Hi there,

I've recently worked with a small (<500 people) call center company that had the ambitious vision to transform their business with AI-Powered Voicebots (of course).

But instead of rushing straight into a big AI project (and burn a lot of cash doing so), this company found a different path. One that's already delivering results while much larger competitors are still struggling with basic implementation.

Today, I'll show you their approach and why it works not just for call centers, but for any company that wants to actually profit from AI rather than just experiment with it.

Let's dive in!

The Reality of AI Implementation

A lot of companies these days face a tough choice between two extremes:

First, they could bet big on transformative projects that might pay off huge (like voice AI bots that might replace human customer service agents) ... or might drain resources for months or even years before showing any return. The sunk cost fallacy is real.

Or, second, they could play it safe with small, disconnected AI tools that deliver quick wins but don't build toward anything bigger. These disconnected "point solutions" might solve immediate problems but don't build on each other. You could save some costs here and there, but you're not really changing the game.

Neither path is entirely wrong - but I believe there's a smarter way that gets you better results, faster.

Orchestration and Sequencing

What made our call center's approach different was effectively combining two principles: Orchestration and Sequencing.

Orchestration means making sure your AI initiatives build on each other instead of existing in isolation. Each project not only delivers immediate value but creates capabilities you'll need later. Think of it as compound interest for your AI investments.

Sequencing is about breaking down ambitious visions into manageable steps that follow a logical order. It means ordering these projects strategically. Instead of trying to leap straight to the end goal, you create a roadmap where each step makes the next one easier and more valuable.

Together, these principles let you establish an actionable AI Roadmap with clear Profit Checkpoints. Each phase has to prove its worth before you move to the next one.

Let me show you how this worked for our call center company.

How This Looks In Practice

Here's how the call center turned their AI ambitions into a profitable reality:

(Keep in mind that while I'm sharing their journey as an example, your sequence might look completely different. The key is following the principles of orchestration and sequencing while adapting them to your specific context, technical maturity, and business goals.)

Step 1: Internal Chatbot (Completed 3 months ago) 
After we got management alignment that starting with an Agentic Voice AI bot might not be the best idea (which took a while), we made a plan that focused on starting small but strategic. The goal was to implement an internal chatbot to help with non-core business tasks like meeting notes and employee onboarding. The efficiency aspect was nice, but that wasn't the core goal. It was about getting people comfortable with AI technology and giving the right people the right amount of exposure to tell AI truth from AI snake oil.

• Profit Checkpoint: Save each employee at least 1 hour per week on knowledge lookup and documentation and overall drive higher employee satisfaction.

Step 2: In-Call Augmentation (Next 3 months) 
Now that the team understands better what AI can (and can't) do, they're developing tools that make their agents more productive. The focus lies on a simple call augmentation solution that helps agents find information faster during customer calls, leading to shorter handle times without sacrificing quality. Instead of trying to replace human agents, they're focusing on making the ones they have more productive.

• Profit Checkpoint: We're aiming for 20% faster resolution times due to faster lookups. If first-call resolution rate goes up, that would be nice, too. These metrics directly impact profitability by allowing agents to handle more calls.

Step 3: External Testing (6+ months) 
This is where the compounding benefits of orchestration kick in. The goal is to launch a text-based chatbot on their website that builds on everything they've learned about customer interactions and knowledge management. But unlike companies that rush into customer-facing AI, they'll have real data about what customers actually ask about and what works given the knowledge data they have. This helps in configuring the chatbot and adding missing knowledge sources. More importantly, this chatbot isn't just about better customer service - it's about collecting more interaction data that will be crucial for future voice AI development.

• Profit Checkpoint: Beyond collecting valuable interaction data, this chatbot must be able to handle 70% of inquiries on its own before handing them off to a human agent.

Step 4: Proactive Support (9+ months)
Here's where investing in those early steps really pays off. The in-call augmentation system will evolve to listen to conversations and suggest answers proactively. Because they've built AI experience across both internal and customer-facing use cases, they know exactly what makes agents more productive versus what just gets in the way.

• Profit Checkpoint: This should allow the company to scale its operations to a call volume equivalent to hiring 20% more staff.

Step 5: Voice AI Pilot (12+ months) Now here's the interesting part: When they finally pilot voice AI (which indeed does have the potential to be a disruptor to their business), they'll be starting with a massive advantage over competitors who rushed in earlier:

• Rich data from months of real customer interactions

• Proven ROI from text-based AI solutions

• A team that knows how to work with AI effectively

• Established AI governance and feedback loops

• Most importantly: clear data showing which calls are perfect for voice AI and which ones need the human touch

When voice AI technology matures further in late 2025, they'll be ready to scale quickly while their competitors are still figuring out the basics. This means they can roll out voice AI much faster than if they'd tried to build everything from scratch.

• Profit checkpoint: The final step will only launch if previous phases hit their targets and show clear path to positive ROI within 6 months.

Conclusion

Here's what makes this approach so powerful: While other companies are either betting big on uncertain AI projects or playing it too safe with disconnected tools, this call center is systematically building competitive advantage with every step.

Most importantly, they're profitable at every stage. Each step delivers its own ROI while laying the groundwork for what's next. There's no massive upfront investment that might never pay off - just steady progress with clear profitability checkpoints.

If you want help creating your own AI Profit Roadmap, I'm launching a 4-Week program in February that will walk you through this exact process, in line with my goal of helping 100 people in 2025 to add $10K+ in recurring profit to their business. You'll get two live workshops plus comprehensive support to build a roadmap that makes sense for your business. Click here for details.

Keep innovating (strategically)!

See you next Friday,
Tobias