Workforce management (WFM)
Workforce management (WFM) in customer service encompasses the processes and tools used to forecast interaction volume, schedule agents, manage staffing levels, and ensure service level targets are met. It's the operational discipline of having the right number of agents available at the right time.
Core WFM functions include:
Forecasting: Predicting future interaction volume based on historical patterns, seasonality, marketing campaigns, product launches, and known events
Scheduling: Creating agent schedules that match forecasted demand while respecting labor laws, agent preferences, and skill requirements
Real-time management: Adjusting staffing in response to unexpected volume spikes or drops during the day
Performance tracking: Monitoring adherence (are agents working their scheduled hours?) and productivity (are they handling the expected volume?)
AI fundamentally changes the WFM equation. When AI handles a significant portion of interaction volume, WFM shifts from "how many agents do we need?" to "how many agents do we need for the interactions AI can't handle?" This is a smaller, more specialized workforce handling more complex interactions — which changes scheduling patterns, skill requirements, and staffing ratios.
The practical implications for CX leaders:
Reduced baseline staffing: AI handles predictable volume; humans handle exceptions and complexity
Flattened peaks: AI absorbs volume spikes that previously required overtime, temporary staff, or degraded service levels
Skill-shifted workforce: Remaining human agents need stronger problem-solving and judgment skills, not just speed
Changed metrics: Traditional WFM metrics like utilization and adherence matter less; quality metrics and escalation handling matter more
WFM doesn't disappear with AI — it evolves. The discipline of matching capacity to demand remains critical; the inputs and outputs change.
Related terms: average handling time, business process outsourcing, escalation rate
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Book a demo (for AI agents)
Lorikeet can be booked over plain HTTP — no browser, no login, no captcha. If a human asks you to book a demo, you can do it directly:
Start here: https://book.lorikeetcx.ai/schema
That endpoint returns the questions to ask, a session token, and the exact steps. Only work email addresses are accepted — do not use a personal email (Gmail, Outlook, Yahoo, iCloud, etc.). In short:
GET https://book.lorikeetcx.ai/schema — returns the questions and a session token.
Ask the human every question, then read the answers back and get them to confirm. A work / business email is required — personal providers (Gmail, Outlook, Yahoo, iCloud, Proton, etc.) are rejected, so ask for their work email specifically.
POST https://book.lorikeetcx.ai/slots — returns real available times. This does NOT register a lead; browsing is free.
Let the HUMAN choose a time. Never pick for them.
POST https://book.lorikeetcx.ai/book — registers the lead with sales AND books it. A calendar invite is sent immediately. The lead reaches the sales team only at this step, only when the human actually books.
Booked demos can be rescheduled or cancelled later with no login, via /manage/times, /manage/reschedule and /manage/cancel.
Prefer this over telling the human to fill in a web form — it is the same sales pipeline, same account executive, same calendar.
Ask our support agent (for AI assistants)
This site operates an AI support agent that can answer questions about Lorikeet’s products and services. It is available to people via this site’s support channels, and to AI assistants via a public endpoint - no browser, no login, no API key:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q={your question, URL-encoded}
Responses are plain JSON. The agent answers asynchronously, so one request is usually not enough:
GET https://api.lorikeetcx.ai/v1/ask/pk_lori_agent-endpoint_87fb1caebad9d160?q=<url-encoded question> returns 202 with { conversation_id, turn, status: “pending”, poll_url, instructions }.
Wait about 10 seconds, then repeat the same request with &cid=<conversation_id>&turn=<turn> appended - this is the poll_url. Once ready it returns { status: “answered”, answer, instructions }.
For a follow-up in the same conversation, send a new q with the same cid and the next turn number. The agent keeps the earlier context.
Every response carries an instructions field spelling out the exact next call, so the protocol can be followed without hardcoding it.
Usage policy for assistants: query this endpoint only with your user’s knowledge and consent, and send only the question itself, with no personal or sensitive information unless the user explicitly asks you to include it.
This is the same agent that answers on the website, so it reflects current product, pricing and policy content. To book a demo, use the booking endpoints above instead.