What would make a conversational assistant useful, recoverable, and accountable in real work?
Where AI Hands Off · AI product research · enterprise fieldwork
Designing the handoff between AI and people.
Field research translated natural conversation into enterprise requirements for workflow, governance, escalation, and trust.
Public-safe caseNames, values, visual language, and identifying details are reconstructed.
Live request · authenticated
“I need help with a leave question.”
I can help clarify that. Before we continue…
Field reactions + workflow probes
Handoff packet + readiness model
Natural conversation felt promising, especially for people who could not or did not want to use a portal. The risk was that a polished interaction could hide failures in contact-center integration, privacy, routing, language, and support operations.
I expanded the research probes, synthesized customer evidence, led the requirements-oriented readout, and translated findings into a concrete readiness model.
The work shifted the product conversation from conversational quality alone to self-service flow, warm transfer, domain boundaries, audit, language, noise, and the employee record after the call.
The readout produced specific action items across product, engineering, governance, and experience. Prioritization, pilot performance, and production outcomes remain unverified.
Reconstructed product and operating flow
The transition defined the experience.
Select a lens to see how conversation, workflow, contact-center architecture, governance, and employee trust intersected.
01
Caller
Intent, identity, context, language, environment
02
AI assistant
Clarify, guide, confirm, resolve
03
Contact center
Routing, queue, adapter, authentication
04
Human support
Summary, last intent, relevant context
05
Employee record
Confirmation, correction, next step
Guide, clarify, and confirm before creating a case or escalating. This protects both the employee experience and operational capacity.
A good conversation was only the beginning.
Customers responded to interruption-tolerant voice interaction and saw potential in repetitive employee questions. Their next questions were about systems, permissions, queues, operating models, and what would happen when the assistant could not finish the job.
Self-service had to come before case creation.
Automatically creating a case for every ambiguous call could increase queue volume. The recommended flow guided, clarified, and confirmed first, then created or escalated only when necessary.
Warm transfer was a continuity and trust problem.
A human handoff needed to preserve authentication state, recent intent, relevant context, and a concise summary so the employee did not repeat the call. The employee also needed a clear record of what was understood and what would happen next.
Enterprise readiness required governance and failure testing.
Multi-topic calls, regional retention rules, sensitive HR data, language switching, accents, noise, profanity, and interrupted authentication were launch conditions, not edge cases to solve later.
What the work enabled
A requirement stack for responsible handoff.
Produced a self-service-first experience model that connected user experience to operational capacity.
Defined a warm-transfer package that preserved context, authentication, and accountability.
Translated privacy, domain, language, and environmental constraints into product requirements.
Connected voice assistance to frontline access and broader enterprise-service strategy.
Public case boundary
This case demonstrates decision input and an explicit requirement stack. It should not imply that the requirements shipped, entered a pilot, or improved production metrics until that evidence is recovered.