It’s easy to get swept up in the AI hype cycle. This is especially true if you’re leading a commercial team trying to hit ambitious targets with shrinking budgets and rising expectations. New tools pop up daily, each promising to revolutionize the way you sell, serve, or strategize. And yet, many teams, while busier than ever, are still stuck.

That’s not a tech problem. It’s a systems problem.

In nearly two decades of working across sales, enablement, and commercial strategy, I’ve seen this movie before. New tools promise productivity but end up adding to the noise. Without intentional design of the tool, the system, and the process, no tool, not even AI, will save you. You need to rethink how work actually gets done.

AI is not a magic wand, but it is a very powerful lever. And if you pull that lever right, it can help with streamlining operations, reducing drag, and expanding team capacity in ways that were impossible even five years ago.

The Productivity Problem Isn’t Lack of Effort

Let’s start with the real issue, which can be a bit hard to swallow. Most commercial teams aren’t struggling because they are lazy or lack ambition. They’re struggling because they’re operating on an outdated productivity model that rewards activity over impact. They are measured on presence instead of progress, with dashboards full of vanity metrics that don’t move the business forward.

When AI is introduced, it reveals this brokenness quickly. When you automate a task and nothing meaningful changes, it tells you something: that task never mattered that much in the first place.

That’s what I’ve found time and again engaging with different organizations. Teams are overengineered for motion and under-designed for progress. They’re loaded up with top-tier tools that don’t talk to each other. They’ve built workflows that evolved out of habit rather than intention and success. And they measure KPIs that track noise and activity instead of traction.

Use AI to Shift from Output to Capacity

In From Busy to Better, I argue that true productivity isn’t just about doing more, it’s about creating the capacity to do what matters. AI helps unlock that capacity by absorbing repetitive tasks so your people can focus on strategic, creative, and emotionally intelligent work that drives revenue and retention.

Here’s what this looks like in practice:

Sales: AI tools can handle tasks like lead scoring, email drafting, follow-up scheduling, and CRM updates. This is a significant time savings, but it also results in mental bandwidth being freed up for reps to actually sell.

Customer Support: Local AI models can categorize ticket sentiment in real-time, while Retrieval-Augmented Generation (RAG) systems pull relevant support documents and FAQ for customer-facing interactions. This allows tier-1 support to be heavily AI-augmented and bringing in your team for escalations.

Enablement: AI can summarize calls, identify coaching moments, and tailor training to the rep. Instead of blanket training programs for everyone, we can personalize development paths and measure their actual impact.

The result isn’t just about faster execution. This is how AI helps teams work at their best.

A Hybrid AI Model Is the Future of Commercial Work

The smartest companies I work with aren’t just plugging ChatGPT into their workflows and calling it a day. They’re building hybrid AI architectures that combine the strength of popular LLMs with the speed and privacy of local models, enhanced by context-specific knowledge via RAG.

Here’s the structure I recommended in the past:

Local AI: Lightweight, privacy-first models (like Mistral or DistilBERT) handle high-frequency, low-complexity tasks like tagging, categorization, and scheduling. This is fast, secure, and cheap—all important aspects of AI implementation for corporations.

RAG Layer: Pulls relevant documents from internal data like CRMs, knowledge bases, and call transcripts so AI responses are grounded in your actual business context. It helps reduce hallucinations from public models and the internet.

LLMs: Reserved for high-impact, strategic outputs like crafting expansion plans, generating market insights, or developing personalized outbound campaigns. This is where the capacity of the larger models really shines.

All of this is wrapped in a unified interface, whether a web app, chatbot, or even a Slack command bar. This delivery makes it invisible to the user. They don’t need to worry about choosing the right model. The tech routes tasks to the right engine. Your people just get results.

When done right, this model can create efficiency and build trust. It helps position AI as more than just a tool.

Technology Alone Doesn’t Transform Teams

It is important to remember that tools don’t create productivity. Systems do. And systems only work when they’re built with clarity, capacity, and outcomes in mind.

AI gives us new capabilities, but without redesigning the underlying system, it may end up speeding up the chaos. That’s why every AI implementation should start with three questions:

What are we trying to achieve? Not in terms of tasks completed, but in business outcomes.

Where is human time being wasted? This is where AI can start relieving the load.

What’s the system that surrounds the work? That means rethinking enablement, workflows, measurement, and culture. Implementation is not just plugging in a bot.

The Human Edge Is Still the Differentiator

AI is actively rewriting the rules of commercial work, but the best teams will still win because of distinctly human traits. Things like strategic thinking, emotional intelligence, creativity, and trust are going to become more sought-after in the coming years.

If you want your team to succeed through the AI-revolution you need to build systems that combine the best of people and machines. You need to emphasize capacity building and redesign your processes and measurements to focus on outcomes.

This isn’t about choosing sides. It’s about designing a partnership where both can do their best work.