have made progress on the first two. The third is where many get stuck, because activating data across channels requires integrations that are more complex than the vendor demos suggest. The fourth is where almost everyone underinvests, because data literacy is a skills problem and skills problems are slower and less satisfying to solve than technology problems.

The martech tools that will win in this environment are the ones that make data activation genuinely accessible to marketing teams without requiring a data engineering team to sit between the platform and the campaign. That gap is real, and it is one of the most important things to evaluate when looking at any new platform.

How Should Senior Marketers Be Evaluating Their Stack Right Now?

When I was growing an agency from twenty to a hundred people, the temptation was always to add. Add headcount, add capability, add tools. What I learned, usually the hard way, was that adding before you have consolidated what you already have creates compounding complexity. The same principle applies to martech.

Before evaluating anything new, the more useful exercise is an honest audit of what you currently have and what it is actually doing. Not what it is capable of doing. What it is doing. That distinction matters enormously. Most enterprise platforms are used at a fraction of their documented capability, and most of the capability gaps are not technology problems. They are training problems, process problems, or data quality problems.

A useful framework for the audit has three questions. First: which tools in the stack are producing outputs that directly influence commercial decisions? If a tool is producing reports that nobody acts on, it is a cost, not an asset. Second: which tools are genuinely integrated with each other, meaning data flows between them without manual intervention? Third: which tools could be replaced by a feature inside a platform you already pay for?

The answers to those three questions will usually identify consolidation opportunities that free up both budget and management bandwidth. That is a better starting point than a vendor demo for the next shiny platform.

For teams working through the broader question of how automation fits into their operating model, the marketing automation resources on this site cover the strategic and operational dimensions in more detail.

What Will the Martech Landscape Look Like in Five Years?

Predictions about technology timelines are usually wrong in the specifics and right in the direction. With that caveat, the direction here is fairly clear.

Platforms will continue to consolidate. The number of distinct martech vendors will shrink, not because innovation stops but because the economics of running a standalone point solution in a market dominated by large platforms are increasingly difficult. The vendors that survive will be the ones that are either deeply embedded in enterprise workflows or genuinely irreplaceable for a specific use case.

AI will move from being a feature that vendors add to their marketing materials to being the actual operating layer of most platforms. The distinction between “AI-powered” and “not AI-powered” will stop being meaningful because AI will be embedded in how data is processed, how audiences are built, and how content is assembled across almost every platform of consequence.

The measurement question will get harder before it gets easier. The loss of third-party signals, the growth of walled gardens, and the increasing complexity of the customer experience mean that attribution will remain an approximation. The organisations that handle this well will be the ones that are honest about the limitations of their measurement and make decisions based on directional confidence rather than false precision. The Moz analysis of Google’s API documentation is a useful reminder of how much signal is controlled by platforms that have their own commercial interests in what they share.

The skills premium will shift further toward people who can think commercially about data. The ability to run a platform is becoming a commodity skill. The ability to ask the right question of the data, interpret the answer correctly, and connect it to a business decision is not. Teams that invest in that capability will get more from their martech than teams that invest in more tools.

For context on where the broader tooling market is heading, the Semrush overview of marketing applications is a reasonable reference point for the range of categories that currently exist, even if the specific tools shift quickly.

What Should You Actually Do With This?

The practical implication of everything above is not a roadmap for buying new technology. It is a set of questions worth sitting with before you do anything else.

Do you have a clear picture of what commercial outcomes your current stack is contributing to? Not what it is capable of contributing to. What it is actually contributing to, measured in a way that connects to revenue or cost.

Do you have the data infrastructure to support the AI-powered tools you are being pitched? Most of those tools require clean, structured, first-party data to perform as advertised. If your data is fragmented, incomplete, or sitting in silos, the tool will underperform and you will blame the tool.

Do you have the team capability to use what you already have at a higher level? Before adding a new platform, the question worth asking is whether additional training or process change would produce more commercial value than a new contract.

And finally: are you evaluating martech against a commercial objective, or against a capability checklist? The checklist approach is how stacks get bloated. The commercial objective approach is how they get useful.

I spent years watching agencies and clients buy tools to solve problems that were not technology problems. The pattern is always the same. A commercial challenge gets framed as a capability gap. The capability gap gets solved with a platform purchase. The platform gets partially implemented. The commercial challenge remains. A new tool gets evaluated.

The future of martech is not a better version of that cycle. It is breaking out of it.

About the Author

Keith Lacy is a marketing strategist and former agency CEO with 20+ years of experience across agency leadership, performance marketing, and commercial strategy. He writes The Marketing Juice to cut through the noise and share what works.

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