AI in HR – how to understand what is happening

AI in HR – how to understand what is happening

There is a considerable buzz these days about so-called ‘AI in HR’. Most vendors are claiming to have some sort of machine-learning within their products and some are making claims that, from the perspective of someone who has been doing this for the last 15 years, seem unlikely.

Understanding what these new technologies can (and can’t) do is vital if HR is able to evaluate purchasing them, or work with their internal teams in designing, developing and deploying their own approaches. Analytical literacy is rapidly becoming a core skill of HR.

Algorithms are free

Many of the technology vendors will market their products as having some amazing, unique algorithms. In almost all instances this is unlikely.

One of the remarkable trends that we’ve seen over the last years is that the big technology companies have acquired large teams of the best data scientists and have been publishing new algorithms in journals, often open-sourcing code at the same time.

Pretty much everyone is using these algorithms – and in many instances much earlier developed ones – as the basis of what they’re doing. They will almost certainly be combining them and changing settings but at the heart we should assume the same freely available building blocks.

What is needed is great training data

In contrast to algorithms being free, data is not and as such is what differentiates decent from great analytics efforts. This matches the message that we always tell clients – to improve early analytic results there is usually a need for better data, not better algorithms. It’s why we built Workometry – to make the collection of the great-quality data as easy as possible.

In 2014 I wrote an article describing the 5 types of HR Analytics vendors. In it I described a category which I called the ‘data aggregator’. This was a firm who, by collecting vast amount of cross-firm, individual-level data were able to build valuable analytics offerings.

In 2018 pretty much every SaaS HR offering is trying this model. In many instances the data doesn’t really have enough value (there is a lot of it, but it’s not really that rich – most survey providers could be put in this category). However some vendors will find true value in this approach.

This data becomes a barrier to entry for new firms wanting to enter the industry – it’s hard and costly to acquire. It’s a good reason why many of the most innovative HR analytics start-ups are in recruitment. In recruitment far more data exists outside the firm in public data sources.

General AI is a long way off

When vendors talk about AI in their product to the lay-person they often conjure-up images of technology that has near-human levels of reasoning. Most data scientists would tell you that this reality is a long way off.

One of the interesting aspects of machine learning techniques is that it can solve some tasks that we humans might find difficult (playing chess for example) yet it might struggle with tasks that even a 4 year old could achieve easily. I suspect that we’re close to developing an autonomous van which can take a parcel from the depot to your house but it might be harder for a robot to take the parcel from the van, up the stairs, enter the building and find the correct letter box.

What today’s current approaches can do is solve certain, well defined problems, usually with lots of available data with extraordinary levels of accuracy. Often, the narrower the problem and the greater the data size to learn from, the more accurate the prediction. These narrow problems are often described as ‘Specific AI’.

Benefiting from Specific AI

Take the example of text analytics. Even within text analysis there are different firms in the HR space doing wonderful things. TextKernel has developed very good approaches to understand CVs and Job Descriptions. We, through our Workometry technology, have probably the leading approach to understanding the answers to open questions (for example in employee suggestions or feedback). We even go so far as building specific models on the organization / question level (arguably our key differentiator is how quickly we can build these models). With such specific models we can out-perform skilled humans at this task in a fraction of the time / cost.

We can think of the implication on work of AI / robots therefore not as automation taking away whole jobs – as most jobs require a variety of tasks, but of AI automating specific tasks. These will be the ones with a lot of repetition or where large volumes of data need to be acquired and synthesised.

When thinking of how to apply AI it’s important to therefore break a job down to tasks, ideally the smallest, most specific tasks possible and identify which are candidates for AI. At the same time we need to identify the value / cost of these tasks to identify which are worth developing solutions to automate.

When doing so we shouldn’t constrain ourselves to tasks that we’re currently doing. Many tasks are possible without AI, but prohibitively expensive. For many firms the sort of text coding Workometry does has been too expensive and time-consuming to perform. For many of our clients Workometry is 10x cheaper & 200x quicker than the alternative solutions and is of higher quality. What was difficult to justify therefore becomes attractive.

Benefits from AI

There are 2 key drivers of benefits from using so called ‘AI’ in HR:

  • To improve a business driver (eg productivity, customer experience) and by doing so enable the business to achieve better results
  • To reduce cost of of delivering HR.

In many instances the first is likely to provide opportunities to realise a greater return to the business, however it is also likely to require greater & more wide-spread buy in to results. Implementation costs and risks are likely to be higher with a greater number of uncertainties influencing the end deliverable.

With this type of analysis it’s highly unlikely that the data needed will be residing in one system or database. Given this we can expect fewer instances where a single system provider will have enough data-coverage to be able to build a complete model. The best work in this area will remain the preserve of data-science teams within a firm who can identify, process and join the necessary data sources into a reliable model.

Cost reduction for HR will ultimately be easier for predicted results to be achieved. In many instances there will be a smaller number of decision-makers (the HR leader) and it’s likely that cost reduction will be a core part of their objectives. Data for this type of analysis will be easily available and more likely to be of high quality / have less measurement error / to be more complete. It will also be more likely to reside in one system. In the medium term we can expect system providers to deliver such capability.

Some points getting the most out of AI for your HR team

  • A little knowledge will go a long way. Think about up-skilling your team so that they have a good understanding of where AI can be deployed in its current state and what the likely benefits are. Several providers (including us) can help here
  • Don’t expect system providers to provide complete solutions where they don’t have access to all the data. There will be a need for the foreseeable future to build good People Analytics capability
  • People Analytics technology won’t solve all your problems, but it might remove routine tasks from the People Analytics team, thereby enabling them to focus on higher-value tasks. Think of these solutions as complements to building capability, not a replacement
  • Challenge your technology vendors (especially if you’re a key client) to develop solutions that can identify cost improvements. With all the transaction data they should be identifying efficiencies. This will soon be a hygiene factor for systems providers
  • Often simple models can be built quickly. In a drive for accuracy you hit decreasing marginal returns pretty quickly. How much more valuable is this solution than what your team could build in 10 days?
  • General models, built on other firms data is unlikely to perform as well as specific models built on your data.