AI Marketing Stack vs. Tool Sprawl: Which One Do You Have?

Marketers have spent several years now exploring AI and adding it to many parts of their work. If you’ve built up a nice collection of tools, that’s great. But now that AI is moving from adoption to operationalization, it’s time to take a look at your stack and determine whether it’s truly an AI-enabled marketing stack… or tool sprawl in disguise.

More than 10 different AI apps now run inside over a quarter of enterprise marketing departments, according to Zapier. But 70% of those organizations say their tools have only basic connections, if any.

A useful stack gives each platform a purpose, connects systems where necessary and makes it easier for your team to get work done. Tool sprawl does almost the opposite: overlapping capabilities, disconnected data, extra subscriptions and more platforms to manage — which can actually work against the productivity goals you might have hoped to achieve by adopting the tools in the first place.

Here’s how to tell the difference between a stack and a sprawl, and how to clean up a stack that’s collected more tools than necessary.

AI Marketing Stack vs. AI Tool Sprawl

What makes a set of AI tools a stack or a sprawl isn’t about the number of tools per se. A large marketing organization could have dozens of AI-enabled platforms and still have a well-designed stack. A smaller team could have five and already be dealing with sprawl.

It comes down to how those tools fit together.

What Is an AI Marketing Stack?

An AI marketing stack is the collection of AI-enabled technology a marketing team uses across its workflows, including both dedicated AI tools and AI capabilities built into existing platforms. And because of that last part, your stack might include more AI than you realize.

AI comes prepackaged in many CRMs, analytics platforms, content tools, search software, advertising platforms and marketing automation systems. It might be scoring leads, identifying anomalies in your analytics, generating ad creative or helping your content team research a topic.

A functional stack gives those capabilities defined roles. Typically, that means:

• Each tool addresses a specific need.
• Teams know which platform to use for which task.
• Important systems can exchange the data they need.
• Capabilities complement rather than duplicate each other.
• Tools meet the organization’s security and governance requirements.
• There is a clear reason for keeping each platform.

A flawless, interconnected ecosystem is not realistic for most organizations or workflows. But an effective AI marketing stack needs to have clearly defined pieces that fit together nicely.

What Is AI Tool Sprawl?

AI tool sprawl happens when an organization accumulates more AI tools than it can effectively manage, integrate or put to use. Instead of working together as a purposeful stack, tools begin to overlap, operate in silos or add unnecessary cost and complexity.

There are several reasons that can cause tool sprawl, but a common one is simply that adoption gets ahead of strategy.

The same Zapier study found that 28% of enterprises already use 10 or more AI tools, while fewer than four in 10 large organizations route new AI apps through formal approval processes.

That can lead to:

• Multiple subscriptions offering essentially the same capabilities.
• Data spread across platforms that don’t communicate.
• Employees using different tools to accomplish the same task.
• Manual copying, exporting and uploading between systems.
• Tools purchased but barely used.
• Inconsistent outputs and reporting.
• Security, privacy and governance concerns.

Three out of four companies in Zapier’s research reported at least one negative consequence from disconnected AI, including wasted spending and increased security risks.

At that point, AI is creating new work alongside the work it’s supposed to support.

How To Tell if Your AI Stack Is Actually Tool Sprawl

The first step for determining if you’re dealing with a nice stack or creeping sprawl is to figure out what you actually have.

Tip: Don’t limit your interrogation to tools explicitly marketed as “AI platforms.” AI may already be embedded throughout your martech stack.

Start by reviewing the major areas of your marketing operation, such as:

• CRM and customer data.
• Analytics and reporting.
• Content creation and optimization.
• SEO and search visibility.
• Paid media.
• Social media.
• Email and marketing automation.
• Design and video.
• Project management and productivity.

Document both standalone AI tools and AI features within existing platforms. Then, for each one, consider the following questions.

1. What Does It Actually Do?

Get more specific than “it helps with content” or “it uses AI for analytics.”

What job does the tool perform? Does it generate first drafts? Analyze search visibility? Scores leads? Summarize customer calls?

Clear use cases make overlap much easier to see.

If four different platforms are all being used to generate marketing copy, for example, that’s an area to investigate.

2. Who Uses It?

Look at who has access, who actually uses it and how frequently. You may be paying for a platform that was enthusiastically adopted six months ago but has since fallen out of the team’s workflow.

On the other hand, you may discover team members are relying heavily on an unapproved AI tool because your official stack doesn’t meet a particular need — and both are useful findings!

3. Does It Connect to the Rest of Your Stack?

Not every AI tool needs to integrate with every other platform. But systems that depend on shared customer, campaign or performance data should be able to exchange that information relatively seamlessly and reliably.

Ask what information each AI system can access and whether that context is sufficient for what you’re asking it to do. If employees are constantly moving information between platforms manually, that’s a good sign an integration needs attention, or that the tool may not be the right fit.

4. Are You Paying for the Same Capability More Than Once?

Some overlap is inevitable. AI features are increasingly bundled into platforms marketers already pay for, which means you may have three or four ways to perform the same basic task without intentionally buying duplicate tools.

That calls for a capability comparison across your stack. A standalone AI platform may have made sense two years ago but be harder to justify now that similar functionality is included in your CRM, analytics suite or content platform.

While that doesn’t automatically mean the standalone tool should go, it does introduce an opportunity to explore it further. If you can demonstrate why it’s better, maybe it belongs in your stack. If you can’t, consider consolidating.

5. Can You Explain Why Every Tool Is There?

Here’s a quick and easy way to determine the baseline importance of a tool. For each in your stack, complete the following sentence:

We use [tool] to [specific job] because [reason it belongs in our stack].

Spoken aloud, this sentence should flow pretty easily for tools that have a clear purpose and belong in the stack. If you’re struggling to fill in one or more blanks, have a closer look at the tool to determine if it’s really necessary.

How To Clean Up AI Tool Sprawl

Once you’ve mapped your stack and determined that reigning in some sprawl could be helpful, here’s how to get started.

Consolidate Obvious Overlap First

Start with anything that’s obvious and unnecessary overlap: tools that perform substantially similar functions.

Compare their adoption, output quality, integrations, cost and unique capabilities. You may be able to standardize around one platform rather than maintaining several. Also check whether functionality you’re paying for separately is now included in software you already use.

Fix Integrations Before Buying More Tools

Sometimes the problem isn’t a missing capability, but rather missing integrations. Before adding another platform, ask whether a better integration could solve the problem.

That might mean connecting campaign data to your CRM, automating a recurring data transfer or standardizing how different systems identify customers and campaigns.

The less manual work required to move context around your stack, the more useful your existing AI capabilities become.

Establish Some Rules for Adding AI

You don’t need a six-month procurement process every time someone wants to test a new tool. But you should have a consistent way to evaluate what gets added. Save yourself from sprawl before it happens!

Before adopting something new, ask:

• What problem does this solve?
• Do we already have a tool that can solve it?
• What systems or data does it need access to?
• Who will own it?
• How will we know whether it’s working?
• What are the security, privacy or governance implications?

Review the Stack Regularly

AI marketing tech stacks — and really any tech stacks — are always a work in progress, even if they look neatly piled. But that’s especially true for tools with AI features and capabilities that evolve quickly.

Regular reviews help keep your stack well balanced. Look at adoption, cost, integrations and new capabilities, and consider retiring what no longer has a clear or compelling purpose.

Build a Better AI Stack

Tool sprawl is a predictable side effect of AI adoption. It’s just what happens when teams experiment and vendors add new AI capabilities to their platforms.

But left unchecked, tool sprawl can quickly lead to poorer information sharing, wasted budget and general confusion into what tools are approved or not.

Know what you have. Give each tool a job. Consolidate unnecessary overlap. Connect the systems that need to share data. And put a basic process in place before something new enters the stack.

You might still end up with five, 10, or 20 AI-enabled tools — but they’ll have clear purpose and value.

Note: This article was originally published on contentmarketing.ai.