Multi-Skill Forecasting in Amazon Connect (Done Right)

Most contact centers still staff like it’s 2010: one forecast, one pool of “interchangeable” agents, and a lot of frantic intraday reshuffling when reality hits. The result is predictable—some queues drown while others sit overstaffed, and your most valuable specialists get pulled into low-value work just because they’re available.

Amazon Connect’s multi-skill forecasting and scheduling is a practical fix for that problem. Instead of pretending every agent is the same, it forecasts demand by skill needs (language, product line, technical capability) and schedules accordingly. For teams trying to hit SLAs without inflating labor costs, this matters—especially in 2025, when customer expectations are high and budgets are tight heading into year-end planning.

This post is part of our AI in Customer Service & Contact Centers series, and it focuses on a specific, high-impact use case: AI-driven workforce optimization. You’ll learn what multi-skill forecasting changes, how Amazon Connect structures it (demand groups, staffing groups, priorities), and how to implement it without creating a brittle “WFM science project.”

Why multi-skill forecasting beats “one big forecast” staffing

Multi-skill forecasting improves staffing accuracy by modeling demand in the same way customers actually arrive: by need, not by your org chart. A single blended forecast hides the real peaks—like a French-language spike at 8:00am or a technical-support surge after a product release.

Here’s what goes wrong with the traditional approach:

• Overstaffing to protect SLAs: You schedule extra headcount “just in case,” which shows up as higher cost per contact.
• Understaffing specialized queues: You hit overall headcount targets but still miss SLA on the queues that matter most.
• Wasted specialist capacity: Bilingual or product-certified agents spend their day on general inquiries because routing can’t reliably preserve skill capacity.

Multi-skill forecasting directly addresses those failures with a simple principle:

If demand is segmented by skill, supply can be scheduled by skill—and SLA becomes a planning outcome, not an intraday emergency.

That’s the bridge to AI in customer service: the “AI” here isn’t a chatbot. It’s the optimization engine that assigns scarce human skills to the right work at the right time.

The core model in Amazon Connect: demand groups + staffing priorities

Amazon Connect implements multi-skill forecasting using Demand Groups (DGs) inside Forecast Groups (FGs), then connects those DGs to Staffing Groups (SGs) with priorities. Think of it as a clean, admin-friendly way to express: “These queues are French,” “these agents can handle French,” and “these agents should be reserved for French first.”

Demand Groups (DG): forecast slices that match skill needs

A Demand Group is a distinct subset of work inside a Forecast Group that is:

• independently forecasted
• associated with specific queues
• used later to drive schedule generation

In the AWS example, a global bank supports English, French, and Spanish across two business lines (Mortgages and Credit Cards). Agents may know both products but have different language strengths. The bank sets up:

• EnglishQueueGroup
• FrenchQueueGroup
• SpanishQueueGroup

A critical constraint (and it’s a good one):

• A queue can belong to only one Demand Group.

That one-to-one mapping prevents messy double-counting and keeps forecasts interpretable.

Staffing Groups (SG): supply pools with skill-aware assignment

A Staffing Group is how you organize agents for scheduling. Amazon Connect supports up to 250 agents per staffing group, and each agent belongs to one staffing group.

The big multi-skill change is this:

• A staffing group can be linked to multiple demand groups, with a priority order.

So you can create a team like TrilingualExpertTeam and tell the scheduler:

1. Prioritize FrenchQueueGroup
2. Then SpanishQueueGroup
3. Then EnglishQueueGroup

That priority list is where the system stops treating agents as interchangeable. It’s also where your operational judgment matters.

Priority should reflect business value and true proficiency—not internal politics.

Implementation workflow: segment demand, link supply, manage schedules

The implementation is straightforward if you follow the same three-step mental model Amazon Connect is built around.

1) Segment demand: set up Forecast Groups and Demand Groups

Answer first: Create a Forecast Group for a line of business, then add Demand Groups to represent skill-based slices of demand.

Operationally, the quality of your forecast depends on two inputs:

• historical contact volume
• average handle time (AHT)

The AWS guidance assumes you’ve been running those queues for 6–8 months, which is a reasonable minimum for seasonality and trend detection.

Practical tips I’ve found useful:

• Choose a segmentation dimension you’ll actually staff to. Language is a great fit. “VIP vs. non-VIP” can be too—if routing and staffing policies truly differ.
• Don’t over-segment early. Three demand groups you trust beats twelve you constantly override.
• Use 15-minute intervals if your center has sharp peaks. Use 30-minute intervals if staffing is stable and you want simpler schedules.

Once the forecast looks sane, publish it. Scheduling in Amazon Connect depends on a published forecast.

2) Link supply: connect staffing groups to demand groups (with priorities)

Answer first: Build staffing groups that reflect real teams, then associate each with the demand groups they can cover—and set priorities so scarce skills are protected.

This is the part that turns “forecasting” into “operational control.” You’re telling the optimizer what to do when demand groups compete for the same people.

Two high-leverage moves here:

• Create specialist teams and flex teams intentionally. For example: a dedicated French team plus a trilingual flex team that backfills peaks.
• Make priorities asymmetric on purpose. If French volume is lower but SLA impact is higher (or talent is scarce), give it higher priority.

3) Manage schedules: generate, review warnings, publish

Answer first: Generate schedules up to 18 weeks out, optimize for SLA or ASA, review warnings/failures, then publish.

Amazon Connect scheduling is designed to generate the least number of shifts that still meet your optimization goal. Scheduling runs can take 30 minutes to 3 hours, depending on constraints and complexity.

A few operational realities worth planning for:

• Your shift structure matters as much as the forecast. If your shift profiles are overly rigid, the optimizer can’t “find” solutions without adding headcount.
• Draft schedules are hidden from agents until you publish. That supports iterative improvements without disruption.
• You can filter schedule views by demand group. That’s useful for intraday: you can see where you’re thin by skill, not just overall.

Amazon Connect also supports partial scheduling for a specific staffing group—handy when you add new hires mid-cycle.

Where the “AI” actually helps (and where it doesn’t)

AI in customer service often gets framed as bots replacing humans. I’m more interested in the other side of the equation: AI making human teams operate with less waste.

In multi-skill forecasting and scheduling, the optimizer helps in three concrete ways:

1. Skill-preserving allocation: Trilingual agents get reserved for the demand groups where they add the most value.
2. Dynamic balancing across demand groups: When French demand is peaking, flex capacity shifts there; when it drops, those agents aren’t stranded.
3. Reduced manual scheduling overhead: Less spreadsheet work, fewer subjective decisions, fewer recurring “why are we missing SLA again?” meetings.

But it won’t fix foundational issues for you:

• If your AHT data is wrong, your forecast will be wrong.
• If your queues aren’t cleanly defined, demand group mapping becomes messy.
• If your routing strategy conflicts with staffing priorities, you’ll get confusing results.

The scheduler can’t compensate for a contact center that hasn’t decided what work is truly “specialized.”

Practical playbook: how to start without breaking everything

Most companies get stuck because they try to model every skill on day one. Don’t.

Here’s a safer rollout plan that still delivers value quickly.

Phase 1: Start with one segmentation that hurts today

Pick the segmentation that causes the most pain:

• language
• technical tier (Tier 1 vs Tier 2)
• regulated workflows (authentication-heavy, payments, healthcare)

Create 2–4 demand groups max.

Phase 2: Protect scarce skills with one flex team

Create:

• one or more specialist staffing groups
• one flex staffing group (your “multi-skill buffer”)

Then set priorities so flex agents get pulled into the specialized queues first when needed.

Phase 3: Add controlled overrides via staff rules

Skills change. People get trained. New products launch.

Use staff rules to override an individual agent’s demand group associations when reality diverges from the staffing group default. This is also your escape hatch during transitions.

Phase 4: Measure what matters (not just “schedule fit”)

Track outcomes by demand group:

• SLA and ASA by demand group
• occupancy and shrinkage impact
• transfer rate (a proxy for skill mismatch)
• quality scores for specialist queues

If you want a simple north-star metric:

Cost per answered contact, segmented by demand group, is where staffing strategy shows up in dollars.

The bigger trend: contact centers are becoming skill marketplaces

Multi-skill forecasting is a step toward a broader shift in AI-driven contact center workforce management: treating your operation like a real-time marketplace of skills.

Customers don’t arrive as “calls.” They arrive with constraints—language, urgency, compliance, sentiment, revenue risk. The centers that win in 2026 won’t be the ones with the most headcount. They’ll be the ones that can reliably align the right human capability to the right interaction, while automation handles the routine.

Amazon Connect’s multi-skill forecasting and scheduling fits neatly into that direction. It’s not flashy. It’s useful.

If you’re evaluating AI in customer service, this is one of the highest ROI places to start: get staffing aligned with skill demand, then layer in conversational AI, agent assist, and analytics on top of a workforce plan that actually reflects reality.

What’s one skill constraint—language, product expertise, or technical tier—that you’d segment first in your contact center, and what SLA would you protect with it?