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Could Artificial Intelligence change the way we sell?
Huthwaite International’s Head of Learning Innovation, Robin Hoyle, argues why the use of Artificial Intelligence in learning is flawed and why, rather than expanding our horizons, it will have the opposite effect.
There is much talk of Artificial Intelligence (AI) and its role in industry to automate certain job functions, in turn driving organisational efficiencies and ROI. And it’s not just about replacing those in low wage, low skill jobs. Increasingly we are hearing it’s actually the well remunerated, judgement-based roles that are most financially attractive targets for AI. After all, setting up AI isn’t cheap. Low cost labour may not cover the costs whereas reducing middle managers and sales people just might.
One area that AI is expected to disrupt is learning and development. In learning, AI is already being used to gather data from learners or from people doing the jobs we want learners to do. Using algorithms, machines accurately predict what learning might be needed next and serves up appropriate content before employees know they need it.
For those working in the field of sales, in theory at least, this could mean teaching sales teams to be more persuasive, behaviourally subtle, and capable of moving a conversation towards a positive customer commitment. But can it deliver?
In short, no. Sometimes more content – focused on new knowledge rather than additional skills and capabilities – isn’t the answer to a seller’s difficulties.
Simply telling people to do things isn’t enough when we want to help them to adopt high performance behaviours.
They need practice, feedback and opportunities to reflect on their experience in order to create an informed action plan for the next call and the next customer. That’s not serving them up yet more digital content – that’s a concerted effort to facilitate behaviour change. It needs people. There is no technological quick fix. More content – however well targeted is a blind alley not a road to a new future.
This rush to AI in learning is not without other challenges.
In the field of learning and development, we’ve been pretty good in the past at attributing importance to things we can easily measure rather than measuring the things that are really important.
To work AI requires a wealth of data. There is a real danger of rushing to crunch whatever numbers we can get hold of, and as a result, we run the risk of magnifying that tendency – valuing the things that can be counted instead of really important things that are more difficult to quantify. For those undertaking sales training, the data generated may be quite limited. The system may know which course was taken and when. Scores from online courses or details of tools and resources which have been downloaded might be available. But it’s pretty small beer in terms of the data required to power an AI driven learning system.
There’s a real risk that organisations will try to implement big data actions on small data sets.
I’ll give you an example. Imagine if your average employee interacts with a Learning Management System or Virtual Learning Environment once a month. The amount of data that is generated is unlikely to be massive.
Small variations – perhaps attributable to the one individual who is a really keen and heavy user of your company’s library of online courses – massively skew the limited data available. What is significant? What worked? What generated a return on investment or led to positive impact? If we look at only the digital footprint of an individual, we are unlikely to find out.
Using algorithms to predict what people want or need actually reduces choice rather than expands it.
AI used to determine learning needs potentially reduces the range of options available and pushes everyone to a kind of ‘learning magnolia’ – a safe, bland alternative which limits our experience rather than expands our horizons. It is, in a real sense, like driving a car by only ever looking in the rear view mirror. ‘People like you also completed this online module’ is barely relevant in an environment seeking high performance.
This use of past data ignores the most important part of learning - that learning is transformative. I have been changed unutterably by learning activities in which I have participated and I hope I have helped change the lives or careers of some of those who have experienced learning activities in which I was involved.
Good learning experiences change us. The application of AI to predict future needs based on past activity risks not delivering on this. Instead, effective learning has to be delivered through a rigorously researched, timeless and validated methodology based on significant data sets obtained via observation of successful people - not counting the clicks of the few to determine a strategy for the masses.
Here at Huthwaite International, having spent four decades studying the behaviours that are needed for successful business, our solutions are among the most researched and validated on the planet, not based on some limited data fields and set of personality types dreamed up by programmers. The idea that we shall forever be defined by a set of numbers and data points and grouped into a narrow profile about things we have done in the past is deeply depressing. We are sleepwalking into a world of capability development in which we hammer round pegs into square holes because the computer says so. A great leap forward it is not.
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