If you are not getting value from AI, you are missing out

Getting the most out of your AI investment

Keri Niven: It’s a challenging fiscal environment, why should organisations invest in AI now?

Dave Mackenzie: To me, if you're not getting value from AI already, then you're missing out. There are so many ways to move the needle and increase efficiency quite simply with AI, and without huge cost. That said, it is a tough climate, so it has to be really underpinned by value and impact. You need to be able to see what’s actionable right now.

If you are unsure where to start, then it is sometimes helpful to understand the journey others have travelled. At Aurecon, we have been on the AI journey for some time and can share our experiences.

We also have a construction request for information (RFI) tool, which has really accelerated our response time. We have cut the time taken to respond to RFIs by as much as 50 per cent, which on some major programmes is, totally transformational.

Keri: We hear all the time, 'we know what AI is and understand the potential but have no idea where to start.' Can you tell us some of the use cases where AI is helpful for technical businesses?

Dave: There are three areas where we have seen AI offer huge value to organisations:

Where you have big, complicated data sets

To give more people in the organisation access to knowledge held by a small group of people

Asset management and operational data

Managing complex data

Keri: Let’s talk about data. Some organisations deal with large amounts of traffic volume data, climate adaptation data and geotechnical data for example – is this the kind of thing we’re talking about?

Dave: That’s exactly right. Anywhere you have messy or unstructured data or information, that’s a great starting place for generative AI. Its superpower is processing unstructured data and turning it into something structured.

We often say that data is the programming for AI, so having lots of good data is what drives great outcomes. Once that data is well structured, that can provide the foundation for more advanced outcomes.

Generative AI can analyse data just like a human. A model can take all of an organisation’s standards and guidelines and context and become an expert in that organisation. Then we can use it to analyse very large swaths of data, to generate insights and perhaps find new or unique connections.

Retaining and accessing organisational knowledge

Keri: You mentioned that AI could give more people access to specialist organisational expertise or understanding. What did you mean by that?

Dave: Every organisation has someone who knows everything about your projects and knows all your standards and guidelines. They can answer every question, and they’re the kind of people who, if they walk out the door, will take a heap of knowledge with them, which will really slow your organisation down.

What we try and do is bring together all your standards and guidelines, technical specifications, all the things that your people need to do their job and fine tune a model specifically on those. If you think of ChatGPT as the bottom of the pyramid, this is going right to the top in terms of answer quality, and relevance to your organisation.

"An AI model can take all of an organisation’s standards and guidelines and context and respond using the unique style and tone of voice of that organisation."

So a transport agency, for example, might have a model that only knows about that entity. It will respond using the unique style and tone of voice of that organisation. Someone might ask it for the specific safety procedure for closing a road and filling a pothole, and it will be able to provide that, because your organisation’s intel is the only content it is accessing.

This is a good place to start as the value is evident early on, it's easy to deploy and it’s relatively inexpensive.

Using AI to solve complex challenges

Keri: How can AI technology be used to solve real world problems?

Dave: Technology must follow the problem, so we need to be developing tools in partnership with organisations in response to real problems. Aurecon’s focus is in using AI to tackle complex problems that matter.

We’ll start by workshopping a few potential use cases or problems an organisation is facing. Last time we did that, the organisation came up with 54 ideas! At that stage, the organisation will select a few of them: the high impact, lower complexity ones, and we will quickly prototype something for them.

"You can experiment and prototype easily, which means you will quickly know if something is of value – typically within a day."

With this technology, you can experiment and prototype easily, which means you will quickly know something is of value – typically within a day. On the other hand, if we start something and it suddenly becomes really complex, you will know that quickly too and can park it for now, without huge investment.

We usually try to prototype a few ideas quickly with an organisation, and once they have something working, we can then expand that out with a pilot, and then onto a longer-term procurement.

Keri: I really like that approach because the organisation has identified the problems. It’s centred on a genuine problem they are grappling with, and our role is to create a tool or approach that can help. Can you share some other new or unexpected areas you’re seeing AI being used?

Dave: One of the big areas coming up is multimodal AI. This is AI that has semantic understanding of images and diverse data sets, such as videos, documents and texts. For example, it could help organisations analyse photos and videos from construction sites at pace and scale to identify trends or areas for improvement for example.

I think the other thing to remember is that AI can be applied quite simply to solve common problems – for example, getting manuals to people on site.

It’s also helpful to get people aligned – to have a shared understanding. When you’re dealing with complex problems that require lots of people to collaborate quickly, you need systems and tools that facilitate that. With AI, you know you are talking about the same data, the same problems, the same language.

Keri: I love the neutrality of AI. When you're asking it a question, the response is based purely on data. That’s helpful when you are bringing various groups of people together, and as you say, you can get to that shared understanding really quickly.