Quality control, customer care and cybersecurity are the top three use cases of artificial intelligence (AI) in businesses today, a new report by MIT Technology Review has found out.
The report shows that artificial intelligence is being deployed widely across various sectors through the technology’s penetration within enterprises is likely to expand slowly.
MIT Technology Reviews survey was conducted on 1004 global executives across various sectors. According to the findings, 72% of surveyed organisations had begun implementing AI solutions by 2018 and a further 87% by 2019.
Key AI use cases
But the concentration of AI use cases is in the areas of quality control, customer service, and cybersecurity. About 60 percent of manufacturers and pharmaceutical companies are using artificial intelligence to improve product quality. In the consumer goods and retail sector, AI is being deployed by nearly half of companies (47%) to improve customer service.
Further, 51% of energy firms are using AI for monitoring and diagnostics while 58% of financial services providers are leveraging it for fraud protection. Over half of technology firms (52%), meanwhile, have AI applications for strengthening cybersecurity.
Sectors such as manufacturing, consumer goods, retail, and health cite the potential benefits to improve speed and supply chain visibility as the main reasons for implementing AI solutions. For technology and financial firms, the gains to customer service, cybersecurity and fraud detection are too strong to ignore.
The report also highlights that despite the increased rate of adoption of AI, many companies remain unconvinced about the technology’s real, as opposed to potential, impact.
To this end, 60% of respondents said that they expect to implement AI in 11% to 30% of their business operations in the next three years, implying that AI deployment is an important but not a dominant influence in their operations. That expectation is highest amongst financial services providers, manufactures and technology companies.
Unsurprisingly, a majority of surveyed organisations agreed on the need to expand data sharing to realise the true value of AI. Roughly two-thirds of the respondents said they are willing to share internal data with third parties to help develop new AI-enabled products, value chain and efficiencies.
However, businesses are still cautious about sharing their data largely because there is less clarity about privacy regulations and also due to the lack of industry standards to help support data sharing.
Change management and data challenges
Another survey results identifies some of the main challenges hindering faced by AI projects trying to scale. About 51% of surveyed firms said they struggle most with the change management involved in modifying business processes to leverage AI.
Data challenges were also cited with the executives reporting difficulties trying to integrate unstructured data as well as problems with interfacing their AI models with open-data platforms.
The MIT Technology report highlights that early adopters of AI have started to realize returns despite the obstacles. Surveyed organizations that first deployed AI projects five years ago are more likely than those deploying later to report underperformance in terms of return on investment (ROI).
However, the early adopters are also more likely than others to say that ROI has exceeded expectations.