Salesforce Commercial and Professional Services UK Company newsroom

The Adecco Group

Adecco needed to respond to changing candidate expectations and handle high-volume, time-sensitive pre-screening faster, while preserving recruiter involvement and the human element in recruitment.

28 companies successfully fill their vacancies
4.6/5 satisfaction rating
57% of these conversations took place outside standard working hours
Thousands of candidates were contacted

Reported impact

Business outcome

The rollout helped Adecco engage candidates faster, extend interactions beyond office hours, free recruiters to focus on soft-skill assessment and meaningful follow-up, and deliver strong candidate satisfaction in production use.

  • Customer Experience
  • Productivity Improvement
  • Speed to Market

Solution

The Adecco Group deployed an AI-powered screening solution on Salesforce's Agentforce as the first wave of a broader global AI strategy. In the UK rollout, recruiters configure and shape the agent's inputs, then Agentforce runs early candidate screening conversations over SMS and email in a written format designed to mirror a live exchange. The system handles always-on outreach and pre-screening outside office hours, automating the first interaction layer so recruiters do not have to manually start every conversation. Agentforce then surfaces lists of engaged, pre-screened candidates ready for recruiter follow-up at the start of each day. The source does not name separate storage or analytics products, so the described architecture centers on the orchestration and serving layers: recruiter-defined prompts and criteria in Agentforce, candidate-facing AI conversations, and a human handoff back to Adecco recruiters for soft-skill assessment and next-step engagement.

Data flow

Recruiters define the agent inputs in Agentforce, which then contacts candidates through SMS or email and runs automated written pre-screening exchanges. Completed conversations are processed into lists of engaged, pre-screened candidates that recruiters review at the start of each day for human follow-up.

Solution architecture

1 components · 1 layer
  1. Uncategorized
    • Agentforce It powers the AI agent that conducts written pre-screening conversations with candidates and returns engaged, pre-screened candidate lists to recruiters.

Architecture clues

  • Candidates are informed from the outset that they are interacting with AI.
  • Candidates interact with the AI agent through SMS or email.
  • First-wave agentic AI rollout launched in the UK six months before publication.
  • Recruiters shape the agent's inputs before candidate engagement.
  • The deployment is described as a full production deployment.
  • The use case automates early-stage interactions and hands pre-screened candidates back to recruiters each day.

Evidence from the source

"AI agents providing lists of engaged, pre-screened candidates ready for contact at the start of each day."
"Candidates engage with an AI agent via SMS or email in a written exchange that mirrors a live conversation."
"Crucially, recruiters shape the agent's inputs, and candidates are informed from the outset that they're interacting with AI."
"Notably, 57% of these conversations took place outside standard working hours."
"The Adecco Group has successfully implemented an AI-powered screening solution with Agentforce from Salesforce."
"The agentic AI first wave rollout, launched six months ago in the UK, supports Adecco's recruiters in high-volume, time-sensitive tasks such as pre-screening of candidates."
"The first wave of the implementation has helped 28 companies successfully fill their vacancies thanks to an innovative AI-powered tool."
Transparency Evidence & methodology 90/100 reference value · 94% confidence · 1 source

Review how this reference was scored and trace each normalized claim back to its documented source.

01

Scoring methodology

How scores are calculated

Customer Reference Value measures usefulness as a customer reference. Evidence Confidence measures the quality and traceability of available evidence, not independent verification.

Customer reference value90/100
  • Problem clarity20/20
    Measures whether the customer's operational problem is specific enough to compare.
  • Implementation depth25/25
    Rewards named components, data flow, integrations, and architecture detail.
  • Outcome specificity20/20
    Measures whether the reported benefit is concrete rather than a generic claim.
  • Quantified impact15/25
    1 metric describe measurable impact.
  • Deployment scale10/10
    Rewards evidence of a live or scaled deployment and meaningful integration scope.
Evidence confidence94%
  • Source authority25/25
    The deployment is described by the adopting organization or a government source.
  • Claim traceability21/25
    10 of 12 key fields link to a named source.
  • Direct evidence20/20
    7 direct source quotes retained.
  • Date provenance10/10
    The publication date comes from the source page or publisher.
  • Source transparency8/10
    1 source reference document publisher, URL, authority, retrieval, and supported fields.
  • Implementation specificity10/10
    Rewards source-backed components, data flow, quantified impact, and a specific outcome.
02

Traceability

Sources & provenance

Field coverage shows which facts each source supports. Dates are shown only when documented by the source.

  1. The Adecco Group

    The Adecco Group customer story

    First-party
    https://www.adeccogroup.com/our-group/media/press-releases/the-adecco-group-completes-successful-first-agentic-ai-implementation-at-scale
    Source type
    Customer-published story
    Published
    Jul 30, 2025
    Supports
    • Customer
    • Business problem
    • Solution
    • Products
    • Solution components
    • Data flow
    • Integrations
    • technical area
    • use case category
    • Business outcome
    • outcome category
    • Metrics
    • Architecture
    • evidence quotes
    • Publication date

Date evidence

  • Published on source page Jul 30, 2025 · The Adecco Group · First-party
    Open date source
Not separately sourced Industry, Region, Company size. Treat these normalized attributes as lower-confidence until a dedicated source is added.