Michael Hutchison is the Head of TME & Customer Experience at eClerx. Michael leads the Customer Operations Division and oversees eClerx’s customer-client portfolios, focusing on sustaining growth and fostering new client acquisitions. Prior roles include McKinsey and L’Oréal.
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Customer experience has always been a defining part of business performance, but the demands placed on companies today have accelerated at a remarkable pace. Customers expect quick, clear, and consistent support across every channel, and they make these expectations known through an enormous volume of conversations. That reality has created new pressure for organizations that once relied on methods suited for slower, more predictable environments.
Manual quality assurance stood as the foundation for oversight in customer support for many years. It worked well enough when interaction volumes were manageable and shifts in sentiment were gradual. That period is long gone. Companies now face unpredictable swings in customer behavior, faster movement across channels, and more complex contact patterns. Relying on limited samples leaves teams with only fragments of the full picture.
This change exposes a deeper truth: customer conversations hold far more value than many organizations realized. They carry signals about product gaps, communication issues, and shifting expectations. They reveal what frustrates customers and what earns their trust. Treating these conversations as simple service events overlooks their potential to guide decisions across an entire organization. When companies begin to view conversations as a form of intelligence, the way they think about quality, training, and improvement starts to evolve.
The rise of automation and AI-driven monitoring has pushed this shift forward. With the ability to review every interaction, companies are no longer tied to guesswork or outdated assumptions. They gain a clearer view of how customers feel, what they need, and where friction appears. This visibility supports faster, more informed decisions, but it also introduces new expectations for how leaders interpret and act on what they see.
Still, even with powerful tools, progress depends on the mindset companies bring to their data. A strong customer experience culture does not form from technology alone. It requires shared accountability, open communication, and a willingness to treat customer insights as a common resource rather than the property of a single team. Organizations that embrace this approach often find that their conversations reveal opportunities for improvement long before those opportunities show up in performance metrics.
These ideas bring us to Michael Hutchison, Global Head of Customer Operations at eClerx. Michael’s work centers on leading customer-client portfolios and supporting sustained growth, and his earlier experience at McKinsey and L’Oréal gives him a broad understanding of how organizations respond when customer expectations rise quickly. He has seen how companies adapt when they begin treating interaction data as a strategic asset, and his perspective reflects the growing awareness that every conversation contains signals capable of shaping long-term decisions.
Michael’s background illustrates why leadership matters so much in this shift. Companies that succeed in building strong customer experience foundations often do so because leaders champion the idea that conversations deserve attention far beyond the contact center. They encourage teams to examine how insights can inform product updates, training decisions, and operational changes. They understand that quality is not a static task but a continuous effort supported by curiosity and collaboration.
Real-time intelligence aligns with this approach by reinforcing the idea that customer interactions are not merely reactive exchanges. When companies listen closely to what customers share in the moment, they uncover patterns that can guide them toward better decisions. These insights support clarity in complex environments, whether the goal is reducing friction, strengthening compliance, improving coaching, or protecting long-term loyalty.
As expectations keep rising, companies face a choice: rely on outdated models that capture only fragments of the customer experience, or build systems that reveal what customers are saying with far greater detail. The path forward depends on how seriously leaders treat the information customers provide every day. Interaction data becomes an advantage only when organizations approach it with intent and recognize that it has a role in shaping decisions well beyond the support function.
This broader understanding of customer conversations sets the stage for our discussion with Michael Hutchison. His work demonstrates how companies can move toward a more informed, responsive, and connected approach to customer experience by paying closer attention to the insights already present in their daily interactions.
Enjoy the interview!
1. Manual QA has long been the default in customer support operations. What factors have made that model insufficient in today’s high-volume, real-time support environments?
Manual QA has been the backbone of customer support for years, but it no longer can keep up with customer service operations today. The sheer volume of interactions is simply too high for traditional sampling to provide full visibility. When teams can only review 1-2% of conversations, they’re making critical decisions based on what amounts to educated guesswork.
Customers expect seamless experiences across every channel, whether that’s voice, chat, email, or social media. This puts added pressure on organizations to maintain standards across every single interaction. On top of that, contact drivers and customer sentiment can shift overnight. By the time manual QA catches these shifts, teams are already behind and end up in reactive mode instead of getting ahead of issues.
That’s why automation and AI-driven QA are becoming essential. They allow us to scale coverage across 100% of interactions, spot emerging trends in real time, and give frontline leaders actionable insights they can use immediately. It’s not about replacing human judgment, it’s about freeing up QA teams to focus on deeper coaching, compliance, and experience improvements instead of chasing random samples.
2. You’ve spoken about interaction data being underused not just operationally, but culturally. What does a healthy data culture around customer experience look like in your view?
A healthy data culture around customer experience starts with breaking down silos across teams. Too often, customer data never makes it to product teams, marketing, or executive leadership, which can lead to missed opportunities for the company.
- Operationally, it means every level of leadership, from the frontline to the boardroom, has access to clear, timely insights about what customers are experiencing. Not buried in spreadsheets, but in a way that drives daily decisions.
- Culturally, it’s about moving away from data being “owned” by one team and instead making it part of the common language across marketing, care, sales, and product. When everyone feels accountable for CX metrics, you start to see real alignment.
- And most importantly, it’s a culture where data sparks curiosity and improvement, not fear. When insights are used to coach, celebrate wins, and experiment with new ideas, you build a cycle where the customer voice is continuously shaping how the business grows.
3. With AI now involved in triaging, scoring, and surfacing trends from calls, what new expectations does this create for cross-functional teams like operations, compliance, and workforce management?
AI changes teams by moving them from reactive, sample-based insights to proactive, comprehensive intelligence. That shift creates new expectations for every function:
- Operations are expected to act faster—AI surfaces patterns in near real time, so leaders can’t wait for the monthly QA readout; they need to pivot coaching and processes on the fly.
- Compliance teams now have a stronger safety net, since 100% of interactions can be monitored. But that also raises the bar—they’re expected to proactively catch issues before they escalate, not just investigate after the fact.
- Workforce Management can no longer forecast just on volumes and handle time; they’re expected to factor in quality trends, sentiment, and emerging drivers AI highlights, so staffing matches not just the “when” of demand but the “what” and “why.”
In short, AI doesn’t just automate QA it creates a culture of real-time accountability across functions, where acting on insights quickly becomes the new standard.
4. You’ve worked with organizations at very different stages of CX maturity. What distinguishes those that are able to scale their monitoring efforts effectively over time?
What I’ve seen is that scaling monitoring isn’t just about adding more technology, it’s about how the organization approaches quality as part of its DNA. In mature organizations, quality insights drive product, training, and marketing decisions, not just compliance checklists. Less mature organizations tend to keep quality insights trapped within the contact center, missing opportunities to address systemic issues.
They also build flexible frameworks. Instead of locking into rigid scorecards, teams evolve their monitoring to reflect new channels, customer expectations, and emerging contact drivers. There’s also a critical people component many organizations overlook. The best companies invest heavily in upskilling their QA teams as they implement AI monitoring to focus on root cause analysis, coaching, and cross-functional collaboration.
Finally, successful organizations close the feedback loop. Insights aren’t left in QA dashboards, they’re integrated into ops huddles, compliance reviews, and WFM planning, so improvements scale as the business grows. That combination of seeing quality as strategic, keeping it adaptive, and embedding it into decision-making is what allows monitoring to really scale and sustain its impact.
5. Customer expectations around speed and personalization continue to climb. What role do you see real-time interaction intelligence playing in helping companies meet those demands?
Real-time interaction intelligence is becoming the bridge between customer expectations and company performance. Customers want answers fast and tailored to their situation and that’s exactly what this capability enables.
For agents, real-time intelligence transforms their capability to provide service without sacrificing efficiency. Instead of relying on their memory of previous conversations or spending time searching through multiple systems, they receive contextual guidance, relevant knowledge articles, and suggested next best actions delivered directly into their workflow, so speed and personalization happen in the moment, not after the fact.
For leaders, it means visibility into emerging issues and sentiment trends as they unfold, so they can adjust staffing, processes, or offers before customers feel the pain.
The customer experience transformation is the most significant aspect. Real time intelligence allows every interaction to build on the previous conversations, anticipating needs, and providing personalized solutions. This creates the impression that the company “really knows” them and values their time to drive loyalty and customer satisfaction.
In short, real-time intelligence turns interaction data from something we analyze after the customer has left into something we use to shape the experience while they’re still engaged.
6. There’s a lot of industry noise around AI and CX. Based on your experience, what practical steps actually move the needle when it comes to retention, first-call resolution, or coaching impact?
There is a lot of hype, but the organizations that actually move the needle tend to focus on three very practical steps:
- Start with visibility. Use AI to monitor 100% of interactions so you truly know what’s driving churn, repeat contacts, or coaching gaps. Without that baseline, you’re just guessing.
- Target the biggest levers. Instead of trying to fix everything, identify the top 2–3 drivers that most impact retention or FCR and design coaching and process changes around those.
- Close the feedback loop. The most successful teams don’t stop at insights—they feed them back into agent coaching, knowledge bases, and even product roadmaps so the improvements stick.
It’s less about “AI everywhere” and more about embedding it where it can drive action; retention saves, quicker resolutions, and coaching that changes behaviors on the floor.
7. For leaders rethinking their CX and compliance strategies, where do you recommend starting if they want to treat customer conversations as a strategic asset—not just a service function?
I always suggest starting with a mindset shift: seeing every customer conversation not just as a service touchpoint, but as a rich source of intelligence. From there, three steps make a big difference:
- Centralize the data. Bring conversations across voice, chat, and digital into a single view so you’re not piecing insights together channel by channel.
- Mine for patterns. Use AI to surface compliance risks, churn signals, or product feedback that might be invisible in manual samples.
- Activate the insights. Feed what you learn back into compliance training, product design, and CX strategy so conversations directly shape business outcomes.
When leaders do this, conversations stop being a cost to manage and become an asset that drives growth, compliance strength, and customer loyalty.
