How Companies Are Doing It, and What You Can Learn
Customer support used to be a cost centre—slow, reactive, staffed by humans handling long queues of repetitive queries. With AI and automation, that is changing fast. Leading firms are re-architecting customer interaction, support workflows, and service delivery to be proactive, personalised, and high-velocity. In many cases the results are dramatic: shorter wait times, more consistency, lower cost, happier customers and employees.
Wyndham Hotels & Resorts, with PwC, built out AI agents to support franchise owners and improve guest experience. Routine owner requests (reservation changes, loyalty account issues, etc.) are now handled by AI agent chat/voice; more complex cases escalate to human agents. The result: brand review times cut by ~94%, and a reduction in staff load as agents handle fewer repetitive tasks.
In the same study, PwC emphasises that adoption succeeded not just because of technology, but because Wyndham built human oversight, and upskilling, and designed workflows to ensure trust.
A regional health system (non-profit) worked with PwC to build AI-powered engagement over 50+ contact-centres, using Salesforce Health Cloud plus telephony / EHR integration. The system handles routine interactions via conversational AI (self-service, verification, etc.), freeing up staff for higher-value tasks. Key metrics: ≈85% drop in call abandonment, 11% of callers resolving issues through self-service, and 3,000+ hours saved per month.
Definity Insurance, working with Deloitte, deployed generative AI to summarise calls and extract topics (for Sonnet brand). By automating call summary notes (which typically took 3-5 minutes manually per call), they reduced burden on agents, enabling more focus for those "human moments that matter." Average call durations dropped, customer satisfaction improved.
Rakuten Securities (Japan) teamed with Deloitte Japan + NVIDIA to build an AI Avatar—a virtual assistant interface to help novice investors get advice on complex financial products. The system aims to help customers access knowledge intuitively, especially for first-time users. More than 90% of customers who tried the Avatar demo said they'd like to use it in the future.
In Deloitte's research and reports, key use cases include:
It's instructive to look at what people building these systems in smaller orgs or testing things in public are saying.
A Reddit thread in r/startups: someone built a RAG-based support bot using internal help documentation. It answered ~9/10 of internal queries correctly; the remaining cases needed human escalation. The author noted that external customers are less forgiving of hallucinations, so strong guardrails are essential.
Another Reddit post describes using tools like Hiver: auto-triaging, suggesting replies to repetitive queries, routing by priority, channel (email, live chat, WhatsApp, etc.). Many small businesses report AI tools reducing response times, cutting repetitive work, allowing staff to focus on edge-cases.
From r/ecommerce: people discuss customer support tools that generate knowledge base articles directly from ticket histories, summarise long threads, translate responses, check grammar. Tools such as Desk365 come up as ones doing this.
These voices confirm what consultancies are saying:
AI in support generates most value when you have:
| Outcome | Examples / Metrics |
|---|---|
| Cost / effort reduction | Wyndham: review times down ~94%; Definity: saving agents minutes per call in summarisation; PwC healthcare case: 3,000+ hours/month saved |
| Faster resolution / self-service | PwC health system: 11% of callers resolving through self-service; more queries deflected from human agents |
| Customer satisfaction / lower abandonment | Wyndham and PwC: lower abandonment / higher satisfaction in areas where AI helps front-line and self-service |
| Agent productivity & morale | Freeing up agents from rote tasks; tools like AMA for Comcast reduce switching cost; agents can focus on harder, more rewarding work |
| Consistency and scale | With AI agents/Wyndham, and Definity, there is more consistency in handling, branding, compliance; and scalability across channels, geographies |
From the above, the recurring patterns that make the difference:
Design from the start where the AI cannot or should not decide; set clear workflows for when human agents step in. Wyndham, PwC, Definity all show this.
Having a well-maintained knowledge base of FAQs, product documentation, historical tickets. Using AI to search, retrieve, summarise from that corpus helps avoid hallucination and keeps responses accurate.
Tools to help agents mid-conversation: AMA for Comcast, suggestion prompts, autocomplete or draft responses. These reduce wait times and cognitive load.
Agents spend time summarising calls and writing case notes. AI automation of post-call summaries both increases speed and consistency. Definity example reduces minutes per call.
Chatbots, voice bots, guided portals that help customers solve simple issues themselves. Use AI to deflect low-complexity tickets and free human capacity for high complexity.
Measure response times, abandoned calls, resolution rates, accuracy of AI responses, customer satisfaction (CSAT, NPS). Use feedback loop to improve models: correct misclassifications, update knowledge base, tune prompts.
While the upside is big, the implementation comes with risk. Commonly cited issues include:
Based on what the leaders are doing already, here are strategic steps to consider if you're planning to push forward:
1. Audit your customer support ecosystem:
Do you have a knowledge base? How clean is it? What channels are used? What are your metrics (abandonment, resolution, CSAT)?
2. Identify low-risk, high-impact starting points:
e.g. call summaries, automating low complexity queries, routing & triage; self-service plug-ins; agent assist tools.
3. Pilot with human oversight:
Put safety nets in place. Test small, iterate. Incorporate agent feedback.
4. Invest in good data and knowledge infrastructure:
maintain documentation; canonical sources; versioning; feedback loops.
5. Measure aggressively:
define KPIs up front; track not just cost/time but customer satisfaction, retention, voices of edge cases.
6. Prepare channels & agents:
design escalation paths, train agents, ensure multi‐channel consistency.
7. Governance, privacy, risk management:
define policies, monitor AI behavior, ensure transparency.
At Epoch AI Consulting, we help organisations build this future. Here's how we approach it, based on what works:
If you're ready to move from reactive, costly support operations to proactive, efficient, customer-centric service powered by AI — we'd love to help. Get in touch to explore a tailored roadmap for your organisation.