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We’re all drowning in tech.
The average sales team uses a dozen or more tools, yet most revenue organizations are still battling the same old demons: disconnected systems, frustrating blind spots across the funnel, and vendors that simply don't talk to each other.
The result is a grim, daily reality. Reps spend more time on manual data entry than on selling. Managers get insights that are two weeks too late to matter. And leaders are stuck trying to make critical decisions with a pipeline full of bad data.
The promise of AI, instead of fixing this, has only added to the chaos. Now, leaders are not only drowning in their current stack, but they're also being pressured to chase every "shiny new tool" that hits the market.
It reads like a tech problem but lands as lost revenue. Your tech stack isn't just a collection of vendor logos—it's the foundation of your entire Go-to-Market motion. And for most companies, that foundation is cracked.
To cut through the noise, we sat down with Brian Nzau, Strategy Consultant at the GTM firm RevLogic, for our latest AI Advantage Webinar. Hosted by Momentum's own Head of GTM Growth, Jonathan "Coach K" Kvarfordt, the session broke down a practical playbook for building a unified data infrastructure that actually powers your revenue team.
Here’s the deep dive.
The "A La Carte" Trap: Why Your Tech Stack is Fighting Itself
The webinar kicked off by tackling the core issue. As Coach K put it, "one thing [RevLogic does] a lot with companies is this whole topic of tech stack." So, what's the most common mistake they see in the wild?
According to Brian, it’s the "a la carte" trap.
"People kind of look for a la carte solutions to a problem instead of really building out connected systems," Brian explained. "What that'll look like is, you know, they'll find a really great tool to do something really specific, but it doesn't integrate with anything."
A problem pops up (e.g., "we need more pipeline"), so leadership buys a point solution. Another problem appears ("our forecasting is a mess"), so they buy another.
The result is a Frankenstein's monster of a tech stack that creates more work, not less.
"Reps are stuck kind of jumping across five, six tabs to get an idea of what the full picture looks like," Brian said. This creates massive gaps and friction. "You'll get things like reps stepping on each other's toes, no clear motion forward, and just kind of revenue leakage from not having access to all of that information in one place."
This is precisely why, as Coach K pointed out, the most common question he gets is the wrong one to start with. "People ask me... 'What AI tech should I get?' And I'm like, I love the question. It's not the right one to start with."
You can't just bolt AI onto a broken foundation and expect it to work. You must first have a clear GTM plan and understand the real problems you're trying to solve.
The RevLogic Playbook: A 4-Step Framework for GTM Architecture
So, how do you fix it?
Instead of buying more tools, you need a smarter strategy. Brian and the RevLogic team—who operate as "enablement as a service" for GTM teams—don't start with solutions. They start with a diagnostic. As Brian explained, one of the first things they do in any engagement is a "tech gap analysis call" to build a 4-step framework.
1. Map Your Current State (The Real State)
First, you have to get out of the land of assumptions and find out what's actually happening on the front lines. This means mapping out:
- Who: What tools is each team (BDR, AE, CSM, Leadership) using?
- What: What are the core integrations (and lack thereof)?
- Why: What is the intended functionality of the tool, and is the team leveraging it to its full capabilities?
This step is critical because, as Brian noted, there's often a "disconnect between how long leaders think something takes versus how long it [actually] takes." You can't build a new process on an idealized, "happy path" version of your workflow. You have to map the real one, warts and all.
2. Diagnose the Gaps
Once you have the map, the friction points become painfully obvious. "Where are reps having to jump through multiple tabs or manually grab and input data in different places?" Brian asked.
This is where you identify the data silos and workflow inefficiencies.
- Where does data flow stop?
- Where does it have to be manually copied and pasted?
- Where is leadership "having to bug reps about updating the CRM" just to get a clear view of the pipeline?
Every one of these gaps is a source of revenue leakage, wasted time, and rep frustration.
3. Prioritize for Impact
You can't fix everything at once. The next, critical step is to "solve the biggest pain first" and "slowly kind of work backwards."
This is about ruthless prioritization. "We really want to understand and solve the biggest pain first," Brian emphasized. If manual CRM updates are collectively costing your AEs 50 hours a week, that's a high-impact, high-priority fix. If a minor tool has a clunky UI, that's a low priority. Start with what's bleeding the most.
4. Align & Architect the Future State
Finally, after you've mapped, diagnosed, and prioritized, you can build the future state. "The goal is we want to get to a place where your entire tech stack speaks to each other," Brian said.
This is where you connect the systems, streamline the process, and (most importantly) leverage AI to automate the manual work.
This is exactly where a tool like Momentum fits into the playbook. As Coach K explained, Momentum is designed to be the connective tissue. It captures the data from all the conversations your CRM misses (calls, emails, chats), analyzes it for critical insights (like deal risks, objections, or competitor mentions), and then automatically pushes those insights into the right systems.
This could mean automatically updating CRM fields, notifying a product team in Slack about a feature gap, or reinforcing a sales methodology (like RevLogic's) by spotting when reps follow the process. This "pulls all those strings together," as Brian said, to create one unified, automated motion instead of a dozen broken ones.
The "Unsexy Work" That AI Demands (and Uncovers)
One of the most fascinating insights from the webinar was Coach K's observation: implementing AI is the ultimate stress test for your existing processes.
"As soon as you bring AI into a company, it uncovers all of the shenanigans," he asserted. "The process is broken, the data is broken... it unveils all this crap, which is a good thing."
He shared a telling example: a $75 million company that had never written down its sales process. They were operating at a significant scale without the basic fundamental framework documented. AI, in this scenario, simply can't help you if you don't have a defined methodology. It’s like trying to navigate a new city without a map: AI might give you directions, but if you don't know your starting point or destination, it's useless.
This crucial mapping and definition process is what Kyle Norton, a past webinar guest, aptly called the "unsexy work." Nobody posts on LinkedIn about spending eight hours documenting their sales stages and exit criteria. It’s not glamorous, but it’s foundational.
Coach K likened this necessary step to "pulling back a rubber band."
"You feel like you're going backwards because it's kind of hard, but as soon as you get the process out on your map, you let go and you accelerate it so fast," he explained. “In the pull-back motion you’re thinking, are we going anywhere? Is this doing anything? What are we even doing here? Just give me the AI button and give me more pipeline: that’s what I’m after.”
Brian agreed, noting that this "unsexy work" is precisely why many teams seek out firms like RevLogic. They either "don't want to do that or they don't really have a system in place to do that internally." This foundational work, though often overlooked, is the bedrock upon which any successful AI implementation or tech stack optimization must be built. Without it, AI becomes a "spam cannon on steroids," amplifying what doesn't work, rather than enhancing what does.
Balancing Human Brilliance and AI Efficiency
This foundational work also helps define the delicate balance between human and AI capabilities. As Brian articulated, the best use cases for AI currently revolve around automating "the more manual processes," such as account research. Reps spend countless hours trying to prioritize accounts, understand financials, and build out value propositions.
"I think that's a really strong area for AI to play a role in," Brian stated, citing tools that can research accounts, prioritize them, and even build "the three whys" for initial outreach.
However, the human element remains irreplaceable for higher-order tasks. "Where reps really need to stay in the loop or take charge is really understanding more of the human side of those interactions," he emphasized. This includes:
- Understanding nuances: Who recently joined the company? What are their new initiatives, and how will that impact the larger motion?
- Crafting compelling stories: Taking data and building a "really great story for how the product fits there" is something AI still struggles with. As Brian quipped, "If you've seen the AI emails that get spit out sometimes, they're not great."
- Strategic communication: Beyond just information, humans excel at empathy, persuasion, and building rapport, the true drivers of complex deals.
The key, then, is to leverage AI to "amplify what's working" by freeing up humans for high-value, uniquely human interactions.
Future-Proofing: Are You "Monolithic" or "Composable"?
The conversation's climax centered on perhaps the most critical strategic choice a leader can make about their tech stack today. Brian introduced a powerful analogy, borrowed from web infrastructure, that perfectly frames the "old way" versus the "new way": Monolithic vs. Composable architecture.
The Monolithic Trap
A Monolithic Architecture is the traditional, all-in-one stack. Think of the legacy, incumbent CRM platforms that try to do everything. All components (the UI, the business logic, the database) are a single, unified, and tightly coupled unit.
"If you have a monolith, it's really inflexible," Brian explained. "The issue becomes if you want to change anything or if you want to update something. It's really painful to change anything when everything is built on this one, really rigid foundation."
This is the stack most companies are stuck with. When one part breaks, the entire system is at risk. When you want to add a new "best-in-class" tool, you're at the mercy of a clunky, outdated API (if one exists at all). You're locked in, and your ability to innovate moves at the vendor's pace, not yours.
The Composable Future
A Composable Architecture is the new, AI-native model. It's a modular, flexible system built from independent, interchangeable, "best-in-class" components. Think of it like building with LEGOs—each tool (your CRM, your conversation intelligence, your prospecting tool) is a separate block, and they all connect seamlessly via modern APIs.
"If you have something that's more composable, you're able to interchange different pieces of technology, you're able to test things quickly," Brian said.
This is the key to future-proofing.
- If a new, better AI tool for forecasting emerges, you can swap it in without blowing up your entire stack.
- If one component fails, it doesn't bring your entire GTM motion to a grinding halt.
- You can adapt and evolve as quickly as the market demands.
The difference is technical on paper, strategic in impact. As Brian noted, teams building composable stacks "have all of this real-time visibility, all these unified workflows... You literally have a massive leg up on the rest of your competitors just because you're able to lead rather than just react."
Final Thoughts: Enablement is the Architect
So, who owns this? The CFO? The CRO? The CIO?
According to Brian, the new owner of the GTM tech stack strategy is Enablement.
He argued that the perception of enablement as just the "people who tell us where everything is" is dangerously outdated. "Enablement really architects the rest of the GTM motion," he stated powerfully. "It is the core of any successful sales team."
Without a strong enablement engine designing the system, "reps are living in the Wild West where they're just doing whatever they need to to hit quota," and the entire organization is left confused. Enablement is the only function that sits at the intersection of process, people, and technology. They are the ones who can perform the "unsexy work," map the real-world processes, and be the architects of a modern, composable GTM engine.
Stop buying random tools and hoping for magic. The path to AI success and GTM efficiency starts with defining your process and intentionally building a composable architecture to support it. Your revenue depends on it.
So What's Next?
- Watch the Full Replay: This was just a fraction of the insight. Catch the full, in-depth conversation with Brian Nzau and Coach K on-demand here.
- Stay tuned for our next Webinars. Register here to be notified when our next episode of the AI Advantage Series is released.
- Become Composable with Momentum: Ready to unify your tech stack and automate your GTM workflows? See how Momentum acts as the "connective tissue" to pull all your tools, data, and processes into one streamlined motion. Get a demo of Momentum.