Arguably the biggest technological challenge, and opportunity, facing organizations today is AI adoption.
When end users think about AI adoption, they will often associate this with disruptive change, unfamiliar technology and even job losses.
For senior leadership teams it is about cost, return and opportunity … oh, and pride. No-one wants to be left behind.
However, true AI adoption, in all its formats, won’t be possible without one key fundamental factor … cultural change.
Embracing, embedding and enabling AI within a business requires the will, trust and commitment of many.
A model I have seen successfully implemented many times in the past, is to appoint a Centre of Excellence. A team the business can look to for use cases, proof of concept and true expertise and innovation. It is a luxury not every company can afford, but if you instil responsibility and accountability within a small group of people coupled with an expert leader, this can trickle down to your regional or departmental teams far more effectively, than trying to transform each department individually.
Education and empowerment will be a huge part of this transition. Businesses will need to hire AI experts who are consultative, resilient and capable of identifying the nuanced opportunities AI represents for each domain. In turn they will need to actively showcase AI success stories to build confidence in, and inspire, end users, whilst supporting them in building AI into their workflows.
Identifying this kind of talent takes deep knowledge and skill. And without expert knowledge of this data landscape, internal Talent teams may struggle to pinpoint the essential behavioural attributes required to navigate such change, coupled with the broad technical armoury needed to support it.
We have seen these seismic shifts in the data and technology job market before, in particular, it mirrors the step-change many had to make during the Big Data revolution around 10 years ago. Analysts had to become Data Scientists overnight, with businesses suddenly requiring advanced programming and modelling skills that could handle millions and even billions of rows of data, working with real time and hyper-personalisation data. New tools entered the market, Python and R, making SAS largely redundant in a matter of months, and organisations started to recognise, and reconcile, the gaps they had around data quality and governance.
So, whilst the road ahead may seem a little unclear, we’ve been here before. And as long as you ensure you have the right PEOPLE in place to lead the charge, from within as well as from above, AI adoption should enable positive change for many.