Shrink RevOps tech stack debt and make your CRM a system of record

Introduction

The RevOps tech stack has grown faster than most teams can manage. According to Scott Brinker's annual landscape research, the martech ecosystem has now surpassed 14,000 solutions — up from roughly 150 a decade ago and nearly 10,000 just a few years back. Despite multiple waves of budget cuts and consolidation pressure, the number keeps climbing.

The economic pressure to rationalize that stack has only intensified. Marketing and sales budgets remain constrained, and leadership is scrutinizing every tool for measurable ROI. The old playbook of throwing more solutions and more headcount at a problem isn't available the way it once was.

But there is now a second and more urgent forcing function: AI. Every RevOps team is under pressure to deploy AI-powered scoring, routing, personalization, and attribution — and almost none of them have the data infrastructure to make it reliable. The reason is the same reason their stacks are bloated to begin with: too many point solutions, too much custom code, too many data fields that nobody owns, and too little consistency across systems. A fragmented RevOps tech stack doesn't just create operational friction. It makes AI outputs unpredictable and indefensible. Stack rationalization has moved from a budget exercise to a strategic priority.

MarTech's replacement survey research has consistently shown that the most replaced software in marketing organizations includes marketing automation, CRM, SEO, and email marketing tools. The leading reason companies replace these tools is better features that integrate more seamlessly with the rest of their technology stacks.

Data centralization and data capabilities are the next factor in choosing a replacement. Marketing and sales organizations don't run on stand-alone applications. They run on stacks of interconnected solutions, which makes connecting applications and sharing data essential.

The last factor listed is the ability to measure ROI — how revenue teams justify their spending decisions and make the business case for continued investment.

CRM overload

Salesforce is the CRM of choice for most companies. It's safe to say that when you think about sales and marketing automation, you think of Salesforce.

Salesforce CRM was launched more than 20 years ago, when there were just a few channels to get leads into the database. Salesforce has evolved over the years through innovation and acquisitions, adding more functionality to keep up with the dynamics of the market.

Today, the explosion of point solutions and digital touchpoints tests the limits of Salesforce's data management and processing scalability. Adding point solutions like LeanData, which shares resources with Salesforce, can choke the system.

Salesforce's sweet spot is being a system of record — a single source of truth about customers and sales numbers. Unfortunately, many companies create a complex martech stack that dumps a lot of duplicate data and garbage data into their CRMs. We know one company that burned down its Salesforce instance twice in two years. Others end up with multiple instances due to M&A, experiencing syncing issues and all sorts of delays. We call this "tech debt," and the root cause is CRM overload.

Tech debt

Applications like Salesforce, HubSpot, Eloqua, Microsoft Dynamics, and Marketo are platforms with workflow engines that can be configured to automate all kinds of tasks. Real-world implementations of these platforms are often chock-full of triggers, validation rules, calculation formulas, webhooks, segmentation rules, and data cleansing programs — many of which are too data-heavy for these platforms to handle.

System performance degrades, synchronization lags, and real-time mechanisms become not-so-real-time. Degradation often creeps into other solutions in the stack, throwing off timing and creating unpredictable outcomes.

Tech debt in marketing and sales operations means anything that impacts your systems of record and that doesn't provide value to your business process. Tech debt originates from:

• Custom functionality (both configuration and code-based)
• Third-party managed packages that compete on resources and cause CRM overload
• Third-party applications that impact your system of record data
• Unnecessary metadata (fields, pages, record types)
• Unnecessary data that doesn't drive your business process
• Misalignment between departments on how to use the fields in your system of record

You may recognize some of the symptoms:

• You have more than 1,000 custom data fields in your CRM or MAP and you don't know what 50% of them are for, how they came about, or who's using them.
• You have five or six seemingly identical data fields, like "industry," "industries," "industry_c," and "industry__c."
• You need to limit your bulk update batch size to fewer than 100 records at a time because of the many triggers, calculations, and validation rules that kick off.
• The sync time between your sales solution and marketing automation solution is longer than 30–60 minutes.
• A task that used to take seconds now takes minutes, or longer.
• Duplicate records are created "mysteriously" by unknown sources.
• You constantly try to sort out which application does what and in which order, leading you to manually orchestrate processes by adding wait steps to referee the applications within the stack.
• You have race conditions where a record's value can change unpredictably depending on which application updated it last and which campaign is triggered first.
• You do the same tasks — data cleansing and standardization — in multiple applications.
• You constantly look at your CRM audit log to try to unravel what happened to your data like it's an episode of "CSI: Marketing Ops."

It's very easy to see how quickly tech debt can get out of hand in growing organizations, and it can have a lot of negative impacts: slowing down system performance, confusing end users, and breaking new functionality.

What RevOps tech stack debt actually costs: real examples

The symptoms above can feel abstract until you see what they cost teams who have lived with them for years. Here is what tech debt in the RevOps stack looks like at companies that have measured it — and what changed when they addressed it.

Rimini Street

Rimini Street had accumulated significant tech debt over years of one-off projects, making even simple tasks complex. Their team of 23 data analysts spent the majority of their time not on strategic work, but fielding ad-hoc requests: list loading, list building, and chasing down data errors and inconsistencies across a fragmented stack. After consolidating their RevOps data work into Openprise and replacing multiple point solutions with a single platform, they:

• Saved at least 108 hours per week in manual human effort
• Reduced ops tickets by 40 per week
• Used Openprise AppFactory to create self-service apps that scaled automation across the team
• Formed a Data Governance Committee to own and evolve data quality going forward

Without Openprise, we would need to hire approximately 15 additional FTEs to manually do the work.

Clari

Clari's RevOps team was carrying a different kind of tech debt: custom, fragile Salesforce APEX code that was burdening their CRM and various point solutions that had created fragmented data silos. Sales and marketing teams couldn't make sound investment decisions because attribution models were unreliable — a direct consequence of the stack complexity underneath them. After consolidating with Openprise and replacing the APEX code:

• Initial tech consolidation resulted in $80k in cost savings while adding increased functionality and flexibility
• Three attribution models were built in just weeks, surfacing insights that had been invisible — including identifying the finance team as a key role in the buying committee
• UTM values were appended correctly and consistently to campaign lead records, eliminating a major source of attribution error

Zendesk

Zendesk's ops team had fallen into a fully reactive posture. Manual data cleaning and standardization were consuming the team's time and delaying lead routing and follow-up — the classic symptom of a RevOps tech stack where data processing responsibility is scattered across too many tools. After streamlining their stack and automating data quality through Openprise:

• $500,000+ was gained from increased team productivity
• 25% improvement in data cleansing efficiency
• 25%+ increase in marketing and sales team efficiency
• The ops team shifted from reactive firefighting to proactive program development

There's no way we could have done this without Openprise. It was like Openprise was a silver bullet.

Minimizing tech debt

Reducing technical debt is very hard, especially when it's been accumulating over many years. That's why it's important to minimize it going forward by taking these steps:

1. Do not over-rely on point solutions.

Lots of applications and managed packages can impact your data without your control. The managed packages on top of your CRM can introduce net new fields, automation, and code that you have no control over.

How to avoid: Invest in platforms that solve multiple problems and maximize internal control of the functionality.

2. Do not use too much custom code.

APEX code that can't be maintained because it was written by a consultant who is long gone is a common example of this problem.

How to avoid: Invest in platforms that offer no-code development. If you have to have custom code, make sure it's well-documented.

3. Do not put functionality above process

It's often easier to throw money at an issue, or just add a new tool, rather than dig deeper into the business process and the supporting business logic. You realize once the new tool is live that it doesn't fix your underlying business process issue — and you're left with tech debt.

How to avoid: Understand the business process, adjust it, then identify the technology gaps.

Follow this technical debt strategy to minimize your legacy debt:

1. Inventory your tech stack and document how each solution is used and what it's capable of. Figure out who's actively using each solution. Determine the ROI you get from each technology.
2. Remove any solution not being used.
3. Remove any solution performing tasks that overlap with capabilities in other solutions.
4. Re-assess your uncluttered tech stack. Ask yourself, "Can any of these solutions be replaced with a single solution, like a data orchestration platform?"
5. Consider using a data orchestration platform like Openprise as a "data firewall" instead of plugging solutions you're evaluating — especially data enrichment services — directly into your CRM. Use the data orchestration platform to incorporate third-party data into your primary data schema instead of letting these services add hundreds of custom data fields into your CRM systems, creating clutter you'll have to remove later.
6. Once you've eliminated the excess solutions, consolidate the data you want to retain into usable fields, and then remove all the data fields these solutions added to your CRM and MAP.
7. Offload data cleansing and other data processing tasks from your Salesforce to a platform that is better at doing it. That will free up resources and let Salesforce do what it is meant to do: be a system of record.

AI is the new reason your RevOps tech stack rationalization can't wait

When this post was originally written, the business case for RevOps tech stack rationalization was primarily economic: too many tools, too much cost, too little ROI. That argument is still true. But there is now a second and more powerful one.

Every RevOps team is under executive pressure to deploy AI — for scoring, routing, personalization, attribution, and forecasting. And across the industry, the same pattern keeps emerging: teams that deploy AI on top of a fragmented, high-tech-debt RevOps stack don't get reliable AI outputs. They get confidently wrong ones.

The reason is straightforward. AI models depend on clean, consistent, complete data to produce trustworthy outputs. When that data lives across a stack of point solutions with inconsistent field definitions, duplicate records, stale enrichment data, and misaligned schemas, the AI model ingests the fragmentation. A scoring model trained on fragmented lead data ranks the wrong contacts. A routing model running on stale firmographics sends deals to the wrong reps. A personalization engine pulling job titles from a CRM with 1,000 unmaintained custom fields classifies contacts incorrectly and fires the wrong message at the wrong persona.

According to Gartner's 2025 research, 80% of AI projects never reach production. The most common root cause identified is not model quality — it's the data and system environment the models are asked to run in. That is a RevOps tech stack problem, not an AI problem.

The teams that are successfully moving AI from pilots to production are the ones that completed the stack rationalization work first. They consolidated point solutions onto platforms that provide centralized data control. They offloaded data processing from their CRM to a dedicated orchestration layer. They got their data clean, consistent, and continuously maintained before asking AI to act on it. That sequence — stack rationalization first, AI second — is what makes AI outputs reliable enough to deploy in production and defensible enough to explain to leadership.

The urgency is real. Openprise's AI Orchestration capability is built on the assumption that data quality and stack architecture are prerequisites — not follow-on projects. Context orchestration, the first of Openprise's six AI pillars, prepares high-quality, enriched data with the relevant context an AI model needs before a prompt is ever constructed. That only works if the RevOps tech stack underneath it has been rationalized to the point where the data is trustworthy. If it hasn't, AI will expose the problem faster than any audit ever would.

You can learn more about how to avoid creating and how to reduce technical debt in the RevOps data platform buyer's guide, or schedule a demo to see how Openprise functions as the data orchestration layer that keeps your RevOps tech stack clean and AI-ready.