As a marketer for over 20 years, I’ve always believed we’re the curious bunch.

We tend to be first in line to try new tools, especially in the age of AI. New platforms, new prompts, new workflows. Marketers usually raise their hands early.

This isn’t just gut feel or professional pride. Research from firms like McKinsey and platforms such as Salesforce and HubSpot consistently show that marketers are among the earliest and most active users of generative AI.

That is why a recent report from MiQ stopped me mid-scroll.

According to MiQ, even though most marketers are already using AI regularly, confidence has not kept pace:

• Only 66% are using AI on most or all projects

• Only 45% feel confident using AI to drive real efficiencies

• Just 25% actually trust AI tools

• Yet 72% still plan to increase AI usage

MiQ calls this gap between adoption and trust the AI confidence gap.

If marketers are the curious ones, the early adopters, and still do not fully trust AI, it raises an uncomfortable question. What does that say about the rest of the organization?

When AI Is Used but Not Relied On

When I switch hats from marketer to AI enabler, the issue becomes clearer.

The challenge is not AI capability. It is mentality and integration.

AI is being used, but not yet relied on.

Touched, but not fully integrated.

Present, but not natural.

In many teams, AI sits outside the real workflow. It is a tool you open when you remember, not something embedded into how work actually gets done.

That distance matters. Confidence does not form through occasional use. It forms when something becomes second nature, like the keyboard shortcut you use without thinking or the spreadsheet formula your fingers type automatically.

Without that level of integration, AI always feels optional. And optional tools rarely earn trust.

Access Does Not Equal Adoption

This is where many leaders get confused.

Teams are given access to AI tools. Licenses are paid for. Internal announcements are made. Sometimes training is even delivered.

Yet usage stays low or surface-level.

People ask AI to rewrite emails, generate social captions, or summarize documents. Helpful, yes. Transformational, no.

The deeper uses, planning, decision support, prioritization, analysis, rarely stick. Not because people are unwilling, but because the system around them has not changed.

If AI is not designed into the workflow, it remains a side activity. And side activities never feel reliable under pressure.

The Confidence Gap Is a System Problem

This is where leaders need to step back and look at the system, not the tools.

Confidence breaks when people do not know:

• When AI is safe to rely on

• Where AI fits into decision-making

• How to judge good output from bad

• What accountability looks like when AI is involved

Without shared expectations, AI feels risky. When risk is high, trust stays low.

The MiQ report maps where confidence breaks, why it happens, and what organizations are starting to do about it. One consistent theme is this: confidence grows when AI use is intentional, guided, and normalized inside real work.

Not as an experiment. Not as a bonus skill. But as part of how work flows.

What Integration Actually Looks Like

Integration does not mean more tools. It means clearer patterns.

It looks like defined moments where AI is expected to be used, for example campaign planning, audience research, or performance analysis.

It looks like shared prompt frameworks, not everyone inventing their own approach.

It looks like leaders modeling AI use openly, including when it works and when it does not.

Over time, this is how AI shifts from something you try to something you rely on.

The AI confidence gap is not a marketer problem. It is an organizational design problem.

If you want to go deeper, the full MiQ report is worth reading. It is a useful mirror for any organization serious about moving from AI curiosity to AI confidence.

At Radiant Institute, this is exactly where we focus, helping teams move beyond access and experimentation toward confident, integrated AI-enabled work.