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.
When discussing GenAI one concern that consistently comes up is a worry around using public models and what happens to my data – there are genuine concerns about data privacy and security when using public AI models. Luckily there are solutions that address these concerns that have been around for quite a while – Ollama , and Open WebUI – tools that empower organisations to run AI models on their own infrastructure.
Remember when we used to install software from CDs? Then SaaS arrived and changed everything. Next, we’ll witness an even bigger revolution: AI agents transforming how business SaaS applications work. While Salesforce, Workday, and countless other platforms transformed how we work, their application layer could be replaced by intelligent agents that bypass interfaces altogether to work directly with you and your data. The applications will change dramatically, but your business data remains the constant foundation of value. Here’s why this seismic shift will reshape every industry and why you need to prepare.
In today’s business environment, staying ahead requires not only quick decision-making but also access to comprehensive, reliable information. GenAI Deep Research Models can help – they are AI driven tools designed to transform how businesses conduct in-depth research and analysis.
Before I get started on this one, it’s largely a thought exercise on where we could be taking Agents and the impact on the traditional API integration model.
The API landscape will shift. Just as APIs revolutionised how applications communicate, AI Agents are likely to emerge as the next solution for system integration. This transformation isn’t just about new technology – it’s about fundamentally changing how our software systems interact, learn, and evolve together.
In today’s fast-paced business environment, Project Managers are continually seeking innovative tools to enhance efficiency and deliver successful outcomes and the Project Management Institute (PMI) seems to be stepping up.
Business Analysts (BA’s) are in an amazing spot right now (and in the foreseeable future). GenAI offers unprecedented opportunities to enhance efficiency, drive process improvements, and deliver greater value both within the BA’s own processes but also within the processes of the organisation.
I have written previously about technical debt and GenAI choices and when you add on Shadow GenAI the situation becomes even more complex. The accessibility of Generative AI (GenAI) tools means that everyone can innovate like never before. However, this surge in un-monitored GenAI usage, often referred to as “Shadow AI”, combined with GenAI only now starting to come down from the ‘Peak of Inflated Expectations’ can lead to significant challenges, including the accumulation of technical debt within organisations.
Agentic AI seems to be talked about constantly and as I’ve talked about in previous articles design patterns matter – same thing applies to AI Agents, design matters. What design patterns, or a least design considerations are there for Agent AI design?
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.