Of the expected $2.5 trillion that corporates are due to spend on AI this year, over $1 trillion is set to be allocated to IT services and dedicated software. That is a huge chunk of change for a technology that many are still grappling with and trying to understand how it fits with their business, team, and procedures.
A few years since the ChatGPT-enabled revolution started, we’re beginning to see the different approaches and challenges businesses are facing when implementing AI.
L&D expert and Chief Learning Officer at 360Learning.
Ford’s recent decision to bring back experienced engineers after AI systems failed to catch manufacturing issues offers an important lesson for every business investing in AI. At the same time, new research from Ramp and Revelio Labs, covering almost 22,000 US companies, found that organizations investing most heavily in AI are actually hiring more people, not fewer.
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Together, these stories tell us something important. Everyone assumed AI would reduce reliance on people, but instead we’re seeing companies discover how dependent AI is on experienced people. AI may be changing how work gets done, but this alone isn’t enough for growth and success.
AI is accelerating work, but experience still matters
Earlier this year, Ford re-hired 350 veteran engineers after AI and automated systems failed to deliver quality work, demonstrating that employees are critical to a successful AI rollout.
It’s also interesting to see this reflected in Ramp and Revelio Labs’ research that organizations investing in AI are hiring more people. Instead of neglecting hiring and skills development, companies increasingly need the right team members to help refine and improve sophisticated AI so that it performs better in the areas they need.
Success with AI still depends on understanding the skills people need to perform, at an individual, team and organizational level, then investing in development that reflects the realities of their work. But before organizations can build those skills, they need to know where expertise already exists. Too many businesses are sitting on critical knowledge without any clear view of who has it or where the gaps are.
One way to achieve this is building a skills ontology, a living record of the skills that exist across the organization.
Rather than relying on assumptions, businesses can identify where they overindex or underindex on capabilities, understand which teams have specialist knowledge and spot gaps before they become business problems. It also creates the foundation for workforce planning, internal mobility and more effective AI deployment.
The most valuable skills inside your organization
Now you have an effective skills ontology, you have an overview of what is going on in your organization. This helps you identify the subject matter experts (SMEs) that exist across a business, but whose knowledge has often never been formally captured – such as that mythical AI ‘power user’. Take Ford, which has said it is using its rehired employees specifically to train younger staff and reprogram AI tools.
Once you’ve identified where expertise exists, AI can help maintain that picture and keep it up to date as roles evolve. But understanding where the expertise exists is only the beginning.
Organizations need to validate that expertise, connecting people to targeted development opportunities and making it easier for SMEs to share what they know with others. Importantly, this learning should be embedded in work as much as possible rather than separated from it, so it improves performance where it matters most.
AI makes knowledge easier to organize and distribute, but it doesn’t create the judgement that comes from experience. It can surface patterns and recommend learning pathways, but it can’t explain why a process works, when to challenge it, or how to adapt it to the realities of your organization.
That’s why experienced team members remain essential. They contribute the context that enables AI, and everyone else, to use that knowledge effectively.
Turn expertise into a competitive advantage
At a time when many in areas like HR and Learning and Development (L&D) felt that AI would diminish their role, it’s clear that this expertise is only becoming more valuable.
L&D teams have the opportunity to operate more strategically by owning the upskilling and reskilling agenda, turning their and team members’ expertise across the business into a competitive advantage, and putting the processes in place to create agile workforces that collaborate effectively with AI.
AI has put organizational expertise firmly in the spotlight, whilst also highlighting that too many businesses are sitting on a wealth of knowledge that exists only in the heads of experienced employees. Not only do they know and understand more about a business and its processes, but they can also provide judgement – a scarce resource that AI cannot replicate.
Getting this information out and deploying it across the business will create a workforce that becomes smarter every time someone learns, rather than losing knowledge when someone leaves, leading to fewer repeated mistakes, better quality and stronger internal mobility.
To get the greatest return from their employees and from AI, the top organizations need to ensure they are investing in identifying, sharing and scaling the expertise they already have instead of simply buying more software.
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