Artificial intelligence is no longer on the horizon; it’s in our budgets. Enterprises have poured an estimated $30 to $40 billion into generative AI, yet the promised revolution feels distant.
According to the MIT report, the State of AI in Business 2025, a staggering 95% of organizations have seen little to no measurable ROI from these investments.
The problem isn’t the technology. It’s the execution.
As writer Vincent Plana’s original analysis highlights, “The majority of enterprise AI systems fail not because they can’t generate value, but because they’re unable to learn, adapt, or integrate with the workflows they’re meant to improve.”
This execution gap is mirrored in the revenue world. A similar study, Traction Complete’s RevOps in the Age of AI: 2025 Leadership Guide, found 59% of leaders and 73% of operators cite incomplete or missing enrichment as their biggest challenges.
And while AI has the most potential to solve data challenges, its value is unlocked in stages.
Both reports point to an undeniable truth: AI doesn’t fix a broken process; it amplifies it. The hype promised a silver bullet, but the reality is that AI’s success is built on a foundation of operational rigor, clean data, and strategic orchestration.
From our vantage point at Fullcast, we see this not merely as an execution problem, but as a GTM planning problem. Your AI is only as smart as the go-to-market plan it operates within. This is where RevOps must evolve—from process managers to GTM architects.
The Great Divide: Why Most AI Initiatives End Up in ‘Pilot Purgatory’
The disconnect between AI investment and impact is stark. The MIT and Traction Complete reports illuminate three core challenges that, while appearing different at the enterprise and RevOps levels, stem from the same root cause: the absence of a unified, orchestrated GTM plan.
1. The Foundation Isn’t Data—It’s the Plan That Structures It
Both studies correctly identify data quality as a primary failure point. But clean data in a vacuum is useless. The real issue is that data lacks context because it isn’t anchored to a coherent GTM plan. You can have perfectly enriched accounts, but if your territories are unbalanced, your segmentation is unclear, and your lead routing rules are chaotic, AI will only automate the chaos faster.
As Vincent Plana observed, “RevOps is battling the micro-level challenges of AI readiness and workflow integration, while enterprise businesses struggle with macro-level challenges that determine whether AI will actually transform the business.”
The bridge between micro and macro is a sound GTM plan. It provides the structure—the territories, segments, and rules of engagement—that gives data its strategic meaning.
2. Build vs. Buy: The Hidden Value of Orchestration
Both reports found that home-grown AI solutions consistently underperform. According to MIT, internal AI builds fail about twice as often as vendor partnerships. Similarly, Traction Complete found that “not a single RevOps leader rated their in-house AI builds as ‘extremely successful.”
Why? Because successful AI isn’t about a standalone model; it’s about deep integration and orchestration. Specialized vendors succeed because they focus on embedding their solutions within existing operational workflows.
From a Fullcast perspective, this is about orchestrating your entire revenue motion around a central plan. The fastest path to value lies in adopting platforms that provide the operational backbone for your GTM strategy, allowing AI to plug into a system that is already aligned and intelligent.
3. Fragmented Tools Reflect a Fragmented Strategy
The term “pilot purgatory” from MIT’s report perfectly captures the state of enterprise AI: high activity, low transformation. Organizations run dozens of isolated pilots that produce localized wins but no cumulative business impact. This is a direct reflection of a fragmented GTM strategy, where each department optimizes for its own goals.
Vincent Plana’s analysis notes this universal pain point: “For both RevOps and enterprise AI leaders, the lesson is the same: value compounds when tools talk to each other.”
At Fullcast, we argue for taking this a step further. Tools talking to each other is integration, and a truly effective GTM motion requires orchestration, where every tool, team, and process executes against a single, unified plan. Without this central blueprint, you’re just creating more sophisticated silos.
The RevOps Mandate: 5 Steps to Build an AI-Ready GTM Plan
To escape pilot purgatory and deliver real ROI, RevOps leaders must shift their focus from chasing AI tools to building the GTM foundation that makes them effective.
Start with an Architected GTM Plan, Not Just Clean Data
Before you clean a single record, define your GTM architecture. Design your territories, define your ICP and segmentation, and establish clear rules of engagement. This plan becomes the blueprint that dictates what data matters, how it should be structured, and what “clean” actually means for your business.
Orchestrate Workflows Around Your Plan
Don’t just find AI that “lives in your workflows.” Design workflows that enforce your GTM plan. Every process, from lead routing and account scoring to territory assignment, should be a direct expression of your strategy. This ensures AI is not just another disconnected tool but an engine for executing your plan at scale.
Use AI to Automate the Plan, Not Just the Task
The promise of AI is freeing up teams for higher-value work. In RevOps, operators spend too much time on “CRM maintenance, manual reporting, and data hygiene,” according to Plana’s summary of the Traction Complete findings.
A well-designed GTM plan, powered by a platform like Fullcast, automates the core operational tasks of territory management and assignments. This allows you to deploy AI where it matters most: strategic analysis, forecasting, and optimization, not manual cleanup.
Guide Experimentation Within Your GTM Framework
Empower your operators to experiment, but provide them with a “sandbox” that is governed by your GTM plan. Encourage pilots for account scoring or churn prediction, but ensure they draw from the central plan as their source of truth. This fosters innovation without creating new data silos or rogue processes.
Measure the Impact on GTM Execution
The organizations crossing MIT’s “GenAI Divide” are those measuring ROI, not activity. For RevOps, this means moving beyond tool adoption metrics. Instead, measure the health of your GTM plan:
- Territory Balance: Are opportunities distributed equitably?
- Lead-to-Opp Conversion: How does it vary by segment and territory?
- Speed-to-Lead: How quickly are reps engaged based on routing rules?
When these metrics improve, you know your AI investments are strengthening your GTM execution, not just creating noise.
The Future is Orchestrated
Both the MIT and Traction Complete reports are a wake-up call. The future of AI isn’t about finding the smartest algorithm; it’s about building the smartest plan.
As Vincent Plana concluded, “When your data is clean, structured, and connected across systems, every insight becomes sharper, every forecast becomes more accurate, and every workflow becomes more intelligent.”
We believe the key to unlocking this potential lies one level deeper.
It starts with an intentional, orchestrated Go-to-Market plan. When your plan is the central source of truth, your data gains context, your workflows find purpose, and AI finally has a blueprint for success. This is the new mandate for RevOps: stop chasing hype and start architecting the future.
