Legacy systems continue to be an integral part of organizations’ IT landscape. For systems nearing the end of life, companies must consider updating, upgrading, or modernizing them to continue providing customers with the highest level of service. However, for the companies that have built them, this can consume substantial engineering bandwidth, causing businesses to incur massive costs and diluting their focus on new product development efforts.

In a fast-paced market, organizations must move away from traditional approaches to sustenance engineering. They must leverage the cutting-edge capabilities of Generative AI (GenAI) to bridge knowledge barriers, accelerate code review and modification, and accelerate workflows.

Learn how GenAI can enable smart application support and engineering and revolutionize software product sustenance.

Understanding Software Product Sustenance and Support

Although older generation and legacy systems are an integral part of modern IT ecosystems, they require constant support and maintenance. Software product sustenance solutions address legacy technology hurdles, revitalize them, and extend their life and value. By enhancing the existing capabilities of software products via bug fixes, new feature implementations, technology upgrades, and UI/UX improvements, product sustenance engineering makes legacy products relevant to today's dynamically changing market conditions.

Regular and robust software product sustenance and support allow for sustained product relevance, enabling organizations to get more value from their software systems. Around-the-clock sustenance engineering for hotfixes and new feature additions allows companies to extend product lifecycles, build a next-gen platform migration roadmap, and streamline product end-of-life management via L1-L4 customer support. Product sustenance also encompasses clean-up of old tickets that are lying dormant. This could require connecting with customers who raised the old tickets so backlog could be bought down drastically.

The Power of GenAI in Smart Sustenance Solutions

When it comes to smart sustenance, GenAI improves engineering engagement, expediting tasks through collaboration, collective problem-solving, and automated workflows. These capabilities help reduce the team’s bandwidth allocated to end-of-life products while assuring customers the highest quality of service.

Here’s what GenAI-powered smart sustenance brings to the table:

• Minimized costs: With GenAI, sustenance teams can enable hyperautomation in code build, test, and release. Such auto-test code generation and execution across unit, functional, regression, and integration testing can minimize the time, effort, and costs of application modernization. Generative AI can also automate maintenance and document generation, allowing teams to divert necessary cash to new product development.
• Improved serviceability: Using Generative AI for software product sustenance and support also helps improve application serviceability. For example, by leveraging an NLP-enabled observability assistant, support teams can get necessary advice and assistance for app performance improvement. Similarly, intelligent RunOps capabilities can automate issue resolution and keep software applications and services running smoothly.
• Faster defect resolution cycle: GenAI allows application support teams to quickly and effectively resolve issues and drive greater value from their applications. For instance, GenAI tools can mine known error databases and logfiles and provide immediate answers to known issues to support staff via self-service. GenAI can also enable proactive error detection via log monitoring and analytics while automating code reviews for greater accuracy and optimization. It provides developers with critical guidance on the fixes applied to similar defects earlier.
• Reduced technical debt: Generative AI models can also help reduce technical debt. Support teams can use AI coding assistants to review existing pitfalls and gaps in code, automate code generation, and accelerate development timelines. Automated testing tools can further improve code robustness and minimize the likelihood of new bugs being introduced.

Advanced Sustenance Engineering with AI-Driven Insights

Utilizing AI for sustenance engineering can significantly enhance efficiency, reduce costs, and improve decision-making throughout a product's lifecycle. By integrating AI to optimize product lifecycle management, organizations can:

• Create optimized product designs, simulate scenarios, and boost user experience and satisfaction.
• Analyze application performance data to predict potential failures or maintenance needs in time.
• Leverage natural language processing (NLP) to assess customer feedback, gauge product sentiment, and discover common concerns.
• Unearth personalized user preferences and tailor product features to meet specific needs.
• Provide 24/7 customer support via AI chatbots to answer queries and guide users through troubleshooting steps.
• Enable data-driven decision-making for continuous product improvement.

Customizing Product Sustenance Solutions

To tailor software product sustenance, companies must embrace flexible engineering services. Such services can help them meet specific product and market needs and adapt to change. Using AI-driven strategies, they can:

• Predicting defects based on historical patterns.
• Create fine-tuned algorithms to predict maintenance needs based on unique operational conditions.
• Develop predictive models that address unique failure modes and performance metrics of their products.
• Build machine learning models specifically trained on data related to their products and users.
• Test their modernized applications in real-world scenarios, collect feedback, and make adjustments as needed.
• Develop dashboards tailored to specific metrics and KPIs, providing real-time product performance and sustenance insights.

Success Stories: AI-Powered Sustenance in Action

Using Generative AI for software product sustenance and support is a great way to enhance product longevity while boosting operational efficiency and customer satisfaction. Here’s a real-world example:

Business Need: A leading global procurement software provider was witnessing a high rate of customer addition post-merger. Managing this large influx of new customers was beyond the capability of internal engineering teams, leading to poor bug resolution.

Solution: Xoriant successfully transferred product knowledge and streamlined engineering processes through an industrialized transition model. A team of 100+ FTEs delivered GenAI-enabled end-to-end sustenance services and feature enhancements across contract management, core requisition, buying, and invoicing.

Business Benefits: The delivery of AI-powered sustenance reduced lead time for new feature delivery, reducing average defect resolution time by 40% and improving CSAT improvement from 4.0 to 4.5.

Choosing the Right Partner for Sustenance Support Services

When choosing the right partner for smart sustenance support services, here are a few factors to consider:

• Look for a partner utilizing a GenAI-first modern delivery model for optimization and continuous efficiency improvements.
• Make sure partner KPIs are aligned with your SLAs for assured value delivery.
• Opt for a partner who is a disruptor in the product engineering space and offers an additional commitment to cost savings.
• Ensure the partner utilizes an industrialized playbook to manage transitions in record timelines.
• Look for a partner utilizing a GenAI-first modern delivery model for optimization and continuous effici
• Make sure the partner offers outcome-based EOL support and utilizes a risk-reward sharing model for upfront asset transition and front-loaded savings.

In Conclusion

Given the current pace of technological advancement, organizations across industries and sectors must modernize and upgrade their legacy systems to make them fit and relevant. GenAI-enabled product sustenance engineering services hold the key to reducing the burden on engineering teams, improving the life and value of software products, and allowing for the reallocation of resources to new product development.

Leveraging a large engineering talent base and a GenAI-first delivery model, smart sustenance solutions via product sustenance engineering services can assure value delivery via committed KPIs and efficient transition management.

Recognized as a significant player in software engineering in the IDC MarketScape: Worldwide Software Engineering Services 2023 Vendor Assessment Report, Xoriant has experience building software from the ground up and modernizing software products and solutions. From product modernization to architecture transformation, cloud migration, native application development, site reliability engineering, and more, Xoriant’s Gen AI-first modern delivery approach focuses on sustenance engineering to extend product lifecycle and support next-gen platform migration.

Explore our sustenance and support services today!