Building Your AI GTM Stack
A Guide for Modern Revenue Leaders
📸 Picture this: You're a revenue leader at a SaaS company that just hit $10M ARR. Your CEO wants to triple revenue next year.
Your sales team is drowning in manual tasks.
Marketing can't properly score leads.
Customer Success is missing expansion opportunities.
Oh, and there are 15 new AI tools in your inbox promising to solve everything.
Welcome to the modern GTM tech stack challenge.
Over the last decade, B2B sales and marketing tech has exploded. What used to be a handful of fields in Salesforce has now become an ecosystem of thousands of tools promising to help you sell better, market smarter, and grow faster. Those tools can be your team’s secret weapon if wielded correctly, or drown you in shiny object syndrome and stack bloat.
Take a VP of Sales we spoke with last week who invested $50,000 into an AI SDR that was supposed to solve all her pipeline problems. Nearly six months later, the AI SDR has garnered … 5 responses.
Or a CRO who has 14+ different tools across his GTM stack, yet is still manually updating spreadsheets to track her most important metrics. Meanwhile, Gong just released Engage, HubSpot launched Breeze, and her CEO is asking about ChatGPT.
They’re not alone - every revenue leader we talk to feels overwhelmed by the pace of change in GTM tech right now. And the challenge isn't just picking tools - it's building a coherent system that actually drives results.
So how should modern revenue leaders approach building their GTM tech stack in 2025? Let's start with the fundamentals.
Start with Problems, Not Products
Last year, we spoke to hundreds of revenue leaders looking to upgrade their revenue stack. Over half of those conversations started with some version of:
"Leadership has tasked us with figuring out how to implement AI in the organization, so we're exploring all the hottest tools."
This is, of course, backwards.
You should approach your GTM stack like an engineering problem, not a shopping spree. That means diagnosing your problems & opportunities before working your way into what products you could consider adding to your tech stack.
This is true for all GTM stacks but becomes even more important in the era of AI. AI tools amplify their inputs. That’s great news if your inputs are perfect. And not so great if they’re garbage. Garbage in, garbage out at scale will be your eternal nightmare if you don’t first focus on building your foundations.
What are those foundations?
We’ve developed a framework to help you assess your current stack and guide your AI GTM Stack research: CRISP.
Breaking down a CRISP AI GTM Stack
When you strip away all the AI buzzwords and feature bloat, the best modern GTM tech stacks all solve the same core problem: good growth ideas die without the right data and the ability to act quickly on it.
To assess your ability to execute against this mission, break down your gtm stack using the CRISP framework:
Collect better data
Reach more prospects effectively
Integrate data across your tech stack
Store data in a clean, organized way
Personalize communication more deeply
We highly recommend creating a CRISP diagram (or spreadsheet) for your current tech stack. Map the tools that you currently use to each of the areas, and break them apart by the GTM function. From there you can more easily identify holes or potential areas for improvement in your CRISP stack.
Where do your teams spend the most manual time across CRISP?
What data do you wish you could collect but don’t?
How do you wish you were better reaching new or existing customers?
Where do your current integration tools have room to improve or further automate?
How can you improve accuracy or accessibility from your data storage system?
Where could you increase personalization or relevancy to new or existing customers?
Once you’ve broken your stack into this framework, you can use it on a go forward basis to assess new tools - where into the CRISP framework does the tool fall, and is it solving a problem you’ve already identified in that tool?
There’s a lot to consider in each tool and element of the CRISP framework - we’ve diagrammed a few examples for outbound, inbound, and CX in this interactive graphic here:
📊 Click to View Outbound, Inbound, and CX CRISP Diagrams
Level Up CRISP with Stack Flexibility
Once you’ve mapped your existing tech stack to CRISP, you’ll see both problems & opportunities in areas of CRISP where you might be missing tools.
But there are still way too many tools to choose from within each part of CRISP.
Just look at this AI market map - it’s incomprehensible:
How do you maximize capability within each part of CRISP while still maintaining lean, effective systems?
The answer is stack flexibility. Austin Hay has an excellent piece with Kyle Poyar that further dives into the importance. We recommend evaluating flexibility across three dimensions:
1. Scalability (Volume & headcount)
2. Integration Capability (How it plays with other tools)
3. Customizability (Can you mold it to your systems or do you mold your systems to it)
Let's make these tangible:
1. Scalability
Founder scenario: Imagine you've built your first GTM stack on a budget using Hubspot's startup program. By year two, you hit 5,000 customers and suddenly discover Hubspot's API rate limits are causing critical data syncs to fail. The painful migration to Salesforce costs your team 3 months of productivity.
Mid-market scenario: When your team grows from 5 to 25 SDRs in six months, your outreach tool's "per-seat" pricing suddenly triples your CAC. A savvy RevOps leader would negotiate a usage-based contract that scales more efficiently with actual results, not just headcount.
What to look for:
Performance at Scale: That email automation tool works great for 500 contacts, but what happens at 50,000? Ask vendors about their largest customers and performance limitations.
Modular Expansion: Your first sales hire needs basic CRM functionality. Your tenth needs territory planning. Choose platforms that let you add modules as you grow without having to start over.
Cost Efficiency at Scale: Be wary of per-seat pricing that punishes growth. Consider negotiating deals with your vendors based on revenue impact or usage, not user count.
2. Integration Capability
Founder scenario: Consider a technical founder who builds their company's first sales motion around a custom-coded integration between Calendly and Copper CRM. When they need to switch CRMs a year later, the brittle integration breaks completely, forcing a rebuild from scratch.
Mid-market scenario: Imagine adding a new customer success platform only to discover your existing enrichment tool can't push data into it. Your RevOps team would be stuck manually exporting/importing data weekly for months until contract renewal.
What to look for:
Open APIs and Webhooks: A strategic RevOps Director wouldn't purchase any tool without reviewing its API documentation first—saving countless headaches down the line.
Pre-built Integrations: When choosing between similar tools, prioritize the one with native integrations to both your current systems and ones you might adopt in the future.
Data Consistency: Without standardized data models across systems, you might discover your MAP and CRM have different definitions for "qualified lead," causing months of incorrect reporting.
3. Customizability
Founder scenario: A founder architects their CRM around a standard B2B sales process, only to realize the product-led motion doesn't fit these stages at all. Six months later, they have no reliable conversion data and must rebuild their entire pipeline structure while training the team on new processes.
Mid-market scenario: After investing heavily in a marketing platform, a mid-market company discovers it can't accommodate their industry-specific compliance requirements. With no ability to add custom fields for regulatory tracking, they're forced into maintaining a separate compliance system that creates data silos and doubles reporting work.
What to look for:
Configurable Workflows: Assess whether tools allow you to build custom automation rules that match your specific business logic rather than forcing you to adapt to their pre-built workflows.
Custom Fields and Objects: Verify that platforms support adding unique data points and relationships specific to your business without requiring developer resources or expensive customization.
UI Flexibility: Evaluate whether interfaces can be tailored for different user roles to increase adoption and reduce training time as your team grows and specializes.
Tips for Building Flexible CRISP AI GTM Stacks from Startup to IPO
Theory is nice, but what would this look like practically? Here are a couple of tips and suggestions for revenue leaders at various stages building their AI GTM stack:
Seed-Stage Startups (Pre-$1M ARR)
Scenario: You've raised a seed round and your GTM is almost entirely founder led-sales, although you’re getting close to product-market-fit and are considering your first sales or marketing hire.
Start with a 30-day stack audit
Don't immediately buy that $50K AI sales suite. Instead, track exactly how your small team spends their time for a month and then surgically identify tools that solve the biggest bottlenecks. You might discover your reps spend 70% of their time just researching prospects—an immediate opportunity for targeted tooling.Test manually before automating
Before investing in a complex email sequence tool, have your SDRs manually send 100 personalized emails using templates. The goal is to know what actually works before codifying it into an automated system.Choose a foundation that fits your motion
If you're product-led, don't default to Salesforce because "that's what big companies use." A simpler CRM with usage analytics might be better. If you're sales-led with high-touch demos, prioritize scheduling and demo tools over marketing automation.Build for your next 12 months, not your 5-year vision
When evaluating that expensive sales intelligence platform, ask: "Do we need this feature now, or when we have 10+ reps?" Choose solutions with monthly contracts that let you experiment without overcommitting.Set aside 20% of your tool budget for integration
The classic startup mistake: buying four tools that don't talk to each other. Allocate specific budget for either integration platforms or developer time to connect systems.
Series A/B Companies ($2-20M ARR)
Scenario: You've found product-market fit, have 10+ sales reps, and are scaling rapidly. Your homegrown processes are starting to break.
Conduct a tool usage audit
You're probably paying for 10+ sales and marketing tools, but how many are actually being used? Survey your team and you might find $100K+ in annual savings from redundant or abandoned tools. Even better, looks for amazing tools that can become the “glue” between your systems.Map your current vs. ideal data flow
Create a visual map of how customer data should flow through your systems versus the reality before evaluating new tools. This often reveals critical integration gaps causing data quality issues that no new AI tool can solve or manual steps that are ripe for automation with existing or new tools.Assess the true value of high quality integration
Is your ops team spending 20+ hours weekly manually transferring data between systems? That hidden labor cost (potentially $50K+ annually) easily justifies investment in proper integration. Be upfront and honest with your self and your team about the value from investing in this integration from the get go.Implement a quarterly tech review
As you grow, different departments will purchase their own tools. Establish a quarterly review where each tool owner must demonstrate continued ROI and streamlined processes to prevent sprawl.
Growth-Stage Companies ($20M+ ARR)
Scenario: You have 50+ sales reps across multiple segments, a complex multi-channel marketing operation, and are expanding internationally.
Create a dedicated RevOps function
At this scale, you need dedicated specialists who sit between IT (who care about security) and revenue teams (who care about usability). This team should own your GTM stack architecture.Understand the costs inherent in your current system Calculate the actual cost of your current system, including manual labor. For example, if you’re a Director of Sales Ops who discovers your team spends 200+ hours monthly maintaining data between systems—you then know exactly what metric to tie potential ROI for a tool evaluation to, (and cost cutting is always easiest to start with).
Standardize your data definitions
You'd be shocked how many $30M+ companies have sales and marketing teams defining "qualified lead" differently. Create a company-wide data dictionary that standardizes definitions across all systems.Build with security and compliance in mind
As you expand, regional regulations like GDPR become critical. Include legal/compliance requirements in all tool evaluations to avoid expensive mistakes later.Balance standardization with team autonomy
One-size-fits-all rarely works at scale. Your enterprise team might need different tools than your SMB team. Create a core stack everyone uses, with approved "add-ons" for specialized needs.Plan for regular re-evaluation
The tool that was perfect at $10M ARR might be holding you back at $50M. Schedule major stack reviews every 18-24 months, with a willingness to make significant changes if needed.
Every Stage: General Tactics & Tips
Whatever your stage, these overall tactics & tips will help you get more value from your GTM stack investments:
Start with a pilot team
Never roll out new tools to your entire revenue organization at once. Select a small, enthusiastic team for initial implementation, gather feedback, and refine before wider deployment.Document SOPs immediately
For every new tool, create Standard Operating Procedures documenting exactly how it should be used. This prevents "tool drift" where different team members develop incompatible workflows.Prioritize adoption over features
When evaluating new tools, remember that a solution with 80% of the features but a much cleaner UI will deliver better results. The most feature-rich tool is worthless if your team won't use it.Measure baseline metrics before implementation
How can you know if that new AI tool actually improved performance without a baseline? Measure key metrics before implementation to enable accurate ROI calculation.Build a feedback loop
Create a regular process for gathering user feedback on tools. The people using them daily often have the best insights on improvements or alternatives.Develop internal champions
Identify and nurture "power users" for each major tool. These champions become internal trainers and advocates, dramatically improving adoption rates and ensuring you’re getting the most out of your tools
The Road Ahead: 2025 GTM Tech Trends
As you plan your GTM tech investments for the coming year, keep these emerging trends in mind:
1. The Rise of the Unified Revenue Platform
The fragmented sales tech stack is converging. Legacy players like Salesforce & HubSpot are rapidly expanding to offer end-to-end solutions. New startups like Clay (hi!) are emerging as the “glue” to help augment your scattered tech stack. This trend means:
Less Tool Management: You may still have the same access or functional tools operating underneath the hood - but you can consolidate the core tools each team member needs to learn dramatically.
Fragmented vs. Unified Systems: The trade-off between best-in-class point solutions vs. unified platforms will grow even more dramatic. Avoid fragmentation that traps GTM value in silos.
2. Data Quality is Table Stakes
As AI tools proliferate, their effectiveness depends entirely on your data quality. Companies that treat data as a strategic asset will outperform:
Build a Concrete Data Foundation: Remember that good growth ideas die without the right data, and that AI amplifies its inputs (no garbage!). Data will become increasingly commodified as the value chain moves from having data to actioning data.
The Future is Providers + AI Scraping: Data providers aren’t going anywhere for specialized datasets - but combining the power of all the data providers + custom AI scraping will separate the winners from the losers in GTM.
3. Human Value is more Important than Ever
Despite AI advances, human judgment, creativity, and relationship-building remain irreplaceable in certain segments of the GTM value chain: :
Invest in Uniquely Human Activities: Instead of data tasks - the best reps will engage more on social, write thought leadership content, host IRL events, and continue to engage on a level only humans can.
Retrain or Rehire Accordingly: The landscape is dramatically shifting, which means your workforce has to too. If you’re investing in new, uniquely human capabilities that you couldn’t invest in before - you’ll have to retrain or rehire – and that takes time.
Good luck building your AI GTM stack. The right tools won't magically solve all your revenue challenges, but the wrong ones will certainly hold you back. Choose wisely.