Organisations will turn to external partners for GenAI implementations and the success of your GenAI project depends heavily on the clarity of your requirements.
A well-prepared RFP with comprehensive requirements isn’t just bureaucratic paperwork – it’s your blueprint for AI implementation success. Let’s dive into some insights on creating requirements that will attract the right vendors and set your GenAI project up for success.
We’ve been seeing Agentic AI hit the press a lot over the last while, especially as we head into 2025. I recently had the chance to build a PoC and find out what Agentic AI was all about and I think I get it now…
A week or two ago I came across an article from Business Standard that summarised a Gartner report suggesting that over 30% of GenAI projects won’t survive beyond proof of concept (PoC) and will be dropped by the end of 2025. Having run a large project portfolio I’m always interested in stats like this so I decided to pick at this a little and see whether this is indeed an issue, or just a headline.
I couldn’t believe the timing this week. As I’m working through the course material for the Microsoft AI Engineer exam a post dropped from Rajani Janaki Ram with a Framework on Adopting AI on Azure – brilliant! I thought I’d review the framework this week and what it means.
After completing certifications in the various platforms from IBM, Amazon Web Services (AWS), Microsoft and Google I wanted to take a high level look at what these platforms really mean for organisations. These organisations are offering powerful AI platforms that provide a way to access AI functionality without having to develop and run your own infrastructure and manage your own AI deployments.
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.
In a Gartner presentation by Max Goss this week the subject of Organisational Change Management (OCM) in AI projects was brought up. OCM has always been important in IT projects but what makes it different in AI projects? Well, no surprise, it’s not just about the technology, it’s about the usual suspects such as communications, involving the right stakeholders, leveraging change networks and delivering training but there are more things to consider in the rapidly evolving landscape of AI.
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…”.
No surprise – it’s a (‘it’ in this case being the deployment of AI from a corporate perspective) a challenge and an opportunity. From the Artificial Intelligence perspective, businesses have access to a variety of tools and frameworks designed to facilitate AI adoption….that often operate in silos.