Vertical-market AI needs industry domain expertise

As AI models become increasingly specialized for industries like healthcare, finance, and law, organizations are realizing that vertical-market domain expertise is the critical ingredient for building truly effective, trustworthy AI solutions.

But where does this expertise come from, especially at a time when the supply of publicly available data is diminishing? The answer: subject matter experts like physicians and financial services practitioners.

But tapping into human domain expertise raises more questions: how do you ensure your AI projects have the right domain experts involved, and that their expertise is used to its fullest potential? Here are practical tips for sourcing, training, and retaining domain experts to keep your vertical-market AI accurate, useful, and safe.

Cast a wide net for sourcing domain experts

Domain knowledge isn’t confined to job titles or internal org charts. Some of the most valuable expertise may reside outside your immediate network, often in places you haven’t thought to look.

Building a high-performing expert network requires looking beyond the obvious to uncover individuals with the right depth of insight and willingness to engage with emerging technologies:

• Professional associations and certification bodies for credentialed experts.

• Academic institutions for researchers with deep subject-matter knowledge.

• Specialized online platforms where vetted professionals offer their expertise.

• Industry conferences and publications to identify thought leaders and innovators. 

Tip: Use AI-powered expert identification tools to analyze publications, patents, and professional networks. Just be mindful of relying solely on algorithms — personal vetting remains key to avoid perpetuating bias. AI can help you spot rising stars and established authorities in your industry.

Vet experts with a multi-layered approach

Domain knowledge isn’t enough if it can’t be applied. Deep expertise must be accompanied by sound judgment, ethical alignment, and the ability to collaborate with technical teams.

A rigorous vetting process helps ensure that the people guiding your AI systems can translate real-world complexity into useful signals for model training and evaluation. To help ensure your experts are truly qualified:

• Verify professional credentials (degrees, certifications, licenses).

• Assess practical experience through scenario-based interviews or case studies.

• Test domain knowledge with real-world challenges relevant to your AI’s focus.

• Evaluate communication skills to confirm they can translate expertise into actionable feedback for AI teams.

• Check for alignment with your organization’s values, especially regarding ethics and compliance.

Tip: Consider using a scoring rubric or structured assessment to compare candidates objectively and transparently.

Prepare experts for AI collaboration

Even seasoned experts can feel uncertain in new AI contexts — it’s crucial to normalize that learning curve. Invest in:

• Foundational AI education that demystifies how large language models (LLMs) operate, their limitations, and where human judgment is essential.

• Hands-on workshops where experts practice prompt writing, annotation, and evaluation in a low-stakes environment.

• Clear guidelines on how to provide feedback, flag edge cases, and identify data quality issues.

• Psychological safety for experts who need to feel safe admitting uncertainty or challenging model outputs.

Tip: Lean into AI to help you train domain experts. At Centific, we’ve used AI to cut prompt review time by 55%.

Build a feedback loop for continuous quality control

Quality assurance is an ongoing process. Expert involvement must extend beyond model development into continuous oversight and refinement. Without a structured feedback loop, even the best-trained AI can drift away from its original purpose and lose accuracy in real-world applications. To keep your AI models sharp and safe:

• Set up regular review cycles where domain experts evaluate AI outputs and suggest improvements.

• Monitor model performance for accuracy drift and emerging blind spots.

• Encourage open communication between data scientists, engineers, and domain experts to quickly address issues.

• Leverage AI-powered review tools to speed up quality checks but always keep a human in the loop for nuanced judgment.

Tip: Track key metrics (e.g., agreement rates between expert and AI assessments) to measure and improve the effectiveness of your quality control process.

Invest in ongoing training and engagement

Subject matter expertise isn’t static. Industry regulations evolve. New trends emerge. AI capabilities shift. Keeping your domain experts engaged means supporting their growth and giving them a stake in your AI’s success as ongoing collaborators shaping its trajectory. Keep your experts up to date by:

• Offering continuous education on both AI advances and industry developments.

• Bringing experts together for regular knowledge-sharing sessions and collaborative problem-solving.

• Recognizing and rewarding contributions to maintain motivation and commitment.

Tip: Create a community of practice where experts feel valued, heard, and empowered to shape the future of your AI.

Ensure ethical and regulatory alignment

The deeper AI goes into regulated sectors, the more it must reflect the ethical and legal standards of those industries. Domain experts are uniquely positioned to guide these efforts, but they need to understand governance, audit, and accountability practices that ensure AI systems are both compliant and fair. Make sure your domain experts:

• Understand relevant regulations (e.g., HIPAA in healthcare, FINRA in finance).

• Participate in ethics reviews to help identify and mitigate bias or unintended consequences.

• Stay involved in governance so that AI systems remain aligned with organizational and societal values.

Tip: Set up cross-functional committees that include domain experts, compliance officers, and technical leads to oversee responsible AI development.

Make expert involvement scalable

AI ambitions often outpace the systems designed to support them. To keep up, organizations must make expert input repeatable, efficient, and scalable without diluting quality.

That means developing the infrastructure and processes to onboard, manage, and retain domain experts at scale, while maintaining the nuance that makes their input valuable.

To scale without sacrificing quality:

• Automate repetitive quality checks with AI tools, freeing up experts for higher-level review and strategy.

• Standardize onboarding and training so new experts can get up to speed quickly.

• Document lessons learned and best practices to build organizational memory.

Tip: Use pilot projects to refine your processes before rolling them out at scale.

Centific can help you

The future of vertical-market AI belongs to organizations that treat domain expertise as a living, evolving asset, not just a box to check.

By connecting human expertise with data-driven insights to solve real-world problems, you'll build systems throughout the AI lifecycle that are not only powerful but also trustworthy and aligned with real-world needs.

Centific helps build AI that treats vertical market domain expertise intelligence as a means to enhance outcomes.

When AI is applied as a tool rather than seen as an all-encompassing fix, its true potential comes into focus. It becomes a bridge: connecting humans with technology, linking raw data to meaningful insight, and translating prediction into understanding.

Explore Centific’s frontier AI data foundry platform.