AI for Contact Centres

Converting forecast demand into delivered service
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AI for Contact Centres
Trusted by leading organisations

How We Support Contact Centres

In Contact Centres, service levels, customer experience, and cost are determined not by average volumes, but by how accurately demand is anticipated and how effectively workforce decisions are made ahead of time.

SolvedBy.Ai connects demand forecasting directly to workforce planning, staff scheduling, opening hours, and resource allocation. This allows contact centre leaders to meet service targets, control costs, and stabilise operations using existing teams rather than relying on overtime, outsourcing, or reactive firefighting.

AI Solutions for Contact Centres

Contact centre demand varies by channel, queue, time of day, day of week, campaign activity, and external events. AI Demand Forecasting predicts contact volumes at the level contact centres actually manage, by channel, queue, site, and time period, rather than relying on historic averages.

In contact centres, this is used to identify where volumes are consistently over- or under-forecast, supporting earlier decisions on staffing levels, channel mix, campaign timing, and service prioritisation before demand arrives.
Labour is the dominant cost and constraint in contact centre operations. Labour demand forecasting converts expected contact volumes into required agent hours by skill, channel, and time period, making capacity gaps visible early.

For contact centres, this supports proactive planning of agent supply, reduces reliance on overtime and outsourcing, and limits service degradation caused by late or inaccurate staffing decisions.
Scheduling determines whether forecast capacity is actually delivered. AI Staff Scheduling aligns shifts, breaks, and skill coverage to expected demand, channel mix, and operating rules.

In contact centres, this improves adherence to service level targets, reduces schedule churn, and ensures skilled agents are available where and when demand materialises, rather than being misallocated across queues.
Not all operating hours deliver the same service or financial value. Opening Hours Optimisation evaluates when queues, channels, or sites should operate based on demand patterns and cost impact.

Contact centre leaders use this to decide where extended hours improve service and where low-volume periods can be consolidated, balancing customer access with workforce efficiency.
Resource allocation determines how limited agent capacity is deployed across channels, queues, and sites. SolvedBy.Ai supports decisions on where to place skilled agents to protect service levels and customer experience.

In multi-channel contact centres, this enables leaders to prioritise high-impact interactions, rebalance capacity dynamically, and improve outcomes without increasing headcount.
Availability of clinical assets directly affects throughput and revenue. Predictive maintenance identifies failure risk in critical equipment before disruption occurs.

For private providers, this is applied to imaging equipment, theatre infrastructure, and facility-critical systems, reducing cancelled appointments and lost revenue caused by unplanned downtime.
Resource allocation determines where care can be delivered. SolvedBy.Ai supports decisions on how clinicians, rooms, and equipment are deployed across services and sites.

In private healthcare groups, this enables leadership teams to prioritise higher-value procedures, rebalance capacity across sites, and improve return on existing assets without expanding estates or headcount.
Opening hours optimisation evaluates when sites or facilities should operate based on expected demand and financial impact. It identifies which hours contribute value and which create unnecessary cost.

This allows leisure operators to adjust opening times by site or facility, maintaining access when demand exists while reducing exposure during low-usage periods.
Budget forecasting builds budgets based on expected demand rather than fixed assumptions. Forecasts adjust as conditions change, giving finance and operations teams clearer visibility of risk and cost pressure.

For leisure organisations, this improves control, reduces variance, and limits late-year corrective action when demand shifts.
(Best suited to asset-heavy leisure operations)

Predictive maintenance identifies early failure risk in critical assets that affect safety, availability, or customer experience.

In leisure, this applies to equipment such as pool systems, HVAC, rides, lifts, and facility-critical infrastructure, reducing unplanned downtime and protecting service and safety.

Case Studies

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The Outcomes We Deliver

Improved service level performance

Demand and staffing plans are aligned earlier, reducing missed service targets and queue backlogs.

Lower cost per contact

Overtime, outsourcing, and overstaffing are reduced through more accurate labour planning and scheduling.

More stable workforce operations

Schedules are built around realistic demand patterns, reducing last-minute changes and agent fatigue.

Greater predictability of cost and performance

Consistent demand signals reduce volatility in service outcomes, staffing cost, and budget variance.

Faster executive decisions on operating model and channel strategy

Leadership teams use a shared, forward-looking view of demand and capacity to make timely decisions on channel mix, opening hours, and investment.

Reduced repeat contact driven by service instability

More consistent staffing and queue coverage reduces abandoned contacts and repeat calls caused by missed service levels.

Why SolvedBy.Ai for Contact Centres

The world’s most granular, most informed forecasts

SolvedBy.Ai produces demand forecasts at the level contact centres actually operate, by channel, queue, skill group, site, day, and time interval. This provides leaders with a clear view of where pressure will occur, rather than relying on averaged forecasts that mask peaks, troughs, and service risk.

Deeper exogenous intelligence

Contact volumes are influenced by factors beyond historic patterns, including campaigns, billing cycles, product launches, outages, and external events. SolvedBy.Ai incorporates these exogenous signals directly into forecasts so staffing and scheduling decisions reflect what is about to happen, not just what has happened before.

Minute-level granularity

Service levels are won or lost in short time windows. SolvedBy.Ai forecasts demand and workload at minute-level granularity, enabling more precise staffing, break placement, and skill allocation to protect service performance during peak periods.

Different models for different channels and queues

Voice, chat, email, and digital queues behave differently, with distinct arrival patterns, handling times, and abandonment dynamics. SolvedBy.Ai selects and tunes different models for each channel and queue rather than forcing a single approach, ensuring forecasts reflect how demand actually arrives and is handled.

Improves as demand patterns change

Customer behaviour, channel mix, and contact drivers evolve continuously. SolvedBy.Ai monitors model performance and adapts as patterns shift, ensuring forecasts and plans remain aligned to current operating reality rather than degrading over time.

Proven ROI

Contact centres achieve measurable returns through improved forecast accuracy, higher service level attainment, reduced overtime and outsourcing, and lower cost per contact. SolvedBy.Ai engagements are anchored to a paid Proof of Concept with a clear ROI threshold, ensuring value is demonstrated before scale.

A partnership built on ROI

We partner with contact centre leaders to deploy AI that connects contact demand, workforce planning, scheduling, opening hours, and resource allocation, improving service level performance, reducing cost per contact, and stabilising operations across channels and sites.

We commit to a minimum 10:1 ROI, with every £1 invested delivering at least £10 in measurable commercial impact.
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FAQ

In practice, it means forecasting contact volumes and workload by channel and time period, then using those forecasts to plan staffing, schedules, and operating hours before demand arrives. The focus is on delivering service consistently, not reacting after queues build.

Traditional WFM tools rely on historic patterns and manual adjustments. SolvedBy.Ai improves the demand signal feeding into WFM so staffing and schedules are built on more realistic forecasts.

SolvedBy.Ai addresses missed service levels, overuse of overtime and outsourcing, poor forecast accuracy, schedule instability, and inconsistent performance across channels and sites.

Decisions such as how many agents to staff, which queues to prioritise, when to extend or reduce hours, how to allocate skills, and how to balance cost against service targets.

Demand is forecast by channel, queue, site, and time period, incorporating historic volumes, campaign activity, and external drivers rather than relying on static averages.

By aligning staffing and schedules to more accurate demand forecasts, capacity is in place when contacts arrive rather than reacting after backlogs form.

Labour requirements are identified earlier, reducing reliance on overtime, agency cover, and outsourcing driven by late staffing decisions.

No. SolvedBy.Ai works on top of existing platforms, improving the planning decisions that feed into them.

Each channel is modelled separately, reflecting different arrival patterns and handling characteristics, allowing capacity to be allocated more effectively.

ROI is measured through improved service levels, reduced cost per contact, lower overtime and outsourcing spend, and more predictable workforce costs.

A time-bound engagement using real contact and workforce data to test whether AI can materially improve forecast accuracy, staffing alignment, and service outcomes.

There is no obligation to scale. The engagement stops if value is not proven.

COOs, Heads of Contact Centres, Customer Operations Directors, and CFOs accountable for service performance and cost.

A call to identify where demand volatility and staffing misalignment are driving service or cost risk, and whether AI can materially improve outcomes.

Demand and staffing forecasts are aligned directly to SLA targets by channel and queue. This allows leaders to see where SLAs are at risk in advance and adjust staffing or priorities before breaches occur.

Yes. SolvedBy.Ai can forecast demand and labour requirements across in-house, outsourced, and hybrid models, supporting better decisions on when to flex external capacity versus internal teams.

Forecasts and labour plans reflect skill groups and cross-skilling rather than assuming interchangeable agents. This ensures skilled capacity is protected for high-impact or specialist queues.

Yes. Voice, chat, email, and digital channels are modelled separately, allowing leaders to plan channel mix intentionally rather than reacting to overflow and congestion.

More accurate forecasts and stable schedules reduce last-minute changes, excessive overtime, and constant re-planning. This improves predictability for agents without increasing workload pressure.

Demand and labour forecasts can be used to inform workforce budgets and cost projections, reducing variance between planned and actual spend across the year.

SolvedBy.Ai typically uses contact volume data, handling times, workforce schedules, and channel information already available in ACD, WFM, and reporting systems.

Real-world contact centre data is often noisy or incomplete. SolvedBy.Ai is designed to work with imperfect data and makes uncertainty visible rather than hiding it behind overconfident forecasts.

SolvedBy.Ai operates under ISO 27001 and ISO 42001 standards, and Cyber Security Plus secure data handling and responsible AI use in customer operations environments.

TL;DR

SolvedBy.Ai forecasts contact demand at the level centres actually operate (by channel, queue, skill, and time interval) and connects those forecasts directly to labour planning, scheduling, opening hours, and resource allocation. This allows leaders to hit service levels, reduce overtime and outsourcing, stabilise operations, and control cost using existing teams.
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Charlotte Street, Edinburgh,
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AI Solutions
AI Demand Forecasting AI Labour Demand Forecasting AI Staff Scheduling AI Opening Hours Optimisation AI Footfall and Visitor Forecasting AI Inventory Optimisation AI Predictive Maintenance AI Resource Optimisation AI Price Optimisation AI Budget Forecasting AI Shift Create AI Task Scheduler
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Retail CPG Supermarkets Hospitality Leisure Manufacturing Private Healthcare Contact Centres
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