E04: Lachlan White on putting AI to work in Australia

While the artificial intelligence (AI) hype continues to outpace adoption across Australia, pragmatic practitioners such as Lachlan White – CTO at Lab3 – are taking a different approach, going against the industry grain to connect market hype to business reality.

To address this gap, White highlighted the most common roadblocks of poor data quality, sub-par education and questionable return on investment (ROI) – providing an in-depth take on an in-demand technology.

“There’s always a scale of maturity when it comes to any new technology,” he observed. “We’re starting to see more focused use cases and, therefore, more focused outcomes.”

Shifting AI into action mode

A critical step when translating customer goals into actionable insights involves in-depth and ongoing education around the potential of the technology in question. In the instance of AI, what is this technology trying to solve?

“When it comes to AI, everyone jumps on the hype and gets excited about generative AI [GenAI] and what it can do but sometimes we’re like a square peg in a round hole,” White added. “The focused content that we would want people to start consuming is just drowned out by the amount of noise that comes from all of the hype material.”

Drawing on frontline experience – and aided further by a strategic partnership with Microsoft – White and the team at Lab3 are taking a lead role in understanding market sentiment and forming a solutions roadmap for customers.

“Microsoft has done a good job surveying companies and seeing where they’re at from a percentage-wise,” he explained.

One way to understand the market pulse is through the vendor’s AI maturity assessment which ranks organisational readiness across five levels:

1. Exploring
2. Planning
3. Implementing
4. Scaling
5. Realising

White estimated that 40 to 50% of the market rests in the initial experimentation and planning phases, with approximately 5% reaching the realisation stage of multiple use cases that yield tangible ROI on AI spend.

“Companies haven’t necessarily got a strong data footprint to date and that means they’re really starting off on an absolute ground zero,” he observed.

There is also a “disparate gap” in the realisation of value as some larger companies are equipped with good data quality and foundational capabilities as opposed to organisations that have just started out on their data and AI journeys.

Data fuels AI

Through the process of experimentation, organisations have been exposed to shortfalls in data quality and infrastructure which at the core, stands in the way of AI ambitions.

In response, White was quick to outline that organisations don’t require “perfect data” to create GenAI use cases however.

“Whilst good quality data is never a bad thing, it’s better to have some experimentation going on,” he added.

“For a long time, we’ve been told that data is the new oil and if you want to get that oil out of the ground and do something with it, you can’t just dig a hole with a spade and then turn it into petrol. You need a refinery.”

For White, having low data quality and falling on the lower spectrum of maturity should not prevent organisations from at least starting down the path of AI adoption. In this game, exploration is everything.

Value of AI expertise

As customers constantly evolve in relation to AI maturity, business requirements are progressing in parallel which in turn, is placing new demands on the partner ecosystem.

In response, Lab3 has embraced “the art of the possible” through a new style of engagement with a human-centred design at the centre, using “proven accelerators” designed to expedite customer goals.

“The problem we’re seeing in a lot of cases is that for every dollar that’s being invested in AI, businesses are seeing a $3.50 return but it’s just taking 14 months to occur,” White explained.

White’s goal is to shorten that timeframe down to possibly three months to maximise that initial investment that customers are putting into building AI use cases.

“How do we bring our expertise – not only in the form of people – but also in terms of pre-packaged code and outcomes that work with clients to accelerate that?” White asked.

Some examples of these accelerator tools include an API-driven AI landing zone and pre-packaged use case formats tailored to specific industry verticals. This covers as much as 60 to 75% of the heavy-lifting for customers when deploying an enterprise-grade AI solution.

This style of solution delivery was curated from lessons learnt during White’s previous experience on the customer side, following roles at AGL Energy, GE, Toyota Motor Corporation and Latitude Financial Services.

White candidly shared that one learning he gathered was that projects are typically categorised in two ways – business successes and technology failures.

“The business will claim success if everything’s great which they’re entitled to do but when something fails they’re always looking at where it went wrong and a lot of the time technology has got a hand in that too,” he recalled.

Consequently, White said Lab3 is moving at pace to get ahead of such stumbling blocks and join hands with the business to ensure mutual success.

“We’re seeing in market anywhere from 30 to 45% of the first few AI use cases the businesses undertake not progressing past experimentation,” White added.

As such, Lab3 is helping organisations avoid “falling into those traps” by providing strategic advice and executive guidance from idea to implementation on AI.

“Just start by having that balance of conversation to unlock the art-of-the-possible thinking that some customers weren’t aware of before,” White stated.

“Businesses aren’t making money because they’ve chosen AI. It’s because they’re doing something different. Losing sight of that from a technology point of view is always a dangerous position.”

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