36
use cases discovered and prioritized

A leading e-commerce platform, founded by one of the largest financial institutions in the EU, was scaling its ~70-person engineering team. AI tools were proliferating without central oversight, opening blind spots in compliance, security, and cost control. Hiflylabs partnered with the company to run a structured AI maturity assessment, surface and prioritize high-impact productivity use cases, model the ROI required before any spend was approved, and deliver a phased implementation roadmap built on a vendor-neutral control plane.
36
use cases discovered and prioritized
5
leadership workshops held around AI vision and strategy
The company came to Hiflylabs with a clear and pressing problem: AI initiatives were already underway across the organization, but entirely fragmented. An earlier centralized rollout of AI tooling had stalled, and not for technical reasons. Developers were running AI coding tools on their own, and business units had launched ad hoc projects, but these initiatives were largely part of “shadow IT” and lacked central governance or oversight. This created real risk; proprietary code policy obligations, cost visibility and access control requirements were left unaddressed. Leadership needed a structured strategy for AI coding integration before fragmentation became a compliance liability – but the CEO holding a firm line on budget meant any investment had to show a projected return first.
Hiflylabs first ran an AI maturity assessment across the company's business units, defining the gaps between goals and the current reality, and set actionable goals for closing the gaps. During the audit and following workshops, we discovered dozens of use cases, 36 of which survived scrutiny. These were prioritized based on potential business impact, with engineering productivity gains coming out on top as the area where AI pays back fastest. Hiflylabs then modeled the ROI of different AI coding tools across the company's developer teams, benchmarking both GitHub Copilot and Claude Code for specific roles and their workflows, using real-life examples as a basis for each calculation.
The question that remained was whether shadow IT would continue to thrive, or usage would be governed. We made the guardrails the “product”. Buying tools off-the-shelf does not make them part of the enterprise fabric. A governed rollout involves investing in the control plane, so that while the tools themselves are interchangeable, the system outlasts any single vendor. The engagement concluded with a four-phase implementation roadmap built on a five-layer reference architecture: identity, data governance, sandboxing, network controls, and observability – ready for IT Security review and full rollout.
AI
SaaS
Claude Code
Github Copilot
Claude Code
Github Copilot
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