For most of my career, the stack was something I learned to live with.

Five-year CRM contracts, clunky workflows and bolt-on tools the IT team never wanted to approve. We worked around limitations and stitched together processes. Proving attribution was near impossible.

Then AI arrived—promising to finally simplify our lives.

But has it?

You’ll have to read on to find out 👇

You Stack Has a Context Problem

Your team is running campaigns across a dozen tools. Some overlap, some were added to solve an urgent gap and when you add AI into the mix things start to break.

This a context problem.

The stack you inherited, was designed to store information and execute tasks. CRM owns the record, marketing automation sends the emails and analytics reports on performance. Each system has a clearly defined role.

But AI has blurred those boundaries.

The stack is now expected to interpret signals, adjust messaging and guide decisions while the buyer is still in motion, but most stacks (and the one you’re living with) aren’t built for that.

The 2025 Martech Map now lists 15,000+ tools across six categories and more than half of the best marketing software available today didn’t exist twelve months ago. Teams use roughly half of what they pay for because expansion has outpaced architecture.

Scott Brinker, the Godfather of martech, calls the emerging model “systems of context”. Tools and platforms that respond to the situation a buyer is in at a given moment.

The leaders responding well are changing their focus from platforms to decisions. They remove software that doesn’t drive conversion, they’re standardizing systems of record, and treat AI as a reasoning layer across the stack.

If you’re suffering from what I call Frankstack, you likely have a context problem. Until you design around decisions (not just storage and execution), it will keep growing without becoming intelligent.

The Six Tool Rule

Many teams I work with describe duct-taping a tech stack together while struggling to demonstrate how it moves the buyer down the funnel.

In cases like this, you start to see friction everywhere. Pipeline reviews require stitching together intent data, product usage and campaign history, budget conversations surface conflicting attribution and prioritization debates stall because key intent signals live in different platforms.

Leaders are solving this problem by simplifying their tech stacks.

A common pattern is what I call the “six-tool stack”: a CRM as the source of truth, an AI engine that reasons across systems, an integration layer that moves data automatically, an intelligence layer that surfaces intent, a primary distribution channel and an attribution layer that reflects the full journey.

Each component has a defined role and information flows seamlessly between them.

Here’s how it works:

The six-tool model works because everything is connected: one system holds the record, one reasons across signals, one moves data, one surfaces intent, one drives reach and one reflects outcomes.

Rewiring Your Team for the New Stack

At some point, every marketing leader realizes their stack has changed faster than their team.

The workflows are different, decisions move faster and intent signals appear in more places, but roles, reporting lines and operating cadence still reflect the traditional model.

McKinsey research shows the stack works best when teams are structured around journeys, decisions and experimentation rather than channels or tools.

Leaders who are getting this right are centralizing data, embedding technical capability inside the team and creating cross-functional pods responsible for specific stages of the customer journey.

In the new model, marketers spend less time operating platforms and more time defining signals, designing experiments and interpreting outcomes. Technical specialists focus on integration, governance and model oversight. RevOps becomes the connective layer that tracks decisions and outcomes.

This also changes hiring priorities.

Leaders are looking for marketers who can reason across systems, are fluent in analytics, can build workflows and are comfortable working alongside AI. Career roles are shifting toward customer interaction, creative strategy and problem framing while automation and AI agents absorb repetitive production work.

Want all the details on this transition? Visit McKinsey to read the full analysis.

The Top 100 Software Platforms of 2026

Each year, G2 publishes its Best Software Companies list based on verified user reviews and market presence. The methodology combines satisfaction scores, adoption and competitive performance across categories, which means the rankings reflect what buyers are actively using (not what vendors claim is leading).

The 2026 lists show two signals marketing and growth leaders should pay attention to:

1. Platforms like Salesforce (#1), Google (#2) and Asana (#7) rank highly because they act as operating environments that connect data, content, analytics and execution in one place.
2. Turnover is accelerating. Roughly a third of companies on the latest global list are new entrants, which highlights how quickly capabilities are shifting and how short the lifecycle of a “modern stack” has become.

Want to know what software to choose this year? Look for vendors who standardize core systems, integrate broadly and treat customer signals as assets that flow seamlessly between platforms.

To see the list of winners head over to G2.

Go From AI Prompts to AI Systems

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