The evolution of martech stacks should focus on capabilities
A lot of people in tech still think martech is about collecting the right tools. That’s outdated thinking. Tools are just surface-level. What really drives value are the capabilities you gain from using them. If what you’re running doesn’t help the company do real work, acquire users, convert leads, automate processes, then you’re collecting software, not building systems.
When you focus on capabilities instead of tools, everything becomes clearer. You start asking better questions: What can this module do that advances the business? Can my team actually use it without friction? Is it integrated with our existing systems, or is it sitting off to the side doing nothing? This is the shift that needs to happen. Businesses are no longer judged by how many tools they’ve stacked together, but by how well those tools enable fast, impactful execution.
This also solves for tool redundancy and cost drag. Most tech leaders admit their stacks have grown bloated. That’s because they’ve bought tools to check boxes, not to cover specific business capabilities. When you step back and prioritize function, whether it’s lead scoring, campaign triggers, or billing logic, you simplify the system. You make it responsive to change. That’s what matters.
MartechTribe analyzed over 1,600 global martech stacks and found that every high-performing system had a unique structure. Why? Because the tools are different, but the real differentiator is capability orchestration, the ability to configure the right components to fit your business model, size, and market position.
AI and SaaS serve different but complementary roles in martech stacks
This idea that AI will “replace” SaaS is lazy thinking. It misses how the systems actually work. SaaS and AI function differently, but they both belong in the same stack. They support one another. SaaS is built around consistency, it runs rules, gives consistent outputs, doesn’t change unless you tell it to. AI is probabilistic, it adapts based on inputs, sometimes surprises you. Each has strengths. Together they get more done than either one could alone.
SaaS is your foundation. It’s where you handle structured, repeatable processes, CRM, email triggers, billing, compliance. It doesn’t need to be creative. It needs to be reliable. AI lives on top of that. It augments the system. It refines copy, personalizes outreach, prioritizes leads based on behavior in ways SaaS can’t.
If you’re running both the right way, SaaS becomes your execution engine, and AI becomes your innovation layer. You get scale and adaptability. Jason Lemkin put it well, SaaS has a steady rhythm, while AI adds surprise and variance. Scott Brinker, VP of Platform Ecosystem at HubSpot, expanded on this idea when he described a next-gen stack as a fabric, where SaaS, AI, workflows, data, and people all interconnect to drive results.
Executives should be thinking less in categories and more in outcomes. If you only see AI or SaaS in isolation, you’re missing the full picture. The best teams build systems where these technologies cooperate, each doing what it’s better at. That’s the difference between a good stack and a transformative one.
AI agents are most effective when narrowly scoped for specific tasks
When you apply AI to a clearly defined job, the results speak for themselves. This is where you see reliability increase and outcomes become repeatable. Broad, unfocused AI systems are harder to control. They generate unexpected outputs, need constant monitoring, and waste time. That’s avoidable. The most valuable deployments we’re seeing now are task-specific, automating lead enrichment, tailoring email sends, repackaging content for different audiences.
These agents aren’t built to replace teams. They expand what teams can do without adding overhead. Platforms like n8n, relay.app, and gumloop are offering templated agents that do one job well, trigger a sequence based on a user behavior, clean form data, or scan signals from multiple sources and stack them into one usable insight. That’s what matters.
A narrow scope also means faster implementation. These AI agents don’t need custom architecture or long onboarding cycles. The job is defined, the boundaries are clear, and the results can be measured. That’s exactly what you want in operational systems, outputs that move the business forward without creating a new layer of complexity.
For leadership, this approach reduces risk while increasing speed. You don’t have to worry about AI misfiring across broad systems. It’s a focused deployment with a clear return. Smart companies are deploying many of these lightweight, targeted agents instead of betting everything on large-scale replacements. That’s how you scale with control.
SaaS tools are often used selectively, with most value concentrated in a few functions
Most SaaS tools come packed with features, but only a small percentage actually get used. That doesn’t mean the platform is failing. It means teams are smart about using only what works. We’ve known this for years, but many buyers still behave as though full adoption is the goal. It’s not.
Real usage tells the story. According to research from Pendo, only about 12% of features in SaaS products account for the majority of user engagement. Gartner backs this with data showing that only a third of tools deployed actually get meaningful use. These numbers aren’t low, they’re focused. Teams activate what drives performance and ignore the rest.
This is not inefficiency. It’s optimization. You don’t get points for running every feature. You get results from identifying what matters and executing at scale. Yet, many vendors and buyers are still judging value based on surface-level metrics like “features used” or “logins per week.” That’s noise.
Executives need to reevaluate how they assess ROI. Ask: What core functions are we running? Are those tightly integrated with other parts of the stack? Are they producing measurable outcomes? That’s what matters. Usage depth, not breadth.
The industry has moved toward a composable model, where companies stitch together the features they need and discard the rest. This is smart strategy. It keeps systems lean, agile, and aligned with goals. Anything else is distraction.
Viewing stacks through capabilities prevents tool sprawl and improves ROI
Too many companies are weighed down by bloated tech stacks, filled with overlapping or unused tools. The issue isn’t that they bought the wrong software, it’s that they didn’t start with a capability-first mindset. If you focus only on tools, you build from the outside in. That’s how you end up with excess software that doesn’t align with core business outcomes.
When you manage the stack around capabilities, you filter tech decisions through a business lens. What functions do we actually need? What parts of the stack drive execution? Once you answer those, choosing and configuring tools becomes straightforward. You minimize redundancy and reduce costs because every element in the system has a specific job.
This also helps with stack cohesion. Tools that weren’t selected with alignment in mind often don’t integrate well. That leads to poor data flow and inefficiency. When you lead with required capabilities, you force clearer decision-making from the start. You buy, build, or modify only what fits into the strategic picture, and shut down what doesn’t deliver value.
Insights from MartechTribe’s analysis of more than 1,600 global martech stacks confirm this pattern across industries. Companies with the leanest, best-aligned stacks had made capability management part of their operating principles. They put less emphasis on tool variety and more on orchestration, getting multiple parts to contribute to a cohesive output.
If you’re running a stack that feels bloated, it’s because it probably is. This isn’t about reducing tech spend for its own sake; it’s about directing investment into components that earn their place, functionally and financially.
Capability management is key to future-ready stack performance
If you’re thinking about martech as a list of tools, you’re already behind. The future belongs to companies that treat their stack like a fluid system of capabilities. The structure doesn’t stay fixed, it evolves quickly to match new challenges, channels, and customer behaviors. That only works when capability management becomes operational, not just theoretical.
Capabilities don’t come from one source. They’re a mix of software functions, AI-driven outputs, workflows, and human skill. The role of leadership is to manage this mix with precision. You need to know what each part contributes, how it connects with the rest, and whether the sum is doing what the business needs.
SaaS platforms deliver on structure. AI models bring adaptability. Workflows connect processes. People add judgment and context. Managing these elements as one capability-based system drives faster decision cycles and better performance. It also gives you optionality. When a market shifts, you don’t overhaul your tech, you adjust the configuration of capabilities.
There’s no reliable performance without continuous oversight of how capabilities operate day to day. Are they producing, connecting, scaling? If not, you don’t need more tools, you need better management of what’s already there.
A system built this way is harder to disrupt. It adapts with less friction. That’s how you move fast while staying aligned. That’s how you scale without breaking.
Organizing stacks by job-to-be-done enhances agility and strategic alignment
Organizing your martech stack around specific business functions, rather than by categories of tools, results in a system that performs with clarity and speed. It forces alignment between what the business needs to accomplish and how the tech is deployed. This structure leaves less room for dead weight. Every component contributes directly to a measurable business task.
When you build this way, you avoid the common problem of stacking tools without a clear plan. Instead of asking “What tools do we have?”, leaders ask, “What needs to get done, and how are we enabling that?” This approach keeps the system lean, operational, and relevant as conditions shift. It also gives stakeholders across departments a better understanding of stack purpose, which improves adoption and cross-functional flow.
A LinkedIn post that went viral in May showcased this clearly. It showed a SaaS startup’s marketing stack structured entirely by capabilities, content production, lead qualification, outbound sequencing, not by tool type or vendor. Each piece of the system was defined by what it enabled, not by what category it fell into. The result was a team that could focus, scale, and iterate faster.
MartechTribe’s analysis of over 1,600 stacks globally confirmed this. Every company’s configuration looks different, shaped by its market, strategy, and size. But the consistent trend was clear: top-performing stacks were built around jobs-to-be-done, not tech silos.
If you want agility, configure to purpose. If you want alignment, remove anything that doesn’t support a defined function. Tool-centric thinking is rigid. Capability-driven structure keeps you adaptive and competitive. That’s what top leaders are opting for now.
Concluding thoughts
If you’re still managing your martech stack by listing out platforms and counting tools, you’re not leading it, you’re inventorying it. That won’t keep up in today’s shifting, complex market. What gives you leverage now isn’t how much software you’ve deployed. It’s how precisely your capabilities are aligned to what your business needs to achieve.
SaaS gives you structure. AI gives you adaptability. Human talent connects the two. The companies that understand how to manage these elements as a unified system are the ones pulling ahead. They run leaner stacks, make faster decisions, and shift gears without burning cycles.
The question isn’t “What tools should we buy next?” It’s “What are we trying to get done, and what mix of capabilities gets us there efficiently?” That’s what drives margins. That’s what supports scale. That’s how you stay competitive.
The smart teams are building for adaptability, not accumulation. If your system isn’t built to evolve, it’s built to lose ground. You don’t need more software. You need more alignment.
