Outsider Insights | What We’re Hearing from CEOs Right Now — And Why AI Keeps Coming Up

Outsider Insights

Across Chief Outsiders, we talk to hundreds of CEOs every month. In this series, we explore the trends and challenges we’re hearing from these discussions – and what you can do if you’re facing the same issues in your business.

What We’re Hearing from CEOs Right Now — And Why AI Keeps Coming Up

In our recent conversations with CEOs, a handful of friction points keep showing up across industries and business models. Here’s what we’re hearing right now:

• “Our growth isn’t repeatable.”

  Results depend on heroic effort, a single channel, or founder-led selling, and it’s getting harder to scale.

• “Our message isn’t landing.”

  Companies aren’t clearly differentiated, and teams struggle to explain value.

• “We can’t trust the numbers.”

  Conflicting dashboards create doubt about what’s really happening.

• “Sales and marketing aren’t working from the same definitions.”

  Pipeline stages, funnel attribution, and handoffs vary by team, creating friction, waste, and funnel leakage.

• “Decisions are slower than they should be.”

  Businesses have more data and more systems than ever, but less clarity, which means they get stuck in analysis and rework. 

There’s an important connection behind most of these issues: leaders don’t have ready, trusted data to make decisions. It’s not that CEOs want more data. They want clarity and they want it fast.

That’s why AI keeps coming up.

AI Can Help Make Sense of Your Data — But It Can’t Fix the Wrong Data

In the best cases, AI tools help leaders and teams get to the truth faster. They can:

• surface patterns in large datasets

• flag inconsistencies across fields and stages

• summarize unstructured inputs

• find and fix gaps or duplicates in data

• highlight outliers worth investigating

That’s real value, especially when a business doesn’t have the time (or the people) to manually spot what’s working and what isn’t.

But AI is not a substitute for a sound data foundation.

You don’t need perfect data to benefit from AI. Most companies don’t have pristine CRM records or perfectly connected systems. In fact, AI can often help improve data quality by identifying duplicates, highlighting or filling in missing fields, and showing where definitions are being used inconsistently.

But there’s a limit. If the underlying data is fundamentally wrong, poorly structured, or inconsistently defined, AI may create confusion instead of clarity.

Why AI Creates Confusion, not Clarity

This is where many organizations get surprised.

If data is inconsistent – for instance, pipeline stages are used differently across teams, customer data sits in disconnected systems, or no one agrees on what counts as pipeline - AI won’t automatically create alignment.

It may help expose those issues. It may even help clean up pieces of them. But at its core, AI augments what already exists — including the flaws.

We recently worked with a client that had more than 20 million customer records. On the surface, it looked like a gold mine for AI. But the data wasn’t consistent, and teams didn’t agree on what the numbers meant. In that environment, AI wouldn’t have solved the problem, it would have amplified it.

If a business lacks shared definitions, governance, or clean data, AI won’t necessarily create alignment. It will still produce outputs teams argue about. It just gets you there faster.

What “Good Enough” Data Looks Like for AI-Driven Clarity

The companies seeing the best results from AI aren’t necessarily the ones with the most advanced tools. They’re the ones with commercial systems and governance strong enough to support insight people can really use for decision making:

• clear definitions and ownership of key metrics

• consistent stage and field definitions and CRM usage

• alignment and trust in which metrics drive key decisions

If you’re evaluating whether your data is good enough for AI to accelerate revenue and pipeline clarity, ask:

• Who owns pipeline (and other) definitions and data quality?

• Do all revenue functions measure the funnel the same way?

• Does everyone trust our data — and how we present it?

• What decisions are we trying to make, and do we agree on what data we need to make them?

If you have solid answers, consider how AI can help you make sense of your data faster. If not, it’s time to fix your data governance first.

The Bottom Line

AI can help companies make sense of messy reality and improve speed to clarity for faster decisions. But it can’t fix wrong data or create alignment where shared definitions don’t exist.

If you want AI to be a growth lever, the work starts with the foundation that makes clarity possible and then AI helps you get to that clear reality faster.