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How Companies Should Prepare For Automation-Led Disruption

How Companies Should Prepare For Automation-Led Disruption

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Businesses in the digital era are increasingly morphing into ‘experience businesses. They are doubling down on creating a positive and consistent customer experience at every digital touchpoint. As more businesses become aware of what it means to prioritize user experience across their digital channels, ‘experience-first’ is turning into a full-fledged customer engagement approach to deepen and nurture customer relationships. Automation lies at the core of these experience-led businesses as it improves efficiencies, accelerates turnaround, and reduces costs – all critical imperatives, given the highly competitive modern business environment. 

The ‘automate for efficiency’ paradigm that has reigned for the last decade or so – wherein businesses focused on manual task automation (for L0, L1 tasks), point initiatives and so on – was designed to cut costs and boost speed and efficiency. But digital transformation and cloud have drastically increased IT operations complexity, given the higher data volumes and diversity of targets under management.  In ESG’s 2018 IT Spending Intentions Survey, 68% of respondents said their IT environments are now more complex than just two years ago, with 39% citing automated IT operations as being critical to survival in the new complicated reality. 



Businesses are therefore moving to the next level of automation – from task automation to IT process automation. This involves orchestrating tasks across tools, processes and systems. With sufficient data and insights, it is now possible to automate even L2 and L3 tasks that have traditionally relied on human judgement, by tapping into the judgment insights that lie within the data itself. Embracing IT process automation also enables businesses to lay the foundation for AIOps or Artificial Intelligence (AI) for IT operations, the new focus area that is expected to be trending for the next four to five years.        

Augmented operations: How ML and AI are making a difference

According to Gartner’s Top 10 data and analytics technology trends for 2019, Augmented Analytics, Continuous Intelligence and Explainable AI are among the key trends that have significant disruptive potential over the next three to five years. The application of AI, Machine Learning (ML) and data science to IT operations (popularly referred to as AIOps) helps IT operations teams tackle the very challenge created by digital disruption i.e. humungous volumes of data, and turn it into a game-changing opportunity. The vast amount of data, together with increasingly powerful processing capabilities of AI, makes it possible to train and execute algorithms at a large scale. Unsurprisingly, the global AIOps market is expected to be worth USD 9.907 billion by 2023.

For enterprises, this means that they can literally find needles in haystacks as AI can sort through billions of data points to answer a single question or ingest humongous data loads to identify patterns. The only dependency? Humans are still needed to define ‘the needle’ as that is something that AI cannot do, at least not currently. This means upskilling and reskilling the workforce in AI and ML technologies

AI’s exceptional compute capabilities help offload the heavy data crunching work from humans to bots by automating the rules. The result: augmented information is available to humans in near real-time to enable smarter decision making and enhance operational agility. 

Transitioning to autonomous operations: The next frontier of IT automation

Autonomous operations (AO) refers to leveraging AI and advanced ML techniques to deliver autonomous responses to IT incidents across the operations lifecycle. AO utilizes the self-learning capability of AI and ML algorithms to continually train and improve their ability to identify patterns and generate appropriate actions. The role of humans in an AO-driven environment is purely supervisory. Think of it like the self-driving cars that are currently taking the automotive industry by a storm. Controlled by software that can detect, analyze, and respond appropriately to events on the road, fully autonomous (level 5) self-driving cars have zero dependencies on human drivers. Their software’s self-learning ability ensures that errors (if any, in the initial stage) will diminish over a period of time. In the IT space, full autonomy would mean zero-touch operations. Are we there yet? No, but we are making rapid strides.

Deploying smarter automation: The role of data 

The more accurate the data, the smarter organizations can get with automation as accurate data allows users to write better rules. Static data has little value today – what organizations need is dynamic data that can be broadly classified into:

See Also

  • Descriptive metadata: Information such as who is the manufacturer of software, what hardware is required, and more.
  • Relational data – Information such as what a particular server means in the context of business, what applications run on it, and more.
  • Behavioural data – Information on how a particular server is behaving, what factors cause a particular behaviour, etc.

To get all this data in the right place and the right format and make it available at the right time, enterprises need a software-defined IT environment. That’s because smarter automation is all about auto-remediation i.e. the ability of business processes to not just proactively identify anomalies but also self-remediate without disrupting business. Traditional IT environments are built around legacy architectures that are not amenable to auto-remediation. Applying automation in such environments does little more than identifying flaws and falls short of creating the desired business impact. 

Moving into the fast lane: Accelerating automation the right way 

What is the right way to get smarter with automation? Consider the ROI of automation by factoring the effort, time, frequency, complexity etc. of a given task, and benchmark these parameters against the cost of automating it. Then create an intelligent Automation Index i.e. the ratio of the number of tasks that can be automated against the total number of tasks. Such targeted prioritization will help enterprises direct their automation investments in the right direction to maximize the potential of intelligent IT operations. The key to accelerating automation and mitigating the risk in the journey towards smarter automation is to create a process that is fully managed, iterative, and incremental.

This article is a part of the AIM Writers Programme. If you wish to write for us, email us at info@analyticsindiamag.com


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