Customer support should be a company’s frontline for building relationships. Instead, it’s a source of frustration for both businesses and their customers.
Think about the last time you needed support. You likely waited too long, repeated yourself multiple times, and dealt with agents who lacked context. This isn’t just inefficiency; it’s a system built for containment, not resolution. And that’s because traditional support infrastructure wasn’t built for modern customer expectations.
For decades, businesses have treated support as a cost center, prioritizing ticket closure over meaningful problem-solving. Billions have been spent on chatbots, outsourced teams, and fragmented systems, yet customer frustration remains the same. I know because I was part of the problem. I kept adding tools and layering automation onto a system that was never designed to prioritize resolution in the first place. It took stepping back to realize the system itself is fundamentally broken.
Why Traditional AI Falls Short
Businesses have turned to AI as a solution, but most AI hasn’t fixed support. It’s made it worse. Why? Because AI has been applied as a band-aid rather than addressing the real problem: traditional infrastructure wasn’t built to support modern expectations.
Most AI tools today are glorified search engines, built to deflect inquiries rather than solve them. They rely on deterministic, rules-based logic; great for resetting passwords, but useless for handling complex, multi-step problems like “Why was my deposit delayed?” or “How do I modify my contract terms?” In short, AI has been built to work within the limits of an outdated system, rather than redefining the system itself.
The reality is that most AI in support today lacks two things:
True understanding of business-specific data – Generic AI models trained on internet data can’t provide meaningful, contextual answers.
The ability to take action – Answering a question isn’t the same as solving a problem. Customers don’t just want information, they want resolution.
Worse, many AI solutions force companies into an “all-or-nothing” approach, requiring a rip-and-replace of existing support tools. That’s simply not how businesses operate. Companies don’t need more patchwork automation. They need a fundamental shift.
Enter a Gen AI-First Approach
AI-first companies like Maven AGI are taking a fundamentally different approach. Instead of layering automation onto broken processes, we’re rethinking the entire paradigm. The goal isn’t just answering support tickets faster. It’s eliminating the need for them in the first place. That’s only possible by addressing the real failure point: support infrastructure built for an outdated world.
Here’s what this means in practice:
AI that understands business context – Instead of relying on internet-trained models, Maven integrates deeply with a company’s CRM, billing systems, and product data. This means Agent Maven can provide answers rooted in real business knowledge, not generic FAQs.
AI that takes action, not just answers questions – Traditional AI can tell you your refund is being processed. Agent Maven processes the refund by orchestrating tasks across multiple systems.
AI that learns and improves over time – Unlike static chatbots, Maven’s AI continuously refines its responses, identifies knowledge gaps, and adapts to new customer behaviors.
This is a structural change. Support infrastructure moves from reactive to proactive, from fragmented to unified, from transactional to relational.
The Shift Has Already Begun
Forward-thinking companies like TripAdvisor, Rho, and ClickUp aren’t waiting for customer expectations to outpace their capabilities. They’re making the shift now.
The results speak for themselves:
Up to 93% of inquiries resolved autonomously – without human intervention.
Up to 81% reduction in support costs – freeing up resources for strategic growth.
Up to 95% customer satisfaction – even as customer volume increases.
This shift breaks away from a support model that was never built for today’s expectations.
The Future of Customer Support: Proactive, Predictive, and Effortless
For years, companies have focused on optimizing support. But optimization only gets you so far when the foundation is broken. The next leap forward is about reimagining support altogether, not just making old systems more efficient.
From reactive to proactive – AI anticipates issues and resolves them before they escalate.
From fragmented to unified – No more juggling multiple tools. Support, sales, and success teams operate with a single graph of record showing a unified view of the customer.
From transactional to relational – Every interaction feels personal, not like another ticket in a queue.
So, to my former colleagues leading support: will you keep patching a system that was never built for modern expectations or will you partner with me to build the future? I’m here when you’re ready.