Intelligent Workforce Planning

Businesses are increasingly using AI in Fieldglass to anticipate rather than react — forecasting when skill gaps will appear, which positions are the most difficult to fill, and how contingent labor spend is evolving across different business units. This shifts workforce planning from a reactive, requisition-by-requisition exercise to something closer to strategic capacity planning.

Automated Candidate Matching

One of the most important capabilities available in 2026 is AI-based skills matching. Once a job requisition has been created, the system examines the job description to determine the competencies required and then matches contractors to that profile. SAP estimates that this could reduce the time it takes to find qualified candidates by around half, allowing hiring teams to concentrate on strategic, skill-based decisions instead of manually screening resumes.

AI Assistant for Faster Hiring

Joule, the SAP AI copilot, has been integrated into Fieldglass workflows to help speed up hiring. Instead of navigating through several screens to create requisitions or check their status, users can engage with Joule conversationally to move tasks forward faster, letting the assistant pull the appropriate information automatically.

Natural Language Queries

Joule lets users ask questions in plain language about workforce data — such as spend by category, supplier performance, or requisition status — instead of manually building reports. As Joule expands across SAP's solutions portfolio, this natural-language interface has become an established way of interacting with Fieldglass data, not a new feature.

Create Better Job Posts in Seconds

Fieldglass now comes with generative AI tools that can create precise, organized job descriptions using minimal input, helping hiring managers avoid inconsistent or unclear postings that slow down the hiring process.

Multi-language Job Description Translation

Since most Fieldglass customers operate globally, AI-powered translation lets an entire job description be translated into multiple languages rapidly, ensuring consistent hiring standards across regions without manual rewriting.

AI-Generated SOW Descriptions

Historically, defining a worker's deliverables and scope in a statement of work was a manual and often inconsistent process that could delay projects. SAP has introduced AI capabilities that can generate SOW deliverables and role descriptions instantly, with SAP reporting a substantial 70 percent reduction in the manual work required to create these documents.

AI Role Recommendations

In addition, the system can now suggest appropriate roles for workers by analyzing the SOW name, description, and historical data from previous SOWs and buyers, making it easier for hiring managers who aren't procurement experts themselves.

Skill Matching

Machine learning models analyze resumes against job-specific requirements to surface the most relevant skills for a particular role, instead of relying solely on keyword matches.

Experience Assessment

Beyond skills, the system also evaluates relevant experience — project history, industry background, and time in role — giving hiring managers a more accurate picture of candidate fit.

Qualification Recommendations

AI can also identify qualifications or certifications that may be relevant but weren't explicitly mentioned, helping recruiters surface high-quality candidates who might otherwise be overlooked.

Automated SOW Creation

Document AI capabilities enhance SOW creation by automating document processing and structuring, producing ready-to-use statements of work and reducing the back-and-forth typically required to finalize contract language.

Intelligent Document Processing

More broadly, Document AI is being used to interpret and extract data from unstructured documents such as contracts and compliance paperwork, turning material that previously required manual review into data the system can process directly.

Supplier Performance Analytics

Fieldglass is increasingly using analytics to track and score supplier performance over time, helping procurement teams make renewal and sourcing decisions based on data rather than anecdotes.

Workforce Cost Forecasting

Predictive models help procurement and finance teams anticipate trends in contingent workforce spend, supporting more precise budgeting and reducing the risk of unplanned cost overruns.

Automated Compliance Checks

As regulatory requirements for contingent labor — worker classification, tax status, data privacy — grow more complex across jurisdictions, AI-driven compliance checks help detect issues before hiring rather than after a worker is already engaged.

Worker Qualification Validation

The platform also supports automatic validation of worker qualifications and certifications, which is especially important in highly regulated industries where non-compliance can carry serious safety or legal consequences.

AI-Assisted Services Procurement

The services procurement space — covering project-based, outcome-driven work rather than pure staffing — is now receiving more AI assistance, including supplier evaluation and SOW-based sourcing, an area SAP has highlighted as a strength in recent industry recognition.

Faster Approval Workflows

Joule-driven automation is being used in approval chains, helping route requisitions, SOWs, and invoices to the right approvers faster and with fewer manual escalations.

Improved User Experience

SAP has partnered with adoption tools like WalkMe to guide users step-by-step through common Fieldglass tasks such as creating and revising SOWs and approving timesheets — particularly useful during system updates or new feature rollouts.