A short note on how AI can help
AI has already changed how you work, and nobody decided it should.
When did you last click through to a website from a search result?
Now most people only read the AI summary at the top and stop.
Nobody was persuaded, nobody ran a pilot. It was simply easier, so it happened.
Pew Research Center, July 2025: users clicked a search result on 8% of searches that showed an AI summary, against 15% without one.
Generative AI is a type of artificial intelligence that creates new content, such as text, images or code, by learning patterns from large amounts of existing data.
People who use generative AI say it saves them about two hours a week. Ask their boards and nine in ten firms report no measurable impact on productivity.
Both are true. The time comes back to the person, and they use it for something else.
Think about your last two hours at a screen. You took information out of somewhere, did something to it, and moved it somewhere else.
Deciding what to do about it is most of what you’re paid for. The rest is moving information between systems.
The fourth working day of the month. The board pack needs a paragraph explaining why marketing is eleven per cent over budget. The actuals are in the ledger, the commitments in the purchasing system, the phasing in a budget spreadsheet, and none of them use the same cost centre names.
The explanation is three sentences. Getting to them takes most of a day.
Give an AI tool the three exports and the layout, and let it learn once how the cost centre names map onto each other. It comes back with the sources reconciled, the odd figure flagged, and a draft for you to disagree with.
And next month, when the same question arrives about a different line, the setup is already done.
Connecting two systems has always been a project: specified, scoped, costed, prioritised, delivered eighteen months later. Anything smaller, a person does by hand indefinitely.
AI has moved that threshold. The person with the problem can now describe it to an AI tool and assemble the fix themselves, often in an afternoon, without waiting for a project.
A one-off client report needed more detail than the reporting system could give. An engineer rebuilt the data by hand and an analyst checked it: two people, two days, every time. Rebuilding the system properly was estimated at three to four months.
An analyst asked an AI model to solve it. It read the public documentation for the data sources and built a connection any AI tool could use; an engineer helped put it live. The report now takes one person an hour, and the same connection answers everyone who asks.
Let AI do the boring part.You do the interesting part.
Pick one repetitive task and try using AI to help. Use the tool your organisation already provides.
Maybe it saves you ten minutes, maybe it doesn’t work the way you hoped, but you’ll adjust and try again.
Your organisation has rules about which data can go into which tools, and they exist for good reasons. Find out what yours are: the task you pick should be one you are allowed to do.
When MIT studied enterprise AI, the pilots that paid off were driven by the people who had the problem, not a central AI function.
MIT, “The GenAI Divide: State of AI in Business”, 2025.
You already know what you want to fix. Pick something small and see how far you get.
If a conversation would help, we’d be glad to have one.
Michael is a software engineer specialising in artificial intelligence and secure, production-scale systems. He spent more than seven years at Amazon Web Services as a full-stack engineer on an AI service team, working across distributed systems, security, computer vision and large language models. His work there included supporting the NHS in scaling its data systems during the COVID-19 pandemic, and applying computer vision to NFL gameplay video.
Since leaving AWS he has focused on independent AI research: how large language models can reason through and carry out complex engineering tasks. He brings an engineering-led approach to AI adoption: finding the valuable applications, building secure systems around them, and fitting them to existing teams and workflows.
BSc Computational and Applied Mathematics; BSc Computer Science, University of Chicago.
Sreejith is a technologist and company founder with decades of experience across banking, finance and technology. His projects include business process re-engineering for the London branch of an international bank and straight-through processing for a multi-billion-dollar finance company.
Most recently he was CEO and co-founder of Attestant, a blockchain technology company entrusted with staking billions of dollars of client assets; its software is used by some of the largest firms in the industry to help run the Ethereum network.
BSc Physics with Electronics and Computing, UCL; PhD Applied Mathematics, Birkbeck; postgraduate AI research in reinforcement learning and neural networks, Imperial College.