CCO Says CX is Changing from a Reporting Function to Operational Infrastructure That Supports Faster Decision-Making
Patrícia Osorio explains why AI is turning customer experience into shared decision-making infrastructure that helps every team act faster with better customer context.
Patrícia Osorio
Co-Founder and CCO of Birdie
CX Transformation: AI is shifting customer experience from a business function to a decision-making infrastructure.
AI Context: Customer context is crucial for AI systems to drive smarter, organization-wide decision-making.
MCP Impact: Model Context Protocols (MCPs) integrate customer insights directly into daily AI tool usage.
Human Judgment: AI accelerates processes and humans apply judgment for strategic decisions requiring accountability.
Friction Focus: Shift from measuring chatbot deflection to improving true customer outcomes and reducing friction.
Patrícia Osorio is cofounder and CCO of Birdie, an AI-powered platform that helps organizations structure and use customer context.
We sat down with Patrícia to learn how AI is changing the field of CX. She said it's going through a fundamental change from a business function to decision-making infrastructure.
The biggest transformation ever in CX
We believe CX is evolving from a business function into infrastructure. Customer context is becoming the shared layer every part of the organization relies on — not just support teams, but product, engineering, marketing, operations, finance, and increasingly AI systems…This is the future of CX.
I'm Patrícia Osorio, cofounder and Chief Customer Officer at Birdie. I believe customer experience is undergoing the biggest transformation since companies first measured customer satisfaction.
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For years, CX has focused on listening — collecting feedback, measuring NPS, building dashboards, and helping organizations understand what customers are saying. Today's companies don't lack customer data, they lack shared context. Every team has its own view of the customer, but no one has the complete picture.
We believe CX is evolving from a business function into infrastructure. Customer context is becoming the shared layer every part of the organization relies on — not just support teams, but product, engineering, marketing, operations, finance, and increasingly AI systems.
At Birdie, we help large enterprises across financial services, insurance, retail, marketplaces, and utilities build that Customer Context Layer. We transform millions of customer interactions across every channel into context that helps organizations prioritize better decisions, align teams faster, and continuously improve the customer experience. We're a team of nearly 60 people, serving enterprise customers across North America, Latin America, and Europe.
This is the future of CX. It no longer reports on what happened. It becomes the infrastructure helping the entire company decide what to do next.
How AI is changing CX into infrastructure
Every company is investing in AI agents, copilots, and automation. But most of them are making the same mistake. They're optimizing individual functions instead of the company as a whole.
Customers don't experience our org charts. They experience the combined effect of every decision we make. So, customer context needs to be available across the entire company.
In the future, product won't prioritize features without customer context. Marketing won't launch campaigns without customer context. Support won't coach agents without customer context. AI agents won't take action without customer context.
That's why I believe customer experience is becoming infrastructure. Every customer interaction creates context. Every decision should use that context and every outcome should make the company a little smarter.
That's a very different role for CX than it has been over the last twenty years. It's no longer about producing reports or even generating insights. It's about providing the customer context that every team — and every AI agent — uses to make decisions.
Because the companies that win won't be the ones with the most AI. They'll be the ones where every decision is grounded in a shared understanding of the customer. To me, that's the next chapter of customer experience: not as a department, but as the decision-making infrastructure of the business.
How MCPs change customer experiences and engagement
Model Context Protocols (MCPs) have a huge impact on customer experience, and we've taken that to heart. For years, customer intelligence was something you visited. You opened a dashboard, ran a report, built a presentation, and shared it in a meeting. It was valuable, but it was fundamentally disconnected from the moment decisions happened.
Over the past year, we changed that by making Birdie's Customer Context Layer directly available inside the AI tools people already use every day. Whether someone uses ChatGPT, Claude, Gemini, or another AI workspace, they can ask questions and engage with their data.
That may sound like a small interface change, but it's an operating model change. And it completely changes how customers experience and engage with your product.
AI owns speed while humans own judgment
AI should own speed. Humans should own judgment.
AI is exceptionally good at processing scale. It can classify millions of customer interactions, detect emerging patterns before they're visible in traditional metrics, connect signals across channels, estimate business impact, and route opportunities to the right teams. That's work no human organization can realistically do at enterprise scale.
The mistake many companies make is trying to replace judgment with AI. Humans are essential where judgment, accountability, and trust matter.
When a customer has had a genuinely bad experience, they don't just need a correct answer — they need to feel heard. When a product team decides whether to redesign an experience, that's a strategic decision involving trade-offs that go well beyond what historical data can recommend. In highly-regulated industries like banking or insurance, escalation and risk decisions require human accountability.
The opportunity, then, is to augment judgment with context. AI should help us understand what's happening, why it's happening, how significant it is, and who should own it. Humans decide what future they want to create.
How AI increases organizational velocity
The biggest CX improvement to come from AI hasn't been productivity. It's been organizational velocity.
One of our customers, Agibank, was analyzing more than 2.4 million conversations and over 33,000 NPS responses every quarter. AI reduced the time to understand that data by 80% and accelerated decision-making by 90%, contributing to a 10% reduction in churn in pilot markets — roughly $60,000 in monthly value preserved.
Another customer, Neon, identified customer friction three to four weeks before it appeared in traditional operational metrics. Acting on those signals led to a 27% improvement in app ratings and a 53% reduction in Android detractors.
The value isn't only that AI analyzes feedback faster. It dramatically shortens the time between a customer signal and a business decision. Organizations move from debating whether a problem exists to deciding how to solve it.
Why AI needs customer context
The biggest failure mode, however, is assuming AI creates wisdom automatically.
AI is incredibly good at recognizing patterns, but patterns without business context can be misleading. If your taxonomy is weak, your customer segmentation is poor, or your organization doesn't understand causality, AI simply produces faster answers to the wrong questions.
That's why customer context matters so much. Context turns pattern recognition into good decision-making.
How chatbot deflection metrics mislead CX outcomes
Chatbot deflection is overhyped. For the past couple of years, we've measured success by asking, "How many contacts did the chatbot prevent?" That's the wrong metric.
Deflecting a conversation doesn't necessarily solve a customer's problem. Sometimes it just makes it harder for them to reach someone who can.
We've all experienced it ourselves: You spend five minutes going in circles with a chatbot, only to end up asking for an agent anyway. The company counts that as automation. The customer experiences it as friction.
The real goal shouldn't be contact deflection. It should be friction removal.
If AI can proactively identify a failed payment, fix it before the customer notices, or surface the right information so they never need to contact support in the first place — that's a great outcome. If AI simply creates another layer between the customer and a resolution, we've optimized the wrong thing.
This reflects a broader shift in CX. We've spent years optimizing operational metrics like handle time, containment, or deflection because they were easy to measure. Increasingly, we're realizing that the metrics that matter are customer outcomes: Did we resolve the issue? Did we remove the root cause? Did trust increase? Did the customer need us less because the experience genuinely improved?
That's why the future isn't AI replacing support. It's AI helping organizations continuously identify and eliminate the sources of customer friction across the business. When you remove the reason customers contact you in the first place, that's when you've created real value — not because you deflected a conversation, but because there was no problem left to solve.
Why AI integration must begin with business problems
AI doesn’t need a broad vision — it needs a very specific job…That’s why I believe the best AI projects don’t start with technology. They start with a business problem that matters.
AI doesn't need a broad vision — it needs a very specific job.
The mistake is starting with, "How can we use AI?" The better question is, "What's the decision we're trying to make better?"
When you're clear about the decision, everything else follows: the data you need, the context you need, the workflow you automate, and how you measure success.
That's why I believe the best AI projects don't start with technology. They start with a business problem that matters.
Ask the right questions and seize this opportunity
Here's my advice:
- Spend less time asking how to use AI and more time asking which decisions AI should improve. That's a much more strategic question. Most organizations don't have an AI problem — they have a decision-making problem. They've become incredibly good at collecting customer data, but much slower at acting on that information.
- Stop treating customer insight as the finish line. The purpose of customer intelligence isn't to produce better dashboards. It's to make better decisions. If AI helps you analyze feedback twice as fast but your organization still takes six months to act, nothing meaningful has changed.
- CX leaders have a unique opportunity right now. For years, people viewed customer experience as the team that measured satisfaction and reported what happened. That's changing. Customer experience is becoming the context layer that helps every part of the business decide what to do next. Product teams use it to prioritize roadmaps. Marketing uses it to understand messaging. Operations uses it to remove friction. Finance uses it to measure business impact. AI agents use it to make better decisions. That's a much bigger role than measuring NPS.
The future of CX isn't becoming more operational — it's becoming foundational. The organizations that will win over the next decade won't be the ones with the most AI. They'll be the ones that learn faster than everyone else because every customer interaction makes the business a little smarter.
Follow along
You can follow Patrícia Osorio's work on LinkedIn. And check out Birdie.
More expert interviews to come on The CX Lead!
