Success Story
“We weren’t afraid of AI. We were afraid of deploying it incorrectly. A failed AI support rollout would have damaged customer trust across our entire portfolio.”
An AI-First Company With an AI-Shaped Problem
IgniteTech isn’t a software company that makes one thing.
It’s a global enterprise software company that acquires, revitalizes, and supports a portfolio of mission-critical solutions across industries — supply chain, HR, communications, IT acceleration, customer experience, sales. Products that organizations depend on to actually run their operations.
By 2026, they had over 30 enterprise products under management. Jive AI powering internal communications for distributed workforces. Knova AI handling complex customer service queries at scale. Gensym AI automating industrial systems and manufacturing processes. Each with its own customer base, its own SLA expectations, its own escalation paths.
In 2023, leadership made a deliberate call. IgniteTech wasn’t going to watch AI reshape enterprise software from the sidelines. They retooled the entire company to become an AI-first innovation organization. New AI-native products were built from scratch. Existing portfolio products got AI capabilities embedded. The brand, the positioning, the roadmap — all of it pointed in one direction.
That bet raised the stakes considerably for every customer-facing function. With an AI-first identity came AI-first expectations. And with 30+ products each generating their own support volume, the question wasn’t whether to deploy AI in customer support. It was whether they could do it without becoming the cautionary tale.
The Challenge: Scale Without Shock
On paper, support was working fine. Resolution times were stable. CSAT was acceptable. Headcount was growing proportionally with ticket volume.
But growth was masking something.
Every new AI-powered feature introduced new customer questions. Every acquisition added new queues. Every product expansion widened the gap in support consistency across the portfolio. And the math’s that had always held — add tickets, add agents — was about to stop working.
AI was becoming central to IgniteTech’s brand identity. Which meant if the support AI failed publicly, it wasn’t just a bad quarter. It was a trust problem across 30+ products simultaneously.
Enterprise AI pilots have a reputation. Most of them don’t deliver real P&L impact. The SVP of Global Customer Support knew the technology wasn’t the hard part. The board had been clear: prove margin impact, protect customer experience, and don’t become the 95%.
“Every product expansion meant new questions our agents had never seen. We couldn’t keep absorbing that with headcount. And we couldn’t afford a botched AI rollout to make it worse.”
Deploy AI in support without becoming the cautionary tale.
The Solution: Discipline First, AI Second
IgniteTech didn’t flip a switch and hope for the best.
They chose Kayako and deployed its AI agent — Kay — one support queue at a time. Defined metrics upfront. Executive oversight at every stage. Expansion only after validation. The objective was simple: prove measurable impact in a controlled environment, then replicate.
1. Lock in success criteria before Kay goes live
Before Kay handled a single ticket, IgniteTech defined exactly what good looked like: First Contact Resolution, Total Resolution Time, Cost per Ticket, CSAT stability, and autonomous resolution rate. Kay would be judged on numbers — not on the novelty of having AI in the stack.
2. Deploy Kay in one queue, not across the portfolio
Instead of exposing 30+ products to AI risk simultaneously, they selected one support queue with high ticket volume, repetitive inquiry patterns, and clear workflow boundaries. Kay operated with confidence thresholds configured from day one — anything below threshold routed instantly to a human. No portfolio-wide exposure.
3. Validate the numbers, then expand
Within 90 days, the pilot queue delivered. 68% autonomous resolution. 73% reduction in resolution time. Stable CSAT. Zero SLA breaches. Only then did IgniteTech extend Kay to the next product line — same process, same validation gate.
How Kay Operated Across the Portfolio
Kay isn’t a chatbot that deflects to help articles. It’s an AI employee that reads tickets, reasons about policy, looks up account data, composes a contextual response, and closes the ticket — as a single coherent operation. At IgniteTech, that meant five capabilities running in parallel across 30+ product queues.
| Kay capability | What it solved at IgniteTech |
|---|---|
| Intelligent Triage | Every inbound ticket auto-classified by product line, customer tier, intent, and priority before any human agent saw it. Agents stopped spending time on intake — they opened tickets that were already understood. |
| Autonomous Resolution | Password resets, billing queries, account updates, order status, known product FAQs — resolved end-to-end by Kay without human intervention. Across the first pilot queue, 68% of tickets never touched a human agent. |
| Agent Assist | For complex queries that required a human, Kay drafted the reply, surfaced relevant KB articles, and flagged suggested actions. Agents reviewed, refined, and sent. Handle time dropped 73%. |
| Escalation Intelligence | When Kay’s confidence fell below threshold, it compiled a full handoff summary — classification, conversation history, actions taken, recommended next steps — before routing to a senior agent. Customers never had to repeat themselves. |
| Cross-Product Learning | Kay’s intelligence layer carried across product lines. Resolution patterns learned in one queue informed performance in the next. Every deployment made Kay smarter across the entire portfolio. |
“From day one, Kay showed high percentage of correct output, on par with our manual counterpart. Over time that shift in trust was everything.”
Implementation: No Portfolio Shock
There was no big-bang launch. No overnight cutover across 30+ products. Deployment followed a controlled sequence, and every piece of it was deliberate.
Risk Containment From Day One
Kay was deployed inside a single support queue with confidence thresholds configured before activation. Human fallback was embedded in every workflow. SLA monitoring was live from the moment Kay went active. If Kay’s confidence dropped below threshold on any ticket, it routed instantly to a human — without delay, without customer-facing friction.
White-Glove Configuration, Not Self-Serve
Kayako’s implementation team worked directly with IgniteTech’s support leadership to map every ticket category, define triage logic per product line, and train Kay on validated historical tickets. Success metrics were locked before go-live. This wasn’t a feature toggle. It was an operational deployment with named owners and defined exit criteria at every stage.
Agent Enablement Before Activation
Each product-line rollout included a one-day agent onboarding session: how Kay’s confidence scoring works, what triggers a handoff, how to review Kay’s decision trail. Agents were introduced to Kay as a specialist colleague — not a replacement. No weekend downtime. No SLA dip during activation. No CSAT drop recorded across any rollout.
Results & ROI: From AI Pilot to P&L Impact
Within 90 days of controlled rollout, IgniteTech moved AI from theory to operating leverage. By month six, Kay wasn’t a pilot. It was infrastructure.
Financial Impact — Year One
Kayako annual cost: ~$820K · Less than 15% of projected incremental hiring cost · Break-even: Month 4
Performance Metrics Comparison
| Metric | Pre-Kay | Post-Kay | Improvement | Timeframe |
|---|---|---|---|---|
| Autonomous Resolution Rate | 6% | 68% | +62 pts | 90 days |
| Avg. Resolution Time | 5.1 hrs | 1.4 hrs | ↓ 73% | 90 days |
| First-Touch Resolution | 56% | 84% | ↑ 50% | 120 days |
| Cost per Ticket | $15.30 | $6.40 | ↓ 58% | 120 days |
| Net Headcount Added | +2 agents/month | 0 net increase | Fully absorbed | Year 1 |
| CSAT | 91% | 92% | Maintained | No dip |
From Pilot Queue to Portfolio Standard
Once the first queue validated, IgniteTech standardized the rollout model. Every new product line followed the same sequence: define baseline metrics, activate Kay with guardrails, monitor confidence and CSAT, expand only after validation.
Kay’s learning carried across products. Resolution patterns built in one queue informed performance in the next. Performance became predictable. Variability dropped. By month six, AI wasn’t an experiment. It was the operating model.
What This Means for Support Leaders
IgniteTech didn’t avoid the 95% failure trap because they had better technology. They avoided it because they had rollout discipline — and because Kay was built to earn trust progressively rather than demand it upfront.
They defined what success looked like before anyone touched a keyboard. They activated Kay in controlled environments. They expanded only after the numbers confirmed it worked.
- → AI became a margin lever — not a cost center or a PR statement
- → Customer trust remained intact across every product line through the transition
- → Headcount growth stopped tracking ticket volume for the first time in the company’s history
- → Performance became predictable and replicable across 30+ products
Kay wasn’t deployed as a gamble. It was deployed as an investment with defined returns and defined kill criteria. That’s why it worked.
The 95% Failure Rate Is a Discipline Problem. Not a Technology Problem.
Kayako’s Agent Kay resolves up to 80% of support tickets autonomously — one queue at a time, with confidence scoring, defined escalation paths, and zero migration risk.
