Sales enablement · Queue Optimizer (beta)

Queue Optimizer sales kit.

From firefighting to service agility. The Product deck (beta) as one working page: the problem, the two days, the orchestration gap, how it works, the summary. No ROI example, discovery set, objection tracks or beta terms exist in this deck yet; the page says so where they would sit.

0 to 100%of SLA management still manual (deck figure)
0stages: capture, forecast, recommend, adjust
0WFM pain points the deck names
0slides in the source deck
ForSales and account management
SourceQueue Optimizer deck (beta) sales kit, Product, modified Jun 12, 2026
Built byDallas Andrews, GTM Engineering
UseInternal. Press P to present.
In one screen
  • The pitch: your WFM forecasts, your ACD routes, your dashboards visualize. None of them decide. Queue Optimizer is the layer that takes the signal from those tools and acts on it, across every commitment simultaneously.
  • The two days: the same demand spike. Today, risks show up late, decisions are firefighting, time goes to dashboard watching. With Queue Optimizer, early risk detection, approval controls, action recommendations.
  • How it works: ACD data capture, forecast and predict, recommend, adjust. The business objective is a service level or ASA for each queue in the ACD, with its constraints.
  • Not in this deck: no ROI example, no discovery questions, no objection talk tracks, no beta terms, no pricing, no target accounts. Product supplies those before sellers need them; nothing here is filled in from another deck or from memory.
01 · The reputation risk

Overstaffing, burnout and constant firefighting force WFM teams into survival mode.

Leaving little time for strategy or scale. Six pain points, in the deck's words.

01Too much time spent reacting
02Manual queue moves eat up WFM bandwidth
03Overstaffing "just in case" is costly
04Burnout is a hidden variable
05Last-minute coverage drives costly overtime spend
06No room for strategic work
The result

Inadequate workforce planning creates cascading operational failures that undermine service delivery.

When performance issues arise, the complexity multiplies

Competing priorities

Managing multiple different queues simultaneously.

Manual calculations

Re-forecasting each affected queue.

System updates

Attendance, scheduling and capacity adjustments.

Time pressure

By the time decisions are made, it's too late.

No one person can handle simultaneous re-forecasts in real time. You need a team, or a better solution.
02 · Two days

The same demand spike can create two very different days.

Switch between the two. The deck shows this slide twice; it is the comparison sellers should hold in their head on a call.

Risks show up lateIssues become visible only after performance starts degrading.
Firefighting decisionsTeams scramble to recover; costly moves under pressure.
Dashboard watchingTime goes to monitoring, not preventing the slip.
Early risk detectionRisk is flagged before queues drift past target.
Approval controlsQueues are stabilized using configurable approval.
Action recommendationsClear guidance on what to change, when, and why.
03 · The orchestration gap

You already have forecasting. You already have routing. What you don't have is orchestration.

Your WFM forecasts. Your ACD routes. Your dashboards visualize. None of them decide. None of them orchestrate. They surface the problem and hand it back to a human, which is why 60 to 100% of SLA management remains manual even in well-resourced organizations.

WFM tools
Forecast
ACD / routing
Route
Queue Optimizer
Decide and orchestrate
Queue Optimizer
Recalculate run rate

The missing layer

The gap isn't visibility. It's the layer that decides what to do with what it sees, automatically, across every commitment, simultaneously. Queue Optimizer is the missing layer: the one that takes the signal from your existing tools and acts on it, autonomously, across every commitment simultaneously.

The 60 to 100% figure is as stated in the Product deck, with no source cited there. Customer-facing use of any figure goes through the Value Repository.
04 · How it works

Four stages. Click each for the deck's description.

  • Deck textAdditional ACD provider services capture call data (like number of calls offered and answered, SL / ASA, and average handle time) and store it to track queue performance and trends over time.
  • Deck textACD data tracks intraday service levels and ASA, helping forecast staffing needs for each queue.
  • Deck textThe Recommendation Engine analyzes inputs from business goals, forecasts, and ACD/WFM systems to optimize queue assignments, deciding which agents to add or remove from each queue.
  • Deck textUser-to-queue assignments update automatically, either with supervisor approval or through full automation.

Business objective (SLA / ASA)

A service level or ASA for each queue in the ACD, along with the associated constraints.

Product screens in the deck

Four slides are product screenshots. They are not reproduced on this page; open the deck for the screens. Their caption, verbatim: "Evolve from reactive firefighting to proactive workforce optimization that delivers consistent performance."

Slide 9
Setting queue targets
Screenshot in the deck
Slide 10
Configuring constraints
Screenshot in the deck
Slide 11
Turning business objectives into measurable results
Screenshot in the deck
Slide 12
Queue recommendations
Screenshot in the deck
05 · The summary

Live performance data, staffing forecasts, configurable constraints.

Intradiem's Queue Optimizer uses live performance data, staffing forecasts, and configurable constraints to dynamically recommend and make real-time adjustments to queue assignments, helping teams hit SLAs, reduce penalties, protect brand reputation, and cut manual work.

Hit SLAs
Outcome one
Reduce penalties
Outcome two
Protect brand reputation
Outcome three
Cut manual work
Outcome four

Through, by integrating with existing ACD systems

  • 01Queue target alignment
  • 02Data-driven staffing predictions
  • 03Automated workforce adjustments
  • 04Penalty risk mitigation
  • 05Comprehensive prioritization and optimization
Predictive analytics and strategic workforce recommendations, as the deck puts it. Outcomes are the deck's claims, not measured results.
06 · Not in this deck

What the Back Office Optimizer kit has and this one does not, yet.

No ROI example in the deck, so no calculator on this page

The Queue Optimizer deck carries no worked example, no customer profile, no benefit lines and no payback claim. A calculator would have to invent its inputs and its formula, so there is none. When Product publishes an ROI example, it is rebuilt here the way the Back Office Optimizer calculator was: the deck's numbers reproduced first, interpretation stated.

  • Discovery questions and listen-fors
  • Objection talk tracks
  • Beta program terms, eligibility, dates, obligations
  • Pricing and ACV ranges
  • Target accounts and ICP definition
  • Deployment prerequisites, connectors, out of scope, dependencies
  • Business impact ranges beyond the 60 to 100% manual figure
  • Who buys and why by persona
Deck figures are internal enablement material. Customer-facing use of any number still goes through the Value Repository.
Open with Product
  • Currency. The source deck is marked beta, "confidential and proprietary, do not distribute", and was modified Jun 12, 2026 in SharePoint (productmanagement / GTM / Sales Enablement / Queue Optimizer / Customer Facing Assets). Product confirms it is still the current beta narrative before sellers use this page on a call.
  • The one figure. "60 to 100% of SLA management remains manual even in well-resourced organizations" has no source in the deck. Product names the source, or the page marks it as an internal estimate.
  • The missing sections. ROI example, discovery, objections, beta terms, pricing, targets, deployment. Product supplies each in any form (slides, a doc, a spreadsheet) and the page picks them up in the same shape as the Back Office Optimizer kit.
  • The screens. Slides 9 to 12 are screenshots. If Product wants them on the page, they export as PNG and embed as is, captioned with the deck's caption.
  • Where it lives. This page is a file. It can sit on the Product SharePoint site or on a noindex Pages link like the Back Office Optimizer kit; Product's call.
Queue Optimizer · Sales kit (beta)

From firefighting to service agility.

Your tools forecast, route and visualize. None of them decide. Queue Optimizer is the layer that does.

The reputation risk

Overstaffing, burnout and constant firefighting force WFM teams into survival mode.

Too much time spent reacting
Manual queue moves eat up WFM bandwidth
Overstaffing "just in case" is costly
Burnout is a hidden variable
Last-minute coverage drives costly overtime spend
No room for strategic work
The result

Inadequate workforce planning creates cascading operational failures that undermine service delivery.

No one person can handle simultaneous re-forecasts in real time. You need a team, or a better solution.

Two days

The same demand spike can create two very different days.

Today (manual + reactive)
  • Risks show up lateIssues become visible only after performance starts degrading
  • Firefighting decisionsTeams scramble to recover; costly moves under pressure
  • Dashboard watchingTime goes to monitoring, not preventing the slip
With Queue Optimizer (guided + controlled)
  • Early risk detectionRisk is flagged before queues drift past target
  • Approval controlsQueues are stabilized using configurable approval
  • Action recommendationsClear guidance on what to change, when, and why
The orchestration gap

You already have forecasting. You already have routing. What you don't have is orchestration.

WFM toolsForecast
ACD / routingRoute
Queue OptimizerDecide and orchestrate
Queue OptimizerRecalculate run rate

The gap isn't visibility. It's the layer that decides what to do with what it sees, automatically, across every commitment, simultaneously.

Why the layer is missing
0 to 100%
of SLA management remains manual even in well-resourced organizations. Tools surface the problem and hand it back to a human.
When performance issues arise

The complexity multiplies.

Competing prioritiesManaging multiple different queues simultaneously
Manual calculationsRe-forecasting each affected queue
System updatesAttendance, scheduling and capacity adjustments
Time pressureBy the time decisions are made, it's too late
How it works

Capture. Forecast. Recommend. Adjust.

ACD data captureCalls offered and answered, SL / ASA, average handle time, stored to track queue performance and trends
Forecast and predictIntraday service levels and ASA forecast staffing needs for each queue
RecommendBusiness goals, forecasts and ACD/WFM inputs decide which agents to add or remove from each queue
AdjustUser-to-queue assignments update automatically, with supervisor approval or through full automation

Business objective: a service level or ASA for each queue in the ACD, with its constraints.

Integrating with existing ACD systems

Predictive analytics and strategic workforce recommendations, through:

The summary

Real-time adjustments to queue assignments, so teams:

Hit SLAs
Reduce penalties
Protect brand reputation
Cut manual work

Live performance data, staffing forecasts, configurable constraints. Deck claims; customer-facing numbers go through the Value Repository.

Intradiem · Queue Optimizer
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