Carmine Rimi, Product Manager AI and Kubernetes, Canonical – the company behind Ubuntu
Artificial Intelligence (AI) has witnessed extraordinary growth in business circles over the past few years. So much so that it feels an omnipresent buzzword in corporate technology conversations the world over. The advent of cloud computing and open source initiatives have supported the rapid expansion of these technologies, which are experiencing huge investment from both multinational businesses and smaller enterprises.
Deloitte’s Digital Disruption Index highlights that 85% of senior executives plan to invest in AI by 2020, while Stanford University’s AI Index reports a six fold rise in the annual investments from venture capitalists into AI start-ups since 2000, with significant quickening of that investment after 2010. Moreover, Gartner predicts that the business value resulting from AI initiatives will hit $3.9 trillion by 2022, rising from $1.2 trillion in 2018. These impressive figures serve to underline the huge potential AI boasts.
The trend towards the adoption of AI shows no signs of abating. In a software dominated world, AI acts as a trigger for business growth, innovation and the launching of new, advanced services for consumers. However, the deployment of AI technology is not a straightforward process, and no industry is immune from this complication. There are many considerations prior to a business being able to enjoy the advantages AI can deliver.
Business have had to discover that rolling out AI technology can be problematic. From concerns around integration with current systems, to a lack of understanding around how AI works, it’s clear that there are broad and complex challenges. A recent report from Databricks, for example, stated that 96% of organisations are experiencing data-related problems such as inconsistent datasets, while 80% reported a lack of collaboration between data engineers and data scientists. Then we come to the question of compute power. AI solutions tend to leverage large reserves of processing power, which will rise as data volumes rocket and the algorithms driving these systems grow increasingly more complex. This presents some large concerns around scalability.
It’s important to note that, from a practical point of view, AI technology is very much in its infancy but is developing rapidly. While the technology has been spoken about for some time, it is only during the last few years that deployments have started to take place and increase.
Challenges to AI deployment can even emerge before the roll-out begins. One of the major barriers for AI is IT teams and business leaders understanding how it can be used for everyday business problems; and also, how it can fit to the specific requirements of the company. As opposed to deploying AI for the sake of it, companies should look at where the technology can make the biggest mark, and what specific processes would benefit from being automated. This is easier said than done. AI requires people with knowledge who can seize the challenge of turning the theory into profitable outcomes. AI encompasses a range of processes and technologies, such as machine learning, data transformation, model creation, natural language processing and deep learning. To derive the most value from these it is essential to understand the differences between these innovations. So, what does it take to deal with these issues to realise the potential of AI?
Making the most of AI
Capitalising on the power of AI comes down to a few key factors. Primarily, it’s crucial that companies understand the importance of rolling out back-end infrastructure and systems which can support the compute-intensive tasks involved in AI and machine learning. Operating systems then have to be adjusted to these sophisticated workloads, which allows businesses to work with huge datasets, deploy applications at scale and manage the complexity that comes with that. Getting AI systems operational takes a lot of time, effort, expertise and resources – which are not always at the disposal of an organisation. AI platforms are only as good as the people who programme them. The industry skills shortage can impact businesses, so it’s important to partner with experts who are able to guide them and address any internal gaps. Enterprises need to be considered in the way they introduce AI, creating a long-term strategy and investing in the right people with the right set of skills and experience.
Harnessing the power of AI may be easier said than done, but no one doubts it presents a phenomenal opportunity. A laser-like focus on how AI can be leveraged to solve business challenges will help. AI has been growing in intelligence for some time – now businesses need to follow that lead and realise the true potential of this remarkable technology.