Why it’s a commercial imperative that your team develops AI skills

Sean Farrington, EVP EMEA, Pluralsight, considers how AI skills are industry agnostic and why market leaders are already putting them to good use.
Sean Farrington, EVP EMEA, Pluralsight, considers how AI skills are industry agnostic and why market leaders are already putting them to good use.

From financial services to the retail marketplace, intelligence-based technologies such as AI are becoming a route to commercial success. AI allows leaders to automate manual or admin-heavy tasks, better process and analyze data, and be more agile to change. Such advantages were especially useful when firms had to keep services online and accessible during the pandemic.

While the initial implementation of AI technologies may be costly, Accenture suggests that the impact on business may boost productivity by up to 40% by 2035. By improving efficiency, companies can reduce operational costs as time is freed up to work on other business priorities.

It comes as no surprise then that AI could contribute up to US$15.7trn to the global economy by 2030. The challenge is that the ability to implement AI effectively requires talented workers, and currently demands heavily outstrips supply. One reason for this is the fact that 66% of hard-to-fill AI and data-science vacancies require a masters or PhD degree, while the bulk of these vacancies were among middle-management and other senior roles that needed three or more years’ experience. 

It is crucial that industry leaders recognize the value of equipping their workforce with AI skills now, so that they can future-proof the business and remain at the edge of innovation. Hiring new talent can be an expensive process, whereas training existing talent within the organization is more economical. Upskilling from within is also more empowering for staff, and will breed a culture that demonstrates investment in its own people.

This is why it is so important for businesses to understand how to run a successful upskilling programme. For those sitting on the fence about investing in AI skills, here are my top three pan-industry examples which show how transformative it can be:

Financial services: Nomura Bank

Traditionally, institutional banks and financial services have been encumbered with legacy tech and have been slow to change. In 2019, a commercial bank was forecast to lose $4.5bn if technology and systems were not updated. However, banks are now undergoing their most profound transformations, with 75% of retail banks citing a digital transformation as their top priority going into 2021, in a bid to stay ahead of competitors and innovation-driven fintech providers. Nomura Bank is one example of effective skills-building; it has upskilled employees in AI to maintain its edge in an expanding market.

Embracing AI skills has streamlined Nomura’s analytical processes, allowing the business to fast-track its strategic decision-making. Speed of innovation accelerated as their understanding of cloud technologies improved. Nomura Bank found that upskilling also boosted its employees’ engagement and morale, and they were more open to collaborating on exciting digital projects as a result.

Retail: Olay

The digital revolution on the high-street has opened the door to new buying experiences and driven significant consumer engagement. Olay, for instance, has pivoted towards technology to capture the imagination of consumers with its AI-driven online skin advisor.

Using the AI skills at its disposal, the software can analyze customers’ complexions simply through a selfie. The tool identifies a ‘skin age’ by comparing customers’ skin to tens of thousands of other women. It also highlights any problem areas so that treatment recommendation is accurate and efficient. By eliminating the trial and error of multiple beauty products, customers can be confident that they are using products tailored to their skin type.

Healthcare: TeamHealth

Against a backdrop of transformational change around data protection and privacy, healthcare companies have begun streamlining and securing their systems. AI has played a significant part in this agenda.

TeamHealth upskilled in machine-learning and automation to maximize efficiency in delivering a new automated billing system which led to an estimated $5 million in annual savings. On a human level, the opportunity to learn new skills and innovate made employees feel more valued and invested in – increasing their motivation to innovate and deploy new prototypes. 

Successfully roll-out your upskilling programme

The above examples show that when AI skills are applied correctly, they are invaluable to a business. Benefits of increased efficiency, higher productivity and faster decision-making all contribute to producing a successful business model. Although new technology adoption may seem daunting, now is the perfect time to begin plotting AI projects.

As a form of best practice, an AI L&D programme that delivers ROI should include the following:

1. A strategic partner that allows leaders to map the skills at their disposal

Businesses should look to find a strategic partner with a learning programme that is measurable, scalable, and flexible. At the heart of this is a need for companies to have a thorough understanding of the skills currently at their disposal. Only when leaders can benchmark their team’s skills and map them against upcoming projects can they understand the skills gaps needing to be bridged.

This will produce an informed and tailored L&D strategy that is superior to the outdated one-size-fits-all model which has proved costly, ineffective and immeasurable. With this intelligence, L&D programs can then be delivered in bite-sized chunks according to the different skill gaps, ensuring training remains engaging and accessible.

2. Demonstrable return on investment

The L&D team has to find a way to show the investment has led to tangible benefits – evidencing new product development or greater levels of productivity provides solid proof that the training has generated a profitable return on investment.

AI-powered tools can be used to measure L&D through bespoke assessments. These are designed to reflect immediate learning and measure employee skill levels against other professionals at a similar level. This transparency ensures that leaders can monitor, and even share with management, the success in the development of their teams. This will ensure the right people are placed on the right projects to guarantee their success.

3. L&D must be a top-down initiative

Finally, it is crucial that the call to drive change and adopt new technologies via an upskilling programme comes from the top. Business leaders that demonstrate personal upskilling, regardless of whether they would be involved in the day-to-day handling of these technologies, will cultivate a culture of learning and maximize business-wide engagement. When all employees in the business participate in L&D, teams are encouraged to engage in friendly competition and work together to achieve common goals.


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Looking Ahead

Having the ability to deploy innovative technologies like AI enables companies to maintain their competitive edge and deliver new and improved services, no matter the industry. But to do this, leaders need to have an effective upskilling strategy in place, or they will quickly fall behind since AI capabilities continue to develop at a rapid pace. L&D can help to expedite projects that have the potential to generate more revenue, pushing business innovation goals to the forefront of strategy. Instilling new technological skills will not only future-proof the business, but will encourage the retention of talented employees by increasing their morale and sense of value as they lead the way in pioneering projects.

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Amber Donovan-Stevens

Amber is a Content Editor at Top Business Tech

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