This is a guest post by Indresh Satyanarayana, VP Product & Technology Labs, Aquant

The paradox of GenAI in service

Generative AI has swept through customer operations. Gartner now predicts that more than 80 percent of enterprises will have a GenAI-enabled application in production by 2026. Yet many CX and service leaders in the manufacturing and machinery industries still chase the same old metrics: first-time-fix, mean-time-to-repair, Net Promoter Score. The bots may quote manuals faster than any human, but there’s a significant opportunity for better outcomes.

Why? Because the dominant technology begging Generative AI, Retrieval-Augmented Generation (RAG), is a one-shot affair: the system retrieves a passage and drafts an answer. Helpful for trivia… hopeless for a broken turbine, a jittery broadband line, or a compliance audit that can tank customer trust.

As Adrian Swinscoe often reminds us, “Outcomes, outcomes and outcomes” are the real currency of experience. If the technology can’t close the outcome gap, it becomes another shiny tool that CX teams abandon after the pilot.

Enter Retrieval-Augmented Conversation (RAC)

RAC keeps the same grounding discipline of RAG, every statement is anchored in live knowledge, but wraps it in an iterative dialogue loop:

1. Clarify the missing facts (“Which firmware build are you on?”).
2. Retrieve new data, not just PDFs but ERP inventory, IoT telemetry, and prior work orders.
3. Advise one next best action.
4. Confirm whether it worked, then decide the next question or gracefully hand off to a human, context intact.

The cycle repeats until the problem is fixed and the customer (or technician, or agent) explicitly says so. Think of it as moving from a Wikipedia-style answer sheet to a digital coach that shadows every service interaction.

Why conversation beats answers in the CX P&L

Across hundreds of field-service calls on the Aquant platform, we’ve seen RAC cut mean-time-to-repair by up to 35 percent and raise first-time-fix rates by double digits, without expanding headcount. More importantly, every closed loop feeds training data back into the model, so the system (and your people) get smarter with each conversation.

Two real-world moments that shift loyalty

1. Turbine alarm at 3 a.m.

Old way: RAG bot quotes a 300-word troubleshooting guide. Technician scrolls, guesses step three, escalates when the error persists, downtime drags on.

RAC way: Bot asks, “Do you hear a high-pitch hum?” When the tech says yes, it surfaces the exact torque spec, waits, confirms the alarm clears, then logs a first-time-fix. The plant manager sleeps through the night; the service brand earns quiet loyalty.

2. Home Wi-Fi drops every evening

Old way: Customer scans forums, reboots twice, rage-dials the call centre. Average handle time balloons; NPS plummets.

RAC way: Bot clarifies connection type, pushes a firmware patch, invites the customer to stream for five minutes, checks telemetry, then closes the ticket with a single CSAT tap. The agent queue stays clear for higher-emotion conversations.

What CX leaders should care about (even if you never read a vector-database spec)

1. Proactive empathy at scale
   Conversation lets the assistant uncover context the customer forgot to give, exactly how your top human agents build rapport.
2. Continuous learning loop
   Every ask-probe-confirm turn becomes labelled data. RAC platforms can identify the steps where customers struggle and auto-generate new knowledge articles, no separate VoC project required.
3. Channel fluidity
   Because the intelligence lives in the dialogue manager, the same loop can run in web chat, WhatsApp, voice IVR or a technician’s headset, without fragmenting the audit trail.
4. Differentiation in a commoditized GenAI market
   With basic RAG chatbots now a swipe-card purchase, RAC is the experience layer your competitors can’t spin up overnight. It demands the marriage of deep domain data, conversation design and outcome analytics, a moat worth building.

Three moves to start today

1. Audit a single outcome-heavy journey
   Count how many back-and-forths a human currently needs. That is your RAC ROI baseline.
2. Prototype with a narrow knowledge slice
   Load one alarm code, one warranty policy or one top-five FAQ. Prove the loop works before chasing omnichannel dreams.
3. Measure containment and sentiment, not conversation length
   A ten-turn chat that ends in a fix is superior to a three-turn chat that ends in escalation. Align incentives accordingly.

The CX leader’s RAC checklist

• 🔲 Governance: Source citations, PII redaction and role-based controls baked in.
• 🔲 Memory: Entity, intent and outcome tracking across turns, no “please repeat the issue.”
• 🔲 Multimodal data: Manuals and parts catalogues and sensor feeds.
• 🔲 Escalation logic: Clear thresholds for handing off with full context.
• 🔲 Analytics: One transcript lake, channel-tagged, to fuel continuous improvement.

Closing thought: build the future in the present

Adrian Swinscoe argues that the best CX leaders avoid “fixating on the possibilities of technology” and instead deliver better outcomes today. Retrieval-Augmented Conversation does exactly that. It converts GenAI hype into tangible customer victories, one clarifying question, one confirmed resolution at a time.

The conversation era has begun. The only real question is whether your brand’s AI shows up ready to talk, and to finish what it starts.

This is a guest post by Indresh Satyanarayana, VP Product & Technology Labs, Aquant

About Indresh

Indresh Satyanarayana is VP of Product & Technology Labs at Aquant, where he leads the team that pioneered Retrieval-Augmented Conversation for field-service and support organizations worldwide.

Credit: Photo by Compare Fibre on Unsplash