In this special guest feature, Tom Pohlmann, EVP of Customer Success at AHEAD, discusses how the road to fully functional Intelligent Operations is bumpy, no matter what stage of digital transformation companies are currently in. Based on his experience, he offers five stages of maturity that organizations typically fall into. Tom joined AHEAD in 2017, and is responsible for implementing programs that create value and success for clients, plus corporate marketing and demand generation. Highly regarded in the technology sector as an innovator and thought leader, Tom excels at guiding businesses through transformations in their strategy and go-to-market efforts. He has 25 years of experience, including leadership roles at data and analytics solutions providers, as well as 14 years at Forrester Research, where he ran the IT research and advisory business unit, plus corporate strategy and marketing.
The growing emphasis on digital-first initiatives has caused business executives to scrutinize how technology investments impact everything from customer experience and uptime to operational efficiency and innovation. While buzzwords like machine learning, predictive analytics and IoT make headlines, the reality is most digital efforts don’t deliver.
According to a recent report from Bain & Company, only 5% of companies actually achieve digital transformation. This is due to a number of roadblocks, including lack of strategy and C-level buy-in, and isolated roles and responsibilities. Another often overlooked reason for stalled digital strategies is the lack of foundation in place on which to transform. It’s those activities beneath the waterline in IT infrastructure that are excluded from digital conversations.
One of the pillars of that foundation is what we refer to as Intelligent Operations. While CIOs have long been expected to improve stability and uptime – and done a pretty good job of it – the stakes are much higher in a digital context, where customer switching costs are at all-time lows. Drastically cutting the time to detect and then fix issues is now a strategic mandate. Unfortunately, many enterprise IT departments still can’t satisfy these demands. Additionally, the focus on implementing tools to monitor performance across the enterprise has resulted in tool sprawl and massive amounts of isolated data.
Without the teams and technology in place to interpret the information and reveal actionable insights in real time, what could be business-boosting data sits in silos, collecting dust. In addition to creating a unified view across an increasingly complex IT environment, Intelligent Operations also enable organizations to automate tasks, remediate issues quickly and incorporate learnings back into the architecture and processes.
The road to fully functional Intelligent Operations is bumpy, no matter what stage of digital transformation companies are currently in. Based on our experience, organizations typically fall into one of the following stages of maturity:
Stage One: Reactive
Organizations in this troubleshooting stage have, at best, a fragmented monitoring strategy across separate teams and disjointed technologies. More often, there is no monitoring strategy at all. With low visibility into the system, the data is meaningless and the detection and repair times are long. At this stage, organizations might require customers to report the issues in order for them to be known at all. We estimate that roughly a third of companies are still in this reactive stage.
Stage Two: Aware
In the awareness stage, monitoring systems are in place, enabling some visibility into IT performance, but the data is still fragmented. For example, monitoring tools are able to detect when a site is down – but only after it has already crashed. That being said, organizations at this stage have more of a grasp on their various IT systems and are therefore at a foundational starting point for Intelligent Operations.
Stage Three: Effective
Effective operations allow for some proactive management of application and infrastructure performance, but are still more focused on resource usage as a proxy of application performance than end user experience. Monitoring tools might be able to understand how one factor is influencing another, allowing for a team member to fix an issue before it happens.
Stage Four: Optimized
Fully proactive in all phases of monitoring, optimized operations help alert team members to mitigate issues before they arise. The monitoring tools in place allow for almost complete visibility, with comprehensive root cause analysis available. Code is in place to signal when an issue might come up; however, optimized operations still require manual code design and implementation.
Stage Five: Intelligent
In mature Intelligent Operations, runbooks are in place to remediate issues with significantly less manual intervention. At this point, a full Intelligent Operations architecture is present, with the ability to detect and diagnose glaring issues as well as smaller, intricate issues that are spread across applications and infrastructure. We estimate that only 5% of enterprises are operating at this stage of maturity today.
Still Down the Road: AIOps
In the future, Artificial Intelligent Operations – or AIOps – will mean much less human interaction in monitoring, detection and remediation activities. At this level, if anything were to go wrong, the system would detect and fix the issue on its own through machine learning and algorithms, and even more so, provide prescriptive and proactive intervention based on those same models. But, until organizations decide to dedicate the time and effort necessary for successful Intelligent Operations, AIOps will remain a theory.
From higher system uptime and availability to reduced time to detect and repair issues, resulting in clear business benefits like higher customer return rates, purchases and customer satisfaction, the benefits of employing Intelligent Operations are essential for organizations to enable, and accelerate, its digital transformation.
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