The Importance of AI Being Aware of Specific Business Domains
In the ever-evolving world of technology, the age of specialist AI has arrived. This new breed of artificial intelligence is designed to speak the language of business, understand specific challenges, and respect the nuances of its domain to become a trusted partner in critical endeavors.
This shift from generalist AI to domain-aware AI is particularly significant in high-stakes environments such as engineering, finance, healthcare, and legal industries. For instance, in the pharmaceutical industry, a domain-aware system would understand the context of company-specific domain knowledge to return a much more precise and reliable answer about a drug's interactions. Similarly, in healthcare, a domain-aware AI can connect a patient's electronic health record with the latest medical research, clinical trial data, and treatment guidelines, providing highly relevant, evidence-based suggestions for diagnosis and treatment plans.
In the legal industry, companies can offer new, AI-powered services like instant analysis of case law. This is possible by building a comprehensive knowledge graph of their domain, which stores information and represents its understanding, much like a human expert's mental model.
In regulated industries, a generic AI can generate plausible-sounding but dangerously incorrect information, potentially leading to compliance breaches. By building a knowledge graph of their domain, companies can mitigate this risk and ensure compliance. For example, in financial institutions and insurance companies, a domain-aware AI can build a knowledge graph of regulations, internal policies, and client data for sophisticated compliance checks and risk assessments.
In Germany, companies like Rheinmetall are using AI in connected military systems to optimise domain-specific processes, leading to improved operational efficiency, faster decision-making, and more efficient resource utilisation. Meanwhile, IT firm Materna is developing AI solutions for automation and data-driven decision-making in public and enterprise-specific processes, with a focus on German engineering. The results are increased process sovereignty, security, and efficiency.
However, not all AI implementations have been successful. Zillow, the American real estate company, shuttered its instant-buying division "Zillow Offers" after incurring $1 billion in losses over 3.5 years and reducing its workforce by 25%. The AI model used in this division struggled to compile all the data needed to properly identify user behavior and value properties.
To avoid such high-risk gambles, enterprises must consider how they can make AI an expert in their domain. By enriching systems with domain-specific knowledge models, as domain-aware AI does, they can unlock significant value, especially in high-stakes environments, and transform vast repositories of specialized information into active, intelligent assets. This strategic business decision can drive innovation, mitigate risk, and unlock new levels of productivity, creating a formidable competitive advantage.
