DECEMBER 2024CIOAPPLICATIONS.COM8CXO InsightsIN MY OpinionDavid Robertson began his career as a programmer for NASA on the International Space Station project before transitioning to lead large-scale technology platform development. A DevOps enthusiast, he has pioneered test automation and infrastructure provisioning, influencing teams globally. You can find his tech musings on X @ShipItSoftware. His struggling career as a musician keeps his focus on architecting software platforms, but he still enjoys playing the guitar whenever he can. Lately, solar and wind systems help entertain his engineer's mind, and give him chances to collaborate on those things with his kids.Through this article, David emphasizes the rapid evolution of generative AI technology and the importance for organizations to adapt and adopt these technologies to remain competitive. He highlights the need for organizations to understand the different types of generative AI tools available, from those that can be purchased to those that can be built in-house. here do we even start with this conversation? LLMs, vector databases, prompt engineering, RAG, and NPUs are all new terms for many of us, and things are changing so fast that it is difficult to keep up. If this is you, please read on, and I will attempt to demystify some of the current generative AI landscape. As technologists--or simply executives trying to stay competitive in today's market--we must adapt and move forward. That doesn't make us experts. Rather, it just makes us better than our competitors.Technology leaders think of generative AI in two broad categories: the things organizations build versus the things organizations buy. In the "buy" category, things enter our workspaces either with a conscious decision to purchase specific AI tools, or they are made available as features to existing tools already available. The former consists of tools like GitHub Copilot or watsonx Code Assistant; tools for knowledge workers that are licensed explicitly as AI assistants. Others are new features added to existing software already in our workspace that may or may not require additional licensing. Most tools in this category are positioned as productivity enhancers: they can summarize our email inbox or create presentations from curated content. Most of these are low risk, and some include customizations to limit any data accidentally exposed to the wrong group. There is a lot of value in them, given the low maintenance and setup. You simply must pay the cost to get the productivity boost and have the right people who can maximize the investment.For things that organizations build, the story gets much more complicated. There is a plethora of tools to use, with more becoming available each month (week??). Sorting through these tools, frameworks, and platforms is not something to be taken lightly, but there are some general selection criteria that can help narrow the list to consider. Like any other technology, in general, the selection should overlap nicely with the current skills possessed by your IT group; fortunately, many of the generative AI tools available today are built with existing languages and platforms, which helps. They are generally "developer-friendly" but still require some nuance. Having a group that includes veteran engineers hosting multi-cloud will make for a longer list, but if yours is a group of junior and senior full-stack developers who only know .Net and Angular deployed to on-premises virtual machines, then you should pick a framework that works for them. Regardless of the tool, much of this INTEGRATING GENERATIVE AI INTO THE ENTERPRISEDavid RobertsonWDAVID ROBERTSON, DIRECTOR ENTERPRISE ARCHITECTURE, EXETER FINANCE
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