A number of threads are converging:
Coding assistants like GitHub Copilot, Codeium, Cursor, and Replit Ghostwriter are integrated directly into IDEs – or in some cases, are the IDEs.
Autonomous agents or agent environments that can generate and execute code, like Microsoft Magentic-One or Langflow .
Application generation environments like Replit takes this even further, generating full applications from natural language prompts and deploying them with just a few clicks.
In preparing last weeks article I came across a series of blog posts from Ardoq (thanks to Ed Granger) that deserves more attention (catch it here). There are quite a few posts (maybe too many) on how GenAI and Agentic AI can be used in organisations but not much on how we conceptualise and plan for this from a Enterprise Architecture (EA) perspective. Ardoq’s insightful blog series delves into GenAI’s impact on EA and more – it’s really worth a read.
As I explore the AI development platforms from major providers like IBM , Google, Amazon Web Services (AWS) and Microsoft it’s clear that they are increasingly offering tools that provide easy access to both pre-trained models and custom model training (we also have easy API access to models like ChatGPT and Gemini). A recent report from the IBM Institute for Business Value included a sentence that resonated with me: “The competitive edge that generative AI delivers today will be table stakes tomorrow.” This insight feels particularly relevant when considering the services these major platforms offer – its power to differentiate will diminish.
Understanding the risks in any organisation or project takes time and usually involves one or more risk workshops, more often than not starting with a blank sheet of paper. Massachusetts Institute of Technology (MIT) have provided us with a short cut to identify risks associated with artificial intelligence using a new resource, the AI Risk Repository – save time and improve the breadth and depth of risks.
In the wake of the recent CrowdStrike incident it’s easy to become an armchair critic. For those with experience in IT, isn’t it likely that such issues are multi-dimensional, spanning technical, managerial, cultural, and even simple human errors?
After posting last weeks article (Deploying AI: What nobody seems to be putting together) where I suggested that we have fragmentation in approaches to AI deployment I saw a number of things come together. EY launched EY.ai (actually, back in Sept 2023), then a great post from Chris Howard at Gartner (Top of Mind – “Are 100% Accurate AI Language Models Even Useful?”) where Chris mentions ‘…as we hit the trough of disillusionment with generative AI…”.
These two subjects, with separate sections below are intertwined – understanding AI risk and the management of AI systems go hand in hand. Luckily NIST and ISO provide us with some tools to help with both.
