The Problem with Silos

Anyone who’s spent time in tech knows the frustration of working in silos. Teams build separate processes, pursue their own KPIs, and operate from disconnected systems. These functional walls often start unintentionally, built to optimize their own team’s goals, but they create misalignment across the business and a fractured experience for the customer.

That problem becomes exponentially worse when it comes to AI. Artificial Intelligence has the potential to radically improve how we engage customers across the Land, Adopt, Expand, Renew (LAER) lifecycle. To do this, teams must be aligned and working from a shared foundation of goals, data, and workflows.

 

A Personal Perspective: Real Silos Are Dangerous 

Growing up in farm country, I haven’t just worked in business silos; I’ve worked in actual silos. They are dark, dangerous places where light is cut off from the outside world and unseen danger lurks.

After harvest on a farm, grain is stored in silos to dry before sale. The drier the grain, the higher the price. After drying in the silos for a few months, it’s time to remove the grain. But removing that grain isn’t always simple. Clumps get stuck to the walls, and someone often has to crawl in and break things up—a dangerous task. Grain entrapments kill dozens of people every year. Grain dust is highly explosive. And the dark, closed space becomes a breeding ground for mold, decay, and pests.

The parallel to business silos is striking: what starts as a useful structure can quickly become hazardous. The longer it stays closed off, the more risk builds up—especially when you try to force automation into those conditions.

 

AI Doesn’t Have an Agenda—So Get Yours Straight

In many tech organizations, siloed decision-making starts with seemingly smart choices: a team implements a tool, designs a comp plan, or creates a process that helps their function perform better. But if that tool or workflow doesn’t integrate with others to reflect the full customer lifecycle, it can actually work against your broader business goals.

For example, we recently worked with one of the world’s leading enterprise software companies. They had defined a value based selling methodology and separately defined an implementation and customer success methodology. This led to a situation where their pre- and post-sale teams were working in a disconnected way. A bigger problem was that customers were not recognizing the value that they were promised during the sales process because the customer success model focused on feature adoption rather than business value. Additionally, their processes and data were disparate and didn’t tell a cohesive story about their customer journey from initial contact through expansion and renewal. 

That’s a huge problem for AI. It doesn’t operate on intuition or organizational context. It simply identifies patterns in data and optimizes for the goals it’s given. If those goals aren’t shared across teams, or if the data it’s trained on is inconsistent, AI becomes as much of a liability as an asset.

This is where organizations often benefit from support. At SOAR Performance Group, we help companies uncover where KPIs, systems, and processes are misaligned, and help them get everyone on the same page to unlock AI’s true potential using common goals and terminology to provide a consistent data set for your AI automation.

 

Data Silos Are Especially Dangerous

When most sales leaders hear “customer data,” they think about what’s in their CRM. But that’s the tip of the iceberg when considering all the customer data your organization collects.

Marketing insights live in Hubspot, including who is interacting with your organization’s content. Support issues sit in Mulesoft or Zendesk, providing insight into who’s having trouble with the software, data that informs a CSM’s responsibility for driving adoption and usage. Financial data lives in ERP systems like Oracle or SAP. Success activities reside in Gainsight, but CSM actions rarely populate into Salesforce. It’s all very important data about the customer.  And none of it connects cleanly.

These systems were built to solve specific problems, but together they create an unconnected mess of SaaS silos and a fragmented view of the customer. That fragmentation limits AI’s ability to generate meaningful insights or automate effectively. Agentic AI workflows can connect systems—but only if:

1. Data is accessible. That means no more hoarding or hiding systems with incomplete or messy data. No judgment, but no exceptions.
2. Language is aligned. Different terms for similar actions confuse AI. Tickets, tasks, CTAs—they need shared definitions.
   
3. Structure is consistent. Even after ETL (extract, transform, load) processes, data still needs to be logically connected.

 

Automating in a Silo? You Might Be Digging a Deeper Hole

As the old saying goes: “If you find yourself in a hole, stop digging.” AI, used carelessly, might make your job more efficient, but make someone else’s job more difficult.

Take this example: Customer Success wants to automate their client review function (QBR). They pull usage data, create charts, and automate the prep process. It’s efficient, but if the conversation focuses only on how the product is used and not why, the customer sees your product as a commodity, not a strategic asset. CS has made their job more efficient, but Sales now has a harder job expanding the relationship.

Or consider Support. AI chatbots can close tickets quickly, but if they don’t surface root causes or direct users to helpful education, the underlying problem persists. Customers may be technically “helped,” but they’re not satisfied. Your renewal team inherits that frustration when the contract comes to expiration.

Automation is powerful–but only when it’s built on cross-functional insight and customer-centric intent. As one of our customers told us, “We are drunk on AI. We have many different tools for different audiences and different purposes. None of them talk to each other and in many cases, we don’t have good governance processes about what to use when.”

 

3 Ways to Break the Silo Cycle and Prepare for AI

1) Audit your customer data ecosystem. Identify how many systems contain customer data, and how consistently key terms and actions are labeled. This audit will show how fragmented your customer view really is. High-quality data is foundational to AI success, so you must understand how many sources you’ll need to access to get a complete picture.

2) Align leadership on an AI strategy. Don’t let each function launch its own isolated initiative. The creation of separate, disparate workflows will lead to even greater segmentation. Your AI strategy should be driven at the executive level and reflect shared goals across sales, marketing, success, support, and finance.

3) Look beyond point solutions. Most AI use cases today are narrow, focused on small tasks or team-specific goals. With AI, you have the ability to finally see the big picture of where your customers need help and where the opportunities lie. But you’ll never see the big picture looking at the inside of your own silo.

 

Final Thought

Silos in business, like on the farm, serve a purpose—but only for a time. Left unattended, they become a risk. If your teams and systems remain isolated, AI will struggle to help you grow. But if you bring people, processes, and data together with shared goals, AI can become one of the most powerful tools in your organization.

Don’t let your silo blow up. Tear it down and build a better, connected foundation for AI—and for your customers.

At SOAR, we are helping many of our customers align on their AI strategy for go-to-market roles through our Change Velocity workshop. We are also working with many customers to build out their customer lifecycle and playbooks in an integrated way across their pre- and post-sales teams. 

To learn more, contact us.

 

This blog was written by Steve Frost, Practice Leader at SOAR.

Steve is a distinguished strategic advisor and industry thought leader with over two decades of experience driving go-to-market strategies and revenue growth for leading technology companies. Currently an independent strategic consultant based in Dallas, TX, Steve advises prominent organizations on transformative initiatives. Read more…

For more from Steve, connect with him on Linkedin.