AI implementation has been subject to much debate recently as organizations try to understand the best ways to tap into the potential of its latest technology offerings. It’s no surprise that companies that can do so will gain a distinct advantage over competitors. Our expert contributor and the founding partner of Transforma Insights, Matt Hatton, unpacks recent research findings on key enterprise use-case of AI to thrive in a competitive market.
The growing importance of artificial intelligence (AI) has triggered significant interest from vendors and potential adopters in understanding where and how it will be used. In recent months Transforma Insights has published substantial research on the adoption of AI. This article examines some of the findings, looking at the verticals in which it will be used most widely, diving into the manufacturing vertical, and examining some of the key implications for competitive positioning.
Which Verticals Dominate AI Deployments?
The recently published Transforma Insights Artificial Intelligence forecastOpens a new window provides a guide to the scale of the AI opportunity, measured in terms of AI instances, i.e. the installation of AI-capable software on a device). It includes segmentation by vertical sector. Based on the sector-by-sector analysis, it is overwhelmingly business-to-consumer applications that dominate AI today, accounting for 83% of instances. The explanation for that is simple: AI is most widely used to streamline interactions between people and machines.Â
The next biggest sector is the government, and particularly the application of video image processing in China. Beyond that the true commercial applications of AI start to emerge. The biggest commercial sector today is manufacturing, accounting for nearly one-quarter of all AI instances. It is followed by Finance & Insurance, which will rapidly accelerate to be the biggest sector, representing 40% of AI instances by 2030. The other big sectors are transportation and healthcare, both of which hover at a little over 10% of instances. Â
Manufacturing Uses AI for System Optimisation
The distribution of AI across different use cases is very wide in the manufacturing sector. The single biggest, System Optimisation, accounts for a little over 20% of AI instances. This is followed by Computer Assisted Diagnostics, Risk Analysis, Intrusion Detection, Threat Detection and Autonomous Systems, all of which account for over 6% of instances.Â
In a way, it is unsurprising that System Optimisation is the highest-rated use case. It revolves around the use of AI to improve the efficiency, speed, output, or functionality of a system comprised of multiple components, or subsystems. As such it is often to be found deployed in highly complex and critical systems where even modest improvements can have a significant impact. For instance, as illustrated in the Transforma Insights case study databaseOpens a new window , the convergence of System Optimisation and Manufacturing tends to involve high-value smart factory deployments, such as the Corning’s Fibre Optic manufacturing plant in North Carolina, Samsung’s Semiconductor plant in Texas, or process optimization such as that implemented for Jaguar Land Rover.
It is also noticeable that companies that have a strong heritage in serving the manufacturing sector have developed capabilities in system optimization. For instance, Hitachi Lumada has a series of vertical solutions that have AI capability. These include Lumada Maintenance Insights, Lumada Field Service Management, and Lumada Asset Performance Management, all of which would be relevant here.Â
While the largest volume of AI instances might be found in consumer-facing use cases, the incremental value will be in these types of complex systems. This explains the relatively strong position of the manufacturing vertical today; the earliest adoption has happened in those deployments that will have the most significant impact.
See More: AI Adoption Will Grow Ten-fold by 2030, Dominated by Use in IoT
Most Enterprises Will Find Themselves Running to Stand Still with AI
While there are some abstract uses for AI, in particular in pursuit of Artificial General Intelligence, it is really in its applied use that value is derived. The value of AI is as an enhancement to existing products and services or as an enabler of new ones; it is not a service in its own right. For instance, an insurance company may choose to introduce AI as a mechanism for handling customer care, claims handling, billing and numerous other functions. If the organization is successful in its implementation, then it will benefit through reduce payouts, improved customer loyalty, reduced costs and so forth. Any company with such a capability will expect to gain market share by reinvesting savings in reduced premiums or increased marketing.Â
However, no company will find itself the only adopter of AI in its field. Or it will be very fortunate if it does. Therefore, AI has become an arms race. If one company’s implementation of AI is superior to another it will gain market share against that company. If it’s inferior it will lose. Most likely, every company will ultimately find themselves converging on a right-sized AI capability that may well see all the organizations settling at similar market share that they would have seen otherwise anyway (save for some market casualties amongst those companies that are laggards in adopting). AI becomes table-stakes for almost any company and, therefore an absolute necessity in securing market share. It is unlikely to have a demonstrable impact on the bottom line. Savings will be comparable for all companies and they will mostly invest those savings in maintaining market share. The net result: everyone stays where they are, but woe betide any company that elects not to make use of AI for critical systems.Â
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