6 Martech Trends to Watch in 2026
The Gist
- AI shifts from speed to strategy. In 2026, AI stops being a productivity booster and becomes a driver of differentiation, experimentation, and net-new growth.
- The stack splits into two modes. The Laboratory accelerates experimentation; the Factory powers scaled, reliable execution — and leaders must structure both intentionally.
- AI agents mature within clear boundaries. They’re production-ready for content, service, and research, but still need guardrails in high-risk or high-autonomy use cases.
- Batch-era tools lose ground. Static DXPs, closed MAP/ESP systems, and sequential workflows give way to real-time decisioning and adaptive experiences.
- Marketing Ops 3.0 takes center stage. The role evolves into the business value engineer, blending strategy, AI fluency, data architecture, and change management.
- Adaptability becomes the differentiator. Top CMOs will run small, continuous bets and build dual operating models that reduce risk without slowing innovation.
The past few years have reshaped what marketing teams expect from their technology. AI has evolved from novelty to necessity, impacting content creation, analytics, segmentation and workflow design. Even so, most of 2025 was spent in a familiar pattern. AI helped teams work faster, but it did not fundamentally change the structure or purpose of marketing programs.
That changes in 2026.
Efficiency is no longer the story. The next chapter is about scale, experimentation and structural shifts that help marketing teams grow in ways that were not possible in the pre-AI era. Marketing leaders are beginning to see that speed alone is not a competitive advantage. Differentiation comes from using AI to expand the range of what marketing can attempt, not simply how quickly it operates.
To help paint a clear picture of what will define martech in 2026, we caught up with the “Godfather of Martech” Scott Brinker, who today released the 2026 Martech Trends and Predictions Report.
Here are six trends he believes will define martech in 2026.
Table of Contents
AI Shifts From Efficiency Tool to Strategic Growth Multiplier
In Brinker’s view, most of 2025 was defined by what he calls the “AI as power screwdriver” phase. As he put it in an interview with CMSWire, “You shaved cycles off content production, segmentation and reporting. Helpful, but not differentiating, because everyone else can buy the same screwdriver.”
How AI Focus Shifts From Efficiency to Effectiveness (2025 vs. 2026)
This table compares the shift in marketing’s AI use from speed-focused gains in 2025 to effectiveness and differentiation in 2026.
| Aspect | 2025 Focus | 2026 Focus |
|---|---|---|
| Primary use of AI | Efficiency: doing the same work faster (“AI as power screwdriver”) | Effectiveness: changing what marketing teams do and can attempt |
| Treatment of time savings | Mostly treated as cost savings and productivity gains | Reinvested into net-new bets, experiments, and micro-segments |
| Campaign strategy | A few large campaigns and journeys run per quarter | A portfolio of many smaller, adaptive journeys tuned to behavior and context |
| Source of differentiation | Limited differentiation: most teams have similar tools and gains | Greater differentiation from experiment velocity and creative variety |
The real shift in 2026 is that AI begins to change the scope of what marketing can accomplish. Instead of using AI to simply accelerate existing processes, leading teams are channeling efficiency gains into net new work. That includes more experimentation, more creative variation and more personalized journeys than would be possible with human-only workflows.
Brinker describes it as a deliberate move from scarcity to abundance. Teams stop relying on a handful of major campaigns and instead pursue dozens of adaptive programs designed specifically around behavior, context and synthetic customer signals. The baseline of “doing more with less” remains, but teams also carve out space for “doing more with more” so they can pursue new revenue opportunities rather than just cheaper activity.
His advice is clear: “If all you get from AI is lower unit cost, you’re leaving most of the value on the table.”
Related Article: The Martech Supergraphic Has Grown Up: 15,000-Plus
The Martech Stack Splits in Two: The Laboratory and the Factory
A central theme in Brinker’s report is the emergence of two distinct operating modes inside modern marketing technology: The Laboratory and The Factor.
- The Laboratory serves as the home for new ideas, early agents, experimental journeys and synthetic customer testing. It is intentionally flexible and optimized for learning speed rather than reliability.
- The Factory, on the other hand, handles scaled and revenue-critical programs. This includes production-grade Customer Data Platform (CDP) operations, customer service automation, core personalization systems and the Content Management System (CMS) environment that shapes everyday digital experiences.
Brinker explained the risk of blending these two modes into one, suggesting that “Where it goes wrong is when you insist on one architecture, one process, one set of KPIs. Then either the Lab gets strangled by Factory governance, or the Factory gets polluted by half-baked experiments.”
His recommended structure keeps both modes healthy. The Lab needs a dedicated sandbox, separate data and budget and shorter cycles. The Factory should only run programs that have formally graduated from the Lab, with clear value cases, integration readiness and operational playbooks.
Marketing Ops becomes the transfer agent between the two. Brinker explained that “The Lab exists to keep the Factory from ossifying. The Factory exists to keep the Lab from burning the building down.”
The Laboratory vs. The Factory Operating Model
This table outlines how the Laboratory and Factory functions differ across purpose, governance, KPIs, and technical environment.
| Dimension | Laboratory | Factory |
|---|---|---|
| Core purpose | Experimentation and discovery of new motions, agents and journeys | Scaled, reliable execution of proven programs that drive revenue |
| Typical work | Early agents, pilot journeys, synthetic customer tests, new playbooks | Core nurture flows, service automations, personalization, core web experience |
| Governance style | Lighter governance with clear constraints on where it can touch customers | Tighter governance focused on SLAs, compliance, brand consistency |
| Primary KPIs | Learning velocity, time from idea to first live test, portfolio of pilots | Reliability, CX metrics, unit economics, revenue and retention impact |
| Tech environment | Sandbox stack, limited data subsets, shorter cycles, higher tolerance for failure | Production-grade CDP, MAP, CMS/DXP, decisioning, monitoring, rollback paths |
| Ownership role | Innovation teams and Marketing Ops acting as experiment sponsors | Marketing Ops and channel owners responsible for scaled delivery |
AI Agents Mature, But Only Within Well-Defined Boundaries
AI agents gained widespread adoption last year, but Brinker was clear about the difference between the capabilities that are ready for production and the ones that still require caution. Teams are leaning heavily on agents for content creation, adaptation and repurposing. “Content production agents are the number one internal use case,” Brinker said, with strong adoption rates and consistently reliable output.
Customer service agents also continue to mature. Brinker said that many achieve resolution rates above 60% when they are grounded in high-quality knowledge bases and customer data. Research agents, enrichment bots and narrow decisioning agents are also proving effective in structured environments.
Where leaders need to be more careful is in areas that generate scale quickly or interact with customers in sensitive contexts. Outbound SDR and BDR agents (AI versions of sales development reps and business development reps) can create a tragedy of the commons if overused, flooding inboxes with personalized outreach and prompting a wave of defensive filtering from AI-powered email clients. Fully autonomous campaign orchestration remains more hype than reality, and anything tied to compliance, pricing or reputation should always include a human in the loop.
Brinker’s rule of thumb summarized the moment: treat agents as powerful tools within clear scopes, not as free-roaming campaign managers.
Related Article: The Next Marketing Stack: AI Agents + Model Context Protocol
Batch-Era Martech Tools Lose Ground as Real-Time Architectures Take Over
Brinker is direct about the parts of the martech stack that are becoming outdated. “Anything that assumes the world is batchy, page-based and purely human-operated is on the endangered list,” he said. That includes overnight ETL (Extract, Transform, Load) processes, sequential marketing automation workflows and legacy systems that rely on fixed personalization rules.
The shift toward real-time interaction surfaces, agentic browsing and adaptive content requires architectures that can sense, decide, act and learn within seconds. Static CMS or Digital Experience Platform (DXP) systems will struggle unless they can generate or adapt experiences dynamically. Closed Marketing Automation Platform (MAP) or Email Service Provider (ESP) tools that cannot expose their signals or decisioning logic to other layers will find themselves sidelined as decisioning moves closer to the lakehouse or warehouse.
Search is also evolving. Traditional SEO practices are no longer enough as more traffic is filtered through AI assistants, AI browsers and generative answer engines. Retrospective BI tools that only describe the past cannot support the demands of real-time orchestration.
The emerging baseline architecture is clear. Teams need a cloud warehouse or lakehouse as a system of knowledge, a real-time context layer that powers both agents and applications and delivery channels that can be directed externally. Tools that cannot plug into this pattern will either evolve or drift into legacy status.
Marketing Ops 3.0 Emerges as the Business Value Engineer
Marketing Ops continues to advance in both skill set and strategic importance. The role has already evolved from tool administration to onboarding use cases. In 2026, it advances again, taking on a clearer mandate as the business value engineer.
This next version of Ops sits closer to the executive discussion and carries responsibility for connecting AI, data and go-to-market strategy. Brinker framed the shift as a movement toward three interlocking areas:
- Strategy and value translation
- AI and data engineering
- Organizational enablement
Ops teams will be expected to build revenue cases for new agentic journeys, design context flows that do not overwhelm models, manage cost observability for AI usage and train marketing teams to work effectively with new tools. They will also own the pipeline between the Laboratory and the Factory, guiding programs through experimentation, graduation and scale.
Brinker is clear about how leaders should prepare for this shift. Identify the role explicitly, give it ownership of cross-functional pilots and invest in training rather than trying to hire elusive unicorn candidates. “If Marketing Ops 2.0 made the stack run, Marketing Ops 3.0 makes the stack pay off.”
The Biggest Challenge of 2026 Is Organizational Adaptability
Technology continues to accelerate quickly, while organizational changes move at a slower pace. Brinker acknowledged that the gap has grown wider in the AI era and says the key to navigating it is to focus on small, continuous adjustments rather than sweeping changes.
He recommended shifting from long, high-stakes AI programs to rolling portfolios of small, time-boxed experiments. Each experiment should have clear metrics, a sponsor and a decision point to scale, revise or stop. This approach helps teams build momentum without overwhelming themselves.
Clear rules around the Laboratory and Factory also help teams know where work belongs, which reduces hesitation and second-guessing. Investing in data quality and stronger schemas creates a stable foundation for future AI work, and a lightweight governance group prevents teams from freezing up while still maintaining structure.
Most importantly, learning should be treated as a legitimate output. Leaders should normalize the idea that insights gained from experiments are valuable on their own, even when projects do not scale. This mindset makes teams more willing to try new approaches and helps them adapt more quickly.
“In an exponential environment, ‘We learned X about this use case, and here’s what we’ll do differently next quarter’ is a legitimate success metric,” Brinker said. “That mindset makes it psychologically safe to run more experiments without everyone feeling like each one has to be a career-defining win. If you do those things, you’re not ‘keeping up with AI’ in some abstract sense. You’re building a marketing org that gets a little more adaptable every quarter. Technology still curves up faster than you do, but your slope gets steeper — and that’s enough to stay in the game while others quietly slide off the cliff.”
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