Gerry Griffin debunks AI myths, explores real-world applications, and highlights its potential for data-driven insights to enhance human potential
The hysteria around AI can make it hard to tell fact from fiction. Listen to the doomsayers and you could be forgiven for thinking your replacement by a machine is both imminent and inevitable. But, once you get beyond the headlines, mostly generated by tech companies clawing back a dip in share price, the truth is markedly different.
AI can provide fresh insight into talent-related challenges such as retention, breaking down barriers, closing skills gaps and keeping people motivated
Trust me, AI won’t cause mass unemployment, hack into security systems, lead a robot revolution (sorry Terminator fans), or replicate the human brain. Why? Because it doesn’t exist in the sci-fi sense that it usually gets reported on.
Take ChatGPT for example. It isn’t self-aware. It’s just a calculator adding sentences together based on a score between zero and 10.
It’s all in the patterns
When people talk about AI, what they actually mean is machine learning, a subset of AI powered by pattern matching – collecting and analysing data to predict user preferences. And these machine learning models are already part of our daily lives.
Have you ever wondered how online retailers and streaming sites such as Amazon, Netflix and Spotify make recommendations for related products or content? Machine learning scans your habits and history for pattern recognition which generates those helpful suggestions.
Do you need a raincoat to go with those wellies? You might like this show because you watched something similar. Try this album because it looks like you’re a fan of rock/country/house/prog/hip hop/insert genre here.
Similarly, social media platforms help you grow your network with people you might know based on connections, comments and likes.
And if we focus on AI’s ability to transform data discovery – the sweet spot between the doom and hype – we can harness its power to unlock talent and help people fulfil their true potential.
How to make AI work for you
If you’re contemplating practical ways to use AI, it’s always best to start with a problem. AI isn’t a master; it’s a tireless servant prepared to undertake tasks humans can’t be bothered to do because of the unrealistic amount of time and effort involved.
This enables it to provide fresh insight into talent-related challenges such as identification, retention, breaking down barriers, closing skills gaps and keeping people motivated.
Let’s look at each in turn to see what AI can do:
Talent identification
Choosing a career is hard, and completing an apprenticeship requires significant effort and investment. However, training pathways are often recommended on little more than a hunch. Part way through, apprentices realise they aren’t the right fit and drop out, which is costly as well as frustrating.
How much better would this process be if applicants and employers understood their abilities and the best fit for their talents?
Clues that lie in the data if you know where to look…
Machine learning can scan for skills unique to organisations and clients. A deep dive into personal profiles (CVs etc) and applications can reveal strengths, weaknesses and any hidden talents and how they align to different courses.
By understanding their true capabilities, apprentices and employers are more likely to choose the right pathway first time around, reducing dropout rate.
And in the same way AI can help young people find a career, it can help older workers feeling stuck, shift to something different. Working in sales, but more suited to social care? Let’s run the same analysis and find out.
Talent retention
Some professions have higher rates of attrition. According to a recent poll, 44% of teachers in England plan to quit within five years. What’s driving this and can it be slowed?
By harnessing the power of machine learning to transform data discovery, candidates can be screened more effectively against the core attributes teachers require to thrive and not just survive.
And if teachers already in post find they’re weaker in one or two competencies, the same technology can recommend further learning in situ to close any skills gaps before they decide to leave.
Removing barriers
When it comes to employment, some people experience significant barriers through lack of skills or social stigma. Again, AI has a significant role to play in helping boost employability, breaking down barriers and improving social mobility.
The unemployed are often the victim of government posturing – a punitive approach to get people into any type of work, even if it’s unsuitable and/or precarious.
This focus on compliance, i.e. spending a set number of hours per week looking for work, misses the mark and does nothing to help claimants into a long-term career suited to their skills.
However, thanks to AI, people can gain important insight into their abilities and any upskilling/reskilling required to find suitable employment.
Similarly, ex-offenders face significant obstacles to re-entering the workforce. And while rehabilitation programmes offer training, they rarely try to unearth any unspoken or hidden talents.
Case study
Own Merit is a charity working to reduce reoffending by helping people with convictions into housing, education and employment. The charity is currently taking its first steps with machine learning to help clients better understand their skills, hone them with further training, and find work that makes best use of their personal strengths, as Co-founder Darryn Frost explains:
“We’re trying to help people find suitable work because this has a higher chance of being sustainable for the individual and is far better for the employer,” Frost says. “At the same time, we need to be realistic about what their capabilities are. AI-supported search provides guidance to people who often struggle to find work and can inspire them to try something different to what they already know.”
This is already making a difference, as Daniel Joseph can testify: “Own Merit has been a huge part of my success since being released from prison,” he explains. “As well as help finding accommodation, I was supported into paid employment with a local construction company. This has given me pride in myself and I feel like a valued member of society again.
“Additionally, I’ve also been able to pursue a long-term goal of becoming a volunteer recovery support worker with Bridge, a substance misuse programme,” Joseph continues. “This is particularly important to me as I’m in recovery from alcohol and substance addiction and am almost three years clean.
“Not only do I feel rehabilitated, but I’m also able to give back to the community. With all the hard work I have put in and the ongoing support from my family, employers, Own Merit, and Bridge, I now have a great life with so much to look forward to.”
With AI already helping people like Daniel navigate complex challenges and cut through preconceptions about background and circumstances, its potential to improve social mobility is becoming increasingly apparent.
And if we apply the same approach within our own organisations, we can remove unconscious bias, uncover skills, help people progress, and create a greater sense of inclusion.
Closing skills gaps
What skills do you have presently? What about the whole organisation? Machine learning can quickly carry out a skills audit and recommend further training to address any deficiencies.
Missed out on a promotion? By drilling into the data you can identify any weaknesses holding you back and upskill accordingly.
Underperforming department? Identify and close the skills gap in exactly the same way.
Motivation
Just like fitness trackers help us stay in shape with goal-setting and progress reports, machine learning can help us monitor our levels of workplace motivation. In the same way inactivity results in a nudge from your Fitbit, if motivation dips, you’re prompted to take mitigating action.
Key takeaways
Tech company hype and media hysteria have distorted our understanding of AI. What we actually mean by AI is machine learning, a subset of AI that recognises patterns in data.
By using machine learning to undertake tasks that are too time-consuming for human workers, we can gain fresh insight into, and solve, a range of talent-related issues with better skills analysis and development that helps people unlock their true potential.
Gerry Griffin is the founder of Springy
