It’s all in the data: How to get your company ‘AI-ready’

In this second instalment in our Livingbridge AI series, our Head of Data & Analytics, Steve Jones, shares insights and practical tips on how to get your company ‘AI-ready’.

Steve knows first-hand the challenges that organisations of all sizes face today when it comes to leveraging data and creating a data approach to generative AI. An expert in the field with decades of experience, Steve creates our in-house solutions. He also works directly with the businesses we support, helping teams evolve analytic capabilities that drive action and lead to informed decision-making.

Here, Steve explains where to get started with creating a data strategy fit for the AI era, explores where to invest time and resources, and highlights key mistakes to avoid in the process.

1. Understanding the landscape

Before we dive into the details, here are a few important things to know about the current AI and data landscape:

• It is evolving all the time
  
  Never before have we seen technology advance this quickly. If we consider Microsoft’s advancement of Copilot - in under a year, we’ve gone from the excitement of ChatGPT, to Gen AI being integrated into my Office 365 workflow. I don't need to learn new skills or adopt new behaviours, Gen AI is fitting seamlessly and naturally into my worklife. The majority of generative AI tools that were popular 12 months ago, with a high price-tag, have already been integrated, and vastly improved on, within Microsoft’s Copilot. Long-term this will lead to exponential productivity gains for companies, but in the short term it means that it is important to get your company AI-ready

Vector databases are changing the game when it comes to leveraging company data

• Imagine you're at a work event with different groups chatting about various projects and interests. Imagine a vector database to be like a pair of glasses. When you wear them, you can see colourful bubbles representing the topics everyone's discussing, floating above their heads. These bubbles are vectors, capturing each person's professional interests and attributes. If you're passionate about green technology, instead of wandering aimlessly from group to group, your glasses instantly highlight who shares your interest by matching your green tech "vector" with theirs. The vector database glasses enable you to swiftly find and join the right conversation, effortlessly connecting you with like-minded colleagues without the need to sift through every discussion. Vector databases will materially change how datasets are connected. This is a game changer for analysis, but also presents a data governance risk.

• The biggest change - and the biggest challenge for companies - is now around data governance

In order to deliver these productivity gains safely, companies urgently need to develop proper data governance, as generative AI tools can’t be allowed unmanaged access to any sensitive data (especially as we don’t have the first idea how the AI is using or processing the data). The good news is that this challenge can be relatively easy to solve with the right approach!

2. How to begin getting your company AI-ready

Our advice to all companies is to spend the next few months getting AI-ready.

This means starting with an internal review, asking: What is the information/data that is really going to help us maximise our use of generative AI? Where can we have the biggest productivity gains?

Once you’ve worked out where you want to focus your AI efforts, the first thing to do is a data audit. This involves going through a process of tagging all your information to understand the security clearance of each piece of data, and it’s the bedrock of good data governance. We suggest working from the ground up to isolate which data you need and why, understand where this data sits, and who has access to it. We are aware this isn’t an incredibly exciting task, but the rewards are great.

We also suggest that companies map out any data that could become an asset. Historical information has gone from being a useless old file to being data that offers really rich insights from the last few decades. 

Businesses that don’t push forward with a data audit now are going to fall behind. Generative AI is going to be everywhere in the future, and if you can’t allow that AI to access your information and drive better insight, better responses and better productivity, your company will react slower to industry changes.

3. Next steps: Invest in getting your core IT infrastructure AI-ready

There is so much excitement around what AI can do, but future gains from generative AI all depend on getting the right infrastructure in place now, not in five years’ time. As we advance, everything is going to be driven by your login and your corporate profile. This means that all your data needs to be available in the Cloud, and that is reliant on your core, internal infrastructure.

AI-readiness really needs to be built in from the ground up and fit into your existing infrastructure. Otherwise, companies risk putting new tech over a “black box” stack, resulting in the business having no idea what the generative AI technology is doing, or what data it is using. This is why we’re advising against investing in generative AI tools outside of your general tech stack unless there is a really strong or well thought through use case.

To create the right internal infrastructure, we advise IT teams to liaise with their managed service provider (MSP) to work out an approach. If you don’t have the expertise in-house, consider hiring a consultant data specialist to help with the project.

4. Get a clear data policy in place, then embrace using external AI tools like ChatGPT for work with non-sensitive data

It is important to note that there is a difference between utilising external generative AI tools to deliver value-add – something all companies should now be doing – and relying on new tooling in your tech stack. For example, ChatGPT and other generative AI products like Microsoft’s Copilot are easy, “quick win” tools that deliver immediate productivity gains.

The one thing to note here is that companies need to have an AI policy in place, so everyone knows where they stand with types of data entry into these tools. All policies should include the instruction: “Don’t paste any sensitive company data into GPT”.

5. Finally, don’t expect getting AI-ready to be a quick and easy process

Thankfully, investing in getting ‘AI ready’ is not an expensive undertaking, in part because Microsoft and Google want companies to be starting this process. However, it does involve a major time investment.

Right now, we’re investing in getting our core infrastructure AI-ready at Livingbridge. We’re leaning on our MSP to help us understand how the tech landscape is changing, and to work out what information is critical and to categorise it properly. This is a lengthy process that will take us around six months. It involves our compliance team, and we may also bring in external experts to help us complete the project. I’m advising our companies with similar projects to be patient and keep going – the process may be boring, but it will be worth the investment in the long run. Once you’re AI ready, your ability to ‘plug and play’ other technology will be maximised and you’ll be ready to go.