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Client case study · Staffing & consultancyDelivered

From AI-aware to AI-enabled.

The firm already had Copilot, ChatGPT and Claude. What it lacked was AI inside the work. We trained every team, automated the repetitive tasks, and wired AI into Slack, Microsoft 365 and Egnyte.

  • 80% faster time-to-fill: AI-assisted screening shortened the path to placement.
  • $150K+ a year in productivity savings: from workflows that used to be manual.
  • $100K+ new annual revenue: from placing candidates faster.
Run a workflow See the story
ClientStaffing & consultancy firm
TeamsRecruitment · Client services · Ops
Built withCopilot · ChatGPT · Claude
AI at work · this weekillustrative

Meet our client

Client
Staffing & consultancy firm
Industry
Staffing, recruitment, consultancy, professional services
Market
Middle-market talent placement and client advisory

In a nutshell

The firm saw that AI could transform recruitment, client service and operations, but giving people access to the tools did not produce real gains. Prompting was inconsistent, use cases were not standardised and AI sat outside daily work. We ran a combined enablement and automation programme: structured training, AI-assisted workflows, integrations with the systems teams already use, coordination of the firm's external AI platform partner, and AI-assisted candidate screening.

Client data protectedThe client is anonymised, and every candidate, client, message and timing in the demos is fictional. The workflows are real; the data is not.
01 · The problem

Access is not adoption

Everyone could open an AI tool. Almost nobody used one the same way twice, and none of it was part of a standard workflow. Compare how AI was used across the firm before and after the programme.

Not usedAd hoc, each person their own wayStandard AI workflow
Illustrative picture of adoption, based on the challenges and outcomes described.
What was going wrong
  • Inconsistent usage: every team, and every person, prompted differently.
  • Low-value use: high-value AI workflows were rarely adopted.
  • Fragmented tools: AI lived in separate windows, outside Slack, Outlook and Egnyte.
  • Admin overload: significant time went on repetitive emails, formatting and scheduling.
  • No one coordinating: internal teams and the external AI platform partner were not aligned.
  • No measurement: nobody could say what AI was actually worth to the firm.
02 · AI training and enablement

Training built on the firm's real work

Structured sessions at several skill levels, reusable prompt templates and one-to-one coaching. Pick a task and compare a typical ad-hoc prompt with the template the team now uses.

Before · ad-hoc prompt
After · team prompt template
Fictional examples. Templates are illustrative of the reusable prompts built in training.
03 · Workflow automation

The repetitive work, handed to AI

Five everyday tasks now run as AI-assisted workflows, with a person reviewing before anything goes out. Choose one and run it.

    Manual
    AI-assisted

    Fictional content. Times are illustrative, chosen inside the measured 30–50% reduction in cycle time.
    04 · System integration

    AI inside the tools people already use

    Instead of another app, AI was wired into Slack, Microsoft 365 and Egnyte, and API integrations connected web forms, the CRM and internal tools. Follow one new application through the firm.

    0times the same data was typed by hand
    0systems updated
    Readystatus
    Fictional candidate. The flow is illustrative of the integrations delivered.
    05 · AI-driven candidate assessment

    Screening in minutes, decisions still human

    An AI-assisted workflow handles early-stage evaluation: it reads each profile, extracts qualifications and checks them against the role. Recruiters then spend their time on the people worth talking to. Run the screen.

    Open role · fictionalSenior Financial Analyst · hybrid
    Time to fill an open role
    Before
    100%
    After
    100%

    Measured: 80% faster time-to-fill, which brought in $100K+ of extra annual revenue from faster placements.

    Fictional candidates and scores. The AI suggests; the recruiter decides who moves forward.
    06 · Coordinating the AI platform partner

    From a team's wish to a buildable request

    We managed the relationship with the firm's external AI platform partner (Metal): gathering requirements, turning them into structured technical requests, prioritising across departments and coordinating delivery. Translate a real-sounding ask.

    Fictional requests. The steps are the coordination process we ran.
    07 · The result

    Measured in money, time and placements

    Following implementation, AI adoption grew across recruitment, client services and operations, and the gains were measured, not assumed.

    Money saved
    $150K+annual productivity savings
    $80Kannual reduction in manual labour costs
    Time and capacity
    30–50%faster workflow cycle times
    $200K+annual operational capacity unlocked
    Placements and revenue
    80%faster time-to-fill for open roles
    $100K+incremental annual revenue from faster placements
    What made it different
    The resultThe firm went from AI-aware to AI-enabled, with the skills and the operating framework to keep expanding AI across the business.
    Microsoft CopilotChatGPTClaudeSlackMicrosoft 365EgnyteMetal AI platformAPI integrationsCRMWeb formsPrompt templates
    From AI-aware to AI-enabled

    Give your team AI that actually gets used.

    Training, automation and integrations that turn AI access into measurable time, savings and revenue.

    What a first conversation covers
    1. 01Your business objective
    2. 02The technical context
    3. 03The right next step
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