A practical, KPI-driven roadmap to evaluate agent assist, chatbots, copilots, and knowledge systems - plus pricing scenarios, benchmarks, and a 5-step adoption framework.
Our AI Buying Guide examines practical uses of AI in customer support and provides a structured approach to technology selection. It organizes the market into four working categories - agent assist, chatbots, copilots, and enterprise knowledge - and explains where each fits in a modern support stack. The guide uses a KPI-first lens so decisions align to measurable outcomes such as average handle time, escalation rate, first-contact resolution, CSAT, and time-to-proficiency.
AI in support isn’t one tool. It’s a set of categories that solve different problems: Customer Service Platforms, Chatbots, Copilots, and Enterprise Knowledge. Use this lens to narrow the field before you compare vendors.
A simple framework to go from exploration to value: Define KPIs, Map your unique criteria, Evaluate archetypes, Gauge TCO, De-risk with trials/POC/pilot. Teams use it to align stakeholders and avoid “AI demo” traps.
Benchmarks vary by industry, but assist AI commonly moves core metrics. Use them as directional goals, then baseline your own data before piloting.
Expect seat, ticket, resolution, or hybrid pricing. Model TCO beyond license: setup, maintenance, vendor flexibility, and scale effects. Include outcome variability if you pay per resolution.
Every path has tradeoffs across implementation, ongoing ops, and lock-in. Compare Add-on (CS platform), DIY (one ecosystem), White-glove (chatbot/search), and Integrator (copilot platform) to match your constraints.
Validate tech, UX, and outcomes in stages: Free Trial, Technical POC, Pilot/POV. Track AHT, FCR, CSAT, backlog, and time-to-proficiency to prove value before scaling.