In the race to harness AI, organisations are layering automation on top of existing operations. But is any of it making business better?
“Strategically, the fundamentals matter more than ever,” says Victoria Place, a fractional COO with more than 20 years delivering strategic transformation. “People, process and technology. AI hasn’t changed that.”
But, she warns, unprecedented speed of change means escalating pressure to focus on just tech.
Here’s an example:
- A recruiter uses AI to automate creating a job description
- Candidates use AI to write CVs and apply
- The recruiter uses AI to screen a mountain of AI-generated applications against the original AI-generated job description
Is it more efficient? Perhaps – 83% of recruiters use AI, but only 36% say it’s improved their time to hire.
Is it more effective? Well, 35% told the same CV-Library study they believe they’d missed out on strong candidates because AI reduces human intuition.
It’s easier to apply for jobs and easier to process large numbers of applications. But it’s potentially harder to work out who should get hired.
The people experience has deteriorated. The process hasn’t changed. Only the tech is different.
People and knowledge
An emerging competitive edge is in finding ways to help knowledge flow more easily across traditional boundaries. Mixing up silos and how expertise gets distributed.
People: Human insight, judgement and empathy. Process. Strategic objectives driving every task and decision. Tech: Harnessing the power of AI to enable higher quality and efficiency.
“The people doing the work are living and breathing the operational challenges,” says Victoria Place. “They need to understand how to use AI, but also how to keep adapting and developing it around what the business needs.”
Some organisations are finding ways to harness frontline knowledge and judgement to identify AI opportunities that might otherwise go unnoticed. Examples include:
1. Adding operational expertise to AI development
One approach is to appoint experienced knowledge workers to key roles within AI R&D teams.
Even if they have little technical background, they bring practical knowledge that technology specialists often lack. They help discussions focus on problems people face rather than what’s technically feasible for AI.
2. Nurturing transformational AI thinking
Another approach is to second experienced employees into AI teams, sometimes for months.
The learning goes both ways. AI specialists gain operational insight. Knowledge workers develop their understanding of how AI can bypass barriers higher up the value chain – inviting intuitions that would never occur to someone looking at their workflow on a process map.
Elevate intuition
“Unlike traditional technology implementations, AI transformation doesn’t end when the new systems go live,” says Victoria Place. “The technology keeps evolving on a daily basis, and so must the ways people work with it.”
If leaders can help their employees develop technological awareness, they’ll find that most are naturally creative problem solvers. The competitive opportunity is giving everyone scope to rethink the processes they know best.
Talk to us about building a workforce ready for AI transformation.
