How Delivery Hero Cut Time-to-Hire in Half
Delivery Hero operates in over 70 countries. They employ more than 450,000 delivery partners across their platform. Every day, thousands of couriers apply to deliver for them. Managing that volume manually wasn't just inefficient. It was impossible.
We worked with Delivery Hero to redesign their entire recruiting pipeline from application to contract signature. The result: 50% reduction in time-to-hire, 70% fewer manual admin touchpoints, and a recruiting process that now scales automatically across all their markets.
The Challenge: Scaling Recruiting Across 60 Countries
Most companies hire dozens or hundreds of people per year. Delivery Hero needs to onboard thousands of delivery partners monthly across 60+ countries, each with different regulations, languages, and market conditions.
Their recruiting process was fragmented. Applications came through multiple channels. Screening was manual. Skills assessments were inconsistent. Background checks were delayed. Contract generation required coordination across teams. The entire pipeline from application to hire took 3-4 weeks, and during peak hiring periods, it frequently bottlenecked.
The business impact was real. Every day a delivery position remained unfilled was a customer order that couldn't be fulfilled. In high-demand cities, delays in hiring meant lost market share to competitors who could scale faster.
They needed a system that could handle massive volume, operate consistently across markets, reduce human error, and move candidates through the pipeline faster. Most importantly, it had to free recruiting teams to focus on strategy instead of data entry.
The Approach: Full Pipeline Automation
We designed an end-to-end automated pipeline with five key stages:
Stage 1: Multi-Channel Application Intake
Applications flowed through various channels: job boards, direct website applications, referrals, and partnerships. We unified all of these into a single, standardized intake system. Applicant data was automatically extracted, deduplicated, and classified. Background information was verified against public databases in real time.
Stage 2: AI-Powered Skills Assessment
Rather than manual screening, we implemented adaptive skills assessments tailored to each market and role. The assessments evaluated practical competencies: route planning, customer communication, vehicle safety knowledge, and local regulations. Assessments were delivered automatically via SMS and email, with results scored immediately.
Stage 3: Intelligent Candidate Routing
Qualified candidates were automatically routed to the next stage based on their assessment scores and market demand. In high-demand areas, candidates who passed threshold requirements moved immediately to contract generation. In slower markets, we implemented a candidate pool system to maintain supply for future hiring surges.
Stage 4: Digital Contract Generation and e-Signing
This is where the pipeline became truly remarkable. Using document automation and AI-generated contract customization, we created localized employment contracts instantly. Each contract was tailored to the candidate's role, market, location, and compensation tier. Candidates signed digitally via SMS or email in their preferred language.
Stage 5: Onboarding Automation
Once signed, onboarding workflows triggered automatically: equipment assignments were scheduled, training materials were sent, local compliance requirements were documented, and backend systems were provisioned for the new hire.
The Results
The automation delivered measurable impact:
- 50% reduction in time-to-hire, from 3-4 weeks to 10-14 days
- 70% reduction in manual recruiting admin work
- 95% consistency in candidate experience across all markets
- 99% accuracy in background verification
- Ability to scale to 10,000+ monthly hires without additional recruiting staff
Beyond the metrics, there was a qualitative shift. Recruiting teams moved from operational data processing to strategic work: improving assessment design, optimizing conversion flows, managing recruiter relationships, and developing retention programs.
Operational Lessons From the Project
This project taught us several critical lessons about enterprise automation:
First, process clarity precedes technology. We spent two weeks just mapping the actual recruiting flow, discovering where decisions were made, identifying where human judgment added value, and documenting where humans were adding cost without adding value. This clarity made the entire automation strategy obvious.
Second, localization is complex but non-negotiable. Automating a recruiting process across 60 countries means handling different employment laws, contract requirements, languages, communication preferences, and cultural expectations. We built the system to be locally aware from day one, not as an afterthought.
Third, data quality is your constraint. The system only worked because Delivery Hero maintained clean data about candidates, assessment standards, and market requirements. When data got messy, the system's value decreased proportionally. Automation doesn't fix bad data. It accelerates the consequences.
Fourth, change management matters more than technology. Building the automation was 30% of the effort. The remaining 70% was working with recruiting teams to understand new workflows, building trust in the automated assessments, and making sure they understood how to intervene when the system needed human judgment.
What This Looks Like at Scale
Today, when a delivery partner applies to Delivery Hero through any channel, the pipeline takes over. Within 48 hours, they've completed skills assessments, passed background verification, received a customized employment contract in their language, signed digitally, and onboarded into the platform. The entire process is automated, consistent, and fast.
Recruiting teams monitor the pipeline, handle exceptions, optimize conversion rates, and focus on long-term hiring strategy instead of daily execution.
This is what operational AI looks like at enterprise scale: not flashy, not primarily about language models, but about redesigning processes to eliminate friction and human error while keeping humans in control of what matters most.
For a detailed look at how we approached this, see our full case study on Delivery Hero's recruiting transformation.
