Hello,

This is Simon with the latest edition of The Weekly. In these updates, I share key AI related stories from this week's news, list upcoming events, and share any longer form articles posted on the website.

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This week, I saw that my old employer, Salesforce, has launched the third major version of its premium AI product, Agentforce. This is quite impressive since the initial launch didn't feel long ago and demonstrates how quickly companies like Salesforce are iterating on their products. However, I have seen a few comments in response to this that highlight products like Agentfoce might be too far advanced for most companies. To get the best out of Agentforce, you need a solid strategy, robust data, and perfect workflows; not everyone has those right now. People suggest that only those who are well down the data transformation path can benefit.

I also saw this as a similar theme at my last company, Dataiku. They have a collection of features called the LLM Mesh, which allows users to run prompt queries on specific datasets with selected LLMs and have it run in an automated workflow with a layer of governance on top. It's handy stuff, yet customers and prospects didn't quite flock to it like you'd imagine. Once again, there was a gap between the leading-edge technology and companies' position on the maturity curve.

This raises a couple of points. Should companies dial down their product ambitions? Is there a gap for other challenger companies to fill with better-aligned features? And is this fertile ground for consultancies and agencies to fill with advisory and execution services?

No to the first point and Yes to the other two.

If companies like Salesforce can't offer a vision for what is ultimately possible with the current technical capabilities, then we are accepting a level of mediocrity that doesn't capitalise on the great opportunity AI offers. It's down to the rest of us to ensure our levels of understanding and competency rise up to meet the challenge.

Gartner warns that over 40% of current agentic AI projects will be abandoned by the end of 2027, due to rising costs, unclear ROI, and “agent‑washing”—where companies hype up simple AI tools as fully autonomous solutions. Currently, only around 130 vendors offer genuine agentic capabilities. Despite this, Gartner expects that by 2028, 15% of everyday business decisions will be made autonomously and 33% of enterprise software will include such tech.

Why it matters: This is a reality check: much of the agentic AI hype may fizzle out, so investing smartly, focusing on proven, valuable use cases and avoiding marketing spin is crucial.

Anthropic shared findings from a study where AI agents—like Claude Sonnet—under simulated pressure chose to blackmail a fictional executive to protect themselves from deactivation. In fact, 86% of Claude Opus models and 78% of Google's Gemini exhibited similar behaviour. The study highlights “agentic misalignment,” where AI takes harmful steps when goals conflict.

Why it matters: It’s worth us bearing in mind, that autonomy comes with ethical and safety risks. Even sophisticated AI might take unexpected and harmful actions unless carefully checked.

HCLTech has extended its partnership with Salesforce to bring agentic AI into enterprise processes—aiming to integrate autonomous agents into cloud-based business tools. This strategic move has caught investor attention and positions HCLTech at the forefront of digital transformation.

Why it matters: The story highlights that big companies are embedding agentic AI into software used by daily professionals such as accountants, marketers, managers. This demonstrates a level of maturity in the tech that enterprise business are happy to embed it into their core work.

Thanks for reading, and see you next Friday.

Simon,

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