5 Key Challenges CX Leaders Face Today: A Think Tank Recap

At the recent Customer Response Summit (CRS) Event by Execs In the Know in sunny San Diego, I hosted a “Practitioner Pop-up,” a think tank session of leaders learning from leaders. As attendees checked into the event, they were asked to write a challenge they were having. Then, the Execs in the Know team and I reviewed the submissions, synthesized the themes, and used them as discussion topics during the session later that same day.

The real-time nature of this session was certainly a new one for me, and it was inspiring. The interactive format sparked some of the most meaningful conversations I’ve had at an event workshop. It allowed us to focus on the real problems that attendees needed help with – not just what sponsors wanted them to hear – and encouraged all of us to imagine the possibilities.

I found this session and the topics discussed reflected much of what we, here at Quiq, hear from our customers, prospects, and partners. So I wanted to share the five key topic areas that emerged, and enable a larger audience to learn from these incredible CX leaders. Here are the challenges discussed and the main takeaways from our collaborative session:

1. Gaining C-Suite Buy-In for AI Projects

A major discussion point was the difficulty contact center (CC) leaders and project executors face when trying to get executive buy-in for AI initiatives.

Key Takeaways:

• Educating the C-Suite is Required: Contact center operations and leaders find that AI project knowledge can range from oversimplified to extremely risk adverse at the executive level, and their complexity and risk isn’t well understood. A primary task for CC leaders is to better educate the C-suite on the nuances of these projects.
• Clarifying Risk Tolerance: Perspectives on acceptable risk for AI projects vary widely within an organization. Transparency is critical—not just about risk, but also how your chosen solution addresses that risk with guardrails, safety checks, etc. Business users need support in articulating potential risks and managing different viewpoints to align the organization.
• Defining AI Value Realization: Executive expectations for the return on AI can range from replacing all agents to questioning whether the project will pay at least for itself. Creating standardized AI Value Realization plans is essential for setting clear, realistic goals—whether it’s reducing agents, reallocating resources, or driving revenue.
• Navigating Self-Education: With countless vendors making big promises and news articles highlighting project failures, it’s difficult for leaders to educate themselves and their teams. A lack of industry standards and concrete examples compounds this challenge. One way to get around this? Look for success stories and how that success was achieved, especially ones in your industry that resonate with the challenges you’re facing and goals you have.

2. Managing the Complex Contact Center Ecosystem

The ever-expanding ecosystem of vendors, systems, and BPOs in the contact center creates significant complexity. The group focused on where to begin when trying to reconcile it all.

Key Takeaways:

• Assess Your Current Tech Stack: Start by taking inventory of your technology. Document the use cases and customer journeys supported by your current stack. Determine if it’s still meeting your needs, and identify opportunities for consolidation or elimination based on workflows.
• Prioritize Legacy Tech: Address challenges with legacy technology first, before modernizing, consolidating, or making new investments. Seek best-of-breed new technology where it’s required.
• Leverage BPO Partnerships and Trusted Vendors: Leverage your BPO partners and trusted software vendors with resources and experience in the contact center space to better understand the broader ecosystem. They often provide valuable insights and flexible options.

3. Creating and Proving Measurable Value

This discussion focused on defining executive-level metrics and driving tangible value within the contact center.

Key Takeaways:

• Connect Metrics to the Bottom Line: While leading indicators are useful for daily operations, the metrics you articulate and track for executives must clearly impact the bottom line. For example, Quiq customer Brinks Home™ demonstrated a reduction in cost per contact by 67%, showing that less dependency on live agents drove costs down.
• Focus on Long-Term Outcomes: Outcome-based measures are what matter the most. Prioritize long-term results and use calculations to project their impact over time. For example, framing a savings of $100,000 in one month as “$1.2 million over the year” or “$6 million over five years” is far more powerful.

4. Balancing AI with a Human Touch

This group explored how to effectively balance the human element within an AI strategy to increase both agent and customer adoption.

Key Takeaways:

• Implement AI that Empowers Both Agents and Customers: Introduce AI solutions that assist agents, in addition to customers. Automating repetitive tasks allows agents to focus on more complex customer interactions. Providing them with better information and tools can help soften the transition.
• Treat Agents with Respect: Many agents fear their jobs will be replaced. While some roles may change, this transformation will also create new opportunities. Be transparent and treat agents like adults, communicating how resources will be reallocated to other valuable areas of the business.
• Communicate Results Widely: Share the intended and actual results of your AI initiatives with both executives and frontline representatives to maintain transparency and build trust.

5. Modernizing Quality Assurance with AI

The conversation here focused on how AI is transforming conversation and ticket QA, and how to effectively QA the AI itself, as the business, technology, and customer needs evolve.

Key Takeaways:

• Onboard Your AI Like an Agent: Treat your AI system like a new hire, not a new piece of tech. First, educate it. Then, test it on designed conversations. Next, have it handle live conversations with human oversight. You can limit the volume, review the interactions, and tune the AI before moving to fully live conversations with ongoing sampling.
• Utilize AI Agent Analytics: Your AI solution should have its own analytics that perform self-review and QA using an AI model. This data will show where the AI agent needs to be tuned or updated.
• Apply AI for QA Across All Conversations: Use AI tools for all conversation QA and analysis. For agent performance, AI can automate the review of large volumes of interactions to identify valuable training opportunities and areas for focused improvement.

Final Thoughts

From securing buy-in for AI projects and navigating complex ecosystems to proving value and maintaining the human touch, my conversations with CX leaders highlighted exactly where we’re at with challenges today.

But there’s plenty of good news, too: By embracing new technology thoughtfully and keeping the lines of communication open, CX leaders are better positioned to deliver meaningful outcomes for their organizations. And of course, the right technology and partner matter. Visit quiq.com to learn more about how we empower CX teams to overcome each of these challenges—and download our free guide to change management, AI-Ready CX: A Leader’s Guide for Change, Adoption, and Impact.

By Mike Zinne, Chief Experience Officer at Quiq