By Balakrishna DR, Senior Vice President, Service Offering Head – ECS, AI and Automation at Infosys.
Digital acceleration following the pandemic is progressing fast, and, for so many organizations, cloud is at the heart of it all. It’s helping them become agile, innovate more and create value even through challenging times. Gartner predicts that cloud spending will grow 18.4% this year, to a total of $304.9 billion. However, even with massive cloud uptake, many organizations are lacking in their cloud maturity, according to Infosys’ 2021 Cloud Radar Report. Capturing one’s fair share of the cloud prize, without the landscape devolving into cloud chaos, is possible only when a company develops a clear view of the value at stake and the business cases that must be prioritized.
Cloud sprawl is a significant challenge. With SaaS enabling business departments to lead their own IT purchase decisions there are immediate gains, but also several challenges. These multiple buying centers bring into the mix the growing risk of siloed services, burgeoning costs and value leakage. AI can help ease the path forward by helping IT organizations counter the hurdles with better clarity, prediction and control.
Clarity not just of cloud spends, but also into the business services and technical drivers around cloud consumption – driven by automated deep analysis.
Prediction of consumption and foresight around seasonal patterns that then also enable better planning and provisioning – using data models. AI also helps manage, structure and monetize large data sets powering big data analytics.
Control that comes from the ability to streamline workloads, while intelligently automating monitoring and tracking of cloud consumption, services and spends. This can in turn bring the dependability of automated rationalization, efficiencies and prioritization; all the fundamentals of good governance without human fallibility.
Beyond monitoring and managing the cloud ecosystem, AI can also serve to self-heal when there is a slip in this orchestrated system. From automating core workflows and then with analytical capabilities to improve processes over time, the role of AI is significantly expanding. In a robust cloud implementation, several processes can be automatically managed by AI, and new insights can continue to help evolve the operational environment, while the IT teams focus on higher-value strategic activities. AI-powered threat detection, data security and network security are increasingly becoming a cloud staple too.
AI can also serve as a much-needed trigger for IT process reimagination and reengineering beyond incremental improvements. Enterprises, especially the digital immigrants, rarely have the operational strengths and organizational constructs in place to manage the complexities of cloud economics. When first applying AI to automate their process environment for cloud readiness, they uncover these operational challenges and often undertake process reengineering of inefficient or ineffective processes, as part of their cloud-powered IT transformation, as a recourse. This can prove to be an invaluable advantage.
Siemens Gamesa Renewable Energy (SGRE) offers us a great example of how some of the principles outlined in this approach is helping the company manage their cloud-first end-to-end IT transformation. The journey for SGRE included hybrid cloud transformation, roll-out of a software defined network, the set-up of an intelligent service desk, and digital workplace services too. They first aligned their existing disparate IT setup into a harmonized and consolidated infrastructure landscape. The hybrid cloud solution – a come together of multiple public cloud platforms with SGRE’s private cloud – was then integrated to bring agility to the IT infrastructure while also ensuring technical and financial synergies. AI for process automation and foresights for continuous improvements of their operational landscape was an integral part of their plan. They leveraged enhanced self-help and self-heal capabilities enabled by AI and automation tools through the process, to ensure that SGRE could count on the benefits from an optimized, stable and always-on infrastructure that will eventually serve their operations across 50+ countries.
As a way forward, with companies building AI models for cloud transformation, engineers tackling the cloud transformation need to keep up with the changes to the tooling and environments. Data scientists are not the only people working with these models – operations engineers and managers will need to work with them too to optimize and improve models. Preparing the talent landscape – in skills and mindset – to match the advances that AI can bring to the IT infrastructure landscape is an area that can benefit from our focus.
- What is Vector Similarity Search & How is it Useful?
- Researchers Use Voice Data and AI For Early Diagnosis of Parkinson’s
- Kaitlyn Albertoli Founder of Buzz Solutions – Interview Series
- ‘Smart’ Walking Stick Helps Visually Impaired Grocery Shop
- What is a Data Analyst? Salary, Responsibilities, Skills, & Career Path