Emerging technologies such as artificial intelligence can add value to a recruitment process, but they can’t fix a strategy that is fundamentally flawed. Steve Phillips looks at why organisations need to get the basics right first.
One of the often-quoted mantras of Silicon Valley is “fail often, fail fast”. The inference is that unless you are failing, you’re not innovating, but you must fail fast to learn from your mistakes and progress.
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Failing in recruitment, though, can prove costly. The Recruitment & Employment Confederation estimates a bad hire at mid-management level can cost up to £132,000.
The cost isn’t just financial either – bad hires and unfilled vacancies directly impact the productivity of the workforce, as well as draining staff morale and damaging confidence in the business. In a worst-case scenario this can snowball, causing highly skilled and valued staff to leave.
The recruitment industry is currently having its own Silicon Valley moment. Machine learning and big data are already starting to revolutionise how we identify and process candidates. This is completely transforming the volume end of the market, while providing those recruiting for specialist roles with new tools to aid their search.
For companies that are already failing at recruitment, however, investment in these new technologies could end up being little more than an expensive sticking plaster.
There are a plethora of reasons why recruitment fails. It’s surprising how often companies don’t really know what they want or need from a candidate. Even if they do, they may be not be able to articulate it to the right target market. If the candidates being sought are bringing new skills and experience into the business, how do those within the organisation know what to look for or which questions to ask? Inaccurate profiling can skew the whole process.
It is a challenge for many to ensure what is being offered accurately reflects the opportunity provided. The most common reasons for people leaving jobs within the first three months are that they either do not have the right skills to fulfil the role, or the job was not what they expected. Getting it right the first time can save a lot of time, money and energy.
Recruiting for the short term
The problem is that all too often recruitment is seen as a short-term transaction. A need arises for new skills, vacancies must be filled, and new roles are created as companies grow. Job adverts are written, sometimes by people who don’t fully understand the role, and a frantic push to find suitable candidates begins. Often there is a gap between what employers are looking for and what the market has to offer. Roles can be advertised and re-advertised for months.
Fail fast and fail often only works when lessons are taken on board and change is enacted.
It’s tempting to believe that, before long, artificial intelligence will be able to reduce attrition by doing all the hard work for us, introducing us to the perfect candidates with levels of match-making that give online data apps a run for their money. Machines, though, are only as clever as the inputs they are given. In order to overcome challenges such as talent scarcity and cultural fit, you need the solid foundation of human strategy.
Any drive to innovate must be guided by long-term thinking. What sort of company are you? What kind of people do you wish to attract? What can you offer to prospective employees that others can’t?
It’s encouraging to see that “employer brand” has become a buzzword in HR in recent years, but this goes deeper than just outward appearances. How can you communicate your values and culture through a LinkedIn advert? Can you? While there are common industry-wide recruitment challenges, each business is unique – so a recruitment strategy that works for one business, may prove completely ineffective for another.
Building on your values and business objectives is a good start. Combined with longer term framing this can allow companies to introduce more lateral thinking and problem solving into recruitment. It is here that data analytics and predictive profiling can really add value. With the right inputs, machine learning can help HR professionals to widen the net and identify candidates with the right attributes from other sectors, in different locations, or from further down the talent pipeline.
Once identified, this talent pool should be nurtured, so that when the need arises it’s less work to connect the right people to the role, and they already know who the organisation is. A lot can be learnt from the tech industry here too – events such as hackathons are often used to engage prospective talent with values and build personal relationships in a soft environment, leaving the door ajar for future approaches.
In order to overcome challenges such as talent scarcity and cultural fit, you need the solid foundation of human strategy.”
All of this might be a big ask for an in-house HR team that is already managing internal culture and development. There will be mistakes made, of course, as organisations adjust to new techniques and technologies, but these can be avoided by working with partners who already have experience and can share the risk. Whether HR directors decide to leave everything to the professionals, through resource process outsourcing, or to continue to manage in-house, the best recruitment strategies are those that are co-designed between in-house teams and recruitment experts with a wider view of the market.
In many ways, recruitment has never changed. It’s ultimately about people and their relationships with each other, something that machines still have some way to go before they can understand.
The primary benefit of time saving techniques such as automated interview booking is that they allow HR teams and recruiters to spend more time focusing on what makes them good at their job in the first place – personal contact and intelligent thinking.
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