The Real AI Maturity Curve: From Insights to Revenue Impact

Hardly a week passes without seeing a bold headline about AI transforming business. Yet when you look inside most enterprises, the story feels different. The gap between what leaders promise and what teams actually deliver has never been wider.

According to Momentum’s recent Voice of the Market and AI Maturity Report, which analyzed more than 1,000 active B2B sales opportunities across 150 industries, only 7.6% of companies have successfully operationalized AI. Another 64% say they’re stuck in the “emerging” stage of maturity, still experimenting or running isolated pilots, and about 31% still handle core processes manually.

This pattern mirrors what broader research shows. McKinsey’s State of AI report reveals that most companies report using some form of AI, but only a small share can point to measurable gains in efficiency or revenue. The reason runs deeper than technology. It’s that most teams aren’t ready for AI, and the systems meant to support it aren’t either.

What’s Really Holding AI Back

When data is scattered across too many tools and systems, speed and accuracy both suffer. The report also found that once teams use four or more tools to address a single workflow, interest in adding new technology drops by 73%.

What looks like skepticism toward AI is often tool fatigue and the resulting fragmentation slows everything down. Tool overload creates another challenge regarding incomplete visibility. When every department uses a different system to solve the same problem, it’s impossible to agree on a single source of truth.

The disconnect shows up most clearly in revenue operations, where CRM data, marketing automation platforms, and forecasting tools often tell competing stories. Momentum’s report also found that CRM challenges sit at the center of these struggles, with nearly one in three pain points tied to weaknesses in core systems. Teams operating in the early stages of AI readiness often lack the connected architecture needed to scale beyond pilots, limiting their ability to turn insight into reliable execution. True progress requires more than clean data. More importantly, most organizations haven’t identified the operational systems and processes that AI depends on. When those foundations are unclear, even the best data struggles to translate into consistent action. This results in teams spending more time reconciling data than acting on it, and million-dollar decisions still relying on unstructured, incomplete information.

Also Read: AiThority Interview Featuring: Pranav Nambiar, Senior Vice President of AI/ML and PaaS at DigitalOcean

The Pilot Trap

Every enterprise leader knows the pattern. A small team runs a pilot, sees promising results, and then struggles to scale it. Part of the challenge is that many AI tools still rely on human prompting or manual input to deliver results. That human dependency slows real transformation, since adoption depends on user behavior rather than automated execution. Without clean data, consistent governance, and an execution layer that connects intelligence to action, even the most advanced AI system gets stuck in neutral.

Most of today’s AI pilots live in isolation, disconnected from the systems that drive daily decisions. When that happens, the experiment becomes another dashboard, which might be interesting to look at, but not particularly helpful when it comes to execution.

Organizations that make progress share one trait and it’s that they leverage a connected architecture that enables intelligence to flow directly into execution. For example, when a forecast is updated, pricing is adjusted automatically. Or if a model flags churn risk, customer teams are empowered to take immediate action in the CRM. That’s how insight becomes action.

Forward-looking companies are investing in what can be called “execution-first intelligence,” a connected approach that links data, systems, and human action. This kind of AI-driven intelligence goes beyond insight and moves naturally into measurable outcomes.

AI-Driven Revenue Data Orchestration: From Signals to Action

Sales teams generate enormous amounts of data and insights, and the real advantage comes when those signals are connected and turned into coordinated action. Revenue data orchestration connects buyer intent, pipeline health, and forecast accuracy into a single system, transforming raw data into operational decisions. Instead of pulling reports from multiple tools, AI Revenue Orchestration unifies data from CRM, email, and customer engagement systems into a single workflow that drives real outcomes.

This provides teams with clearer visibility, enabling them to act faster and with greater confidence. The ROI can be seen in faster response times, cleaner handoffs, and deals that move through the funnel without friction.

What Leadership Often Gets Wrong

A common mistake is assuming AI adoption is purely a technology problem. It’s not. It’s an operational one. Leaders often approve AI initiatives without addressing the data quality and governance issues that determine whether those initiatives succeed. When information is incomplete or inconsistent, automation serves to magnify the problem instead of solving it.

Momentum’s report shows that among the 64% of organizations still in the early stages of AI maturity, the most frequently cited barriers are fragmented data and disconnected execution. Real enterprise AI requires three things:

1. Verified data
2. Automated business processes centered on systems like the CRM
3. Intelligence that operates without constant human prompting.

When any one of these is missing, results stall. Again, the takeaway here is that AI can’t fix broken systems on its own, but it does expose them faster.

That said, the story behind these numbers is changing. Most enterprises are done debating whether to invest in AI. The question now is how to make it work in practical, measurable ways. That shift marks progress and signals the end of the hype cycle. Real adoption depends less on adding new tools and more on connecting the ones already in place so AI intelligence can finally turn into execution.

The Shift from AI Promise to Performance

AI delivers value when it’s built into the workflows that shape revenue, efficiency, and customer experience. When companies focus on how intelligence moves through the organization, adoption starts to feel less like experimentation and more like progress. Ultimately, that’s the difference between an AI initiative and an AI operation.

For enterprise leaders, 2025 is the year to focus less on chasing new headlines and more on making AI more predictable, reliable, and measurable. When that happens, the ROI will finally match the promise.

About The Author Of This Article

Santiago Suarez Ordoñez is co-founder and CEO at Momentum