Bridging the AI Expectation Gap
Enterprises need an AI strategy, but articulating this is just as necessary.
More and more enterprises are expressing an interest in artificial intelligence (AI) and machine learning (ML), with many companies moving forward with projects to harness the technology. This means that it is rapidly being embraced as a means to digitally transform businesses, moving beyond the hype and into reality.
According to IDC, 31 percent of organisations are in the discovery and evaluation stage of AI, while 22% plan to implement AI within the next 1-2 years. In addition, another 22% are running AI trials. It seems that every company is betting on AI or at least trying to avoid their business being disrupted by it.
As predicted, many industries will face disruption due to advancements in artificial intelligence and machine learning both in the short and long term.
Already we’ve seen the influence of chatbots which are coming to the fore in contact centres that can answer customer questions through live chat – something that can sort out queries in seconds rather than minutes. It therefore seems reasonable to assume that such chatbots could replace entire call centres in years to come.
An additional benefit that such automation brings over human counterparts is data analysis and collection. These chatbots can help retailers to increase the amount of data they can collect about customers in a number of different ways, giving them a competitive edge over those who do not deploy such chatbot solutions.
There are other reasons too. According to PointSource, the tactical use of AI results in more than one third of shoppers increasing their online spend, with a further 49% suggesting they will shop more frequently online when AI is present.
In the energy industry, cognitive AI can be applied to the tracking of tankers to determine when they should leave port, how much oil they should carry and where they go in a much more efficient way. The Japanese shipping group, Mitsui OSK Lines (MOL), has been testing Rolls-Royce’s intelligent awareness systems to make its vessels safer and more efficient to operate as it and others navigate towards a future of autonomous surface vessels operating without human interaction.
In manufacturing, AI can be used to focus on automation and optimisation in order to help augment the decision making process. Indeed, according to the Annual Manufacturing Report 2018 (AMR 2018), 92% of senior executives believe that AI and ‘smart factory’ digital technologies will bring about advances in productivity whilst empowering staff to work smarter.
In healthcare, machine learning can help analyse complex medical data and research to better inform the doctor’s diagnosis and treatment suggestions. That, says McKinsey, means that application within the fields of pharma and medicine could be a $100bn annual opportunity, based on optimised innovation, improving the efficiency of research and clinical trials, and building new tools for physicians, consumers, insurers, and regulators.
Furthermore, IT Service Management (ITSM) can use AI to overcome shortages in analyst resources in dealing with many requests and incidents from front line IT support calls.
However, to get your enterprise ready for AI, you need to set out a vision of what it will do for it. AI is a journey and not the magic bullet a lot of people believe it to be, so the vision has to be based in reality, not only taking into account practicalities, but also offering a glimpse into the future organisation and where people and processes fit in.
The vision should lay out what opportunities there are for enterprises to engage customers, find new markets, evaluate sales leads and involve influencers.
It must also anticipate the disruption to an industry artificial intelligence is likely to cause. This can be used as a clarion call to prompt the organisation into taking action.
However, there is a difference between what is expected of artificial intelligence and what it is able to deliver. This gap, if not properly addressed could stymie efforts to get any such projects off the ground in an organisation.
That’s why it is important for the contemporary enterprise to have an AI strategy. Having a strategy puts in place what the company intends to do, why it wants to do it, and how. It will also set out the expected outcome of such projects.
When executed properly, AI can enable businesses to cut costs and boost productivity by freeing up employees from more routine tasks. In turn, this increases agility and flexibility, and stimulates innovation. Figures from Narrative Science indicate that around 61% of firms with an innovation strategy are harnessing AI to identify opportunities in data they would otherwise have missed.
As technologies such as IoT produce ever more data, AI will be essential in managing that data. But enterprises need to control the scope of how AI makes decisions. This means having transparency on how decisions are being made by AI. Therefore, it must provide information about its behaviour.
Ultimately, AI needs to be built into the heart of an enterprise. To do that these organisations must embark on a journey to reconstruct and reimagine themselves as a collaboration between human and artificial intelligence. Doing so will help enterprises become better, faster and more agile than ever before and overcome their limitations.