We’re all experiencing the hype and excitement about what generative AI can do for our organizations, but the stories of early adoption are often stories of disappointment or disaster. When generative AI is implemented, it quickly reveals how dependent the results are on the quality and organization of the data it’s fed (GIGO) and…
There are a lot of conversations about AI and most of them are focused around Generative AI. But because AI is a very large ecosystem, I think we need to start to break it down into some of its subcomponents when we’re looking at how to most effectively apply it to the enterprise. …
The recent Gartner article on Generative AI (GenAI) entering the “trough of disillusionment” marks a pivotal moment for technology leaders. As the initial rush to adopt GenAI begins to cool, we are presented with a valuable opportunity to focus on the groundwork necessary for sustainable AI adoption.
As organizations race to leverage AI, many are finding that the biggest challenge is managing the “shadow AI” implementations that often occur outside the formal oversight of IT and risk management. These unseen, unsanctioned uses of AI can lead to a lack of awareness and control, resulting in potential risks that could undermine the very…
As we move into an era of AI-assisted decision making, the adage “garbage in, garbage out” has never been more relevant. Yet, the traditional approach of exhaustively cleaning data before leveraging it is becoming obsolete.
Incomplete or inaccurate data fed into AI systems reveals valuable insights. When AI produces suboptimal responses, it can effectively highlight…
As AI becomes increasingly integral to our operations, we’re faced with a critical challenge: how do we scale these systems while maintaining robust compliance and security?
When we introduce multiple AI components into our ecosystem, we’re not just expanding capabilities but creating a complex web of interactions that demand careful oversight. Each new AI solution…
In the rush to adopt AI, many organizations overlook a critical opportunity: using AI to integrate their enterprise infrastructure. This approach can not only prepare you for broader AI adoption but can also streamline your existing systems.
A common topic currently is how to confidently implement AI while maintaining solid data GRC (Governance, Risk, and Compliance). After two decades of navigating data GRC requirements, it’s clear that AI introduces new challenges to traditional and proven ways of handling them.
CIOs are often challenged by simple questions that cannot be addressed without complex data navigation. Our enterprise data landscapes are intricate webs spanning multiple departments, regions, and legacy systems.
Many enterprise leaders are asking, “How do I integrate AI into my business?” but this question is actually not helpful for developing an AI strategy. Instead of starting with how to integrate AI, we should be asking, “How do I overcome my business challenges and enhance performance?”
As we embrace AI, it’s crucial to remember that architecture strategy has always been the backbone of successful tech infrastructure. Adding AI is no different – it’s an evolution of our existing approaches, not a complete overhaul.
Our transition from on-premise data centers to cloud adoption offers valuable lessons. We must apply this knowledge as…
Many businesses see AI agents as tools to automate existing processes, but this approach limits their potential. It’s like strapping a jet engine onto a horse-drawn carriage. The carriage moves faster, but it is still following the same old path. The real value of AI agents isn’t in making outdated systems more efficient, but in…
