In the movie Iron Man, Tony Stark’s computer system J. A. R. V. I. S. (Just A Rather Very Intelligent System) controls everything for the genius billionaire- from helping him destroy criminals to running his business and taking care of his estate.
True, we are not quite in Iron Man territory yet; a utopia (or dystopia) where machines and intelligent software reign supreme and redefine the boundaries of what we previously thought possible is still way off.
But, data science & engineering technologies are fast evolving, and they are helping to inject intelligence powered by advanced machine learning and AI into processes to deliver a new class of smarter, more intelligent applications.
Just what are intelligent apps?
One can see intelligent apps as an appliance of artificial intelligence and advanced machine learning in the form of an application.
Over the last decade, access to large amounts of data and ever-improving processing power have enabled machine learning to take a huge leap forward. Unlike expert systems, which take a lot of coding to embed knowledge, machine learning doesn’t require much programming at all. The more the system is used, the better it gets.
So, these apps intuitively adapt and learn from interaction with an enterprise’s data to deliver unparalleled customized insights that enhance business results. The key requirement is to have access to vast quantities of high-quality data.
A good example of an intelligent app is a VPA (Virtual Personal Assistant), which helps you manage priority tasks such as emails and highlights important information for the user. These assistants still have limited options but are getting more and more sophisticated.
But, the use of intelligent apps isn’t limited to this alone. Organizations like Salesforce and Oracle are increasingly infusing AI into various enterprise applications that span sales and marketing, supply chain, HR, and other areas of business, providing them with actionable business and customer insights that enhance business results.
For instance, an intelligent app with a predictive analytics engine collecting data from industrial machines on a customer’s premise can automatically alert a field technician that a part approaching end of life needs to be replaced, and prevent downtime.
Or supply chain managers can automatically figure out the best options to distribute goods around the world, while providing the best value freight and transportation options for enterprise shippers.
Are we there yet?
There are multiple trends that suggest the transformation has already begun in many business segments and others too will get on to the bandwagon soon. Industry watcher Gartner expects that by 2018, a notable percentage of the world’s largest 200 companies will exploit intelligent apps and use the full toolkit of big data and analytics to refine their offers and enhance customer experience. Within the next decade, nearly all digital applications are expected to contain some form of AI.
- A notable use-case is that of Forbes. The business magazine has employed a writing software powered by artificial intelligence, called Quill, created by Narrative Science. Quill takes structured data such as numbers and symbols; it then extracts meaning from that data, and transforms that into rich content through natural language. Importantly, the software is capable of testing data to figure out what’s important and relevant for the report and what’s not. It chooses to omit information that isn’t pertinent.
For example, if a project’s net incremental cash flow hasn’t changed considerably for a given year; it might suppress that information, whereas if there’s been a rise in marketing budget it may highlight that figure.
The content generated by Quill can be anything, from a tweet to a 20 page investment research report that can be passed off for a human equity analyst who has looked up a stock, conducted research and written up a report.
(Image Source: https://www.econsultancy.com/)
- Google DeepMind has formed a medical research partnership with London-based Moorfields Eye Hospital in a bid to use AI for rapid detection of common eye diseases such as age-related muscular degeneration and diabetic retinopathy.
The disease is diagnosed using optical coherence tomography (OCT) scans, which are highly complex and take too long for health professionals to explore.
An OCT Scan
(Image Source: https://deepmind.com/)
DeepMind is investigating how machine learning can help in quicker analysis of these scans for more effective medical management.
- The use of artificial intelligence in the legal profession is an emerging area that can have significant implications in the foreseeable future.
AI software for legal applications facilitates the review of huge numbers of documents during the discovery phase of the trial process as opposed to large teams of lawyers and paralegals required traditionally.
One company Cataphora, develops technologies intended to detect conspiratorial behavior through analysis of employees’ recorded communications. For instance, their software reveals suspiciously deleted messages by representing them as unresolved nodes in graph representations of email exchanges.
- Intelligent technology is also redefining the future of talent acquisition. AI is being used in job-candidate searches to fine-tune results and provide recommendations based on previous searches—a feature that could fundamentally alter hiring by helping to better identify candidates.
One example is TalentBin, which helps recruiters quickly identify promising candidates by evaluating work skills of a candidate, taking into account all possible information: personality traits, social media posts, word choice, work samples, etc. in their resumes.
It can also boost engagement by following up with unresponsive candidates, offering them more chances to read recruiter e-mails. With its automated e-mail messaging campaigns, there’s no need to manually type e-mails or remember if a particular candidate has been followed up with.
- Complex supply chains are increasingly relying on artificial intelligence to reduce waste and boost efficiencies. This is useful in myriad ways, such as locating inefficiencies in the production process and figuring out ways to mitigate them.
AI can also analyze data from other operational aspects, such as financial information and team structure and help find insights that would otherwise be missed.
Hitachi Ltd. is using AI to provide appropriate work orders based on an assessment of demand fluctuation and on-site kaizen derived from big data aggregated in enterprise systems.
There is a significant shift underway- artificial intelligence enabled apps are clearly moving from technologically niche areas to domains impacting every industry in the world. It is time for CIOs to embrace the new paradigm and reap its rewards in the enterprise.
Latest posts by Krittika Banerjee (see all)
- Aspire is Back Again at Temenos Community Forum 2018! - April 23, 2018
- Discover the Hyper-testing Marvel at STAREAST 2018 - April 11, 2018
- Enhancing Customer Loyalty using Market Basket Analysis - April 2, 2018