Global Food Technology Company Case Study Header Video
Global food technology company

From vision to reality: AI-powered data framework for scalable growth

Together we have achieved

Results worth seeing

269

AI and BI use cases identified

43

High-impact AI use cases prioritized

€64m

Uplift potential over six years

Close up of unrecognizable chemist pouring purple liquid into a beaker at laboratory.
About the company

A global leader in natural ingredient innovation

The company is a global food technology leader specializing in the development and production of natural ingredients and color solutions for the food and beverage industry. With a strong focus on sustainability, innovation, and quality, it operates across multiple continents, serving a diverse portfolio of international brands and manufacturers.
Its expertise spans the entire value chain — from sourcing natural raw materials to delivering scientifically advanced, customer-specific solutions that enhance product appeal and nutritional value. By combining decades of R&D excellence with data-driven innovation, the company continues to redefine how natural food ingredients contribute to healthier and more sustainable consumption worldwide.

Our approach

Establishing an AI-driven framework for data-led transformation

01

AI strategy and use-case identification

Mapped and prioritized more than 260 AI and BI use cases to define strategic value and technical feasibility.

02

Current-state analysis and architecture review

Assessed the company’s data infrastructure, governance, and technology stack to identify bottlenecks and scalability potential for AI implementation.

03

Holistic AI-driven data strategy design

Developed a unified data-excellence framework integrating AI, analytics, and governance models to ensure scalability, transparency, and business alignment.

04

Implementation roadmap

Outlined a phased rollout plan with seven core AI initiatives to operationalize analytics, automation, and AI capabilities across the organization.

2025 AI Implementations

Launching seven core AI use cases

01

Copilot enablement

Empowered employees to integrate Microsoft Copilot into daily workflows.

02

Satellite data & Power BI

Combined satellite and field data to predict harvest outcomes for natural raw materials.

03

Mail-to-order automation

Automated email order processing using LLM and RPA, achieving >90% accuracy without API access.

04

Patent monitoring

Applied AI to track global patent applications and research trends.

05

Supplier questionnaire automation

Automated the completion of 100+ page supplier forms based on prior data.

06

Legal document automation

Automation of standardized legal documents such as NDAs and General Terms & Conditions.

07

Molecular simulation

Molecular dynamics simulation by computational platform for molecular discovery and design.

Key deliverables

Delivering the foundation for AI-driven transformation

01

AI-driven data strategy blueprint

Comprehensive roadmap defining data, analytics, and AI transformation priorities and measurable milestones.

02

Data governance and excellence framework

Unified governance structure and clear data ownership model ensuring cross-departmental accountability.

03

AI and BI use-case portfolio

Identified, prioritized, and quantified business impact across departments including operations, R&D, and marketing.

04

Target data architecture design

Defined a scalable, cloud-based data infrastructure enabling analytics, IoT, and automation integration.

05

Execution and transformation roadmap

Developed a multi-year plan with seven key AI and data initiatives, setting the foundation for expansion into additional use cases.

06

Change enablement and capability building

Empowered employees through Copilot training and AI literacy programs to enable adoption and sustained transformation.

Get in touch

Interested in working with us? Talk to our experts!

Dr. Christian Fürber

Partner Data & AI

Christian Riede

Vice President Tech Advisory

Dr. Felix Schulz

Director Data and AI Strategy