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Enterprise spending on artificial intelligence (AI) is increasing, with 82 percent of early adopters saying they have seen positive returns on their investment. That’s according to a new report from professional services giant, Deloitte.
Fifty-five percent of respondents said they had launched six or more pilot schemes (up from 35 percent a year ago), with 58 percent undertaking six or more full implementations (up 32 percent year on year).
According to the second annual State of AI in the Enterprise report, which surveyed 1,100 US executives with early-stage AI projects, the average return on these programmes is 17 percent.
What’s winning in the enterprise
Deloitte looked at four types of AI: machine learning; deep learning; natural language processing; and computer vision.
Natural language systems are spearheading AI’s enterprise growth, with 62 percent of the companies surveyed adopting it, up nine percent year on year.
Machine learning comes second in the survey, with 58 percent adoption (up five percent since last year’s report), with computer vision (57 percent) and deep learning (50 percent) bringing up the rear.
Thirty-seven percent of the senior executive respondents said that their companies have set aside $5 million or more for cognitive technologies.
However, some of this expenditure is on the rising number of enterprise software suites that come with AI built into processes, rather than on discrete investments. In recent years, IBM, Microsoft, Google, Salesforce, Oracle, SAP, Box, and others, have all focused on adding AI to their cloud platforms and other services.
But why are enterprises adopting cognitive systems?
Where is the strategy?
Deloitte’s report implies that the dominant reason is tactical, rather than strategic – echoing the findings of some other recent surveys. Sixty-three percent of respondents said they are using AI to compete with other companies, while 37 percent said the technology is helping them widen their lead in their market.
Just 11 percent of all respondents said that AI is of critical strategic importance today, while 42 percent – still a minority – said that the technology will be of critical strategic importance in two years’ time.
Nearly two-thirds of respondents (63 percent) expect that AI will automate tasks that are currently overseen by human workers. Ten percent of all those surveyed said they had a clear preference for retaining and retraining employees as AI is rolled out into the enterprise, but 78 percent said they were either on the fence about whether to replace staff, or leaning towards bringing in replacement talent.
Nearly 70 percent added that they are facing moderate to extreme skills gaps in AI, with coders, developers, and data scientists in particular demand.
Not the right approach
These findings underline those from other industry surveys. In July, a credible report warned that the hype cycle surrounding AI is coming to an end, which will trigger a wave of consolidation, not to mention of disappointment as many organisations fail to experience the technology’s benefits.
The core reason isn’t that the technology doesn’t work, but that many organisations are implementing AI for the wrong reasons, it said.
Think tank Rethink Technology Research found that many organisations are implementing AI tactically to slash costs, rather than as a long-term strategic bet to improve the business, capitalise on new opportunities, and get closer to their customers.
A PwC report in the summer made the same point, suggesting that many organisations are implementing AI and automation for internal cost-cuts, rather than to make their businesses smarter. This is despite evidence from other studies that AI can underpin customer loyalty if applied strategically.
In this sense – the quest for easy cost-cuts – AI, robotics, and automation are simply ‘the new outsourcing’. And as with offshoring customer contact centres and other functions, organisations may find that the reality is more complex than the hype promised, with savings not always guaranteed.
The problem of overhyped dangers – which is apparent from popular press coverage about AI and robotics – should therefore be set alongside another problem: overhyped benefits.
Deloitte’s latest survey appears to share this assessment: the number of respondents who think AI will transform their companies or industries within three years is down 20 percent from a year ago.
And not all industries are faring well with AI, adds Deloitte. For example, life sciences and healthcare have invested heavily in the technology, but have experienced lower returns relative to telecoms firms, for example.
The report says, “Healthcare and life sciences companies are investing in AI but, according to our data, have less to show for it. Certainly, some healthcare ‘big bang’ projects have disappointed thus far.
“However, advances in fields as diverse as radiology and hospital claims management show that AI offers substantial potential for value in healthcare, despite some high-profile stumbles.
“For example, in a recent study, deep-learning neural networks identified breast cancer tumours with 100 percent accuracy by analysing pathology images. Such advances, however, are only in the lab and will likely take time before entering clinical practice.”
Regulations and ethics
Many respondents had other concerns about AI. For example, nearly one-third of respondents (32 percent) said they have experienced an AI-related data breach in the last two years.
As many as 20 percent of respondents said that they had shelved their AI plans as a result, while twice as many expressed concerns about the legal and regulatory risks.
Thirty-two percent of the business leaders interviewed said that the ethics of AI deployment and the risk of bias were of particular concern.
Forty-three percent cited “making the wrong strategic decisions based on AI/cognitive recommendations” as a worry, while 39 percent mentioned fears of AI failing in a mission-critical or life-and-death situation.
Deloitte says that a challenge for AI adopters is “the growing complexity of machine learning and the increasing popularity of deep-learning neural networks, which can behave like black boxes, often generating highly accurate results without an explanation of how these results were computed.”
In all, a useful addition to the AI research canon, and one that reinforces a critical point: AI should be implemented tactically to make businesses smarter, and not tactically to cut costs and jobs.
The core reason is simple: these tools are designed to augment human skills, not replace them – and that’s according the CEOs of most enterprise providers, including Microsoft and IBM.
And as the World Economic Forum (WEF) noted in its own analysis of Industry 4.0 technologies, the organisations – and the countries – that invest in re-skilling their workers will win in the long run.