Talk Is Cheap, Unless You’re Using It Right: Where LLMs Matter Most for Enterprises

The transformative potential of generative AI, particularly large language models (LLMs), is widely understood at this point. What’s less clear for many C-level and operations leaders is where to begin.

Should LLMs support sales teams with proposal generation? Automate routine HR communications? Summarize legal contracts? Assist finance with reporting and forecasting?

Each of these is a viable direction. But starting in the wrong place can slow momentum, drain resources, and result in limited (or invisible) impact.

This post is designed to help business leaders think through how to prioritize LLM adoption across the enterprise, with an eye toward both short-term wins and long-term scalability.

What Makes an Area Ideal for Initial LLM Deployment?

Before selecting a starting point, it’s important to define what good looks like. Through our work across enterprises, we’ve seen five criteria that consistently predict successful outcomes:

1. Language-Dense Workflows

The task should involve a significant amount of natural language, whether spoken or written. This is where context, nuance, and variability are important and where LLMs thrive.

2. Operational Complexity with Human Touchpoints

Look for areas with both repeatable patterns and high variability, often involving human judgment, decision support, or empathy. LLMs are not workflow or RPA systems. There is an element of decision-making where the system uses judgment to take action. If you're only updating a simple workflow, then you’re missing the point.

3. Clear Business Metrics

Choose functions with well-established KPIs, such as handle time, task completion, satisfaction scores, or conversion rates, to ensure AI impact is measurable.

4. High Volume, Moderate Risk

Target workflows that occur frequently enough to justify investment, but don’t involve high emotions or sensitive decisions (at least to start).

5. Opportunity for Hybrid Workforces

Finally, look for areas where AI can work with humans, not replace them, by offloading routine tasks and augmenting decision-making.

Surveying the Enterprise Landscape

Based on these criteria, several functions across the enterprise stand out as LLM-ready:

(Subtext to table) Readiness score considers complexity, scale, and ability to drive measurable value.

While each function has valid use cases, customer support stands out as the most compelling place to start, not necessarily because it’s the easiest, but because it aligns most closely with high-value outcomes.

Why Customer Operations Offers the Most Strategic ROI

Customer support and the broader contact center have long been viewed as a cost center. But in the LLM era, that perspective is shifting. The reason is simple: it’s where AI can most visibly enhance the collaboration between humans and machines to serve customers and drive business growth.

Here’s why it’s the right proving ground for generative AI:

1. Massive Volumes of Natural Language Data

Contact centers generate an enormous amount of unstructured data through voice, chat, and email. This volume is what LLMs are designed for: recognizing patterns, understanding intent, and dynamically adapting responses.

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2. Clear, Measurable Outcomes

Every interaction is tied to tangible metrics:

Containment rate
Average handle time
Customer satisfaction
First call resolution
Agent QA scores

LLMs can be evaluated and improved based on these performance benchmarks.

3. Opportunity to Optimize Human Time

Much of the typical support workload is repetitive:

Identity verification
Information retrieval
Explaining known policies
Writing post-call summaries

LLMs can handle these tasks, freeing human agents to focus on empathetic, high-impact interactions.

4. Creating a Hybrid Workforce

This isn’t about replacing humans. It’s about deploying AI agents alongside human agents to create a more efficient, responsive, and resilient operation.

The future of customer operations will likely be hybrid by default:

LLM-powered agents contain routine calls
Human agents handle edge cases and emotional complexity
Both are trained, coached, and measured using AI

5. Better Experience, Not Just Lower Costs

The real value isn’t just in deflection or savings. It’s in building a support system that is:

Always available
Responsive to policy and product changes
Consistently on-brand
A feedback loop for the entire business

AI agents trained on actual call data and fine-tuned to QA frameworks can even identify gaps in operations, policy, and product, turning the contact center into a driver of continuous improvement.

Final Thoughts: Start Where You Can Show Strategic Value

Generative AI is not a race to deploy the most models. It’s a strategic opportunity to reimagine how work gets done across the enterprise.

For CIOs and operations leaders, the most important question to ask isn’t where can we use LLMs? It’s where will it matter most?

Customer support delivers on that promise. It’s where language is rich, metrics are clear, impact is broad, and the combination of AI and human talent can create something better than either could achieve alone.

The companies that lead with a thoughtful, hybrid AI strategy in customer operations will not only reduce costs; they’ll build better experiences, unlock new insights, and create a modern workforce equipped to scale.