When Amazon's Alexa in-home virtual assistant startled customers by emitting unprovoked snickers earlier this year, it appeared as comic proof of the online retail giant's deployment of commercial use-cases leading the adoption of artificial intelligence technologies. Just like customer service chatbots and social media marketing, the descriptive and predictive consumer applications of AI remain in the headlines.
Providers of enterprise resource planning solutions have had a harder time demonstrating to buyers with more at risk from exercises in the digital transformation that the submission.
According to a 2017 survey of more than 600 companies worldwide by Tata Consultancy Services, most are using AI to mine the huge amounts of business brains data they generate; for security, for technical support of system users, for automating production, and to track the use by employees of vendor systems. Having built the core systems and artificial intelligence software that run a global business, ERP makers know there is more to be done, and are developing applications and interfaces with AI for the wider gamut of operations they support.
Underpinned by market forecasts running as high as $400 billion in annual expenditure globally on AI over the next dozen years, the pace of change is heady, with product and platform launches, iteration and purpose releases and functionality upgrades from whole-of-enterprise providers and business-process specialists over the past 18 months.
Like the headline-grabbing commercial applications of artificial intelligence, these combinations of analytics, machine learning, sensors, and robotics are aimed squarely at the user. Whether through automating rhythmic tasks and initiating event-driven processes or mining data flows for more accurate decision-making, the goal is to engage employees in recognition of organizational agility.
A primary feature of AI-enabled ERP systems is the intelligent user interface, which combines AI process technologies with speech and image recognition and natural-language processing. free users from having to request data from a range of applications and key in commands as they interact with chatbots to gather in turn initiate processes and team up with colleagues.
Tools for commerce, customer service, finance, human resources and CRM have all been improved, along with applications for manufacturing supply chain, marketing and sales and all in ways that leverage insights from the San Francisco-based company's data cloud, where 7.5 trillion data points are collected each month. Developers can tailor Intelligent Bots, a feature of the Oracle Mobile Cloud that also came to market last year, to tailor the user interface for work and discussion flows and to extract insights from unstructured data.
Cloud services provider Salesforce, also based in San Francisco, is applying AI in CRM, where its Einstein platform uses predictive analytics and natural-language processing to generate forecasts and identify opportunities for touch points that can lead to sales conversions without the need for undoing tools and spreadsheets.
The scattered nature of AI development is a testament to the early-stage nature of the technologies at work in enterprise and reflected in the limited return on investment from AI projects.
More often than not, the consultancy adds, the use-cases best placed to produce immediate ROI are at the edge level, where performance costs are lower and demand is high for customization rather than in the cloud, where AI investments aimed at scale tend to be made.
Despite the relative paucity of immediate ROI from AI investment, industry experts predict a business boom will eventually emerge from raising venture in digital transformation underpinned by artificial intelligence deployed in the cloud. The rollout in 2020 of 5G communications know-how, involving low-frequency, low-power edge devices that make up the Internet of Things, will boost ERP use cases and further impact the development of workforce efficiency.