From Pandemic Pivot to 1M+ Project Hours: Inside Mastercard's AI-Powered Talent Marketplace
When 93% of a 33,000-person workforce voluntarily registers for a single internal platform, something unusual is happening. When that platform logs more than one million project hours and one in three active users makes a career move, the case becomes impossible to ignore.
Mastercard's internal talent marketplace, Unlocked, did not start as a strategic initiative. It started as a scramble. In early 2020, the company needed to redeploy employees whose roles had been disrupted by COVID-19 — and the manual approach failed to keep up. Five years later, the platform born from that scramble has become one of the most fully scaled AI-powered talent marketplaces in enterprise HR, generating $21 million in first-year productivity gains and fundamentally changing how Mastercard thinks about skills, mobility, and workforce planning.
Here is what they built, how they governed it, and what HR leaders can take from the playbook.
The Problem: When Spreadsheets Hit a Wall
In early 2020, Mastercard launched "Project Possible," an internal initiative to match displaced employees with teams that needed help. The concept was straightforward: employees volunteered their skills and availability; HR coordinators matched them to open needs.
The results were instructive — but not encouraging. Using spreadsheets and manual coordination, teams could successfully pair only about 30% of volunteers with relevant opportunities (Gloat case study). The bottleneck was not willingness. Employees wanted to contribute. The problem was matching complexity: skills, availability, geography, and project requirements across tens of thousands of people could not be processed manually at the speed the moment demanded.
Project Possible proved two things simultaneously. First, employees were eager to move beyond their defined roles. Second, Mastercard's existing HR infrastructure could not capture or deploy that energy at scale.
The Solution: Unlocked
Mastercard partnered with Gloat, a workforce agility platform, to build an AI-driven talent marketplace that could do what spreadsheets could not: ingest thousands of employee skill profiles and generate intelligent matches across the entire organization (Gloat case study).
The resulting platform — branded Unlocked and launched in 2022 — uses AI algorithms to match employees with four categories of internal opportunity: short-term projects, mentorships, open roles, and learning paths. Unlike a traditional internal job board where employees browse and self-select, Unlocked actively recommends opportunities based on an employee's skills, interests, and career trajectory.
The philosophy behind the platform is deliberately skills-first. Rather than filtering by job title, tenure, or reporting line, the AI evaluates what employees can actually do — and surfaces connections that managers and HR teams would never spot on their own.
Lucrecia Borgonovo, Mastercard's Chief Talent and Organization Effectiveness Officer, captured the logic: "If you focus on skills, you're going to expand your talent pool and respond to business needs much faster" (UNLEASH).
Critically, deploying talent through Unlocked requires no dedicated budget from the receiving team — employees contribute to projects using their existing work capacity, removing one of the most common friction points in internal mobility programs (Gloat case study).
Results at Scale
The metrics tell a clear story of enterprise adoption, not just pilot success:
93% employee registration. Approximately 30,000 of Mastercard's 33,000 employees signed up for Unlocked — a penetration rate that most enterprise software deployments never approach (Mastercard corporate blog, 2025).
42% monthly active engagement. Registration alone means little; nearly half the registered base actively uses the platform each month (Mastercard corporate blog, 2025).
1 million+ project hours logged. By early 2025, Unlocked had facilitated more than one million hours of project-based work — short-term assignments, stretch projects, and cross-functional initiatives (Mastercard corporate blog, 2025).
1 in 3 engaged employees made a career move. Among active users, one-third experienced a tangible career shift — a lateral move, promotion, or new role — connected to marketplace participation. Michael Fraccaro, Mastercard's Chief People Officer, highlighted this metric at UNLEASH World as evidence that internal mobility drives retention (UNLEASH, Oct 2024).
$21 million in first-year productivity gains. Per the Gloat case study, the platform generated measurable ROI in its first year through reduced time-to-fill for internal projects, lower external hiring costs, and improved utilization of existing talent (Gloat case study).
90% reduction in interview scheduling overhead. The AI matching eliminated most of the back-and-forth traditionally required to coordinate internal interviews for project placements (UNLEASH).
According to industry reports, 57% of projects on the platform are cross-functional and 49% of mentorships span regions — metrics that, if sustained, suggest the marketplace is actively breaking down organizational silos rather than reinforcing existing team boundaries.
The platform has also attracted academic attention. Columbia Business School published a teaching case study — "Mastercard Unlocked: An Enterprise Talent Marketplace Journey" — authored by professors Stephan Meier and Jeffrey Schwartz, examining the strategic decisions behind the platform's evolution (Columbia CaseWorks). The fact that a top business school formalized this as a teaching case signals the model has moved beyond early-adopter experimentation.
Governance and Trust: AI as Enabler, Not Decision-Maker
For HR leaders evaluating AI-powered talent tools, the governance question is often the deal-breaker. Mastercard built its approach around a principle articulated by Anshul Sheopuri, Executive Vice President of People Operations and Insights: "AI is the enabler, not the decision-maker" (UNLEASH).
In practice, this means the AI recommends matches and surfaces opportunities, but humans make the final decisions — employees choose which opportunities to pursue, and managers approve assignments. The algorithm expands what is visible; it does not narrow what is possible.
Mastercard's AI and Data Governance Council oversees the platform's algorithms alongside the company's broader AI portfolio, applying fairness principles and regular audits to ensure the matching engine does not introduce or amplify bias (HR Leaders Podcast, Feb 2025). Sheopuri uses the platform's data "to learn more about our global skills across the enterprise," treating Unlocked as a live workforce intelligence layer — not just a matching tool (UNLEASH).
This governance architecture matters because it addresses the most common objection HR leaders raise about AI in talent management: the fear that algorithms will make consequential career decisions without adequate human oversight or accountability.
Lessons for HR Leaders
Mastercard's journey from Project Possible to Unlocked offers four actionable takeaways for organizations evaluating or scaling internal mobility programs:
1. Start with a pilot, then scale deliberately. Mastercard did not begin by purchasing enterprise AI software. It began with a crisis that revealed genuine employee willingness to contribute beyond defined roles — and a genuine inability to harness that willingness at scale. The 30% manual match rate from Project Possible was not a failure; it was a diagnostic that quantified how much value was being left on the table. Organizations that skip the pilot phase risk deploying technology without understanding what success looks like.
2. Adopt skills-first matching over title-based filters. The core insight behind Unlocked is that job titles are poor proxies for what people can actually do. As Borgonovo put it, focusing on skills expands the talent pool and accelerates response to business needs. HR leaders implementing talent marketplaces should invest in skills taxonomy and inference before worrying about matching algorithms.
3. Governance is a prerequisite, not an afterthought. Mastercard's AI and Data Governance Council was not bolted on after deployment. The "AI is the enabler, not the decision-maker" principle was a design constraint from the start. For organizations entering this space, establishing governance guardrails before launch — not after the first incident — is the difference between sustainable adoption and expensive remediation.
4. Internal mobility is a retention lever. The 1-in-3 career move rate among engaged employees is not just an efficiency metric — it is a retention signal. Employees who can see and access growth opportunities inside the company are less likely to look outside it. In a talent market where replacement costs routinely exceed 50% of annual salary, reducing attrition through internal mobility has direct financial impact.
What Comes Next
Mastercard's Unlocked is no longer an experiment. It is an operating system for internal talent deployment, backed by enterprise-scale data, executive sponsorship, and academic validation.
For HR leaders still evaluating whether AI talent marketplaces are ready for prime time, the answer from Mastercard is unambiguous: they are — provided you build on a skills-first philosophy, govern the AI transparently, and measure outcomes that go beyond adoption rates.
The practical next step is straightforward. Audit your organization's internal mobility friction. Quantify the gap between employee willingness to move and your infrastructure's ability to facilitate it. If the gap is wide — and it almost certainly is — Mastercard's playbook shows what closing it looks like at scale.