The Best of Applied Artificial Intelligence, Machine Learning, Automation, Bots, Chatbots

When starting their AI initiatives, many companies are trapped in silos and treat AI as a purely technical enterprise, sidelining domain experts or involving them too late. They end up with generic AI applications that miss industry nuances, produce poor recommendations, and quickly become unpopular with users. By contrast, AI systems that deeply understand industry-specific processes, constraints, and decision logic have the following benefits:

• Increased efficiency — The more domain knowledge AI incorporates, the less manual effort is required from human experts.
• Improved adoption — Experts disengage from AI systems that feel too generic. AI must speak their language and align with real workflows to gain trust.
• A sustainable competitive moat — As AI becomes a commodity, embedding proprietary expertise is the most effective way to build defensible AI systems.

Domain experts can help you connect the dots between the technicalities of an AI system and its real-life usage and value. Thus, they should be key stakeholders and co-creators of your AI applications. This guide is the first part of my series on expertise-driven AI. Following my mental model of AI systems, it provides a structured approach to embedding deep domain expertise into your AI.

1. Data: The bedrock of expertise-driven AI

AI is only as domain-aware as the data it learns from. Raw data isn’t enough — it must be curated, refined, and contextualised by experts who understand its meaning in the real world.

Data understanding: Teaching AI what matters

Only domain experts can validate whether the data is complete, accurate, and representative of real-world conditions. If these nuances aren’t accounted for, the AI might learn an overly simplified view of supply chain dynamics, resulting in misleading risk assessments and poor recommendations.

✅ Implementation tip: Run joint Exploratory Data Analysis (EDA) sessions with data scientists and domain experts to identify missing business-critical information.

Data source selection: Start small, expand strategically

One common pitfall when starting with AI is integrating too much data too soon. Instead, start with a couple of high-impact data sources and expand incrementally.

✅ Implementation tip: Start with a minimal, high-value dataset (normally 3–5 data sources), then expand incrementally based on expert feedback.

Data annotation

AI models learn by detecting patterns in data. Domain experts help the AI understand what matters and make better predictions by labelling key attributes.

✅ Implementation tip: Define an annotation playbook with clear labelling criteria and involve at least two domain experts per critical label.

Synthetic data: Preparing AI for rare but critical events

Synthetic data solves the problem of underrepresented rare, high-impact events by creating more datapoints.

✅ Actionable step: Work with domain experts to define high-impact but low-frequency events for synthetic data generation.

Intelligence: Making AI systems smarter

Once AI has access to high-quality data, the next challenge is ensuring it generates useful and accurate outputs. Domain expertise is needed to define clear AI objectives aligned with business priorities.

Training predictive models from scratch

Define clear objectives and constraints with domain experts before training AI models.

✅ Implementation tip: Define clear objectives and constraints with domain experts before training.

Designing ergonomic user experiences

In B2B environments, the user experience must be seamlessly integrated with existing processes and task structures.

✅ Implementation tip: Involve domain experts early in UX design to ensure AI interfaces are intuitive.

Conclusion

Vertical AI systems must integrate domain knowledge at every stage, and experts should become key stakeholders in your AI development. They refine data selection, annotation, and synthetic data, guide AI learning through prompting, RAG, and fine-tuning, and support the design of seamless user experiences that integrate with daily workflows.