Chatbots are old news. Everyone expects them. They automate FAQs. They try to diffuse common support traffic. But they’re just the tip of the iceberg. The real power of AI in customer service is unfolding in subtler, more complex, or more daring ways — ways that blur the line between “customer support” and “customer experience,” “operational efficiency,” and sometimes even “product development.”
Some of these ideas are being deployed; others are nascent or speculative. I’ll argue that the ones that break through will do so by solving context, empathy, proactivity, and value creation — not just cost-cutting.
What we already have (setting the baseline)
To appreciate what’s unconventional, first a quick flyover of what many companies already do. Between sources:
- Chatbots / virtual assistants handling routine customer queries.
- Agent assist tools: AI suggesting responses, retrieving relevant knowledge/documentation during live agent interactions. E.g. Comcast’s “Ask Me Anything” LLM for agents.
- Predictive analytics: forecasting churn, customer needs, upsell opportunities.
- Sentiment analysis: detecting angry/upset customers, prioritizing tickets, etc.
- Automated ticket routing and workflow management.
These are useful, well-established. But they often still feel incremental. The unconventional ones push boundaries. Maintaining a strong workplace pulse during such transitions is equally crucial, ensuring that AI adoption improves both customer outcomes and employee morale.
Unconventional or Emerging Applications: Where AI Gets Creative
Here are less obvious, more advanced, or emerging use-cases. Some are already in practice; others are possibilities with tech just reaching maturity.
- Agentic AI Co-Pilots / Autonomous Support Agents
- What it is: Systems that do more than suggest: they act, decide, adapt in real time. They maintain context, track conversation flow, trigger workflows, anticipate needs. They reduce agent cognitive load by not just supplying information but guiding the entire process.
- Examples:
- Minerva CQ: a system integrating real-time transcription, intent & sentiment detection, entity recognition, dynamic profiling, partial conversational summaries, modular workflows. Deployed in voice support.
- Similarly, Ask Me Anything for agents: letting agents ask the LLM in real time vs switching contexts/searching; reducing search time.
- Minerva CQ: a system integrating real-time transcription, intent & sentiment detection, entity recognition, dynamic profiling, partial conversational summaries, modular workflows. Deployed in voice support.
- Why it’s interesting: Because it blends human + AI in a richer way. Not replacing but augmenting, in the middle of complexity. It has the potential to improve first call resolution, agent satisfaction, speed, and consistency. It’s not simply automation of simple tasks; it’s helping with the messy stuff.
- What it is: Systems that do more than suggest: they act, decide, adapt in real time. They maintain context, track conversation flow, trigger workflows, anticipate needs. They reduce agent cognitive load by not just supplying information but guiding the entire process.
- Proactive / Predictive Intervention
- What it is: Rather than reacting when a customer complains, the system anticipates problems before the customer even reaches out. That could be due to detecting rising frustration, system issues, logistical delays, etc.
- Manifestations:
- Predictive analytics for churn or for service issues.
- Monitoring supply chain/logistics (for physical products) to warn customers in advance: “Your delivery might be delayed because…” “We noticed a problem with your flight; here’s how to rebook.”
- Predictive analytics for churn or for service issues.
- Why it’s compelling: Because it moves customer service toward experience management rather than cost optimization. It builds trust. But also risk: you have to be highly accurate; false alarms annoy more than they help.
- What it is: Rather than reacting when a customer complains, the system anticipates problems before the customer even reaches out. That could be due to detecting rising frustration, system issues, logistical delays, etc.
- Emotionally Aware / Empathy Augmentation
- What it is: AI that picks up emotional cues (tone of voice, word choice, hesitation, etc.), flags them, adapts responses in real time to inject empathy (or switch modes), perhaps escalate or route to human agents who are better suited.
- Examples & potential:
- Sentiment analysis is already common. But new work combines this with voice features (prosody, pauses) to detect frustration or irritation early.
- Agentic systems that detect emotional state and suggest apologies, defer, or rephrase. This is emerging.
- Sentiment analysis is already common. But new work combines this with voice features (prosody, pauses) to detect frustration or irritation early.
- Why it matters: Because a lot of customer dissatisfaction comes less from what you say than how you say it / how you make people feel. Emotion-blind support (no matter how fast) can feel robotic and worsen retention.
- What it is: AI that picks up emotional cues (tone of voice, word choice, hesitation, etc.), flags them, adapts responses in real time to inject empathy (or switch modes), perhaps escalate or route to human agents who are better suited.
- Multimodal Customer Understanding
- What it is: Using more than just text & voice: images, video, sensor data, metadata. For instance, customers might send pictures of a broken product; or video showing a problem; or have past usage data, geolocation, etc. AI can integrate all this to better assess the issue.
- Examples:
- E-commerce support where customers send photos of a faulty item: AI identifies the defect, matches it with known issues, instructs repair/replacement without human verification.
- Technical support for machinery or devices: video feed + remote diagnostics.
- E-commerce support where customers send photos of a faulty item: AI identifies the defect, matches it with known issues, instructs repair/replacement without human verification.
- Why unconventional: Because it demands richer data, privacy concerns, more sophisticated models. But the payoff is fewer back-and-forth messages, fewer misunderstandings, faster resolution.
- What it is: Using more than just text & voice: images, video, sensor data, metadata. For instance, customers might send pictures of a broken product; or video showing a problem; or have past usage data, geolocation, etc. AI can integrate all this to better assess the issue.
- AI for Customer Journey Memory / Long-Term Personalization
- What it is: AI that remembers not just the last conversation but long-term preferences, trade-offs, past issues, tone, style, etc., to personalize future interactions. Example: a customer previously complained about shipping tardiness, so preferring express shipping; in next order, support can proactively mention shipping options, or apologize if a delay is expected.
- Why it’s powerful: Makes the difference between “we see you” and “just another ticket.” But it also raises privacy and data storage issues. The technical architecture (secure, reliable memory, relevant and not creepy) matters.
- What it is: AI that remembers not just the last conversation but long-term preferences, trade-offs, past issues, tone, style, etc., to personalize future interactions. Example: a customer previously complained about shipping tardiness, so preferring express shipping; in next order, support can proactively mention shipping options, or apologize if a delay is expected.
- AI Agents in Physical or Hybrid Environments
- What it is: Using AI not only in digital channels but in physical / hybrid contexts: drive-throughs, kiosks, store assistants, robots, voice assistants in retail or hospitality, AR/VR support.
- Examples:
- Why unconventional: Because these systems must handle noisy, imperfect real-world conditions. But success here could shift large parts of customer service that currently require human staff to AI systems, especially in retail, QSR (quick service restaurants), travel, etc.
- What it is: Using AI not only in digital channels but in physical / hybrid contexts: drive-throughs, kiosks, store assistants, robots, voice assistants in retail or hospitality, AR/VR support.
- Knowledge Base Creation & Maintenance via Generative AI
- What it is: Instead of manually writing/fixing FAQs, product docs, internal KBs, using generative AI to draft, update, tag, cluster, and surface knowledge. Also summarizing customer support tickets to extract new patterns that become new support content.
- Examples:
- Generative AI used to auto-generate customer replies or knowledge base articles.
- Systems analyzing ticket histories to identify emerging issues, then auto-update documentation or support flows.
- Generative AI used to auto-generate customer replies or knowledge base articles.
- Why interesting: It scales knowledge management, which is often neglected; improves freshness; reduces agent confusion. But risk: hallucinations, obsolete/inaccurate content, brand-voice drift.
- What it is: Instead of manually writing/fixing FAQs, product docs, internal KBs, using generative AI to draft, update, tag, cluster, and surface knowledge. Also summarizing customer support tickets to extract new patterns that become new support content.
- Workflow Automation & Tool-chain Integration
- What it is: AI that doesn’t just respond but triggers entire processes: order refunds, schedule service engineers, initiate repairs, send replacement items, adjust billing, etc., often by integrating with external systems (inventory, CRM, logistics). Essentially, AI isn't just the front line but embedded in the backend flows.
- Examples:
- AI that automatically escalates and triggers field service.
- AI recognizing return request + initiating shipping label generation automatically.
- AI that automatically escalates and triggers field service.
- Why unconventional: Because more moving parts, more risk—if auto process fails, or if system misidentifies. But it's where huge inefficiencies live, so huge potential gains.
- What it is: AI that doesn’t just respond but triggers entire processes: order refunds, schedule service engineers, initiate repairs, send replacement items, adjust billing, etc., often by integrating with external systems (inventory, CRM, logistics). Essentially, AI isn't just the front line but embedded in the backend flows.
- AI-Mediated Trust, Verification, Fraud Prevention in Customer Interactions
- What it is: Using AI to detect fraud, confirm identity, ensure compliance, keep interactions secure and trustworthy. Could include biometric voice recognition, anomaly detection in transactions or support requests, detecting fake reviews or spam.
- Examples:
- Banks using AI to detect suspicious transaction queries. (Some of this overlaps with fraud detection already.)
- Using AI to verify identity in voice dialogues, possibly in drive-through or phone support.
- Banks using AI to detect suspicious transaction queries. (Some of this overlaps with fraud detection already.)
- Why valuable: Because customer service is often exploited via phishing, impersonation, social engineering. If you can secure support interactions without making the customer jump through hoops, you both improve trust and reduce risk.
- What it is: Using AI to detect fraud, confirm identity, ensure compliance, keep interactions secure and trustworthy. Could include biometric voice recognition, anomaly detection in transactions or support requests, detecting fake reviews or spam.
- Customer Service Analytics & Strategic Feedback Loop
- What it is: Not just metrics (speed, resolution, etc.) but deeper analytics: trend detection, root cause analysis, clustering of complaints, mapping product defects to support tickets, feeding back into R&D / operations. AI that ties together support data with product data and with behaviour to help make better strategic decisions.
- Examples:
- Summarizing large volumes of support tickets to find emerging issues.
- Using Voice of Customer (VoC) data with ML clustering to feed product teams.
- Summarizing large volumes of support tickets to find emerging issues.
- Why often overlooked: Because it lives behind the scenes; companies tend to under-invest here, though it may have the greatest long-term ROI.
- What it is: Not just metrics (speed, resolution, etc.) but deeper analytics: trend detection, root cause analysis, clustering of complaints, mapping product defects to support tickets, feeding back into R&D / operations. AI that ties together support data with product data and with behaviour to help make better strategic decisions.
Risks, Challenges, and What Makes “Unconventional” Hard
Pushing the frontier is exciting but comes with strong friction. Some considerations / trade-offs:
- Data privacy & regulation: Using multimodal data, long memory, emotional detection, voice recognition involve sensitive data. GDPR, local laws, user trust: big constraints.
- Accuracy vs embarrassment: If AI misclassifies, misdiagnoses, or gives incorrect answers / faulty promises, blowback can be worse than doing it slowly. The higher the stakes (financial, safety, brand reputation), the more risk.
- Maintaining brand voice & authenticity: Especially when personalization and generative AI are involved. If the AI speaks like a robot, or gives off weird style artifacts, people notice.
- Human in the loop still matters: Some issues require judgment, empathy, nuanced understanding, cultural sensitivity. Over-automation can alienate.
- Operational complexity & integration burden: These unconventional applications often require deep integration across systems: CRM, inventory, billing, field operations, product defect tracking, etc. Many companies are siloed and lack the infrastructure.
- Cost, training, monitoring: Building, maintaining, and continuously improving these systems is resource-intensive. Need ongoing QA. Need feedback loops. Need to guard against drift (model drift, data drift).
What I Think Will Work — My Opinions
Here are my thoughts on which of these applications will likely make big waves, and which are more likely to remain fringe, plus what separates winners from losers.
- High potential:
- Agentic co-pilot systems for agents. These systems, when well-designed, deliver strong leverage: fewer errors, faster resolution, better training. They reduce the cognitive overhead from switching contexts, looking up documents, etc. I expect these to become standard in sophisticated contact centers within 2-3 years.
- Proactive / predictive support. Companies that can anticipate issues (logistics, delays, known recurring defects) will earn massive loyalty. Customers do notice when a company says, “we noticed something may delay your order, here’s what we’ll do,” vs finding out after frustration builds.
- Multimodal support, especially for product/tech troubleshooting. As devices, IoT, AR/VR become more common, the ability to see/hear/sense the problem in context will become a differentiator.
- Agentic co-pilot systems for agents. These systems, when well-designed, deliver strong leverage: fewer errors, faster resolution, better training. They reduce the cognitive overhead from switching contexts, looking up documents, etc. I expect these to become standard in sophisticated contact centers within 2-3 years.
- Medium / cautious potential:
- Emotion detection / empathy augmentation. It has promise, but implementation is tricky. Risks of false positives, overstepping into “manipulation”, sounding insincere. It could backfire if customers feel they’re being toyed with.
- Generative knowledge base auto-creation. Useful, but must be tightly controlled. The risk of “hallucinations” or outdated content is real. Winners will be those that combine human validation with AI drafts.
- Physical/hybrid AI agents in stores/drivethroughs. Context-heavy, environment variable, costs high. But in specific sectors (fast food, retail, hospitality) could yield big ROI.
- Emotion detection / empathy augmentation. It has promise, but implementation is tricky. Risks of false positives, overstepping into “manipulation”, sounding insincere. It could backfire if customers feel they’re being toyed with.
- Lower upside / more speculative for now:
- Fully autonomous AI replacing human agents for complex complaints. At least until models have very strong contextual and ethical consistency, I'd be wary of using them in sensitive domains (health, legal, finance, high emotional stakes).
- Very deep emotional or psychological modeling. Might cross into creepy, or ethical problem if not transparent.
- Anything that demands wide sensory input (video, biometric) unless privacy / user consent are fully baked in and accepted by customers.
- Fully autonomous AI replacing human agents for complex complaints. At least until models have very strong contextual and ethical consistency, I'd be wary of using them in sensitive domains (health, legal, finance, high emotional stakes).
What Makes a Good Use-Case: Criteria for Success
If you (or your clients) want to push into unconventional AI in customer service, here are the elements that tend to separate projects that succeed vs those that either under-deliver or backfire:
Criteria
Why It Matters
Relevance to pain points
The more urgent / costly / frustrating a problem is, the more value in solving it unconventionally.
Data quality & integration
AI needs good training data, relevant user history, access to systems (inventory, product, logistics, refunds etc.). Without a unified backend, even the best AI output falls flat.
Human oversight & fallback paths
Especially when dealing with emotions, correctness, ethical issues — always have humans in loop, easy escalation.
Transparency & trust
Let customers know when they’re chatting with AI; allow opt-out; ensure data privacy; avoid being “too clever” in ways that feel disingenuous.
Incremental deployments, feedback loops
Don’t bet everything in one go. Pilot, measure, iterate. Monitor failures, edge cases.
Cost versus ROI clarity
Some unconventional AI is expensive (hardware in stores, voice recognition, multimodal sensors). Need to model what you save or what extra revenue you get (retention, fewer returns, fewer complaints, etc.).
Some Case Studies & Anecdotes
- Minerva CQ: The case study in the recent paper (Sep 2025) shows measurable improvement in agent efficiency, handling time, etc., by using Agentic AI which maintains context dynamically.
- Lyft + Anthropic's Claude: Lyft using Claude to handle driver-side customer service requests, reducing resolution times by ~87%.
- Wendy’s FreshAI drive-thru: expanding to support ordering in Spanish; AI voice order takers at drive-thru lanes, integrating with POS; showing how physical/hybrid support is being tested.
My Strong Warnings / What To Watch Out For
- Brand tone disasters: Generative responses going off-brand, sounding weird. AIs that are too “friendly” in contexts where formality might be expected or vice versa.
- Biases and fairness issues: If AI picks up patterns from biased data (e.g. treating certain accents, languages, demographics worse), that becomes visible and harmful.
- Privacy backlash: Using voice, location, device data, images etc., without explicit consent or with over-reach will generate negative reactions. Legal risk (GDPR, etc.).
- Overpromise & underdeliver: If you advertise “AI support 24/7” but the AI is easily stumped, or QoS (quality of service) is inconsistent, customers will resent. It might degrade brand more than improve.
The Future: What “Beyond Chatbots” Will Look Like
Here’s where I think things are headed in the next 3-5 years, if current trends continue and computing / models keep improving.
- Blended reality support: AR headsets, mobile cameras, remote video + AI-guided repair. E.g. tech support where your phone shows you where to screw, or “AI sees your machine part and tells you if you need a replacement” in real time.
- Voice as first class everywhere: In many settings, customers will prefer speaking, especially in automobiles, while walking, etc. AI voice agents that can handle dialects, background noise, etc., will become more robust.
- Continuous learning loops from every channel: Not just tickets, but social media, in-person interactions, product returns, voice calls—all feeding into models that adapt support flows, content, policies.
- AI as a service quality auditor: AI listening in (with consent) to support calls, customer interactions, evaluating agent performance (tone, adherence to script or brand voice), identifying best practices, slipping policies, automatically suggesting training.
- Emotion and context awareness becoming baseline, not luxury: Recognizing frustration early, adjusting style; using customer profile / history to adjust responses; remembering avoidances (e.g. “don’t ask me to spell my name”) — small things that feel delightful.
- Hybrid human-AI teams rather than vs: The most successful customer service orgs will weave AI smoothly with human agents; AI handles repetitive / simpler tasks, humans handle nuance; but humans will also lean on AI co-pilots.
Conclusion
Going “beyond chatbots” isn’t just about using fancier models; it’s about rethinking what customer service can do. It’s about moving from reactive to proactive, from transactional to relational, from rigid to empathetic and context-aware.
That said, doing so well takes care: the right data, the right design, the right guard rails. When done badly, unconventional AI applications risk alienating customers or compromising brand. But when done well, they can differentiate companies in a crowded space, reduce costs in ways customers don’t even notice, and turn support into a source of delight and loyalty. That loyalty can go even further when paired with advocacy. Platforms like ReferralCandy make it easy to transform delighted customers into promoters, ensuring that exceptional AI-driven support not only reduces churn but also fuels referral-driven growth.