Smarter IT Lifecycle Management with AI-Powered End-of-Life Lookup

In the effort to modernise IT, outdated technology often goes unnoticed until it begins to slow progress. Systems that once supported operations start showing their age. Maintenance becomes more demanding and security gaps begin to appear. Over time, these issues add pressure on budgets and make each upgrade more difficult to manage. Technology that should drive change instead ends up holding it back.

Despite the urgency to modernise, many teams still struggle with a simple yet fundamental question: When exactly does this technology reach its end of life? Manually tracking end-of-life (EoL) dates is slow and error-prone. Vendors rarely provide this information in a single source, leaving IT teams to waste countless hours navigating datasheets, vendor portals, and forums in search of lifecycle details.

This is where AI begins to transform enterprise architecture. ADOIT brings automation and intelligence directly into your EA workflows with its AI-powered End-of-Life Lookup, turning what was once a tedious research task into a proactive, data-driven decision process.

End-of-life data is critical to planning. You need to know when a database version loses support or when an operating system becomes a security liability. But that information is buried across:

• Vendor websites and obscure product pages
• Technical PDFs hidden in knowledge bases
• Online forums of mixed credibility

Even when lifecycle information is found, it often remains scattered across teams. Application owners, cybersecurity experts, and financial planners may each rely on different assumptions, leading to inconsistent data, delayed decommissioning, and risky investments in technologies already nearing obsolescence. To resolve this, ADOIT’s AI-powered End-of-Life Lookup removes manual effort and brings all lifecycle data together in one reliable source of truth.

Instead of searching vendor sites one by one, ADOIT automates the process inside your architecture repository. Here’s how it works: 

1. Input the technology – Select any system software.
2. Trigger the lookup – ADOIT’s AI retrieves verified EoL and support data from trusted sources.
3. Apply the result – With one click, update your repository and keep every stakeholder aligned.

No more spreadsheets, no conflicting timelines — just accurate, validated data you can rely on.

Working this way transforms how enterprise architects approach lifecycle management:

Faster investment clarity
Identify which technologies to modernize and which to retire before they drain budgets.

Reliable risk prevention
Plan proactively around vulnerabilities and strengthen compliance by addressing lifecycle risks before they become findings.

Clearer roadmaps
Map technology transitions in sync with projects and reduce technical debt across your application landscape.

Stronger rationalization support
Justify sunsetting or consolidation decisions with transparent, fact-based data — not assumptions.

For CIOs and portfolio leads, this means modernization roadmaps that are defensible, budgets that stay focused, and transformation programs that advance with confidence.

A large enterprise aims to modernize 20% of its application landscape in the next fiscal year. With manual lifecycle tracking, the team would spend weeks auditing hundreds of apps and guessing lifecycle status. With ADOIT’s EoL Lookup, they can instantly identify outdated technologies, prioritize rationalization targets, and support investment cases with trusted external data.

A bank preparing for a regulatory audit must prove that unsupported technologies are not in use. Instead of chasing down dates across dozens of vendors, ADOIT flags systems approaching EoL and provides lifecycle transparency across the entire portfolio — reducing audit risk and avoiding last-minute remediation projects.

Across industries, the result is the same: faster progress, lower risk, and better alignment between IT and business strategy.

Today, ADOIT’s AI ensures lifecycle data is accurate, centralized, and always available. In the future, this intelligence could go even further — proactively detecting lifecycle risks before they impact roadmaps, or suggesting migration paths when technologies near end-of-life.

For enterprise architects, this would mean less firefighting and more forward planning, with AI keeping execution on track while teams focus on driving transformation.

Legacy thinking leads to legacy systems. If your architecture team is still manually tracking technology lifecycle data, it’s time to rethink the approach.

With AI doing the heavy lifting, from research to repository updates, you gain the confidence and speed to modernize effectively.