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AI in contingent workforce planning: How enterprises can improve forecasting, visibility and control

September 23, 2026

TL;DR

Contingent workforce planning is becoming more complex as demand, skills requirements and workforce models change. AI and workforce analytics can help identify demand patterns, skills gaps, improve spend visibility and assess sourcing options earlier.

Why contingent workforce planning needs to become more predictive

Contingent workers are increasingly being used to provide specialist skills, project capacity and flexibility that may not be available through the permanent workforce. For enterprises, the challenge is no longer simply how to fill contingent roles quickly. It is knowing when external talent will be needed, which skills will be required, how much capacity should be secured and which sourcing channel is most appropriate.

That requires contingent workforce planning to move beyond reactive requisition management.

When workforce demand is assessed only after a hiring request has been raised, organizations have fewer options. Critical skills may already be scarce, supplier costs may be higher and talent pools may need to be built under time pressure.

Strategic workforce planning is increasingly being used to anticipate future capability and capacity needs rather than simply responding to immediate vacancies. McKinsey notes that organizations using strategic workforce planning can take a longer-term view of future skills and capacity gaps while connecting workforce decisions with broader business priorities.

For contingent talent, this means workforce planning needs to connect demand forecasting, sourcing, technology, cost management and governance.

A structured contingent workforce solution can provide the operating model, technology and oversight required to bring these elements together.

Where AI can improve contingent workforce forecasting

The value of AI in contingent workforce planning is not simply faster data analysis. Its greater potential is helping workforce leaders identify patterns early enough to influence the staffing decision.

Historical requisition data, project timelines, workforce volumes, skills requirements, supplier activity and spend patterns can be analysed to identify signals of future demand.

For example, an enterprise may find that:

demand for certain technology skills increases before major transformation projects

specific business units repeatedly use contingent workers for the same skill categories

certain suppliers consistently provide talent for particular roles

assignments are frequently extended beyond their original end dates

contingent spend increases significantly during predictable business cycles

These patterns can inform workforce planning before demand peaks.

The same principle applies to skills planning. As technology changes the skills required across organizations, the distinction between permanent and contingent talent becomes less useful when planning future capability. The World Economic Forum’s Future of Jobs Report 2025 found that AI and big data are among the fastest-growing skills, reinforcing the need for organizations to understand how skills requirements are changing.

For contingent workforce leaders, the practical question becomes:

Which skills should be developed internally, and which should be accessed externally when demand changes?

AI can help surface the patterns. Workforce leaders still need to decide what action should follow.

For a broader view of the market, the 2026 Contingent workforce imperatives report from AMS and Everest Group examines the forces reshaping contingent workforce management and the implications for enterprise programs.

Better workforce data creates better planning decisions

AI is only as useful as the workforce data behind it. This is a significant challenge for enterprises where contingent workforce information may be distributed across suppliers, business units, geographies, VMS platforms and other systems.

AMS research reports that AMS research reports that 75% of HR leaders struggle with cost visibility for their contingent workforce, while 70% lack visibility into their workforce because of legacy systems.

Without reliable data, AI can produce sophisticated analysis without necessarily producing better decisions. A stronger data foundation gives workforce leaders a clearer view of demand, skills, costs and workforce activity across the contingent program. This makes it easier to identify patterns, spot emerging gaps and make planning decisions with greater confidence.

A vendor management system can provide a central source of operational data while workforce analytics can help identify trends across the program. This is outlined in this guide key components of a contingent workforce strategy, where technology and workforce analytics are positioned as important components of visibility, supplier management and workforce planning.

The objective should not be to create more dashboards.

The objective should be to answer questions that affect workforce decisions.

For example:

Where is contingent demand increasing?

Which skills are becoming difficult to source?

Which suppliers are delivering the strongest results?

Where are rates increasing without a corresponding improvement in outcomes?

Which contingent workers or talent pools could be redeployed before new sourcing begins?

That is where workforce analytics starts to become a planning capability rather than a reporting function.

From workforce analytics to scenario planning

Historical reporting tells workforce leaders what has already happened. Predictive analytics can help them consider what could happen next.

This creates an opportunity to model different workforce scenarios before a staffing decision is made.

Consider an enterprise preparing for a large transformation program. Workforce analytics may show that similar projects previously required a significant increase in contractors with specialized technology skills.

Instead of waiting for individual requisitions, workforce leaders could assess several scenarios:

Scenario 1: increase permanent hiring for critical recurring skills.

Scenario 2: use contingent talent for the initial project period.

Scenario 3: build a direct-sourced talent pool for recurring contractor requirements.

Scenario 4: combine internal skills development with targeted external sourcing.

The right answer will vary by role, market, cost, duration and business strategy. AI can help process the underlying data and compare patterns across scenarios, but human judgment remains essential when deciding which model is appropriate.

This distinction matters because AI should support workforce decisions rather than become the decision-maker.

AMS’s Next Gen Talent Acquisition approach illustrates how unified data, predictive analytics and AI can be connected across permanent and contingent hiring while keeping people and business outcomes at the center of decision-making.

Leveraging AI for workforce forecasting

Forecasting only creates value when it changes what happens next.

If recurring demand is identified for a particular skill category, the organization can start building a talent pool before the requirement becomes urgent. When the same roles are repeatedly sourced through staffing suppliers, direct sourcing may provide another route to qualified talent.

AI and analytics can help identify these patterns earlier, while direct sourcing can provide a more targeted way to build and maintain access to talent. Together, these approaches can help organizations move from reacting to hiring needs to preparing for them in advance.

Rather than starting from zero each time a requisition is raised, organizations can use previous hiring activity and workforce data to identify talent that may be relevant to future requirements.

AMS’s Branded Direct Sourcing solution focuses on building relationships with contingent talent and creating reusable talent communities.

There is also tangible evidence that proactive talent pooling can affect workforce outcomes. As per our client case study, more than 70,000 qualified candidates were added to a talent pool and 60% of contingent roles were filled directly from that pool, creating more than $6 million in annualized savings.

That illustrates an important principle: workforce planning should not end with forecasting demand. It should create a mechanism for responding to that demand more efficiently.

Contingent workforce governance cannot be automated away

More sophisticated workforce analytics can also expose risks earlier, but AI should not replace governance.

Contingent workforce programs can involve worker classification, co-employment, supplier compliance, assignment duration, contract terms and different regulatory requirements across jurisdictions.

AI-supported monitoring can help identify patterns such as unusually long assignments, inconsistent rates, repeated extensions or supplier activity that falls outside expected parameters.

However, these signals should trigger review rather than automatically determine an outcome.

Human oversight remains particularly important where a workforce decision could have legal, financial or employment consequences.

This becomes even more important as organizations expand contingent hiring across multiple countries. A workforce planning model that optimizes cost without accounting for classification, compliance or local employment requirements can create risk rather than value.

A modern contingent workforce operating model therefore needs to connect analytics with controls.

AMS’s CWS model brings together consulting, next-generation MSP capabilities, direct sourcing, services procurement and insourced vendor management to address cost, compliance, talent and workforce visibility together.

What enterprises should measure

AI-enabled contingent workforce planning should ultimately be measured through business outcomes, not the sophistication of the technology.

The focus should be on whether forecasting is becoming more accurate, critical skills are being filled faster and contingent workforce costs are becoming more predictable. Supplier performance, direct sourcing and talent pool utilization can show whether access to talent is improving, while redeployment, compliance and assignment outcomes provide a view of how effectively the workforce is being managed.

Hiring manager satisfaction and time-to-productivity then show whether those improvements are translating into better experiences and faster business impact.

For example, a predictive model that accurately forecasts demand but does not improve sourcing outcomes has limited business value. Similarly, a large talent pool has little value if qualified workers are not being engaged and redeployed effectively.

Building a more predictable contingent workforce strategy

AI can make contingent workforce planning more predictive, but technology alone will not create a better workforce strategy.

An effective contingent workforce strategy brings together five core components:

Reliable workforce data to establish a clear view of contingent labor.

Demand and skills forecasting to identify future capacity requirements.

Workforce analytics to understand spend, suppliers, utilization and sourcing performance.

Flexible talent channels such as direct sourcing and talent communities to respond to recurring demand.

Governance and human oversight to manage compliance, risk and important workforce decisions.

This reflects a broader shift in contingent workforce management. External talent is increasingly being treated as part of how organizations access critical skills and deliver business outcomes rather than simply as an additional source of temporary capacity. AMS research describes this shift toward more talent-centric contingent workforce programs and greater visibility and control across the extended workforce.

The goal is to know where contingent talent creates value, when it is needed, which skills should be accessed externally and how that workforce should be managed alongside permanent talent.

When those decisions are supported by reliable data, predictive analytics and a clear operating model, contingent workforce planning can become more proactive, measurable and closely connected to business strategy.

For enterprises reviewing their current approach, the next step is to assess whether workforce demand, sourcing, technology, spend and governance are being managed as connected parts of the same strategy. AMS’s contingent workforce solutions provide a framework for managing different workforce models.
Ready to get started? Contact us to learn more.

FAQs

How does AI support contingent workforce planning?

AI can analyze workforce data to identify demand patterns, skills requirements, supplier trends and potential capacity gaps. It can also support scenario planning and help workforce leaders identify where action may be required earlier.

Can AI predict contingent workforce demand?

AI can support demand forecasting by analyzing historical workforce data alongside relevant business signals. Forecast accuracy depends on the quality, consistency and completeness of the underlying data.

How can workforce analytics reduce contingent workforce costs?

Workforce analytics can identify supplier rate differences, sourcing channel performance, utilization patterns, recurring demand and opportunities to improve workforce mix. The resulting insights can then inform sourcing, supplier management and workforce planning decisions.

What role does direct sourcing play in contingent workforce planning?

Direct sourcing can help organizations build reusable talent communities for recurring contingent requirements. When demand patterns are understood in advance, talent pools can be developed before hiring needs become urgent.

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