Hawaii Businesses Must Regain Control Over Their 'AI Layer' to Avoid Third-Party Dependencies and Data Risks

The enterprise AI ecosystem is rapidly evolving. No longer confined to simple chatbots that answer questions, AI is now powering core business functions and executing tasks across organizations. This shift introduces a critical question for Hawaii businesses: who will own the foundational ‘AI layer’ that orchestrates these complex operations? Understanding this ownership is paramount for safeguarding data, ensuring future flexibility, and mitigating risks associated with expanding third-party dependencies. Companies like Glean are positioning themselves to be that central AI layer, raising questions about how businesses will manage their digital infrastructure moving forward.

The Change

Enterprise AI is transforming from conversational interfaces to sophisticated operational engines. This evolution means that AI is no longer just an add-on; it's becoming the foundational operating system for many business processes. The critical change is the emergence of a distinct ‘AI layer’ that sits beneath various AI applications and tools. This layer is responsible for data integration, workflow orchestration, and decision-making logic. As this layer becomes more central, questions of ownership, control, and data governance become increasingly complex and vital.

Who's Affected?

This development impacts a broad spectrum of Hawaii businesses and stakeholders:

• Entrepreneurs & Startups: Founders must consider who owns the core AI infrastructure of their burgeoning products and services. Relying on third-party AI layers could introduce scalability barriers and long-term vendor lock-in, impacting future funding rounds and exit strategies.
• Investors: Venture capitalists and angel investors need to scrutinize the technological architecture of companies they fund. Understanding the ownership of the AI layer is crucial for assessing a startup's defensibility, intellectual property, and long-term viability.
• Small Business Operators: Local businesses, from restaurants to service providers, are increasingly adopting AI tools. They need to be aware that the AI layer powering these tools might be owned and controlled by external vendors, potentially leading to unforeseen costs, data privacy concerns, and limited customization options.

Second-Order Effects

In Hawaii's unique, constrained economic environment, the implications of relying on third-party AI layers can be amplified:

• Increased Dependency on External Tech Vendors: As businesses adopt AI solutions where the core layer is managed by third parties, Hawaii may see a significant outflow of capital to mainland tech companies for software licensing and data integration services, reducing local economic retention.
• Data Sovereignty Concerns: Centralized AI layers controlled by external entities could pose risks to sensitive local business and customer data, potentially impacting local privacy regulations and creating vulnerabilities if these external providers experience breaches or regulatory scrutiny elsewhere.
• Talent Specialization Gaps: A focus on integrating third-party AI layers might divert demand for local IT talent away from developing foundational AI infrastructure expertise towards more specialized application integration roles, creating a mismatch in workforce development needs.

What to Do

Given the current landscape, a WATCH approach is recommended. Businesses should monitor the evolving AI infrastructure market and assess their current and future AI strategy. Here’s what to monitor and potential trigger conditions for action:

• Monitor: The market for enterprise AI integration platforms and ‘AI layer’ providers. Pay attention to companies like Glean, Microsoft, and Google Cloud as they define these foundational capabilities.
• Monitor: Emerging standards and best practices for AI data governance and third-party risk management. Look for guidance from organizations like the National Institute of Standards and Technology (NIST).
• Monitor: The total cost of ownership for AI solutions, considering not just upfront costs but also long-term licensing, potential vendor lock-in, and data egress fees.

Trigger Conditions for Action:

• If a significant portion of your operational workflows becomes dependent on a single third-party AI layer provider, and
• If the cost of switching or integrating alternative solutions becomes prohibitively high,

Then: Evaluate the feasibility and cost of bringing core AI infrastructure management in-house or diversifying your AI toolset to avoid vendor lock-in. For startups, this means prioritizing architectural decisions that allow for control over the AI backbone.