Your trusted partner through the AI journey.

Start with alignment: a half-day leadership workshop that explains what AI can and can't do and agrees a short, prioritised list of use cases. Then get your existing users trained properly, automate one or two high-value reporting or document tasks, put light governance in place, and deliver quick wins you can show the board. Small, visible steps beat a big programme that takes a year to report back.

Free consumer versions may use your data for training; business and enterprise versions can be set so they don't, with retention and admin controls. Use company accounts on a business plan, check the data settings, and keep highly sensitive information out of any tool that isn't approved for it. We help you choose the plan and settings and explain the security layers in terms your stakeholders can sign off.

It will change jobs, and repetitive desk work will be affected first. But for the next several years the dominant pattern is people working with AI: less re-keying and drudgery, more time for judgement and customers. Experienced people become more valuable, because AI needs someone who knows the business to direct and check it. As we often say, it's not AI that replaces people, it's people using AI who replace those who don't.

It won't be perfect the first time, but it gets better fast. We break the work into small, well-defined tasks, feed the AI only the relevant source material, make it cite where each answer came from, and build review steps into the tool. Early outputs are reviewed closely. As accuracy is proven, reviews lighten, and your feedback is used to improve the instructions.

There are three parts: per-user licences for the AI tools staff use, usage charges ("tokens") for automated work, which grow with volume, and the time of the people who build and maintain your tools. Hosting and connectors are usually modest. People's time is the cost that gets underestimated. Give AI its own budget line and a simple spend dashboard, because it's easy to overspend when you push thousands of documents through a model. Good engineering keeps running costs down, for example by pre-processing documents once rather than re-reading them on every question.

The underlying models are broadly comparable; the difference is in how they are deployed. Copilot is built into Office apps, which is convenient but can feel restrictive. ChatGPT and Claude are more flexible and stronger for building automations and connecting to your systems. Choose on data controls, connectors, automation and what your people will actually use. Most companies settle on one main platform, sometimes two.

Decide case by case against four tests: cost, including your people's time, which is usually the biggest cost; skills (can we build and maintain it?); criticality (if it must be 100% right, like payments, lean towards buying); and speed (building a wrapper around what you have is often much faster). Building also makes sense when no product integrates with your systems or fits how you work, and in-house tools are easier to change later. A common answer is "keep the core system, wrap AI around it, buy where accuracy is critical".

Usually not. Replacing an ERP is slow, expensive and disruptive. The pattern we see working is "keep the core, wrap AI around it". Leave the system, its data and its permission structure exactly as they are, and add read-only views, a secure connector and AI tools on top. People who don't use AI carry on working just as they do today. Many companies are doing exactly this with ageing ERPs.