AI-Driven Cognitive Operations Driving Enterprise Agility
AI-defined architecture unifies three typically human characteristics to automate IT and business processes – learning, understanding and solving. Using these skills, cognitive systems today can talk to people, plan and optimize tasks, make forecasts, recognize patterns, and analyze image and voice signals.
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IT operation managers are under ever-increasing pressure to increase performance while reducing budgets. With increasingly complex technologies, increased globalization, and more reliance on technology for critical business capabilities, they face constant pressure to reduce business downtime that negatively impacts customer experience, reduces employee productivity, and leads to loss in revenue. They face highly advanced security threats, which are amplified by difficulty in having the right security staff with the right skills. And they are often responsible for managing legacy systems, which come with an extra burden of manual mundane operations and heavy dependency on the IT workforce.
As advancements in technologies create additional pressures on IT operations, they can also provide new forms of relief. The introduction of cognitive technologies is an increasingly useful solution to these growing problems.
Evolution of Cognitive Technologies
Cognitive has moved dramatically beyond the conceptual stages in 2016, evolving from simple isolated automation systems to integrated enterprise-class digital automation solutions. Today, we have such diverse abilities as Runbook Automation (a sequence of steps leading to the implementation of automated tasks started by a system or user), AI Ops platforms (leveraging big data and AI or machine learning functionality to improve and partially replace a myriad of IT operations processes and tasks), and AI-led Intelligent Automation (context-aware robots that cement a strong foundation for applications and services).
Over the next half a decade, we expect the impact of cognitive technologies on different industries to grow significantly. A recent survey conducted by Deloitte of more than 200 IT executives found that over 64% intend to increase investments in cognitive technologies. Driven by new investments, AI-led cognitive systems will scale up across industries and functions including financial planning, analysis and decision making.
Simplifying IT Operations with Cognitive
Within the IT operations, cognitive technologies (especially intelligent automation) are the next game changers. These systems detect and analyze vast quantities of data and automate complete IT processes or workflows while learning and adapting in the process. This key feature allows them to facilitate autonomous and seamless decision making even when there are aberrations in the process implementation. Some of the practical applications include:
1. Predictive Maintenance
Cognitive-based predictive maintenance systems leverage huge data sets to anticipate failures before they happen. Companies can schedule preventive maintenance before likely failures, while getting important recommendations on how to prevent potential security breaches.
2. Interdependency Analytics
Many current infrastructure and application performance management suites offer visibility to a specific component of the stack, but not full stack visibility, causing the IT workforce to spend increasing amounts of time unraveling and troubleshooting complex environments. Cognitive-based interdependency analytics map relationships between systems, predict events based on dependencies and help engineers make well-informed decisions about data center optimization and planning.
3. Self-healing and Autonomous Remediation
Critical infrastructure, applications and software must be recovered after they crash, powered by either external or internal system breaches. Until recently, there was no accepted mechanism to automate this. Now, thanks to three integrated AI-based capabilities (automatic instrumentation, machine learning analytics, and integrated remediation), self-healing and auto-remediation systems can now detect the need of healing resources and utilize them appropriately.
4. Self-learning System and Knowledge Management
Intelligent (AI-based) learning systems, with self-regulated content, support self-oriented learning, enhance performance and generate competitiveness for the enterprises. Knowledge management ensures intelligence is always in reach and yielded in the context of the task. It makes it simple to find relevant information and resources, and offer relevant methods, tools, templates, techniques, and examples.
5. Role of Smart Agents to Manage L0/L1
Smart agents are intelligent and connected AI-based virtual assets. These agents can detect and respond to internal and external environment. With them, enterprises can make an intelligent shift from real-time control to predictive control to ultimately, autonomous control.
A Compelling Value Proposition
The bottom line is that using cognitive in IT operations is good for the bottom line. Compared to traditional models, companies can achieve significant operational cost reductions. Enterprises with limited developer skill set can automate most of the high-volumes, rule-based work processes round-the-clock at the fraction of a cost of a human resource.
But cognitive isn’t just cheaper – it’s also better. Preventing issues, rather than responding to them, is a win-win for business operations and IT operations. Thorough understanding of systems, through independency analysis, self-learning and knowledge management, reduced the frequency of issues as well as the time to restore.
Implementing cognitive tools also provides value in the form of critical AI experience. According to New IDC Spending Guide, global spending on cognitive and AI solutions will achieve a compound annual growth rate of 54.4% through 2020 when revenues will be more than $46 billion, and the US will be the largest market. It makes sense then to look at IT to lead the charge, adopting them internally first as a precursor to implementation throughout the organization.
Looking to the Future
AI-defined architecture unifies three typically human characteristics to automate IT and business processes – learning, understanding and solving. Using these skills, cognitive systems today can talk to people, plan and optimize tasks, make forecasts, recognize patterns, and analyze image and voice signals. In the not-to-distance future, self-aware machines might possess thoughts, feelings, and consciousness. As the capabilities of these systems expand, the opportunity to use them in administering IT operations only grows.
Disclaimer: The views expressed in the article above are those of the authors' and do not necessarily represent or reflect the views of this publishing house
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