As businesses search to deliver even greater efficiency and cost savings, Artificial Intelligence (AI) powered automation has the potential to drive forward the fourth industrial revolution. There are huge factors pushing companies towards this new zeitgeist; however, building and training sophisticated AI models can be hugely complex.

Regardless, automation has made an impact in almost every industry, from manufacturing, to retail and even transport.

Just look at the likes of PayPal, the secure money transfer service is using machine learning to combat money laundering by analysing millions of transactions between buyers and sellers to identify fraudulent transactions. It is success stories like this, which are pushing businesses to reimagine how they work.

In marketing, automation is already well embedded into industry practices. German software provider, Sensape, is a great example of this, providing cognition to digital signage solutions by teaching the operating systems to see, understand and interact with people at trade events and in retail locations. Through a combination of AI, computer vision and augmented reality (AR), content is adapted in real time, improving the rate of interaction by 14.7 times in comparison to traditional digital signage.

While the list of AI powered projects grows daily, for businesses within traditional sectors like finance, there is still work to be done to achieve full automation. Encumbered by legacy systems and data security fears, these industries experience bottlenecks. But, with competition from cloud native fintechs, the pressure is on for the financial services industry to quickly adapt and bring themselves up to speed with technological developments like automation.

To do this, it’s useful to take a close look at both the obstacles to using AI to automate, and the opportunities.

Overcoming the cost of adoption

Firstly, the cost of developing intelligent AI and machine learning models in-house is often too high. This ultimately boils down to two things: having the computing power and data available to build and train a sophisticated model. The UK’s Digital Catapult Centre found that the cost of a single training run for a machine learning system can be well over £10,000. When you consider that this cost is in addition to the price of storing a large volume of clean and consistent data, it’s no surprise that it is one of the main barriers to adopting AI.

Despite these expenses, businesses are still rushing to create their own AI applications, which means having the right infrastructure for development is crucial.

Increasingly, organisations are turning to cloud and open-source platforms to acquire AI capabilities without the hefty price tag. When storing and computing in the cloud, organisations can exploit on-demand payment models and customisation to streamline tech investment to exactly those capabilities which it needs. Similarly, open-source models typically have limited or no cost at all and require less input from in-house developers. The very nature of open-source means that businesses can build on publicly available AI applications to create software that is fit for purpose.

Combatting the skills gap

Despite the expense, the availability of both data and computing power has been on the rise in recent years, resulting in a deluge of automated and intelligent services, which developers must now differentiate between to deliver a successful implementation. This goes beyond merely finding an AI solution that addresses business concerns. It requires consideration of factors like identifying the right DevOps partner to provide the right consultation and on-hand support, particularly during the initial phase of the programme where employees require training.

Whilst on-hand support is invaluable, businesses looking to expand their automation strategy must address the skills limitation, either by investing in AI experts or training for existing staff. A recent report found that there are only 300,000 AI professionals worldwide, but millions of roles available. As such, skilled developers have a monopoly on the market and are able to drive the price for their services up.

Thankfully, cloud and open source platforms are helping to combat the global skills deficit. Open source initiatives such as Fast.ai are delivering a free open source framework which offers training in coding AI models such as image classification and natural language tasks. Aside from being a cost-free tool, training can be delivered on an ongoing basis, ensuring that in-house developers have the latest information and are therefore able to drive forward an organisation’s innovation strategy.

Making the right choices for your business’s automation strategy

Whilst the IT team are at the heart of this adoption, successful digital transformation initiatives require a company-wide shift in attitude. With automation, this is even more important as employees’ roles will, in all likelihood, be completely transformed.

At its best, augmentation is the collaboration of AI and robots with humans, to automate repetitive or strenuous tasks which drive productivity and employee satisfaction. Businesses who do implement augmentation see dramatic results, with studies showing that augmented organisations achieved 28 per cent higher overall performance, did 31 per cent better financially, and reported employees being 38 per cent more engaged.

The question is not whether organisations should implement AI solutions in to their business, but how? Applying automation to business challenges which are labour intensive, delivers positive return on investment and greater productivity, enabling companies to retain and grow their competitive proposition.

Similarly, an increasingly open technology landscape offers CIOs and CTOs much greater flexibility and agility when adopting new technologies like AI. They have greater access to the tools needed to develop, adopt and innovate in the future, which assures the success of these businesses long-term. The focus for business leaders now will be on applying the right mix of technology to achieve this, whilst reducing the risks associated with complex implementations.