Duration: 25:00
PART 1 — Analytical Summary 🚀
Title: Using Generative AI to Automatically Sort & Score Thousands of Applicants in Odoo Enterprise
Speaker: Simon (Managing Partner, Much Consulting)
Context 💼
In this session, Simon from Much Consulting demonstrates how to deploy generative AI to triage, score, and advance job applicants within Odoo Enterprise, with a special focus on the Recruitment app. He frames recruiting as a high-ROI, text-heavy process that is ideal for generative AI and shares hands-on implementation patterns across Odoo 17/18 and the new Odoo 19 AI features. The talk addresses productivity, bias reduction, compliance (especially in the EU), and practicalities like cost, environmental footprint, and change management with recruiters.
Core ideas & innovations 🧠
The approach centers on using generative AI to evaluate CVs and applications against job criteria stored directly on the job profile in Odoo. Applications arrive via email, web forms, or third-party portals (e.g., LinkedIn), and Odoo creates an applicant record with attachments. After a short delay (about five minutes to ensure all files are available), a background process sends the context (job description, evaluation criteria, candidate data, and attachments) to an AI model using a structured prompt. The AI returns a concise summary, a confidence-based score, and a recommendation (advance, reject, or “uncertain”). In the EU, a human-in-the-loop confirms the decision to meet legal and compliance expectations; outside the EU, the workflow can update stages automatically (e.g., trigger first-interview scheduling) with no recruiter touch.
A notable design choice is to keep scoring rules in the job profile—what Simon calls an “internal evaluation guide”—so prompts can stay stable across roles while pulling job-specific criteria. The team emphasizes prompt engineering, parameter tuning (e.g., temperature for creativity tolerance), and robust context passing (CVs, chatter, emails, attachments). They reported challenges with Odoo 19’s AI properties when trying to pass attachments and to adjust model parameters there, expecting quick improvements from Odoo. As a proven alternative, Much Consulting maintains an open-source “AI Automated Actions” module that lets teams run AI from automation rules or server actions, choose among multiple models, tune parameters (temperature, max tokens), include attachments, reports, chatter, and write results back to fields or chatter.
The team’s view on bias is pragmatic: avoid training on historical CVs or similarity models (which often embed legacy bias). Instead, use prompt-based generative AI, which a cited study suggests can reduce bias by around 41% while improving selection quality. They also recommend introducing a third path (“uncertain”) to prevent forced choices and to surface atypical but promising profiles.
Impact & takeaways ⚙️
The operational benefits are substantial. For specialized roles (consultants, engineers), recruiters often process 150–250 applications per hire; the “read + decide + click” work averages two minutes per CV, or roughly 500 minutes per hire. By offloading first-pass screening to AI, teams reclaimed hours (Simon cites around six hours saved per hire in their deployments) and cut response times from about four working days to next-day replies—reducing candidate drop-off (which commonly hits 25% of qualified applicants due to slow processes). Recruiters spend less time on the least enjoyable task (CV sifting) and more on sourcing and interviews.
Cost and sustainability points are surprisingly favorable. Each application consumes around 3,000 tokens, costing less than $0.01 per API call. Measured narrowly at inference time, the process is estimated to be roughly three times more CO2-efficient than a human simply breathing for the equivalent review time (about 6g vs. 16g CO2, not counting model training). On data protection, the recommendation is to use paid APIs from providers like OpenAI or Google Gemini with strict settings (no training on customer data, appropriate data residency), and to reflect processing in the company’s privacy policy.
Change management advice is straightforward: prototype in a staging database with historical applications, tune prompts and parameters, then compare the AI’s scores to hires you actually made—this builds trust through measurable reliability. On calibration, adjust temperature to allow a bit of creativity for atypical but promising profiles (e.g., for consulting roles) and near-zero creativity for deterministic functions (e.g., accounting). The same pattern generalizes far beyond recruiting to any text-heavy workflow in Odoo (support, sales qualification, compliance summarization), where automation rules, template prompts, and context capture drive end-to-end reliability.
Overall, the talk’s message is that the AI-enabled recruiting flow in Odoo is practical, fast to implement (sometimes in under an hour with the open-source module), compliant with EU expectations when using a human-in-the-loop, and immediately impactful on time-to-reply, recruiter satisfaction, and candidate experience. 💬
PART 2 — Viewpoint: Odoo Perspective
Disclaimer: AI-generated creative perspective inspired by Odoo’s vision.
Our north star remains the same: simplify complex processes by integrating them natively. AI is not a bolt-on for us; it’s a capability that should feel invisible—available wherever users work, respecting context, privacy, and local regulations. I’m encouraged to see partners use Odoo’s templates, automations, and AI hooks to solve real problems like recruiting, where speed and fairness matter.
We’re iterating quickly on Odoo 19’s AI features so developers can pass richer context—attachments, chatter—and fine-tune behavior without friction. The goal is to make 80% of AI use cases “drag-and-configure,” with reliable defaults, auditability, and room for expert tuning. Community feedback like this session sharpens the product and keeps the ecosystem moving together.
PART 3 — Viewpoint: Competitors (SAP / Microsoft / Others)
Disclaimer: AI-generated fictional commentary. Not an official corporate statement.
Odoo’s pitch resonates for SMBs and mid-market firms: fast setup, cohesive UX, and the ability to wire AI directly into recruiter workflows. Their human-in-the-loop stance for the EU is sensible. The next hurdle is enterprise-grade scale and controls—governance over prompts and models, data lineage, rigorous bias monitoring, and audit trails that satisfy global compliance regimes.
For large organizations, depth in HCM, security, and integration remains decisive. Platforms like SAP SuccessFactors and Microsoft’s Dynamics 365 with Copilot emphasize model governance, data residency, and advanced policy controls. Odoo’s momentum on UX and time-to-value is impressive; turning that into standardized, globally compliant AI operations at scale—without sacrificing the product’s elegance—will be the real differentiator.
Disclaimer: This article contains AI-generated summaries and fictionalized commentaries for illustrative purposes. Viewpoints labeled as "Odoo Perspective" or "Competitors" are simulated and do not represent any real statements or positions. All product names and trademarks belong to their respective owners.