Soraia Soraia

AI Readiness Assessment · Anonymous Sample

AI Readiness Assessment for Acme Recruitment Srl.

Process mapping · Bottleneck identification · Stack evaluation · Operational action plan

Client Acme Recruitment Srl. (anonymized name)
Industry Recruiting & HR · Executive Search
Team 18 people (12 recruiters, 4 ops, 2 management)
Assessment duration 12 days · 8 internal interviews · 3 sessions with sponsor
Date March 2026

Section 01 · Executive Summary

What we found in 2 weeks of work.

Acme Recruitment is a fast-growing executive search agency (+38% revenue YoY) with a structural problem: 56% of recruiter team time goes to repetitive administrative tasks — CV screening, data entry on legacy ATS, interview scheduling, manual client reporting. Management knows they need freed-up hours to scale commercially, but internal initiatives (ChatGPT prompt templates, sporadic Make automations) have not generated systemic impact.

56%
Recruiter time on administrative tasks — measured
€186k
Recoverable annual value (12 recruiters × baseline)
3
High-priority processes identified for Sprint 1

Recommendation: proceed with a Co-Building Sprint of 5-7 weeks focused on the 3 priority processes (see Section 04). Estimated investment €34,000-€42,000, expected payback 7-10 weeks, "hours recovered or refund" guarantee included.

Section 02 · Operational Context

How the team works today.

Current stack

The team uses a fragmented stack: Bullhorn as primary ATS, Microsoft 365 for email/calendar, Slack internally, scattered Google Sheets for commercial tracking, Make for 2-3 isolated automations. No intelligence layer on top of the ATS.

Mapped processes (top 6 by time consumption)

1. Inbound CV screening ~14 h/week/recruiter

Recruiter downloads CVs from Bullhorn, reads manually, evaluates fit against JD, rejects or advances. 70% rejected after 3-5 min of reading. No fit-scoring automation.

2. Passive candidate outreach ~9 h/week/recruiter

Manual email personalization on LinkedIn + Bullhorn. Reply rate 6%. Generic templates hurt the brand.

3. Interview scheduling ~5 h/week/recruiter

Back-and-forth email coordination between candidate, client, and internal team. Calendly barely used because enterprise clients don't want public booking links.

4. Weekly client reporting ~4 h/week/recruiter

Manual Excel files updated every Friday. Candidate status, pipeline, next steps. Duplicate of data already in Bullhorn.

5. JD parsing & client intake ~3 h/week/recruiter

PDF/DOC received via email. Hard/soft requirement extraction, reformatting for Bullhorn. Frequent errors.

6. BD prospecting for new companies ~2 h/week/recruiter

Identification of companies in hiring spike. Mixed public sources, manual, not scalable.

Section 03 · Prioritized Bottlenecks

Where AI delivers the highest ROI, fastest.

We ranked 6 bottlenecks by time impact, technical feasibility, and speed to steady state. Only the top 3 warrant an immediate sprint.

Bottleneck Impact (h/week) Feasibility Priority
Inbound CV screening
Agent: JD-driven fit-scoring + classification
168 h/week High. Bullhorn has API P0
Personalized outreach
Agent: drafting + multi-touch sequence
108 h/week High, LinkedIn Sales Nav integration P0
Weekly reporting
Agent: Bullhorn extraction → narrative client report
48 h/week High, direct on Bullhorn P0
Interview scheduling
Agent: email-based coordinator
60 h/week Medium, requires 3-way orchestration P1
JD parsing
Agent: intake doc + Bullhorn fill
36 h/week High, low volume P1
BD prospecting
Agent: companies-in-hiring spotter
24 h/week Low, depends on unstable external sources P2

Focusing the sprint on the 3 P0s recovers 324 h/week in total (~57% of the team's administrative time). We tackle P1s in a subsequent sprint, once the 3 P0 agents are running stably.

Section 04 · Stack Assessment

What to keep, what to integrate, what to cut.

Tool Status Recommendation
Bullhorn (ATS) Keep Remains source of truth. Agents write via API.
Microsoft 365 Keep Email + calendar OK. Add Graph API for scheduling agent.
Make (automations) Reposition Stays for light glue. Complex agents move to a dedicated layer.
Google Sheets tracking Remove Duplicate of Bullhorn. Replaced by automated reporting.
LinkedIn Sales Navigator Keep Required for outreach agent + BD prospecting.
AI agents layer (new) Add Company LLM provider (Anthropic Claude Business) + workflow orchestrator + observability. Stack agnostic, details in Action Plan.

Compliance: candidates are personal data. The technical proposal includes EU infrastructure (Cloudflare Workers + EU-hosted LLM), full audit log of every agent decision, configurable retention (default 90 days post-closure), Art. 28 DPA with Soraia included.

Section 05 · Operational Action Plan

What we do in the next 5-7 weeks.

1

Week 1 · Kick-off + baseline

Full-day session with sponsor (CEO + COO + lead recruiter). Lock targets for the 3 P0 agents with measurable metrics. Dev environment setup + Bullhorn API access.

Deliverable: baseline document signed
2

Weeks 2-3 · Build Agent 1 (CV Screening)

Build fit-scoring agent against JD. Test on historical dataset from the last 6 months. Threshold calibration with 3 senior recruiters. Deploy in shadow mode (parallel to manual screening).

Deliverable: agent live + 1 week shadow data
3

Weeks 3-4 · Build Agent 2 (Outreach)

Email drafting personalization agent. Brand-approved templates. Multi-touch sequence (3 steps). LinkedIn Sales Nav + Bullhorn outbound integration.

Deliverable: agent live + A/B test setup
4

Weeks 4-5 · Build Agent 3 (Weekly Report)

Pipeline extraction from Bullhorn → client-facing narrative report. Custom template per client. Auto-send Friday 5pm with human preview in approval queue.

Deliverable: agent live + 2 weekly cycles tested
5

Weeks 5-6 · Team training + Adoption

4-hour workshop with all 18 team members. Agent demos, governance, escalation rules. Definition of internal "agent champion" (1 senior recruiter).

Deliverable: internal prompt-pack + policy 1-pager
6

Weeks 6-7 · Go-live + measurement

Shadow mode switched off. Week-1 measurement vs baseline. Final agent adjustments. 30 days of hypercare with weekly check-ins.

Deliverable: 30-day report with real metrics vs baseline

Section 06 · Investment + Guarantee

What it costs and what we promise.

Item Weeks Amount
Co-Building Sprint (3 P0 agents) 5 €28,000-€34,000
AI Adoption workshop (18 people, 1 batch) 1 €5,000
Hypercare (30 days post go-live) 4 Included
Total 5-7 weeks €33,000-€39,000

"Hours recovered or refund" guarantee. Sprint target: average recovery of 7 hours/week per recruiter at go-live + 30 days of hypercare. If we don't hit it, we work for free until we do, or we refund the sprint. Measurement against the baseline defined in Section 02.

12-month projection

~4,400h
Hours freed year 1
(12 recruiters × 7h × 52 weeks)
€220-€350k
Value generated year 1
(fully loaded €50-80/h)
7-10 wks
Sprint payback
(conservative scenario)

Next Steps

How we get started.

  1. Alignment (1 week): report review with sponsor + formal budget commitment.
  2. Sprint sign-off + 50% invoice: delivery weeks locked in. Kick-off within 10 days.
  3. Sprint start: 5-7 weeks to go-live. Assessment €2,000 credited against the sprint.

To align on next steps: [email protected] · [email protected] · soraia.io/en/contact.

This document is an anonymized sample. All client data, operational metrics, and projections are indicative of a real Soraia case but have been anonymized. The actual assessment is custom for each client and includes data measured directly against their operations.