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In 2021, Barcelona-based delivery platform Glovo was faced with a law that threatened its business model. Spain had passed the Rider Law, which introduced a presumption of employment when a digital platform organizes, directs, or controls couriers’ work, including through algorithmic management. Glovo came up with what seemed like a clever fix to avoid having to classify its couriers as employees: emphasizing courier autonomy by redesigning its app so they could log in freely, reject orders without explicit penalties, and choose a daily rate multiplier.
Three years later, however, it was clear that the fix had failed. Glovo and its parent, Delivery Hero, announced that they would hire roughly 15,000 couriers as employees in Spain, incurring a significant financial hit. The workaround had simply delayed the reclassification the company had sought to prevent. During this period, courier protests over lower earnings, along with mounting employment-related penalties and a criminal case against Glovo, increased its exposure.
Glovo reached for a common leadership response to unwanted regulation: minimal compliance. This involves making the narrowest changes a company believes will satisfy a rule while preserving how the business operates. But as algorithms become embedded in how companies deliver services and manage workers, that response is becoming a trap.
Leaders often default to workarounds or temporary fixes because they preserve the existing business model while avoiding a costly redesign. But this leads companies into what we call the algorithmic compliance trap. As Glovo’s case demonstrates, in algorithmic businesses, such fixes expand the audit surface, increase internal complexity, and invite scrutiny that can push the company toward the very redesign it was trying to avoid.
That said, not every regulatory change or instance of enforcement calls for a complete redesign. The hard question for senior leaders is when simple fixes are sufficient or a more significant rethinking of the business model is warranted.
We have developed a practical diagnostic for making that call. Drawing on five cases of algorithmic businesses facing major regulatory challenges in Europe — Glovo, Deliveroo, Airbnb, Uber, and Meta — we identified four signals that can help leaders judge whether workarounds are likely to stabilize a business model or accelerate a costly redesign.
Algorithmic Minimal Compliance: What It Is and Why Leaders Are Drawn to It
When regulation arrives, every business faces the same basic choice: absorb the cost of full compliance or find a way to meet the formal requirements while preserving the existing model. Historically, compliance could be layered onto the organization through policies, training programs, and designated controls while keeping core operations largely intact.
For algorithmic businesses, that separation is harder to maintain. Compliance often has to be built into the product itself: into decision logic, ranking systems, access gates, pricing rules, and automated enforcement. Changes in one part of the system can affect outcomes elsewhere and must continue to work at scale as the product evolves. That can make even genuine, good-faith compliance expensive and disruptive, and it creates a strong pull toward minimal compliance: modifying code, contracts, and workflows just enough to satisfy the formal requirements of a rule while preserving the underlying business model and what the system optimizes.
Algorithmic minimal compliance tends to appear in a few recurring forms:
- Contract terms designed to pass a specific legal test.
- Interface redesigns that signal autonomy while the platform continues to coordinate outcomes through incentives and information.
- Data collection and reporting tools built for recurring obligations.
- “Human review” steps that satisfy oversight requirements without shifting real decision authority.
- Compliance programs that produce reports and process artifacts while the system continues to evolve.
None of these moves is necessarily made in bad faith. When rules are new and enforcement standards are still taking shape, a workaround can keep a business operating while leaders learn what regulators will actually test for.
Algorithms make these workarounds harder to contain for two reasons. First, code changes, contract amendments, and workflow redesigns leave auditable traces that a revised policy memo does not. A workaround intended to satisfy a formal requirement becomes a record and, in adversarial settings, may serve as evidence. Second, workarounds in complex, interdependent systems rarely stay contained. A change in one part of the logic can have ripple effects elsewhere, creating new inconsistencies and new exposures. What begins as a targeted fix can expand the audit surface faster than it closes it.
Four Red Flags That Suggest Minimal Compliance May No Longer Hold
Across the five cases, four signals help explain when minimal compliance remained viable and when it began to compound the problem.
1. Tightening legal clarity. Minimal compliance thrives in gray zones. As laws, rulings, and guidance become more specific, loopholes close and the design space for a workaround shrinks.
2. Increasingly visible harm. When social costs become visible to workers, users, communities, journalists, or public officials, regulators become less willing to accept fixes that change the optics without changing outcome
