Anyone who shops online or uses a music streaming service will have experienced recommendations. Their accuracy can be surprising at first glance, but these recommendations aren't made by accident. They are based on sophisticated machine learning techniques, pattern analysis and automated decision-making.
Systems like these rely on a technology infrastructure that can import, analyse and interpret huge volumes of data and take appropriate action without the need for human intervention. While this is already a reality for the types of recommendations referenced above, the next step for AI or machine learning technologies is to form an established part of customer service and broader business operations, says Neil Sholay, Head of Oracle Digital, EMEA.
The race for automationMachine learning was high on the agenda at Oracle OpenWorld, with CTO Larry Ellison announcing the addition of machine-learning technology to the Oracle Cloud Platform and into some of Oracle's cloud applications.
The launch of so-called Intelligent Applications represents a major development in the adoption of machine learning technologies across broader business functions, and relieves some of the burden of innovation and development from businesses looking to create services that automate actions based on analysis of employee, customer or financial data.
Furthermore, with Internet of things products and services becoming ever more established, customer data will soon come from a wide range of sources and will be created in exponentially greater volumes. Indeed, Gartner expects 6.4 billion connected things to be in use by 2020. As a result, the race is on for companies to develop AI services to automate processes and take actions based on the massive amount of data signals.
The next great leap
The convergence of machine learning and IOT to enable the next great leap forward in commerce, manufacturing and trade is a prime example of what many refer to as Industry 4.0. The goals for this area of innovation are to provide better, smarter, faster automated services based on more accurate understanding of specific environments.
This is happening right now, and not just in consumer-facing organisations. Healthcare, urban infrastructure management, transportation, industrial manufacturing, consumer services, and more are already benefiting.
In healthcare, real-time monitoring coupled with feedback on behaviours could result in improved personalised treatments and more efficient spending. In agriculture, more accurate and hyper-localised weather prediction could help farmers obtain greater yields at lower cost.
Many organisations understand that machine learning and automated services are essential in making the most of the massive amount of data that will flood in as the IOT takes hold. In fact, our recent research found that 62% of businesses are currently implementing or plan to implement AI in the near future.
Where to start?
To be able to effectively implement and enjoy the benefits of automated AI solutions, companies must have technology systems that are as cohesive and flexible as possible. The best way to achieve this is by taking an integrated approach that combines Compute Services and Cloud Platforms, meaning that data can flow easily between different tools and functions.
More than half (56%) of the organisations we polled understand the importance of integrating these cloud functions in order to capitalize on AI.
In the age of Industry 4.0, data is an organisation's most important asset, and making the best use of it should be a priority. An integrated cloud strategy can ensure all data is used to its fullest potential.
Those organisations that fail to embrace the new AI-enabled world of Industry 4.0 are in danger of being left behind. And with the current speed of change, this could happen sooner rather than later.
Our comments policy does not allow anonymous postings. Read the policy here