In the rush to bring artificial intelligence into go-to-market teams, it’s tempting to assume that automation alone will solve problems of efficiency, forecasting and scalability. But as this RevOps Co-op session made clear, without the right scaffolding underneath, AI amplifies disorder instead of eliminating it. Systems become brittle, workflows collapse, and instead of creating leverage, teams end up firefighting broken processes.
Panelists Charanyan Venkataraghavan, Co-Founder at RevenueHero, and Patrick Spychalski, Co-Founder at The Kiln, delivered a message was blunt: you can’t automate chaos. Durable, scalable revenue operations infrastructure is the prerequisite to unlocking any meaningful AI impact.
The Cost of Over-Automating Too Early
Many GTM organizations fall into the trap of “future-proofing” by layering in automation before their business has achieved real process maturity. The result is a complex, highly customized CRM or CPQ instance that feels impressive at first—but eventually blocks innovation, slows down execution, and creates costly reimplementation projects.
Key pitfalls of automating too soon:
- Brittle systems: When workflows depend on dozens of interconnected automations, a single schema change can break everything.
- Lost agility: New pricing models, compensation rules or sales motions become impossible to roll out quickly because the system can’t adapt.
- Wasted headcount: Instead of enabling reps, Ops teams spend their days untangling automation spaghetti and fixing sync errors.
As Chara from RevenueHero put it:
“Every time your company doubles in size, you’re essentially running a new company. Automations built for 20 employees won’t survive at 200.”
The alternative is to embrace scrappiness early on—lightweight tools, spreadsheets, simple flows—and focus on proving repeatability before investing in automation-heavy infrastructure.
For more on the complexities associated with automation, check out the blog post The Hidden Complexity Behind Meeting Automation That Nobody Talks About.
Data and Process: The True AI Prerequisites
Generative AI and predictive analytics sound powerful, but they can only ever be as good as the data and workflows feeding them. AI doesn’t magically fix misaligned processes or dirty CRM records. If your enrichment layers are inconsistent, if your pipeline stages don’t match reality, or if sellers are skipping mandatory fields, AI will generate noise instead of insight.
The building blocks of AI-ready GTM infrastructure include:
- Clean, normalized data pipelines: Lead-to-account matching, deduplication and standardized field definitions across CRM, MAP, CPQ and billing.
- Governance frameworks: Clear rules for how data is captured, updated and maintained so everyone speaks the same “data language.”
- Enrichment and orchestration layers: Third-party data services that supplement first-party CRM records with job changes, technographics, intent signals and usage metrics.
- Workflow documentation: Standard operating procedures for everything from SDR-to-AE handoffs to closed-won-to-CS transitions.
Without these fundamentals, AI ends up hallucinating—or worse, surfacing false confidence to sellers and leaders. With them, it can deliver trusted recommendations, like prioritizing ICP-fit accounts, flagging renewal risks or suggesting next-best actions.
For more on AI and Agents, check out the post Speed to lead: In the age of agents, this still is the simplest fix to more revenue.
From Point Automation to Orchestration
RevOps leaders stressed that automation is necessary—but insufficient. The real unlock is orchestration: coordinating systems, processes and people so that data flows seamlessly across the GTM engine.
What orchestration looks like in practice:
- End-to-end handoffs: SDRs, AEs and CSMs all work from the same records and context, ensuring nothing gets lost when customers move through the funnel.
- Closed-loop feedback: Sales learns from marketing, CS feeds insights back into product, and leadership gets an accurate view of revenue health.
- System interoperability: Instead of 20 point solutions with shallow integrations, orchestration layers harmonize data across the stack—creating a single, consistent narrative.
This is the only way to make AI trustworthy at scale. Orchestration ensures that when an AI model surfaces a lead score, a churn risk or a pricing recommendation, operators can trust it because the underlying systems are aligned.
For more on automation solutions for RevOps, check out Top Sales Automation Tools for Faster B2B Conversions.
Designing Infrastructure That Scales With You
Another theme of the discussion was resilience. Infrastructure must be designed with the expectation that business models, team structures and product strategies will evolve.
Best practices for resilient design:
- Audit complexity regularly: Every quarter, ask if each workflow or integration still adds value. If not, strip it out.
- Invest in documentation early: Processes should be simple enough that a new hire can understand them on day one without tribal knowledge.
- Plan for change every time you double: Org doubling is the stress test that breaks fragile systems. Bake this assumption into your roadmap.
- Avoid vendor lock-in: Over-customizing inside a single tool can make switching impossible later. Keep an eye on portability and interoperability.
By designing for adaptability, RevOps teams ensure that GTM infrastructure becomes an enabler of innovation rather than a constraint.
For more on design best practices as it relates to your tech stack, check out The GTM Tech Stack That’s Actually Driving Pipeline.
Practical Next Steps for RevOps Teams
- Run a system health check: Document your current tech stack, identify duplicate tools and flag workflows that create friction for reps.
- Map your core processes: Define exactly how leads move through the funnel, what each stage means and who owns which handoffs.
- Start small with AI pilots: Focus on low-risk, high-impact use cases like lead scoring, churn prediction or forecasting accuracy.
- Tie automation to outcomes: Every workflow should be linked to a tangible business metric—faster cycle times, better forecast accuracy, improved NRR.
- Build cross-functional buy-in: Orchestration requires collaboration. Involve sales, marketing, finance and CS in infrastructure decisions.
Check out these playbooks from RevenueHero - they’re like your B2B cheat code for more qualified meetings and pipeline.
Final Thought
AI raises the bar for RevOps. It doesn’t eliminate the fundamentals of process, data and system design—it magnifies them. Teams that master orchestration will build durable GTM engines capable of scaling AI initiatives with confidence. Teams that don’t will find themselves buried in broken automations and operational chaos.
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