1. Human thinking vs machine thinking
Artificial intelligence is already pervasive across the globe, enhancing and powering various parts of our lives across many industries. It is particularly suited to situations requiring direct, human-scale interactions. Examples include self-driving cars, asking Siri for directions or when Facebook recognizes familiar faces in photos.
But ITOps are not conducted on a human-scale. Indeed, IT networks are growing ever larger, and becoming more complex and widely distributed. Artificial intelligence is not yet sufficiently advanced to look after a network on its own, simply because machines are not yet able to think as creatively as humans. However, machine learning – which is the leading driver of AI development – is far more efficient than humans at executing the fundamental tasks of network management. Machine learning has already been used to automate and enhance trading, an environment where real-time responses and extensive user customization are needed for dynamically-changing conditions. Going forwards, it will become crucial for ITOps teams to harness such efficiency.
The global volume of business data doubles every 1.2 years.
Waterford Technologies, ‘Big Data Statistics & Facts for 2017’ (2017)
The IT professional’s move from specialism to generalism is not a downgrade in significance, however. Rather, it emphasizes the creative potential of human thinking that machine learning – and therefore AI – is nowhere near close to imitating. With machine learning responsible for the raw labour of extracting knowledge from data, ITOps will have the freedom to become creative. As a result, IT teams will no longer need to spend time chasing down faults or security alerts across the network, for instance. This freedom will empower teams to implement ideas that would be impossible to execute without the scale and efficiency of algorithms. The potential of the network increases as more insights are exposed, and this encourages new ways to think about problems and plans for the future.
In this way, AIOps contributes to the new digital trend of augmenting the human worker. In the new data-centric paradigm, automation and analytics make decision-making easier for IT teams. As these teams move closer to the business’ frontline, AIOps becomes essential in helping them focus on building scalable networks to meet business growth. IT operations will no longer be a merely reactive firefighting service. Instead, it will become exponentially more informed about the past, present and future state of the network, and therefore more valuable to the business’ success than ever before.
2. AIOps skillsets
AIOps can bring many benefits. But it is not simply a case of turning on machine learning, sitting back and waiting for results. As discussed above, the limitations of machine learning mean that human workers remain integral to ITOps.
Over 40% of organizations practicing advanced analytics are challenged by a lack of adequate skills.
Gartner, ‘How to do Machine Learning Without Hiring Data Scientists’ (2017)
Between 2014 and 2017, demand for engineers developing AI and machine learning algorithms has increased by 485% in the UK, and there are 2.3 roles available per qualified candidate.
Computer Weekly, ‘UK at risk of AI skills crisis’ (2017)
However, one of the purposes of AIOps in particular, and digitalization in general, is to deliver to businesses the benefits of a data scientist without necessarily hiring one. To overcome this issue, Gartner has suggested that businesses can train IT professionals as citizen data scientists, work with academic institutions, or employ third-party professionals or applications.
3. How IT operations will operate
Alongside skillsets, businesses must think about how they deploy their operational resources when adopting AIOps. Specifically, ITOps will be reorganized according to the data sources used, rather than by existing technology or infrastructure layers. In the age of digitalization, data is king, and without data there can be no analytics.
As is common to the digital transformation, AIOps requires the removal of information silos. Despite the complex algorithms that underpin machine learning, the quality of output from AIOps still depends on the quality of input. If only a few data sources are used, or plans are executed only within a certain business silo, then the results and analytics will only be as good as those silos.
Indeed, Gartner has specified the datasets that constitute true AIOps, as we have previously covered. This further underlines how important it is to unite separate silos from across a business network. To achieve this, IT teams need to familiarize themselves not just with machine learning, but also the roles of both network engineers and software developers.
A 10% increase in data accessibility will result in more than $65m additional net income for a typical Fortune 5000 company.
Forrester, ‘ROI of Data Quality’ (2017)
With silos being eliminated and businesses increasingly unified in purpose, the impact of AIOps will spread across departments and within applications. As it does so, IT should retain governance of AIOps deployment strategy, and ownership of data collection and analytics across departments. With consistent ownership comes consistent processes. It will also enable and encourage uniform interaction between IT teams and the business development professionals, who rely upon the insights provided by AIOps to improve the business’ offerings.
AIOps is about harnessing AI to enhance the human worker, remove friction from workflows, and increase efficiency across the business. Implementing this disruptive and technology-heavy paradigm across the entire business is labor-intensive and cannot be undertaken lightly. IT teams will take on more generalist, wide-ranging and creative responsibilities – uniting IT functions – whilst supervising the work undertaken by the machine learning. They will collaborate with business drivers across departments to ensure successful delivery. In this way, both the human and the machine can work to their respective strengths for the benefit of the business.