The Era of Specialized AI - Part IV

Part IV: The Rise of Specialized AI and The Need for Domain-Specific Customization

There is real expertise required to know how to combine different models, customize them, personalize them and scale them within a specific business environment. There is work to be done to ensure these models can scale responsibly. This is crucial for building trust and preventing AI systems from perpetuating harmful stereotypes. Finally, model explainability is non-negotiable, aiming to make the decision-making processes of AI models more transparent and understandable. This is essential for building trust and ensuring accountability.

General purpose models, while impressive, cannot replace any job overnight. They are designed to be broadly applicable, but lack the specialized knowledge and skills required for specific domains. To truly harness the potential of AI models (both generalized or specialized) businesses will need to stand up four functional skills - domain-level evaluation metrics, red-teaming and risk mitigation process, domain-specific model customization/re-architecture, scaling with testing and simulation capabilities.

New skills and jobs need to be defined and developed in any business domain that aims to take advantage of any general-purpose and domain-specific model for their specific use case. Technology leaders need strong partnerships with the business domain experts and HR expertise is critical to ensure jobs and employees are set up for success.

Beyond customizing these general-purpose models, there will continue to be a growing need for specialized AI models tailored for specific industry functions and tasks. These models leverage domain-specific knowledge and data to achieve higher accuracy and efficiency in areas like finance, marketing, supply chain, operations, etc.

If you are a business leader with a budget and a growth target, prioritize creating space for your organization to learn and define the scope of specialized intelligence you need and iterate on it daily/weekly in close partnership with your technical teams and your preferred vendors. Imagine two teams that are given a growth target of say 10%, one is given a budget which they use to outsource the work required and the other is given access to resources amounting to a similar budget. This second team is more likely to solve their most urgent pain point with AI and technology and upskill in that process because they are not wasting time sending domain knowledge to AI, they will bring AI closer to their domain. This is the winning strategy every single time, the AI needs to work within your domain and the leaders are critical in orienting and incentivizing their teams to make this happen.

The highest urgency and focus should be on finding ways to embed AI within your domain and use it as a catalyst to up-skill, re-skill, and strategically evolve the talent base in your organization. Any AI/AGI hype, generalized large models, race to implement AI Agent decoupled with talent strategy is just short term firework, it is not the electrical wiring you need to power your business into the future.