AI Governance: Pulling the Pieces Together

As AI accelerates along an exponential curve of features and intelligence, there are hidden risks and opportunities that require active governance strong enough deliver transparency, but also robustly adaptable to emerging regulations and unintended consequences.  Domain and industry leaders are facing unfamiliar challenges as the rush for scalable results breaks proven deployment and oversight solutions.

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Prior to 2023, the lure of artificial intelligence (AI) was researched in universities but limited in its progression due to scalability, performance, and capability.  What a difference 15 months has made on our business models and what must be considered for the future:

  • Today and looking into any part of the future, AI innovations are seemingly limitless, unbounded digital ecosystems, and layers upon layers of intelligent features that seemed impossible just a budget cycle back (e.g., Sora). 
  • Today in a pervasive age of AI, business solutions discussing how to capitalize on innovation and data advancements dominate IT, marketing, and even traditional enclaves such as audit, tax, legal, and boardrooms. 
  • Today we scan the headlines, seek out needed skill sets, and have interdepartmental communications and we banty phrases such as cloud computing, neural networks, federated learning, and even next-horizon solutions that include large action modules (LAM’s, e.g., Rabbit 1.0). 

Yet, beyond these innovative, singular ideas how, who, and where are the intersections points, the active management, and regulatory oversight?  For the last 16 months, Gen AI has reworked traditional practices and spurred innovation, but it represents the tip-of-the-spear.  To come to grips with the AI hyper-innovation cycle now underway, industry leaders must look beyond vendor solutions. 

The future of tomorrow requires a proactive integration of innovative research tempered by domain market forces, consumer behaviors, AI technology (e.g., chips and software) and digital data explosions all glued together by security, legal, and regulatory requirements.  It is a future that demands layers of integrated solutions all requiring transparency, heterogeneity, and risk-attributions.  Herein is the core business challenge: how will all the pieces fit—and at what costs?

At its core, AI is a data-driven solution.  At its edges, AI represents an ability to extend data ideation using building blocks of functionality uniquely assembled—but how?  Let’s discuss an illustrative representation of delivering AI Governance by design versus the traditional siloed product mindsets of “one-and-done.” 

What Figure 1 represents is that organizations will be required to cost-effectively blend results (i.e., vendor solutions) with research (i.e., deep-channel innovations) to arrive at a federated model that adapts to changing technology, business, and regulators.

While this idea is not completely foreign, it blurs the lines between what is coming and what is possible (i.e., research), with what is available and how can it be adopted (i.e., vendor and outsourcing solutions)….