From cash-burning lead-gen to 2× ROI in 5 months.
The Challenge
A global bank had built a heavily funded B2B AI lead-generation system that was burning cash and yielding poor-quality prospects. Token spend, vendor fees, and infrastructure were ballooning month-over-month with no corresponding lift in qualified pipeline. In parallel, the same business unit needed a predictive churn-reduction engine built from the ground up — without further bloating OpEx.
The Optimization
We forensically audited the existing lead-gen infrastructure end-to-end: token traces, vendor contracts, agentic prompt chains, and downstream attribution. We restructured the enrichment workflows around tighter intent signals, replaced over-spec'd proprietary endpoints with optimized open-source local inference for high-volume text processing, and rebuilt the messaging layer for the bank's actual ICP. Every architectural decision was validated against a CFO-Ready cost-per-lead model before deployment.
The Financial Yield — Optimization Timeline
| Priority KPI | Before | After 2 mo | After 5 mo | Stabilized | Why it matters |
|---|---|---|---|---|---|
| Multi-channel Conversion Rate | 0.8% | 1.3% | 1.9% | ~2.0% | Main commercial KPI. Validates that targeting, sequencing, and AI messaging are improving. |
| Monthly TCO (AI Lead-Gen) | $129K / month | ~$45K–60K / month | ~$20K–25K / month | < $15K / month | Measures infrastructure, tokens, and operating costs. Critical for P&L health. |
| Operational ROI | Negative | Break-even / positive | ~15–20% | > 24% ROI | Proves the transition from a technically interesting project to a commercially viable asset. |
| Cost per Converted Lead | $1,600–2,000 | ~$600–900 | ~$300–450 | ~$200–300 | Highly actionable for sales leadership — combines conversion lift and OpEx cuts. |
| Qualified Lead Volume | 80 / 10,000 | 130 / 10,000 | 190 / 10,000 | 200 / 10,000 | Demonstrates absolute business impact and top-line growth. |
ITSM AI agent that resolves tickets autonomously.
The Challenge
An Italian energy utility was running a heavily manual internal IT Service Desk supporting thousands of field engineers, control-room operators, and back-office staff. L1 triage was a dedicated cost centre, MTTR was hovering around an entire business day, and SLA breaches were a recurring board-level reporting line. The CIO needed measurable cost-per-ticket reduction without degrading employee experience.
The Optimization
We deployed and rigorously tuned an ITSM AI Agent purpose-built for utility-sector ticket taxonomies, then operationalised it: high-accuracy routing on intent classifiers, automated resolution playbooks for the long-tail of routine tickets, and tight integration with the existing ITSM platform so resolution telemetry fed straight into the CFO's cost-per-ticket dashboard. Continuous reinforcement loops keep the agent's autonomy rate climbing as it sees more of the company's actual ticket distribution.
The Financial Yield — 4–6 Month Maturation
| Priority KPI | Before AI Agent | Optimized State | Why it matters |
|---|---|---|---|
| Autonomous Resolution Rate | 0–5% | 25–40% | Tickets fully solved by AI with zero human intervention and no SLA breach. |
| Mean Time to Resolve (MTTR) | 18–24 hours | 7–10 hours | Proves the agent is actively accelerating workflow — not just a complex FAQ. |
| SLA Compliance Rate | 80–85% | 92–96% | Critical enterprise KPI. Reduces missed SLAs via instant automated triage. |
| Reopen Rate | 7–10% | 3–5% | Quality-control metric. A tuned AI resolves the root issue, reducing repeats. |
| Cost per Ticket | $18–25 | $10–15 | Direct executive ROI metric. Savings from eliminating L1 manual actions. |