Lessons from AI Assistants in Customer Service and Healthcare

/ AI, Customer Experience, Enterprise Software, Verint Engage

Session Overview

This session presented two distinct but complementary AI implementations: an AI assistant for customer service agents and an interaction wrap-up bot for clinical coaching at Cigna Healthcare. Both case studies showed the importance of user adoption, knowledge management, and iterative customization as critical success factors.

  • Customer Service AI Assistant: Deployed first for agents, then extended directly to customers through WhatsApp, Facebook Messenger, and a public webpage. The assistant integrates with LMS, policies, and official documentation to give accurate, real-time answers. The outcome is a service time reduction from 20–30 minutes to just four minutes, faster resolution, and higher CSAT.
  • Clinical Wrap-Up Bot: Introduced to ease documentation burden on clinicians handling 30–60 minute coaching calls. The bot captures 100% of conversations and generates summaries to reduce post-call work. While early adoption is positive, clinical needs require deeper customization (e.g., capturing diagnosis, treatment, and resources).

A central theme emerged where direct involvement of end users through beta groups, review teams, and “voice of the customer” sessions is essential for AI adoption. Expansion is underway with nearly 2,000 medical clinicians already using the wrap-up bot, with another 2,000 in behavioral health to follow.

Industry Perspective

These case studies reflect the dual mandate enterprises face when scaling AI: prove rapid ROI while managing change.

For customer service, the results are obvious:

  • CSAT rose from 40 to nearly 70, placing the company 30 points above its score four years ago.
  • Contact centers handled 3× the volume with the same headcount.
  • Service time reductions and faster escalations freed agents to focus on higher-value work, with cross-sell opportunities driving incremental revenue (e.g., luggage add-ons).

For healthcare, the path is more complex. Clinical contexts demand accuracy, compliance, and trust. Out-of-the-box summaries were insufficient; clinicians needed detailed, structured notes. Adoption succeeded only after a beta group of 50, later 180 champions, validated the solution and influenced design. The bot now supports nearly 2,000 clinicians, but challenges remain such as outdated knowledge bases, voicemail mis-summaries, and technical character limits for long calls.

theCUBE Research finds that over 70% of AI projects stall due to adoption and governance hurdles, not technology itself. These examples confirm that insight. In customer service, initial resistance was addressed by forming a SWAT team of agents and QA staff to tune responses and rebuild trust. In healthcare, leadership buy-in was secured only after clinicians tested customized summaries that captured the details they needed.

The lesson is that AI must fit into user workflows, not the other way around. Success is measured not just in KPIs but in whether frontline employees believe the system makes their jobs easier.

Moving Forward

For enterprises pursuing similar AI rollouts, several priorities emerge:

  • Fix the Knowledge Base First: Outdated and disorganized content undermines accuracy. AI effectiveness is only as good as the data it pulls from. Enterprises should establish a continuous curation process and use AI itself to flag knowledge gaps.
  • Tailor Clinical AI to Context: Healthcare use cases demand domain-specific customization. Summaries must include mandatory details (reason for call, diagnosis, resources provided). Features like voicemail summarization should be disabled when irrelevant.
  • Measure What Matters: Establish clear before/after metrics for after-call work, case volume per day, and documentation accuracy. Without quantified proof points, adoption risks being labeled as anecdotal.
  • Engage End Users from the Start: Beta groups, champions, and “voice of the customer” reviews should be embedded into every AI rollout. Direct involvement is the fastest way to build trust and drive sustained adoption.
  • Balance Automation with Human Judgment: Guard against over-reliance. Clinicians and agents must still review, edit, and approve AI outputs, ensuring accountability and maintaining engagement.
  • Plan for Expansion Thoughtfully: Scaling from 2,000 → 4,000+ clinicians or thousands of customer service agents requires operational procedures for resets, updates, and ongoing support.

Bottom line: These implementations show that AI assistants can deliver measurable ROI with CSAT +30 points, service time cut by 80%, clinical documentation offloaded, but only when paired with clean data, user-driven design, and continuous customization. Organizations that prioritize adoption strategies alongside technical deployment will see AI become not just a tool but a trusted partner in both customer and employee journeys.

Authors

  • With over 15 years of hands-on experience in operations roles across legal, financial, and technology sectors, Sam Weston brings deep expertise in the systems that power modern enterprises such as ERP, CRM, HCM, CX, and beyond. Her career has spanned the full spectrum of enterprise applications, from optimizing business processes and managing platforms to leading digital transformation initiatives.

    Sam has transitioned her expertise into the analyst arena, focusing on enterprise applications and the evolving role they play in business productivity and transformation. She provides independent insights that bridge technology capabilities with business outcomes, helping organizations and vendors alike navigate a changing enterprise software landscape.

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  • Paul Nashawaty

    Paul Nashawaty, Practice Leader and Lead Principal Analyst, specializes in application modernization across build, release and operations. With a wealth of expertise in digital transformation initiatives spanning front-end and back-end systems, he also possesses comprehensive knowledge of the underlying infrastructure ecosystem crucial for supporting modernization endeavors. With over 25 years of experience, Paul has a proven track record in implementing effective go-to-market strategies, including the identification of new market channels, the growth and cultivation of partner ecosystems, and the successful execution of strategic plans resulting in positive business outcomes for his clients.

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