Running a vendor trial for an AI assistant is one of those projects that looks deceptively straightforward until you’re three vendors in and your inbox is full of demo recordings, feature matrices, and slippery promises about “human-like” understanding. I’ve run more than a few trials like this, and the difference between a trial that leads to a successful deployment and one that wastes months of team time usually comes down to two things: clarity of objectives up front, and a practical scoring rubric that everybody trusts.
Set crystal-clear objectives before you talk to vendors
The first mistake teams make is starting vendor conversations without a clear plan. You’ll get dazzled by a demo and end up measuring the wrong things. I always start by translating business outcomes into measurable objectives. Examples I use:
- Reduce repetitive ticket volume by X% for a defined set of use-cases (billing, password resets, returns).
- Improve first-contact resolution (FCR) for chat interactions by Y percentage points.
- Deflect FAQs to self-service and automated channels, increasing deflection rate to Z% while keeping CSAT >= target.
- Reduce average handle time (AHT) for assisted conversations by enabling agents with suggested replies and knowledge snippets.
- Deliver consistent tone and policy compliance across automated responses.
Make these objectives time-boxed and tied to a specific dataset or channel. “Improve chat FCR” is too vague; “Increase chat FCR for billing inquiries to 70% within 90 days” is actionable. Share this objective doc with vendors — it forces them to propose targeted test plans instead of generic demos.
Design the trial scope: use-cases, traffic, and success criteria
Define what you’ll actually test. I recommend three to five focused use-cases that represent the most common and most painful parts of your support load. Typical categories:
- Account and password management
- Billing and invoices
- Order status and returns
- Product troubleshooting for top issues
For each use-case, specify:
- Sample volume: how many conversations or queries you’ll send (real or replayed).
- Channels: web chat, email, SMS, or in-app messaging.
- Data sources: which knowledge base articles, product docs, or internal FAQs the model can use.
- Success metrics: accuracy, FCR, CSAT, escalation rate, and latency.
Collect the right baseline data
Before the trial begins, capture baseline metrics over a representative period (2–4 weeks). Key numbers I always collect:
- Volume by use-case and channel
- Current FCR and escalation rates
- Average handle time for assisted vs. automated handling
- CSAT or NPS for support interactions per use-case
- False positive/false negative rates for any existing automation
Baselines let you prove lift (or lack of it). Vendors that push back on sharing tools to gather this data are a red flag — you’ll need those baselines to measure real impact.
Build a practical scoring rubric (and share it)
A scoring rubric turns subjective impressions into comparable numbers. I always use a blended rubric across three dimensions: accuracy, usability/UX, and operational fit. Below is a sample table I use during vendor evaluations. You can copy and adapt this for your team.
| Category | Sub-metrics | Weight | Scoring |
|---|---|---|---|
| Accuracy | Intent recognition, answer correctness, hallucination rate | 40% | 5 = >95% correct; 4 = 90–95%; 3 = 80–90%; 2 = 70–80%; 1 = <70% |
| Customer Experience | Response clarity, tone, personalization, CSAT | 25% | 5 = CSAT >= target & consistent tone; 1 = poor tone or confusing replies |
| Agent Experience | Suggested replies quality, context surfacing, ease of handover | 15% | 5 = seamless handover & time saved; 1 = increases agent effort |
| Operational Fit | Integration complexity, security, data residency, scaling | 10% | 5 = fits infra & policy; 1 = major blockers |
| Vendor Support & TCO | Onboarding speed, training tooling, pricing clarity | 10% | 5 = clear roadmap & transparent pricing; 1 = opaque costs |
Walk stakeholders through the rubric and ask each reviewer to score vendors independently. Aggregate scores give you a defensible recommendation and reduce recency bias from an engaging demo.
Run both synthetic tests and live shadow mode
Synthetic tests (pre-defined transcripts and edge cases) let you validate accuracy against known questions. But synthetic tests alone are misleading — they’re often too “clean”. Always run a live shadow mode where the AI responds in parallel to real conversations but doesn’t serve customers directly. This uncovers real-life noise: typos, multi-intent queries, and context switching.
Monitor these metrics during shadow mode:
- Suggested response acceptance rate (for agent assist)
- Escalation triggers/false escalations
- Latency under production traffic
What to watch for — red flags that should stop the trial
Not every vendor is worth piloting. I stop trials early when I see any of these:
- Inflated demo claims: They promise capabilities in the sales deck that don’t exist in the environment we provide.
- Poor data handling answers: No clear approach to PII, data retention, or compliance (e.g., GDPR). If your legal or security teams aren’t satisfied, pause the trial.
- Lack of explainability: You can’t see why the assistant made a decision or surface the training sources — dangerous for regulated responses.
- High hallucination rates: The assistant confidently provides wrong answers; this erodes trust fast.
- Opaque pricing and hidden costs: Extra fees for connectors, training runs, or production scaling are common traps.
- Poor integration roadmap: If connectors for your CRM, ticketing system, or knowledge base are piecemeal, the work falls on your engineering team.
Governance checklist for production readiness
If a vendor passes the rubric and shadow mode, run through this checklist before greenlighting production:
- Data privacy and retention strategy approved by legal.
- Rollback procedure and clear escalation points for customer-facing errors.
- Monitoring dashboards for accuracy, latency, and customer feedback.
- Training and ongoing tuning plan with responsibilities assigned.
- Contract terms that include SLAs, security attestation, and clear pricing for scale.
Finally, treat the trial as a learning process, not procurement theatre. Document every experiment, every failed prompt, and every surprising success. I’ve seen teams spin up entirely new workflows off a single vendor insight (better context passing, or a micro-journey for returns) — and that’s the point. A well-run trial should give you a definitive answer, confident stakeholder buy-in, and a practical roadmap for rolling an AI assistant into production.
