“Time is money.” Ben Franklin coined the phrase more than two centuries ago, but it’s just as true today for modern companies. In a recent survey of large businesses, 98 percent said that a single hour of IT downtime costs them more than $100,000 in lost productivity. For a third of those firms, the losses exceed $1 million. Such losses underscore the need for IT systems that can recover from errors quickly and efficiently, without going offline.
Fortunately, advances in artificial intelligence and machine learning have the power to help companies identify and fix issues and outages across systems without human intervention. This “self-healing” approach is poised to become a business imperative.
Remediating operational failures
Systems failures used to be an unavoidable part of life for most businesses. But thanks to recent developments in AI and machine learning – the process through which machines leverage huge data sets to teach themselves – that’s no longer the case.
Self-healing platforms are capable of identifying and fixing operational issues automatically, before they cause full-scale systems failures. The technology monitors how systems normally function and flags any deviations from those patterns as potential errors. If the system identifies a certain problematic pattern, it automatically makes adjustments to restore normal operations.
Compared to the traditional, more manual approach to solving system failures, the self-healing approach is transformational. Typically, when an IT incident occurs, a human employee must troubleshoot the system – a process that includes triaging, running diagnostics, and carrying out a root cause analysis. From there, the worker must find a resolution, implement it, and document the entire process.
Letting AI handle this process – at superhuman speed – works wonders for human productivity. North American companies can lose up to $700 billion a year in lost worker productivity due to IT outages. Not to mention frequent IT failures also chip away at employees’ morale by making their work mundane and tedious.
While executing system remediation manually can take up to 30 minutes, a self-healing approach can bring that time down to two minutes. That’s a substantial 90% improvement in time saved, and one that could save companies millions of dollars and lots of employee grief.
Improving employee productivity
Another application for self-healing platforms is analyzing tickets in help-desk software to detect patterns that often lead to productivity impact. This can proactively fix the problem before it slows down a business.
Consider a recurring problem we used to have at Adobe. During certain weeks in July and December, we found that employees would flood our help desk with requests for assistance resetting their passwords. By leveraging our own self-healing platform, we found the influx of requests coincided with our company shutdown weeks – a time when the help desk had limited resources. With a collaboration API framework, we now have a notification system that sends reminders (via email, chat) to employees to reset their passwords a few weeks before crunch time. The bottleneck at our help desk has disappeared.
Providing frictionless online experience
Speeding up IT services with AI can also help drive sales. Delays in the purchasing process can repel prospective customers, who are accustomed to frictionless online experiences. If an e-commerce site takes more than three seconds to load, 40 percent of customers won’t hesitate to take their business elsewhere, according to a study by Forrester Consulting.
At Adobe, self-healing programs applied to our online purchasing systems immediately begin identifying slowdowns in our registration process and fix them preemptively.
AI for business enablement
AI solutions can tackle all sorts of internal business problems – not just systems failures.
For example, an AI program employed at research firm Nielsen gleans and records product ingredients by scanning a picture of the product’s packaging. That saves employees from having to manually record product information.
And an AI solution used in the manufacturing industry predicts failures via superhuman hearing. The program detects irregular sounds in machines to predict potential problems. Another manufacturing AI system uses sensors to spot issues up to two weeks before they happen.
Adobe has leveraged AI and Robotic Process Automation (RPA) to automate certain aspects of the procurement workflow including purchase order transactions and contract creation. Both previously entailed time-consuming data-entry tasks. As a result, we spend 80 percent less time and effort on these procurement tasks – an improvement that has enabled us to get products to market more quickly.
Offloading internal business problems to AI programs frees up IT professionals to spend time on more creative and analytical tasks – like developing design-led portals for employees and driving predictive analytics to run the business. In other words, it creates the capacity for our people to do more value-added work without the constraints of manual processes.
Next wave of business value
AI and machine learning constitute the next wave of IT business value by augmenting the human workforce with the virtual workforce to improve operations, simplify processes, and create capacity. On the product side, it’s about helping customers make tedious tasks fast, and offer relevant experiences across all channels. On the business process and operations side, IT leaders are using those same AI and ML capabilities to improve uptime and productivity.
With self-healing platforms, we have reached an inflection point where machines can heal themselves, and humans can focus on higher level tasks.
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