3 Reasons Internal Enterprise AI Projects Fail—and what CIOs must do to succeed

The following is an executive summary of our April 2025 white paper available for download:
‍3 Reasons Internal Enterprise AI Projects Fail —and what CIOs must do to succeed.

Artificial Intelligence (AI) is no longer a future trend—it’s a business imperative. Across the SaaS landscape, CIOs are under pressure to deliver results with AI: to cut churn, forecast revenue, prioritize accounts, and fuel smarter, faster go-to-market decisions. 

Yet despite the investment and excitement, recent surveys reveal a troubling truth: over 70% of enterprise AI projects never make it past the pilot stage. And of the few that do, most struggle to deliver tangible business value. 

So what’s going wrong?

Based on extensive research, case studies, and lessons drawn from digital transformation leaders, three root causes consistently stand out:

‍

1. Your Data Isn’t Ready (And You're Not Alone)

One of the most consistent blockers is poor data readiness. In a recent study by HFS Research, 70% of enterprise leaders admitted they don’t know whether their data is even suitable for AI.

This isn’t surprising. In most large SaaS environments:

• Data is siloed across CRMs, billing systems, product analytics tools, and support platforms
• Definitions of key metrics like “active user,” “churn,” or “sales-qualified lead” vary between teams
• Unstructured data (like emails, notes, and chat logs) goes underutilized
• Data governance and documentation are limited or nonexistent
  

Even when there’s plenty of data, it’s often incomplete, inconsistent, or disconnected. That makes it hard to train accurate models—and nearly impossible to operationalize them at scale.

CIOs must view data readiness as a strategic capability, not a backend function. Without strong data foundations, no AI initiative—no matter how well-intentioned—can succeed.

Great AI is built on great data. Without the right foundation, even the smartest model will fail

‍

2. LLMs Are Powerful—But Not Always Practical

With the rise of Large Language Models (LLMs) like GPT, many enterprises are rushing to embed generative AI across their organizations. And while LLMs are undeniably powerful, they’re not the answer to every problem—especially in RevOps.

Most RevOps use cases—like churn prediction, lead scoring, and CLTV forecasting—depend on structured data: timestamps, numerical values, product events, and CRM fields. These aren’t tasks where generative models shine.

Even when LLMs are useful, they come with tradeoffs:

• High infrastructure and compute costs
• Risk of hallucination and misinformation
• Governance, privacy, and compliance challenges

CIOs must separate hype from reality. LLMs have a place—but building enterprise-ready systems often requires more focused, modular, and interpretable models.

Not every problem needs an LLM. But when used wisely—with the right scaffolding—LLMs can unlock new capabilities for knowledge work, automation, and decision-making.

‍

3. From Tech Project, to Product Mindset

One of the most common—and least addressed—reasons internal AI projects fail is that they are treated as traditional technology initiatives rather than as long-term, cross-functional product development efforts.

Traditional IT projects tend to have well-defined goals: digitize a process, modernize infrastructure, build a dashboard. AI, however, doesn’t fit that mold. It’s not about implementing known solutions—it’s about discovering new ones.

Internal AI tools require deep integration into workflows, thoughtful user experience design, long-term support plan, and iterative refinement based on real-world feedback. This is the essence of product development—not IT delivery.

Successful AI systems require:

• Long-term ownership and accountability
• A clear product roadmap
• Embedded user feedback loops
• Continuous monitoring and retraining

In other words, they need to be treated like products, not side experiments.

CIOs must recognize that predictive AI initiatives live or die based on how well they are productized. Productization is what connects technical capability to business value. 

Unfortunately, many enterprises still run AI projects like ad hoc pilots. There’s no product manager. No go-to-market integration. No long-term support plan. As a result, even models that perform well in testing don’t get adopted.

Without a product mindset, your AI model is just an unused file on a server; With it, your model becomes a living part of how the business runs.

‍

RevOps: AI’s Most Undervalued Opportunity

If there’s one domain where AI has the clearest path to business impact, it’s Revenue Operations. 

RevOps use cases—like churn prediction, CLTV, and account prioritization—are ideal for AI. They’re data-rich, high-frequency, and directly tied to revenue. Yet RevOps AI still fails more often than it should. Why?

Here too our research shows that the most common reasons for failure have little to do with model performance:

Because the outputs don’t reach the people who need them. Data scientists produce insights, but those insights live in dashboards—far from the salespeople, marketers, and CS reps making day-to-day decisions.

Here’s how to change that:

• Embed predictions into GTM tools (e.g., Salesforce, HubSpot, Gainsight)
• Use plain-language scoring and labeling (e.g., “High Churn Risk” vs. a 0.72 probability)
• Provide visibility into why a prediction was made—so users trust it
• Create feedback loops to improve model accuracy and usability over time

AI works in RevOps when it’s part of the workflow—not just an overlay.

RevOps is where AI can stop being an experiment—and start becoming embedded intelligence that drives revenue, retention, and growth.

‍

Why CIOs Are Buying AI Instead of Building It

Given all of these challenges, it’s no surprise that many CIOs are rethinking their approach. Instead of building internal AI solutions from scratch, they’re turning to specialized platforms designed to solve common problems like churn, scoring, and forecasting.

Why?

• Speed to value: Prebuilt models and integrations shorten time to impact
• Embedded best practices: Vendors bring knowledge from dozens of implementations
• Lifecycle support: Monitoring, retraining, and updates are built in
• Lower risk: Proven ROI reduces the chance of failed projects

This doesn’t mean “never build.” But it does mean CIOs should buy when the use case is common and the need is urgent—and reserve internal development for problems that are truly unique or proprietary.

‍

Final Thought

The companies that win with AI aren’t necessarily the ones with the most data or the biggest budgets. They’re the ones that treat AI not as a tool to be implemented, but as a product to be discovered and developed.

As a CIO, your leadership in framing internal AI initiatives as product ventures—not just technical ones—may be the single most important factor in whether your organization captures real value from AI.

In the end, AI success is not about building smarter models. It’s about building smarter organizations.