Agri-Business Leader
Innovation sector
The client's enthusiasm for AI was clear, but they were facing a classic "AI Hype" hangover. Our initial analysis, confirmed by our on-the-ground consultant, identified several core issues that were blocking progress: "AI for AI's Sake" Initiatives: Projects were often technology-led rather than problem-led. As our consultant noted, "They want AI and they haven't really understood the problem." This resulted in initiatives that lacked a clear connection to measurable business value, with teams appeasing stakeholder requests for new features without a strategy. A Disconnect from Foundational Principles: In the rush to innovate, core disciplines were being forgotten. There was a tendency to abandon design thinking, proper product management, and lean methodologies, leading to solutions that were complex but not valuable. Inconsistent Development Practices: The lack of standardized environments—the "it works on my laptop" problem—created a significant technical bottleneck. Without a unified approach, scaling projects from a local machine to the global AWS platform was inefficient and fraught with errors, blocking rapid experimentation. Organizational Inertia and a Capability Gap: A culture of "this is how we've always done it," combined with underinvestment in upskilling internal teams, created a dependency on external hires who couldn't build lasting capability. This was exacerbated by a "fear culture" that stifled the very experimentation needed for AI to thrive.
Riverflex was engaged not just to build models, but to build a sustainable engine for AI innovation. Our approach was pragmatic, hands-on, and focused on embedding new ways of working. Building an Engine for Experimentation: Instead of a theoretical "AI Lab," we focused on creating a practical capability. The cornerstone was a monorepo with standardized, production-aware templates. This ensured that every new project was built for the cloud from day one, allowing developers to build and test locally but scale seamlessly on AWS. Hands-On Coaching & MLOps Enablement: We provided direct coaching and MLOps support to data science teams adopting AWS Sagemaker. This wasn't just training; it was about establishing best practices for CI/CD, data versioning, and creating a robust, repeatable path to production for every model. Driving Strategic Clarity and Prioritization: We acted as the critical bridge between business and data science. We implemented a structured intake process to ensure every initiative was anchored to a measurable business outcome. Crucially, we challenged assumptions and translated ambiguous stakeholder needs into concrete, viable technical requirements. Delivering Value While Building Capability: While embedding new processes, we co-delivered on critical use cases, including a sophisticated customer churn model. This allowed us to demonstrate the value of our methods in real-time and build momentum for the wider transformation.
Our partnership moved this agri-business leader from a series of fragmented projects to a cohesive, value-driven AI program.
Significant Business Value Delivered: The new churn model, built and deployed using our methodology, is projected to deliver €10M in annual value by identifying new customers in a key market.
Accelerated Time-to-Value: The standardized monorepo and templates dramatically reduced onboarding time for data scientists. New projects could be spun up in hours, not weeks, leading to 10+ AI concepts being explored and 5+ solutions moving toward production in a fraction of the time.
A Clear Path to Self-Sufficiency: By embedding best practices and upskilling internal teams, we broke the dependency cycle. The client is now on a clear journey toward owning its AI capability, equipped with the tools and processes to scale independently.
Increased Adoption & Standardization: We successfully drove the adoption of modern MLOps tooling (like MLflow, SageMaker, AWS Fargate) and standardized development environments, eliminating key technical bottlenecks and fostering a more collaborative and efficient engineering culture.