Here’s a puzzle that should keep every business leader awake at night: the teams with the most to gain from AI are the ones struggling most to use it effectively.

According to Section’s 2025 AI Proficiency Report, customer service teams scored dead last in AI proficiency at just 33 out of 100. Marketing teams, despite being in the perfect position to leverage AI for content creation and audience insights, barely cracked the top five with a score of 41.

This isn’t just an interesting data point, it’s a strategic blind spot that’s costing organizations millions in missed opportunities.

The Functions That Should Be Winning

Think about it logically. Customer service teams deal with repetitive inquiries, need to access information quickly, and could benefit enormously from AI-powered response suggestions and sentiment analysis. Marketing teams create content at scale, analyze audience data, and need to personalize messaging, all perfect AI use cases.

Yet these frontline, customer-facing functions are getting left behind while engineering and strategy teams race ahead with AI adoption.

The irony runs deeper when you consider industry predictions. Gartner forecasted that 80% of customer service organizations would be applying generative AI by 2025. We’re already there, and the reality is starkly different. Instead of leading the AI revolution, customer service teams are struggling to get started.

Marketing faces a similar paradox. This is a function built around creativity, data analysis, and audience understanding… areas where AI excels. Yet marketing professionals are underperforming compared to their engineering and finance colleagues when it comes to AI proficiency.

Why the Front Lines Are Falling Behind

The AI Proficiency Report reveals several factors that explain this paradox, and they all point to the same underlying issue: these teams are being systematically under-resourced for AI adoption.

Customer service and marketing teams are disproportionately staffed with individual contributors and lower-level managers – the exact groups that have the least access to AI tools and training. While 73% of C-suite executives have access to company-provided AI tools, only 44% of individual contributors do.

This creates a vicious cycle. The people who could benefit most from AI efficiency gains can’t access the tools, while the people with the most access often have the least day-to-day operational need for them.

There’s also a training gap that goes beyond simple access. When these teams do get AI training, it’s often generic rather than function-specific. A customer service representative doesn’t need to know how to use AI for financial modeling, they need to know how to use it for faster case resolution and better customer communication.

The Real Cost of This Misalignment

When customer-facing teams lag in AI adoption, the impact extends far beyond internal efficiency. These are the teams that directly shape customer experience, brand perception, and revenue generation.

Consider what happens when a marketing team can’t leverage AI effectively. They’re slower to create content, less able to personalize at scale, and missing opportunities to analyze customer sentiment and behavior patterns. Meanwhile, their competitors who have figured out AI-powered marketing are moving faster and more precisely.

In customer service, the cost is even more immediate. Every inefficient interaction, every delayed response, every missed opportunity to understand customer needs affects retention and satisfaction. When Gartner predicts massive AI adoption in customer service, they’re not just talking about efficiency, they’re talking about competitive survival.

The AI Proficiency Report shows that AI experts save more than 12 hours per week, while most employees save less than 2. For customer service teams handling dozens of interactions daily, or marketing teams producing multiple pieces of content weekly, this time differential compounds quickly.

Breaking Down the Barriers

The solution isn’t to wait for these teams to catch up organically. It requires intentional intervention and resource reallocation.

Start with access equality. If your organization provides AI tools to executives, those same tools should be available to frontline teams. Customer service representatives need access to AI writing assistants just as much as (if not more than) senior managers.

Invest in function-specific training. Generic AI training that covers every possible use case is less effective than targeted training that shows customer service teams exactly how to use AI for ticket resolution, or marketing teams how to use it for campaign development and audience analysis.

Create use case libraries tailored to these functions. Instead of abstract examples about AI capabilities, give customer service teams specific prompts for common scenarios, and show marketing teams proven workflows for content creation and data analysis.

Most importantly, measure what matters. Track AI adoption and proficiency in customer-facing roles just as rigorously as you track it in technical roles. Make AI competency part of the performance conversation for these teams.

The Competitive Opportunity

Here’s what makes this situation particularly urgent: while most organizations are making the same mistake, the ones that figure it out first will have a significant advantage.

Imagine competing against a company whose customer service team can resolve issues twice as fast because they’re effectively using AI for research and response drafting. Picture trying to keep up with a marketing team that can produce personalized content at scale because they’ve mastered AI-powered workflows.

The AI Proficiency Report shows that only 10% of workers are truly AI-proficient. In customer service and marketing, that percentage is likely even lower. This means there’s enormous room for competitive differentiation for organizations that invest in building AI capabilities in these critical functions.

Practical Steps to Bridge the Gap

If you’re ready to flip this dynamic, start with a focused pilot program. Choose one customer service process or one marketing workflow and systematically upgrade it with AI assistance.

For customer service, this might mean implementing AI-powered response suggestions, automated sentiment analysis, or intelligent case routing. For marketing, it could be AI-assisted content creation, automated audience segmentation, or real-time campaign optimization.

The key is to start with high-frequency, high-impact activities where the benefits of AI assistance are immediately obvious. Success in these areas builds confidence and creates advocates who can drive broader adoption.

Invest in champions within these teams: people who can become the local AI experts and help their colleagues learn. These don’t need to be technical experts, just people who are curious and willing to experiment.

Most importantly, connect AI adoption to the metrics these teams already care about. For customer service, that’s resolution time and customer satisfaction. For marketing, it’s engagement rates and conversion metrics. When teams see AI helping them hit their existing goals, adoption becomes much easier.

The Teams That Should Be Leading

Customer service and marketing teams interact with customers, understand pain points, and see opportunities that other functions miss. They should be at the forefront of identifying valuable AI use cases, not struggling to keep up with internal adoption.

The organizations that recognize this will create sustainable competitive advantages in customer experience and market responsiveness.

The choice is simple: continue to under-invest in AI for your most customer-critical functions, or recognize that the front lines of your business deserve the best tools available.

The data shows that most organizations are making the wrong choice. The question is whether yours will be different.

Is your customer-facing team falling behind in AI adoption? I help organizations build AI capabilities where they matter most – in the functions that directly impact customer experience and business results. Let’s connect on LinkedIn to discuss how to turn your frontline teams into AI leaders.