Does AI Have Your Context?

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.

Think back to the first time you used generative AI. You were likely impressed by its ability to instantly answer a question or draft an email.

However, after a few weeks of playing around, the novelty probably wore off. You likely realised that while the AI was smart, it lacked context. It didn't know you, and it certainly didn't know your business.

Even with upgrades allowing tools to search the internet, they remain disconnected from your internal reality.

For example, if you ask a standard AI tool, "Which of my customers spends the most with us?", it will have no idea.

To get an answer, you currently have to do the heavy lifting:

This works, but it is manual, clunky, and creates data privacy risks. Wouldn’t it be better if the AI could simply look at your CRM and find the answer itself?

Having an AI system take actions for you, is now becoming a reality. Connecting AI tools to other systems is done via the Model Context Protocol (MCP).

I won’t bore you with technical details (though reply to this email if you want to get into it), but here is what you need to know:

When you hear people discuss Agentic AI, MCP is a massive piece of the puzzle. The true power of AI comes from systems talking to one another.

Consider booking a holiday. Currently, an AI can tell you where to go. With Agentic AI (powered by connections like MCP), the AI doesn't just search; it connects to flight platforms, hotel systems, and payment gateways to actually book the trip.

This technology will fundamentally change the jobs we do and how we behave as customers. It is both exciting and, admittedly, a little terrifying.

A question for you:

Have you moved beyond just chatting with AI, or have you tried working with agents?

Most Voice AI deployments fail for the same reasons: unclear logic, limited testing tools, unpredictable latency, and no systematic way to improve after launch.

The BELL Framework solves this with a repeatable lifecycle — Build, Evaluate, Launch, Learn — built for enterprise-grade call environments.

See how leading teams are using BELL to deploy faster and operate with confidence.

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In this section, I highlight a real-world example of AI. This week I talk about how businesses are using MCP to increase efficiency and service levels

Multiple major AI labs (including Google, OpenAI and Anthropic) have been featured in the press for releasing or previewing compact AI models that run efficiently on laptops, phones, and private company servers. These “small-but-capable” models are being pitched as cheaper, more private alternatives to giant cloud-based systems. For businesses nervous about data privacy or spiralling subscription costs, this trend has been welcomed with open arms.

Why this matters: Smaller models accelerate adoption by organisations that previously avoided AI due to cost or confidentiality concerns.

A wave of new AI video products has been highlighted across creative-industry news this week, with tools now able to generate short adverts, training clips, and social content from simple text prompts. Marketers love the speed boost, but filmmakers and designers are debating what this means for creative jobs. Expect a growing divide between those using AI to enhance creativity and those worried it might replace it.

Why this matters: Video was one of the last expensive, slow content formats and AI is now transforming it just as quickly as it disrupted text and images.

Economic think tanks and labour researchers have been in the news for publishing updated forecasts on AI’s job impact. The consensus remains: AI will automate tasks, not whole jobs, but the jobs most exposed are “middle-skill, middle-income” roles like admin support, customer service, and basic analysis. Interestingly, the studies highlight that workers already using AI are reporting higher job satisfaction, largely because tedious work is reduced.

Why this matters: The conversation is shifting from “Will AI replace jobs?” to “Which tasks will change, and who benefits?”

Thanks for reading, and see you next Friday.

Simon,

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