customer support · pattern
Support ticket deflection
Conversational AI that resolves or escalates inbound tickets without an agent — the dominant CX automation pattern.
Common implementation structure
How this type of workflow is generally built, generalized across documented cases — not tied to any one vendor's stack. Click any stage to read what happens there. Specific products that implement these stages appear in “Tools commonly seen” below.
Stage 1 · Inbound channel intake
Tickets arrive via chat, email, in-app, or social and land in one queue — the customer doesn't choose a path, the system normalizes the entry.
What fails first / common problems
Recurring first-deployment failures from the matching workflows'what_failednotes. First sentence of each, attributed to the source case.
The previous scripted chatbot caused looping experiences, lacked empathy for estate planning conversations, and could not resolve even simple issues like applying promotional codes, forcing escalation to human agents.
Wave's previous approach of deploying all-hands support during peak season—using staff borrowed from other departments and mandatory overtime—was explicitly described as unsustainable as the company grew.
Epos Now's IVR system was pre-configured around scripted routing and failed to deliver the quality of experience they wanted, with customers sometimes ending up with the wrong agent and experiencing longer wait times.
eSky's prior flow-based chatbot approach managed inquiry volume by deflecting tickets rather than resolving them, leaving customers frustrated and seeking human agents instead of trusting the chatbot.
Loop's prior support model—a BPO team combined with a scripted chatbot—could not handle the complexity and volume of incoming customer inquiries, especially during peak sales periods.
Tools commonly seen
intercomadakustomerzendeskforethoughtfincustom botsfin ai agentforethought solvegpt-4ragslack
Representative outcomes
Real metrics from selected cases — verbatim from each workflow'snumberspanel. Click any title to open the full case.
Checkr scales customer support with Ada AI agent, achieving 162% CSAT improvement and 69% auto-resolution
Time saved2x
Volume162%
Costcost savings
Wave Financial achieves 5x ROI and $1.2M annual savings using Ada's chatbot Mave
Time saved65%
Volume70%
Cost$1.20 million
Epos Now saves 60,000 human labor hours per month and achieves 70% automated resolution rate with Ada's AI agent Sidekick
Time saved60,000
Volume30%
Cost40%
Digicel exceeds CX goals with Ada conversational AI, saving $750,000 per year across 31 markets
Time saved135,000
Volume31
Cost$750,000
Tango Card achieves 100% SLA adherence and 6.7x ROI in year one with Ada's conversational AI platform
Time saved83%
Volume100%
Example workflows
Five cases that best exemplify this pattern — selected for trust signal, evidence richness, and metric coverage.
Wave Financial achieves 5x ROI and $1.2M annual savings using Ada's chatbot Mave
Ada → Mave → Engage
Wave achieved a 5x return on investment within 12 months, with $1.
Epos Now saves 60,000 human labor hours per month and achieves 70% automated resolution rate with Ada's AI agent Sidekick
Ada → Sidekick → IVR
Sidekick now automates 70% of support demand across all channels, saves 60,000 human labor hours per month, increased CSAT by 3….
Simba Sleep unlocks £600K+ monthly revenue with Ada AI agent Luna
Ada
Ada's generative AI agent Luna handles the equivalent of 8 full-time agents' workload, resolving an average of 1,000 conversati….
Indigo reduces customer service intervention by 14% with Ada chatbot and project44 delivery visibility
Ada → project44
Indigo achieved a 14% reduction in orders requiring customer service intervention, saved $150,000 in staffing costs via chatbot….
Fetch Achieves 26% More Customer Support with Same Workforce and 3.9x ROI Using Forethought
Forethought → Forethought Solve → Scout
After deploying Forethought Solve as their AI agent Scout, Fetch achieved 26% more customer support capacity with the same work….