There’s little doubt that AI is already being widely adopted by enterprise. But its true capabilities are still far from being fully realised and even for companies that have adopted AI processes, many face challenges getting their organisation to take full advantage of its potential.
A 2018 McKinsey survey of over 2000 global businesses found that nearly half had already embedded at least one AI capability. Of these, robotic process automation, computer vision, and machine learning were the most commonly deployed. However, while there are huge opportunities to integrate AI into nearly every industry and business function, only around one-fifth of those companies had embedded AI into more than one part of the business. And investments in AI still represent a very small fraction of overall company expenditure on digital technologies.
So given its enormous potential, what are the main barriers holding back companies from being able to make the most of AI technologies?
A lack of access to talent
Due to the relatively recent development of AI, there simply isn’t the quantity of talent available with the appropriate skill set for AI work. This means organisations may struggle to have the in-house technical capabilities to install or optimise AI processes.
There are talent shortages across almost every kind of IT role, but as AI research and engineering are specialised niches within computer science, it takes time for large numbers of people to be qualified in these areas. Inevitably, the AI talent shortage will change as data science becomes one of the most lucrative careers.
There is no way around it – developing and implementing AI programs and strategies is expensive.
This is partly related to the high staffing costs of AI experts as well as the fact that cutting-edge technology always represents a high upfront capital cost. Therefore, getting approval to implement the latest systems can be difficult or program costs can be underestimated.
One way to bring management on board and change a company’s attitudes towards AI tech is to initially target low-cost AI solutions such as chatbots. These provide a lower level of entry and can then be used as a demonstration case of good value for money and process improvement. Once one piece of AI tech has been shown to add value to the business then it’s much easier to seek increased resources for other acquisitions.
Lack of strategy
Many implementations of AI systems don’t have the necessary strategy to go with it. This can be a result of AI being implemented without a clear problem to solve or a lack of defined goals or targets to meet.
Strategy creation is imperative to success. A clear use and business case for implementing AI should be developed to demonstrate the objectives of the tech and how its impact can be measured to determine its success over existing or legacy systems.
At the end of the day, it is common for people to fear change. Especially when it comes in the form of AI that many see as a threat to existing human tasks and roles. Like all change management, AI shouldn’t be implemented without first considering the human impacts. Staff need to be trained to see AI as something that benefits them, by boosting performance and reducing the amount of human input required on mundane tasks.
The possible applications of AI across all industries is hard to overestimate. AI techs have particularly strong use cases in the telecoms, high-tech, and financial services areas. In retail settings, the use of AI is most commonly being used in marketing and sales processes. Managers and leaders in these industries need to recognise that challenges in implementing AI are inevitable but should not be considered insurmountable or discourage them from reaping the productivity and efficiency benefits that AI can deliver.
Are you considering implementing an AI strategy in your business? Finite can help you find the right people to help you build a strategic AI plan and gain a competitive advantage over your competition. Get in touch for a confidential chat about your needs here.