Enterprise AI Team

AI-Powered Support, Done Right

February 20, 2025
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AI’s Growing Pains

For years, enterprises have been caught in the excitement of AI’s promise—efficiency, cost reduction, and scalability. Yet, as many companies have found, AI adoption isn’t a straight path to success. Amazon’s early foray into AI-powered customer support chatbots promised to revolutionize service interactions. Instead, it led to customer frustration as the system’s limitations became glaringly evident.

Munir Hafez, CIO at TransUnion, understands this dilemma well. His approach? AI should augment, not replace, human agents. “The key,” he explains, “is to leverage AI where it enhances productivity without sacrificing the customer experience.” TransUnion’s AI strategy is focused on balancing automation with human intervention—an approach that acknowledges AI’s current limitations while setting the stage for future advancements.

Smarter Support, Not Just Bots

TransUnion’s customer support transformation was driven by a clear mandate: improve operational efficiency without losing the personal touch. Instead of fully automating customer service, Hafez’s team implemented AI-assisted support tools that enhance agent performance.

By integrating Microsoft 365 Copilot, the company equipped service representatives with AI-generated summaries, action items, and real-time recommendations during customer interactions. These features allowed agents to resolve issues faster while maintaining a personalized experience. Additionally, TransUnion adopted Converso, a chatbot platform with GPT integrations, to handle routine inquiries while ensuring seamless escalation to human agents when needed.

The rollout followed a structured implementation strategy:

  • Pilot Testing: A six-week testing phase for Microsoft 365 Copilot, with iterative feedback loops to refine AI outputs.
  • Business Case Prioritization: Analyzed 88 AI use cases across Salesforce, DocuSign, and other platforms to identify high-value implementations.
  • Custom LLM Training: Focused on proprietary data grounding to improve AI decision-making and contextual accuracy.
  • Continuous AI Literacy Training: Workshops for employees on prompt engineering and AI augmentation best practices.

When AI Goes Too Far

Amazon’s struggle with AI-powered chatbots underscored a critical lesson: full automation without an effective fallback mechanism can alienate customers. Early adopters of AI-driven customer service often underestimated the frustration that rigid, rule-based chatbots could cause when they failed to comprehend nuanced queries.

In traditional models, customer service relied on large teams of human agents managing inquiries manually. While effective, this approach lacked scalability and was prone to inefficiencies—high operational costs, long wait times, and inconsistent service quality. AI chatbots promised a fix, but many enterprises, including Amazon, discovered that AI’s inability to grasp complex customer needs led to friction.

Hafez recognized that AI’s role is best suited for enhancement rather than replacement. “Is AI good for customer support? Yes. Is it going to replace human agents? Not yet,” he asserts.

AI That Delivers Results

By adopting AI as an augmentation tool, TransUnion achieved significant efficiency gains without degrading service quality:

  • Productivity Gains: AI-assisted agents reported a 10-15% improvement in handling customer queries.
  • Faster Resolution Times: With AI-generated action items and summaries, response times decreased by 20%.
  • Cost Savings: Reduction in manual documentation efforts translated to operational savings in training and support.
  • Customer Satisfaction: Unlike fully automated systems, TransUnion’s hybrid AI-human model maintained customer trust and satisfaction.

What Comes Next?

Hafez’s experience highlights a critical AI adoption lesson: AI should empower, not displace, the workforce. For companies navigating AI implementation, key takeaways include:

  • Prioritize Augmentation Over Replacement: AI is most effective when it enhances human capabilities rather than attempting to replace them.
  • Pilot and Iterate: Controlled experimentation ensures AI tools are refined before full-scale deployment.
  • Invest in AI Literacy: Employees must be trained in prompt engineering and AI management to maximize efficiency.
  • Custom AI Models Matter: Leveraging proprietary data for AI training improves decision-making and contextual accuracy.

As AI continues to evolve, TransUnion’s strategy offers a pragmatic blueprint for enterprises looking to harness AI’s potential without falling into the pitfalls of over-automation. By focusing on augmentation, efficiency, and customer-centric AI, businesses can future-proof their AI investments while maintaining trust and service excellence.