How businesses can shift AI from an initiative to an expectation

A newly developed framework for AI adoption is simplifying the process and focusing on what makes a difference.

In its recently released AI Adoption Blueprint, Employment Hero took lessons from its own deployment and redesign to form a framework that businesses can implement, whether they are a team of five or 500.

The framework is broken down into four phases, beginning with aligned leadership. As the blueprint explained, effective AI integration is often a top-down approach, with leaders having a degree of visibility to set expectations and model the behaviour they’re asking for.

Phase 2 dictates that building clear guidelines is crucial to AI adoption and eventual scaling, as it empowers a workforce to actually make moves. In contrast, those with deficient guidelines risk “hidden” AI use or absent AI use.

Further to the establishment of practical guidelines, Employment Hero encouraged employers to lay the foundations for AI knowledge company-wide, but train in segments, starting from those required to use AI the most often and visibly, and then to earlier adopters or those with internal proof points.

Organisations will then be ready to measure their results. A step sometimes forgotten in the AI adoption process, metrics reflect the reality of operation, and show where confidence is high and low, what additional training may be needed, time saved on specific processes, and how workflow has been changed with AI.

Speaking to HR Leader, Employment Hero head of people operations Liam D’Ortenzio explained that simply adding AI as one of the platform’s operating principles shifted organisational sentiment dramatically.

“We documented what happened next because there’s a clear gap between how Australians are using AI and how their employers are supporting them,” he said.

“Our AI Paradox Research found around one in three Australian employees use AI at work without their employer knowing, and 42 per cent feel that using it [to do] parts of their job is like cheating. Yet only 18 per cent of Australian employers say AI skills are essential.”

D’Ortenzio explained that workers remain sectioned from company-wide policies or frameworks, often left to teach themselves without connection to the work their business needs done.

This sentiment aligns with ongoing coverage of the importance of employee trust with regard to AI and the culture surrounding it.

Reflecting on the biggest lessons learnt from the addition of AI to its core principles, D’Ortenzio said that “you don’t get people to change one by one”.

“You change the conditions they work in. That means leaders using the tools themselves, peers sharing what works and a clear policy that tells people what they can do, not just what they can’t,” he said.

“In one year, our top developers moved to coding at roughly 30 times their previous rate. Our CX team cut data configuration per customer onboarding from 10-11 hours to 2-5 hours. And by week four of our AI bootcamp, four times as many people were building agents as on day one.”

In the same way, D’Ortenzio reflected that, at times, expectations for leaders were not sufficiently set, meaning teams were also not able to follow leaders effectively.

“The order of steps is the same for every business. Get leadership aligned, set guidelines that give people permission to start and find the people already getting results so they can bring others along,” he said.

“You don’t need a perfect plan. You just need to start.”

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