Overview
Imagine you can resolve the issue within a few minutes, whereas it used to take several days. The MTTR (Mean Time to Resolve) is the average time it takes to resolve an incident from detection to resolution. With AI, it has gone down approximately to an impressive 40%. Isn't it interesting what the power of new-age technologies can do for us? That’s how AI can make your systems intelligent enough beyond automation.
A decade ago, CIOs focused on automation to reduce manual efforts in day-to-day operations. However, today, the play is much broader. Enterprises are looking for modern or intelligent operations and automation is just a tiny piece of the puzzle. Within four to five months of including AI capabilities, organizations can reach a zero-touch resolution, i.e., from issue occurrence to its solution, no human is involved.
We all know that traditional operations rely on monitoring tools to detect and report events or failures automatically. Leveraging service management to track the status and the SLAs while the human workforce will deal with the resolution and RCA (Root Cause Analysis), a total manual intervention. To make the situation worse, identifying problems was never easy due to the complexity of interconnected devices and multi-environment IT infrastructure. However, analytics has played a crucial role in identifying and predicting operations problems. Analytics-driven by ML and AI can predict 30% more accurate patterns.
Event correlation platforms can be leveraged with AI in intelligent service management and made smart enough to separate the application issue from the infrastructure issue, mute hundreds of related events from different underlying layers and identify RCA. With the power of AI, incident recovery has been 2605X faster than ever.
Unlocking the potential with AI and ML
Predictive analytics and forecasting: Events and anomalies can be detected before they occur, and patterns can be identified to predict future events using ML algorithms. Without the concern related to manual checking of the status of backup jobs or going through tedious reports, segregating, filtering and tracking, ML can help predict the anomaly by comparing the historical data in real time. The impact of predictive analytics is that it saves resources and hundreds of millions of dollars resulting from downtimes.
Self-healing: Coupled with AI and ML, automation platforms can self-heal the system before it is broken. The remediation action can be applied by detecting anomalies and recognizing patterns. These may include killing the job and freeing up the resources and network or restarting later.
Post-healing works well when an issue is detected and resolved, and an ITSM ticket is closed on its own without human intervention. A self-healing system can be made smart enough to learn independently, continuously improve and recommend resolutions.
Automated incident resolution: Have you ever worried about missed SLAs or inefficient operations? Well, AI drives automated incident resolution, which reduces non-compliance and brings maximum efficiency to IT operations. This leads to freeing up SMEs and contributing to their more revenue-generating efforts. As a result, engineers are able to free up almost 40% of their time and contribute to innovative solutions.
Automated service requests: Generally, requests are raised by owners and the app support team in the ITSM portal and then manually assigned to relevant groups and infra engineers. This adds up to a lead time of several days, which also includes follow-ups for clarity or confirmation of requests. AI- and ML-powered solutions can completely automate the fulfillment of these requests where adding Vlans in the network or adding RAM to a server is fulfilled. Further, it reduces the touch points to zero. These new-gen technologies can offer zero-touch points and reduce day-to-day service requests by 40% to 50%.
Conversational AI: IT Operations have a dedicated service desk office that caters to stakeholders and manually routes the incidents to respective departments. The chatbots based on NLP (Natural Language Processing) and Conversational AI help eliminate the need for human-assisted operations to report, direct and guide the issues. Intelligent chatbots provide a human-like experience to users and help avoid a decent number of issues with numerous guides, SOPs and self-help books. These smart tools also assist in optimizing costs and saving on time-consuming efforts by finding the right teams to work on respective issues.
Self-service solutions: Automation has been a driving force, from automated CI/CD pipelines to self-service infrastructure, which are differential features. Automated CI/CD pipelines propel your projects forward, eliminating tedious manual processes and enabling your team to focus on what truly matters – creating exceptional software. But that's not all – with intuitive self-service portals for infrastructure, accessing and managing resources becomes as easy as a few clicks, decreasing deployment time from weeks to a few hours. The power of AI has also been able to provide a cloud-like experience for on-prem platforms. AI works for you and empowers you to drive innovation at every turn."
Key takeaways
The transformation of AI and ML unlocks unprecedentedly efficient and seamless working methods. Human error reduction, cost savings, manual effort reductions, zero downtime, quick resolutions, efficient operations, autonomous intelligent systems and quality results are just a few highlights of innovation-led operations.
We at HCLTech help clients achieve advanced operations by carefully assessing them and leveraging our homegrown, best-in-class tools based on AI, ML and NLP to start the journey of next-gen operations and gain momentum along the way.
To know more, you may write to us at HCBU-PMG@hcltech.com.
