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Almost every contact center says it uses AI, but usage and maturity are not the same thing. Enterprise contact centers reach a level of AI maturity where the technology actually changes outcomes at roughly double the rate of smaller businesses, and the gap is wide enough that it shows up directly in profitability, not just in tooling.
The Gap, in Numbers
The gap is not about whether a business has tried AI, it is about whether that AI usage has scaled into something that actually changes how the operation runs. Companies with more than $5 billion in revenue reach AI-scaling maturity roughly 50% of the time, compared with only 29% for companies under $100 million in revenue, according to McKinsey research from November 2025[1]. On the smaller end of that scale specifically, only 51% of US small businesses have integrated AI into customer service at all, per Talkdesk research from October 2025[2].
That maturity gap has a real payoff attached to it. AI-mature contact centers are 85% more profitable than low-maturity peers, according to Deloitte Digital[3], which means the businesses stuck on the wrong side of the adoption gap are not just behind on technology, they are leaving profitability on the table that competitors with more mature AI programs are already capturing.
Why the Gap Exists, and Why It Is Not About Access to Technology
The AI tools themselves are widely available to businesses of any size, so the gap is rarely a matter of access. It comes down to capacity: enterprise contact centers have dedicated teams, budget, and time to pilot AI tools, integrate them into existing workflows, and iterate until they work. A growing business running lean is usually solving the day’s staffing and volume problems first, and evaluating a new AI tool competes directly with that.
Labor is also the single largest lever in a contact center’s cost structure, representing up to 95% of total contact center costs according to Gartner[4], which is exactly why AI maturity translates so directly into profitability for the businesses that reach it. A business without the internal bandwidth to properly pilot and integrate AI tools is effectively leaving that lever untouched, while competitors with more resources close the gap faster.
What Closes the Gap for a Smaller Business
The fastest way for a growing business to close this gap is rarely to build an internal AI program from scratch. It is to work with an outsourcing partner that has already made the investment, has already piloted the tools against real call volume, and can bring a working AI-augmented model to a smaller program on day one rather than asking the business to build that capability internally first.
Frequently Asked Questions
Is the AI adoption gap really about company size, or is it about industry?
McKinsey’s research frames the gap primarily around revenue and resources rather than industry, with larger companies reaching AI-scaling maturity at nearly double the rate of companies under $100 million in revenue regardless of sector. Industry does affect which specific AI use cases matter most, but the underlying resource gap driving overall adoption maturity holds broadly across sectors.
If almost every contact center uses AI in some form, why does maturity matter more than usage?
Usage without maturity often means a business has adopted a point tool without integrating it into how the operation actually runs, which limits the payoff. The profitability gap Deloitte identified between AI-mature and low-maturity contact centers shows that the businesses capturing real value are the ones that have scaled AI into their workflows, not just turned a feature on.
Can a smaller business realistically close this gap on its own?
It is possible but resource-intensive, since closing the gap requires the same piloting, integration, and iteration work that gives larger companies their advantage. Many growing businesses close the gap faster by partnering with an outsourcing provider that has already built and tested an AI-augmented model against real call volume.
Does closing the AI adoption gap mean replacing agents with AI?
No. The AI adoption gap is about maturity in how AI and human agents work together, not about reducing headcount. The businesses seeing the biggest profitability gains are the ones using AI to handle repetitive volume so trained agents can focus on the interactions that actually need a person.
Ready to Skip Straight to an AI-Mature Program?
You do not have to build AI maturity from scratch to benefit from it. Redial pairs Voice AI and workflow automation with trained, in-house managed agent teams so your program starts closer to enterprise-level maturity from day one.