By Steve Oriola, CEO of Unbounce Go-to-Market Solutions

The AI era is forcing a reckoning for bloated tech stacks.

According to recent sales and marketing alignment research , 56% of go-to-market teams said tool bloat was becoming a serious issue, while 60% admitted they use less than half of their sales and marketing stack.

For years, go-to-market teams invested in new SaaS platforms to improve efficiency, move faster, and gain an edge over competitors. Every new tool promised to solve a specific problem – better lead routing, faster follow-up, cleaner reporting, stronger personalization, or richer customer insights.

In my experience, many organizations didn’t anticipate the shift from growth at all costs to efficiency at all costs. Now, with tighter teams and tighter budgets, organizations are being forced to pare back the tech stacks they spent years building. Many of those platforms were never fully adopted, integrated, or maintained, leaving teams with poor data hygiene and underused tools that create more friction than they solve.

AI platforms are also changing how teams interact with their tech stacks. Instead of logging into multiple disconnected tools to find answers, teams are beginning to use platforms like Claude and ChatGPT as a central interface. Through MCP servers and other integrations, AI can pull context from the tools teams already use and return answers faster.

Companies can’t treat AI like just another tool and hope it will fix an already bloated stack. For AI to deliver useful answers, the systems it relies on need to be clean, connected, and trustworthy. That means reevaluating which tools are actually being used, improving data hygiene, and identifying which platforms are built to work together.

Without that work, AI will not reduce complexity. It will simply make fragmented workflows, incomplete data, and underused tools harder to ignore.

Before organizations hit the gas on AI adoption, they need to make sure the systems underneath it are ready to support it.

Review your stack. Identify and prioritize your core systems

Every modern GTM stack should revolve around a clear system of record. For most organizations, that starts with the CRM. But the CRM’s role has evolved. It is no longer just a place to store account information or complete administrative tasks. It is increasingly the operational backbone that connects sales, marketing, customer success, finance, and leadership.

That shift matters because the data inside these systems directly affects how efficiently teams sell, market, forecast, and follow up with customers. If the CRM is treated as optional admin work, adoption suffers. If adoption suffers, the data becomes incomplete or unreliable. Once the data is unreliable, teams lose the shared view of the customer they need to make better decisions, improve handoffs, and drive revenue.

This is also where AI readiness begins. AI depends on the quality of the data it can access, and that quality starts in the core systems where customer and business information is created, updated, and maintained. When reviewing your tech stack, start by identifying which systems act as the hub of the business, then evaluate whether the tools around them actually support that hub. The goal is to build a hub-and-spoke model where core platforms centralize critical data, and surrounding tools strengthen those systems rather than fragment them. This is about building a connected, foundational stack that teams can adopt, maintain, and trust, not cutting tools for the sake of it. 

Fix your foundation. Lack of adoption leads to poor data hygiene

AI won’t fix your tech stack until you fix the mess underneath. That starts with adoption.

Today, 60% of GTM teams admit they use less than half of their sales and marketing stack. That’s not just a utilization problem. That’s a data problem. When teams do not consistently use the tools they have, the data inside those tools becomes incomplete, outdated, and unreliable.

Tool bloat rarely happens all at once. It often starts with a missing capability. A team needs better lead routing, more advanced reporting, or a faster way to personalize campaigns, so they buy a point solution to fill the gap. Later, a core platform adds a similar feature, but the original tool stays in place. Multiply that pattern across teams and years, and the stack becomes crowded with overlapping tools, inconsistent data, and unclear ownership.

This is where tool bloat compounds. As tools accumulate, ownership becomes less clear, workflows become harder to standardize, and data becomes harder to trust. Team departures and reorgs can make it even easier to lose track of the stack, leaving underused tools sitting idle and quietly auto-renewing in the background. If your existing tech stack is not optimized, AI cannot fix that on its own. It will either expose data gaps or become another layer of complexity teams struggle to get value from.

Simplification does not mean cutting everything. It means creating a foundational set of systems that teams can actually use, maintain, and build on. The goal is not fewer tools by default, but a more connected and reliable foundation for AI to build on.

Create connectivity and optimize for output

AI is making connectivity easier. MCP connectors, along with native AI features like copilots and agents, are making it simpler for systems to share context and automate work across tools.

But easier connectivity does not mean every tool should be connected. If anything, it makes it more important to be selective about which systems connect and why. Teams need to start by asking what output they are trying to improve, whether that is better lead quality, faster handoffs, cleaner reporting, stronger personalization, or more consistent follow-up.

The goal is to use AI as an intelligence layer that improves the systems and workflows teams already rely on, not treat it as another tool category. For teams in the early stages of AI adoption, embedded AI capabilities inside existing platforms may be the most natural starting point because they already sit inside established workflows. When those capabilities are connected to clean data and a clear use case, they can help surface insights, recommend next actions, automate routine work, and identify risks earlier. Without that foundation, AI can cause the same problems teams are already trying to solve, creating more silos, more complexity, and more work to manage.

This matters because misalignment is often treated as a people problem when it is really an operational one. According to Unbounce’s GTM alignment research, 53% of GTM professionals say alignment challenges stem primarily from operational issues like tools, workflows, and processes rather than culture or strategy.

Highly aligned sales and marketing teams are more effective at sharing data, and highly aligned teams are twice as likely to describe their tech stack as lean and focused. That suggests simplification, not expansion, may increasingly become a competitive advantage.

Tech stack hygiene is a revenue problem

Tool bloat is not just an operational inconvenience. It has direct consequences for revenue.

Only 29% of GTM teams rate their lead quality as excellent. Better leads depend on shared data, shared context, and shared understanding across sales and marketing. Alignment is not just another meeting, dashboard, or quarterly planning exercise. It is a shared understanding of customers, handoffs, data, and goals.

The tech stack is the foundation for that shared understanding. But only half of teams are highly confident their tech stack supports sales and marketing alignment. Highly aligned teams are 3.5 times more likely to report excellent data sharing across teams and twice as likely to report high-quality leads.

AI does not change the fundamentals. It raises the stakes. GTM teams that prioritize adoption, data hygiene, and connectivity will be better positioned to get value from AI because they will have a cleaner, more reliable foundation to build on.

The payback for years of stack expansion is coming due. AI will reward teams with clean, connected, well-adopted systems, and expose the ones still trying to automate their way around a messy foundation.

The teams that win in the AI era will no longer be the ones with the most tools. They will be the ones that have simplified their systems, strengthened adoption, and built a data foundation AI can actually rely on.