Want to work with AI? Make sure you level up your domain expertise

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Artificial intelligence and data analytics skills are in hot demand but cannot deliver business success in isolation. Domain expertise is vital to success, so professionals aspiring to a career in AI must extend their learning beyond technology.

That's the word from Tendü Yogurtçu, PhD, chief technology officer at Precisely, who advocates for such expanded roles.

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"We need more people to run AI, but also we need more people to pair with those AI and data skills with domain-specific skills," she said to ZDNET at Precisely's recent Trust 24 event at Drexel University in Philadelphia. 

"In order to harness the best language models -- whether small or large -- we need to have domain experts who can provide that deep expertise, to make the most trusted outcome."

She said to think about an insurance company that manages location data, suggesting AI skills alone will not add value to this data: "Eighty percent of data has a location attribute. If you are an insurance company, and you are trying to price with the right risk levels, it's very important to understand the property boundaries, the difference between two properties, and whether you are close to the water line to make an assessment."

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Likewise, AI and technology skills alone will not deliver success in healthcare, where deep domain expertise in medical procedures is required. AI skills alone also cannot deliver success in financial analysis or factory assembly. 

This situation explains why Yogurtçu urged professionals or students seeking careers in AI and related technologies to add another area of expertise. Industry experts agree that members of AI teams need a cross-section of both business and technical skills. 

"In the case of AI-suggested outputs, there is potential for contextual misunderstanding, biased results, or hallucinations," Junaid Saiyed, CTO at Alation, told ZDNET. 

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"It is essential to have domain experts or humans in the loop to verify AI suggestions -- an approach aptly summarized as 'Machine Suggested, Human Verified.' Effective human oversight requires clear monitoring roles and transparency in AI models for easy interpretation."

The domain expert or user "with the most knowledge about a situation or initiative should have the authority to overrule or reverse AI decisions," said Saiyed. "However, it is essential that anyone within the organization can highlight issues through a transparent governance process to ensure accountability and continuous improvement."

This requirement for a blend of domain expertise and AI skills was recently highlighted in an analysis by consultant Accenture. While "machines automate and augment human work for major business processes," organizations can employ an "internal talent marketplace for on-demand collaboration, where dynamic project teams can rotate on and off projects as per strategic needs."

The Accenture authors recommended that companies keep evolving technology and domain-specific roles to move forward. The key to this approach is implementing a domain-centric approach to data modernization. 

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"Reinvention-ready companies have centralized data governance and a domain-centric view of data modernization," they stated. "This creates a strong data foundation that is ready for AI-led reinvention." Part of this process is ensuring "people have a clear understanding of how to create, handle, and consume data." 

The analysis also said it's important to "put people at the center of reinvention," the Accenture authors urged. "In the age of AI, that means reshaping the workforce so that new roles align to business needs as the technology evolves. It means offering comprehensive training to workers so they can thrive in their roles and take full advantage of the power of gen AI. 

"It means reinventing work and rethinking processes and entire workflows to gain a clear view of where gen AI can have the most impact in serving customers, supporting people, and achieving business outcomes."