As AI reshapes the digital landscape, CIOs have an opportunity to redefine the role of enterprise architects. Traditionally focused on long-term planning and technical standards, architects are now poised to take a more active role in driving AI adoption across the enterprise.
Given AI’s reliance on vast amounts of data, enterprise architects should prioritize data…
Many of us have already taken our first steps in AI implementation, often in customer service, sales, development, or support, but those initial deployments are just scratching the surface. While these efforts are a good start, the real value of AI lies in transforming our entire organizations so we don’t have AI trapped in isolated…
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.
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…
