Looking at cloud specifically, multi-cloud environments are now prevalent across the enterprise landscape, offering the flexibility and agility to meet diverse business and technology requirements, and advance digital transformation strategies.
The challenge, however, is clear. As these highly dynamic ecosystems scale, with multiple clouds, services and applications in action, process management becomes evermore complicated, and silos emerge preventing users from seeing the full picture and identifying potential issues.
In an effort to tackle these blind spots and complexity, organisations are recognising the need to move from a reactive to a more proactive approach to cloud management and IT operations.
For many, the future success of business transformation strategies will depend on the ability to manage workloads proactively, detect threats, predict incidents, streamline collaboration and workflows, and correlate events across various distributed environments, teams and toolsets.
Machine-learning and artificial intelligence (AI) could well provide the answer.
As we are increasingly able to witness in enterprise settings, AI can play a significant role in automating process, learning and adapting as it goes.
With the acceptance of and interest around AI growing rapidly, techniques such as machine-learning, natural-language processing and computer vision promise to solve many business process management challenges, particularly in multi-layered cloud environments.
By analysing big data sourced from IT operations tools and devices, new management platforms powered by AI can intelligently forecast potential incidents, and reduce risk and time spent identifying and resolving them.
Artificial intelligence for IT operations, named AIOps by IT research group Gartner, is for now relatively unheard of in the business landscape, with only 5 per cent of all large enterprises deploying the strategy.
This number, nonetheless, is expected to jump dramatically to 40 per cent by 2022. If these predictions become reality, AIOps has the potential to transform process management completely in our cloud-centric economy.
In a report, Colin Fletcher, research director in IT operations management at Gartner, describes AIOps as enabling “leaders to meet the proactive, personal and dynamic demands of digital business by transforming the very nature of IT operations work via unprecedented, automated insight”.
However, this is not to say that the deployment of AI in a diverse cloud environment comes without its own challenges.
Pitfalls can arise if businesses are not fully assessing the real investment and effort levels required by an AIOps implementation. Machine-learning demands huge amounts of data and real-life interactions; these are critical to an effective AIOps strategy and take time to build and perfect. Any investment in AIOps platforms would need to follow comprehensive assessment of the demands for data, human-machine interaction and time commitment.
As with any digital transformation journey, AIOps must also be approached in an iterative fashion. Wider than its technological impact, the methodology is ultimately about people and process. The main objective is to drive greater collaboration to improve quality, deliver efficiencies and reduce operating expense – this takes time and careful planning.
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