How Omya Built a Focused AI Strategy in Industrial Minerals
Omya wanted a clear plan for using artificial intelligence across its operations. In a four-phase project we assessed readiness, set goals, selected pilot uses and built a scale-up roadmap. The result is a ranked list of AI priorities and a governance model that keeps efforts aligned.
Omya supplies industrial minerals and specialty ingredients worldwide. Its R&D teams search for technology that boosts efficiency and opens new revenue streams.
The Challenge
AI pilots had sprung up in different units and were competing for the same data and talent. Leaders needed to see where AI created the most value, set shared goals and avoid scattered effort.
Our Approach
Phase I: Company Analysis – Understanding the AI Potential
The first step is a deep dive into both external and internal factors that shape the company’s AI strategy. This phase helps in identifying where AI can create value and what internal capabilities need to be developed.
- External Analysis: Understanding market trends, customer needs, emerging AI technologies, and industry developments. This helps in identifying AI opportunities and potential disruptions.
- Internal Analysis: Evaluating the company’s core competencies, current capabilities, and AI readiness. Key areas include data infrastructure, technology expertise, talent, and operational processes.
- Strategic Alignment: Ensuring AI initiatives align with business objectives, long-term growth strategies, and competitive positioning.
At the end of this phase, the company gains a clear picture of its AI potential, the market forces influencing AI adoption, and the internal strengths and gaps that need to be addressed.
💡 Insight: At Omya, an AI project mapping exercise based on external trends and internal competencies helped identify overlapping initiatives and streamline focus areas based on business value and feasibility.
Phase II: Setting AI Goals and Identifying Opportunity Areas
With a solid understanding of the company’s AI potential, the next step is to define clear AI goals and identify opportunity areas where AI can drive impact.
- Defining AI Goals: Establishing the strategic direction—whether AI is aimed at process automation, cost reduction, customer experience improvement, product innovation or business model innovation
- Setting KPIs: Determining the key performance indicators (KPIs) that will measure AI success, such as efficiency improvements, revenue growth, or customer satisfaction.
- Selecting Opportunity Areas: Prioritizing business functions where AI can add the most value, such as predictive maintenance, AI-driven decision-making, or personalized customer interactions.
By the end of this phase, the company has a roadmap that defines AI priorities, success metrics, and specific business areas for AI implementation.
💡 Tip: Omya realized that AI projects often compete for the same resources—by setting clear KPIs and prioritization filters, they focused on the highest-value applications.
Phase III: Identifying Use Cases and Pilot Selection
With goals in place, this phase focuses on choosing AI applications (use cases), selecting strategic approaches, and launching pilot projects.
- Use Case Selection: Identifying specific AI applications that align with strategic goals, such as automated customer support, AI-powered sales forecasting, or smart supply chain management.
- Evaluating Strategic Options: Deciding how to implement AI—whether through in-house development, partnerships, or third-party solutions.
- Pilot Project Selection: Choosing a small-scale AI implementation to test feasibility and business impact in a controlled, iterative, and fast-paced manner.
By the end of this phase, the company has defined AI use cases, chosen the best implementation strategy, and launched pilot initiatives to validate AI potential.
💡Tip: Omya used small-scale pilots to test AI feasibility quickly, ensuring that only high-impact, scalable projects moved forward.
Phase IV: Testing, Evaluating, and Scaling AI Solutions
The final phase ensures AI solutions are tested, refined, and prepared for company-wide rollout.
- Testing & Measuring Results: Assessing pilot projects using predefined KPIs to measure effectiveness in cost savings, performance improvements, or customer engagement.
- Iterating & Refining: Using insights from pilot results to improve AI models, optimize data usage, and address limitations.
- Scaling AI Solutions: If pilots are successful, AI is expanded across the organization, ensuring integration into existing systems and business processes.
By the end of this phase, the company has validated AI solutions, fine-tuned them for optimal performance, and developed a strategy for scaling AI-driven innovation across the business.
The collaboration with the BMI Lab helped us to gain more clarity and focus on our AI strategy while still ensuring the flexibility to adapt to the fast-paced AI landscape.
Vice President R&D
Impact
Business Impact
The first pilots proved which AI applications can scale and which need more work. Each priority now ties to a clear KPI.
Capability and Culture Impact
A shared governance model prevents resource clashes. Teams now use a common framework to start any new AI work.
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Key Learnings
Mapping existing work early prevents wasted effort. Small pilots cut risk and build proof fast. Omya will expand the best pilots and refresh the roadmap as AI technology shifts.
Prof. Dr. Christoph Wecht
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We share what shapes our work and creates pivotal seconds. Mostly through our scientific and entrepreneurial perspective.
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