Maximizing Value through AI-Driven Automation in Application Management Services

IT landscapes today are getting more complex and costly, with more than 64% of IT budgets spent on maintaining existing technologies. Most companies have specialized teams managing applications and infrastructure to cut costs, simplifying and enhancing how software is managed. At its core, AMS combines solutions, processes, and methodologies to maintain, improve, and manage custom, packaged, and network-delivered software applications. It involves tasks like patching, upgrades, and performance troubleshooting, each with specific workflows, analysis, and solution components.

Need for AI-Driven Automation in AMS

Traditional AMS relying on manual processes needs to catch up in handling the surge of raised tickets, potentially leading to delayed and inadequate solutions in a hyper-dynamic business environment. However, remarkable advances in cognitive computing, machine learning (ML), and robotics have spurred organizations to adopt artificial intelligence (AI)-driven AMS.

The key reasons that make AMS automation essential are:

• Service quality: AI-led automation enhances service quality by leveraging dependable, swift, and consistent bots. These trained bots promptly tackle fundamental issues, freeing teams to focus on more complex tasks.
• Speed: Automation expedites the routing of AMS tickets, streamlines incident analysis, and transforms software release cycles from quarterly or monthly updates to weekly or even daily deployments.
• Cost savings: By preventing delays in service delivery and reducing human efforts for software maintenance, AI-driven automation reduces associated costs. It also integrates all AMS requirements in one place, facilitating high-value returns on initial investments.
• Risk reduction: Once configured correctly, bots are less prone to errors than humans. In ML-driven automation, bots can learn from mistakes to avoid recurrence, contributing to a reduced risk profile.
• More innovation: When free from the encumbrance of basic maintenance tasks, IT teams gain valuable time to explore, implement, and innovate new AMS solutions and business services, cultivating a dynamic culture of continuous innovation.
• Business value generation: Several processes learned, tested, and implemented in IT can also be applied to business processes. For example, AI automation to close IT service tickets may also work in some customer service functions. Such extension of IT capabilities amplifies the influence of technology on business outcomes, strengthening enterprise credibility in the market.

Building an AI-driven automation program for AMS

Automating AMS is not merely about building AI bots to administer software and apps. It stands as a pivotal enabler of business transformation, augmenting service delivery. Guiding principles that help organizations understand their digital maturity and design a practical AI-based AMS automation roadmap include:

Prioritizing objectives over technology: When evolving service level agreements on the back of numerous AI-powered automation tools, it is easy to get distracted by the bells and whistles of technology. However, the right approach begins with listing the organization’s automation objectives. Instead of getting entangled in what, when, where, and how, the initial focus should be on ‘why.’ The expected outcomes should guide automation decisions for AMS.

The scale of automation: The economy-of-scale factor is essential for AMS automation. Extensive IT service desks and application maintenance setups warrant investment in sophisticated AI capabilities. Conversely, smaller enterprises may only need to amplify the utility of their existing IT service management (ITSM) tools. The automation approach must harmonize with the scale and attributes of AMS processes.

Pre-requisites of automation: Ideally, AI-based AMS automation rests on a well-defined and consistently executed process. For example, the effective deployment of incident resolution analytics would need a knowledge base or data repository of historical activity. Automation must be guided by consideration of the ‘as-is’ application management operating framework to ensure a holistic approach.

Application architecture: Automation used in an enterprise resource planning (ERP) environment would differ from that used in a custom software development landscape. Even within ERP, automation solutions differ among various vendor tools. Cloud-based solutions introduce another layer of complexity. Hence, AMS automation principles must aptly accommodate the unique intricacies of the company’s application ecosystem.

Possibilities of quick wins: As companies map their AMS automation journey, it’s crucial to pinpoint areas of maximum potential. Prioritizing tools that yield desired outcomes with minimal investment is critical. Employing an automation heatmap that outlines processes ripe for AI-driven automation throughout the organization aids in identifying opportunities that synergize IT and business objectives effectively.

Unlocking AI’s value for automated AMS: The support from YASH

With over 27 years of extensive AMS expertise across diverse industries, YASH Technologies’ Next Generation Application Maintenance and Support empowers enterprises to enhance end-user satisfaction, reduce costs, and ensure secure and optimized technology utilization. Our comprehensive AI-powered application management solutions cater to custom software and cloud applications.

YASH harnesses AI solutions from international vendors to effectively tackle performance challenges, oversee transaction metrics, and rectify issues about APIs and middleware at all support tiers. Whether it is incident management, change management, service requests, monitoring, or language layer support, our AI-powered AMS is engineered to elevate service delivery value and amplify business productivity exponentially.

Explore our AMS success stories for more information, or write to us at info@yash.com.

• AI
• AI-Driven Automation
• Automation in AMS