[AI for Business #6] - Injecting domain expertise into your AI
Shape your competitive advantage with an expertise-driven AI approach
Dear AI friends,
As generic AI assets like mainstream LLMs and copilots are purchased by more and more companies, access to the technology is not enough to stay ahead. Rather, you need to provide your AI with ways to make sense of your unique organizational knowledge - which goes well beyond simply giving it access to your company’s data.
In my experience, one of the most solid ways to establish AI in a company and turn it into a strategic asset is embedding unique domain expertise throughout the different components of your AI system. My latest article Injecting domain expertise into your AI system explores this approach using the example of supply chain optimization. The article will introduce you to a bunch of techniques to gain:
✔ Greater efficiency – AI that understands domain-specific workflows reduces manual effort and improves decision-making.
✔ Better adoption – Users disengage from AI whenever they feel it doesn’t “get” their industry. By contrast, an AI that aligns with real-life facts and processes is quick to gain user trust.
✔ A sustainable competitive moat – As AI building blocks - models, frameworks, infrastructure - are increasingly commoditized, encoding proprietary expertise and amplifying it with AI creates long-term differentiation.
What You’ll Learn
Create datasets that drive specialization and value – Start with 3–5 high-impact data sources and expand strategically, rather than flooding AI with noisy data.
AI that mirrors real-world decision-making – Use domain expert annotations to teach AI what really matters, not just statistical correlations.
Beyond generic LLMs – Enhance GenAI applications with Retrieval-Augmented Generation (RAG) and fine-tune them to ensure they provide relevant, domain-specific insights.
Neuro-symbolic AI & knowledge graphs – Structure business rules and relationships to make AI more explainable, reliable, and accurate.
By integrating deep domain expertise into AI, companies can accelerate AI adoption and transform their industry knowledge into a scalable competitive advantage.
Read the full article here.
If you have already been at the interface of domain knowledge and AI, please share your insights and challenges in the comments and let’s discuss!
Keep innovating!
Best wishes
Janna
Injecting domain expertise into your AI system
In B2B, there is a lot of talk about “tuning” AI with domain- and company-specific knowledge, but how exactly can this be done? My guide offers a systematic walkthrough through the interface between domain knowledge and AI. It provides you with plenty of methods and techniques to choose from if you want to build a solid and sustainable competitive advantage with AI.
Trend insight: Knowledge graphs
Knowledge graphs are emerging as one of the most powerful—yet often underutilized—tools for integrating domain expertise into AI systems. While machine learning models excel at recognizing patterns in raw data, they are often less reliable for real-world relationships, business logic, and contextual reasoning. This is where knowledge graphs step in, structuring information into an interconnected web of entities, attributes, and rules and making the AI more robust and interpretable.
Knowledge graphs were one of the two key AI technologies highlighted on Gartner’s AI hypecycle 2024. Best of all, they are past the rollercoaster of inflated expectations and are a realistic, hands-on technology for businesses that seek explainable, context-aware AI solutions. The rise of GraphRAG (Retrieval-Augmented Generation using knowledge graphs) has further fueled their adoption, enabling LLMs to retrieve structured, business-critical information instead of relying purely on probabilistic text generation.
Knowledge graphs are best suited for use cases that rely on complex, partially implicit, knowledge, such as:
Healthcare & pharma: Mapping drug interactions, clinical trial data, and patient history to enhance AI-driven diagnosis and treatment recommendations.
Finance & risk management: Connecting fraud detection signals, transaction histories, and regulatory compliance rules for better fraud prevention.
Enterprise knowledge management: Linking customer support tickets, product manuals, and employee expertise to power AI-driven enterprise search and chatbots.
By structuring your company’s knowledge into an explicit knowledge graph, you not only improve the performance and reliability of your AI system but also ensure that valuable knowledge remains with the company even when your employees leave.
Use case spotlight: Supply chain optimization (SCO)
Disruptions from geopolitical tensions, climate risks, and evolving trade regulations shake up global supply chains. Companies with complex logistics—such as automotive, consumer electronics, and pharmaceuticals—can no longer rely on traditional forecasting methods alone. To stay competitive, they need dynamic, AI-powered systems that deliver real-time insights, dynamic risk assessment, and proactive decision support in an increasingly unpredictable world.
An AI-powered Supply Chain Optimization (SCO) system integrates multiple intelligence layers to predict disruptions, recommend alternative strategies, and automate key logistics decisions. By combining predictive analytics, large language models (LLMs), knowledge graphs, and workflow automation, such a system helps businesses:
Predict and mitigate disruptions – AI models analyze historical data, live shipping conditions, and external risk factors (e.g., port congestion, geopolitical instability) to anticipate bottlenecks.
Optimize supplier and logistics decisions – AI suggests the best suppliers, routes, and inventory strategies by weighing costs, lead times, and compliance factors.
Enhance operational resilience – Knowledge graphs structure supply chain relationships, enabling AI to simulate cascading effects of disruptions and recommend contingency plans.
Automate routine logistics tasks – AI can dynamically reroute shipments, flag at-risk suppliers, and assist planners with intelligent recommendations.
The following chart depicts supply chain optimization using the AI use case framework outlined here:
By embedding domain expertise at every level—data, intelligence, and UX—businesses can ensure AI systems provide meaningful, context-aware insights rather than generic predictions. SCO AI doesn’t just analyze supply chains; it thinks like a supply chain expert, making decisions and operations smarter, more agile, and future-proof.