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.
Following on from last weeks article on GenAI readiness assessments, to harness its full potential, organisations must adopt a strategic approach. Developing a Generative AI Roadmap is crucial to guide this journey effectively, no only for the GenAI solutions but the development of the systems and processes around GenAI to ensure it’s long term support.
Generative AI (GenAI) is revolutionising industries by automating content creation, enhancing decision-making, and sparking innovation. But before jumping in, it’s essential to pause and do the groundwork. In today’s fast-moving, hype-driven tech landscape, that can feel a bit old-school, but understanding your organisation’s readiness isn’t just prudent, it’s foundational. A GenAI Maturity and Readiness Assessment is a crucial step to ensure that the excitement of innovation is backed by the stability of preparation.
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.
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.
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.
While mulling over what seemed to be a lack of GenAI frameworks and design patterns last week, this post from Debmalya Biswas dropped into my feed on A Comprehensive Guide to Agentic AI. This post and the accompanying slides are more than the title suggests – they are a great start on a set design patterns and reference architectures – something that we seem to be sorely missing.
A little while ago I wrote an article that talked about the urgency of adopting Generative AI (GenAI). The more organisations I speak to, who then see the potential of GenAI, the more I realise that it’s an imperative – that the world is changing around organisations and those that are not planning to adopt are leaving both time and cost savings on the table (the ROI is tangible). This hesitation could be costing businesses more than they realise – the loss of a competitive edge.