As technology advances, two things are certain — users can more easily complete day-to-day tasks, and IT teams have more problems to solve. Agile analytics have emerged to help technology teams react to emerging issues and resolve problems faster but there’s a broader aim, a kind of “Holy Grail” spoken of in hushed tones and wistful longing: Prediction. The power to reliably and accurately predict IT outages or performance problems before they happen and take corrective action, eventually automatically.
According to a recent Forrester report, however, the rise of AI-driven cognitive operations may put the Grail within reach for many organizations. Here’s a look at the evolution of thinking outside the box.
Why cognitive operations? Why aren’t cloud-based analytics tools enough to support IT efforts? According to Forrester, because “technology has grown too much and too quickly for humans to monitor and operate it effectively.” Consider the discipline of climate research — as noted by Inside Science, predictive models are now so large that research teams are both running out of storage and struggling to manage the sheer complexity of their experiments. Enterprise IT teams have the same problem: Massive amounts of data generated daily coupled with emerging IT issues potentially connected to network faults, performance problems or mobile device adoption.
The result? It’s no longer possible for IT teams to both manage incoming data and address emerging issues. Current analytic techniques offer the ability to react quickly and reduce the impact of potential compromise, but this creates an all-too-familiar cycle: Break, fix, repeat.
The Cognitive Concept
It’s one thing to talk about AI-enabled cognitive computing — and another to clearly define its form. According to Forrester, well-positioned vendors in the market must include four critical components:
- Reduced Effort — Intelligent discovery of potential issues by cognitive solutions reduces the effort and time required by IT to make basic predictions.
- Improved Reaction — With access to predictive data on-demand, IT teams can quickly determine the best course of action before serious network consequences occur.
- Proactive Prevention — Access to both end-user experience and underlying issue data helps achieve the “Holy Grail” of proactive IT: Problems solved before they impact the end user.
- Business Meaning — The next step in IT analysis: Going beyond technology outcomes to assess the business impact of networking and device issues. This both solidifies the place of IT in the boardroom and provides C-suite members relevant, actionable data to inform ongoing strategy.
Improving Employee’s Experience
But what does this mean in practice? First is the direct benefit of improving workforce productivity — if IT can detect issues on user desktops or mobile devices before end users submit a ticket or report an issue, both employee satisfaction and performance improves. And as noted by the report’s authors, the use of adaptive, cognitive solutions “helps IT Infrastructure & Operation professionals monitor and manage larger, more complex environments with less effort.” By shifting the analysis and reporting workload onto autonomous processes it’s possible to both increase the processing performance of network analysis and give IT the time and space they need to determine the root of technology issues rather than simply managing their symptoms.
AI and cognitive operations are popular IT buzzwords — but as noted by the Forrester study, also offer the potential to improve IT operations performance and enhance employee productivity and satisfaction. Bottom line? For companies to exceed the expectations of digital natives and corporate shareholders, it’s time to think outside the box and move into the direction of predictive automation.