AI skills shortage poses considerable challenges

Most organisations are fully invested in AI but more than half don’t have the required in-house skilled talent to execute their strategy, according to new research from SnapLogic. The study found that 93% of US and UK organisations consider AI to be a business priority and have projects planned or already in production. However, more than half of them (51%) acknowledge that they don’t have the right mix of skilled AI talent in-house to bring their strategies to life. Indeed, a lack of skilled talent was cited as the number one barrier to progressing their AI initiatives, followed by, in order, lack of budget, lack of access to the right technology and tools, and lack of access to useful data.

 

The new research, conducted by Vanson Bourne on behalf of SnapLogic, studied the views and perspectives of IT decision makers (ITDMs) across several industries, asking key questions such as: where is your organisation in its AI/ML journey, what are the top barriers your organisation is facing when executing your AI initiatives, does your organisation have employees in-house with the required skillset to execute your strategy, and what are the top skills and attributes you are looking for in your AI team?

 

Where are organisations in their AI/ML journey?

When asked where organisations are in their AI/ML journey, most (93%) ITDMs claim to be fully invested in AI. Nearly three-quarters (74%) of organisations in the US and UK have initiated an AI project during the past three years, with the US leading the UK at 78% compared to 66% uptake.

 

Looking at specific industry sectors, the financial services industry is most progressive with 80% having current AI projects in place, followed closely by the retail, distribution and transport sector (76%) and the business and professional services sector (72%). Surprisingly, the IT industry was found to be among the least progressive in AI uptake with 70% having projects actively in place.

 

Key barriers holding AI initiatives back

Despite strong levels of AI uptake, organisations are being held back by significant barriers. More than half (51%) of ITDMs in the US and UK do not have the right in-house AI talent to execute their strategy. In the UK, this in-house skill shortage is considerably more acute, with 73% lacking the needed talent compared to 41% in the US.

 

In both the US and UK, manufacturing and IT are challenged the most from this in-house talent shortage. In the UK, 69% of manufacturing organisations and 56% in those in the IT sector raise lack of in-house talent as the top barrier. Likewise, in the US, those two sectors face similar challenges, with 50% in manufacturing and 41% in the IT industry citing lack of in-house talent as the primary barrier.

 

Behind lack of access to skilled talent, ITDMs in the US and UK also consider a lack of budget (32%) to be a key issue holding them back, followed by a lack of access to the right technologies and tools (28%), as well as access to useful data (26%).

 

Building the right AI team

Interestingly, the priority skills and attributes that organisations are looking for in their AI team are coding, programming and software development (35%), with data visualisation and analytics considered to be a priority by 33% of ITDMs. An understanding of governance, security and ethics is also considered a necessary skill (34%). Just over a quarter of ITDMs (27%) are looking for talent with an advanced degree in a field closely related to AI/ML.

 

To build the right AI team, an impressive 68% said they are investing in retraining and upskilling existing employees. Nearly 58% of ITDMs indicated they are identifying and recruiting skilled talent from other companies and organisations, while almost half (49%) believe that recruiting from universities is important to getting an effective AI team in place.

 

Gaurav Dhillon, CEO at SnapLogic, commented “The AI uptake figures are very encouraging, but key barriers to execution remain in both the US and UK. For organisations to accelerate their AI initiatives, they must upskill and recruit the right talent and invest in new technology and tools. Today’s self-service and low-code technologies can help bridge the gap, effectively democratising AI and machine learning by getting these transformative capabilities into the hands of more workers at every skill level and thus moving the modern enterprise into the age of automation.”