Use AI to perform rationalization analysis and technology investment scenario modeling

Overview

Rationalization decisions are inherently comparative: which technologies should be invested in versus which should be rationalized, which migration sequencing produces the fastest risk reduction, which investment combination produces the greatest combined return on portfolio improvement effort. These comparative decisions benefit from scenario modeling — the ability to evaluate multiple alternative investment and rationalization scenarios against the portfolio evidence and identify the scenario that best satisfies the organization’s priorities. AI tools can perform this scenario modeling against the Technologies Inventory family data rapidly and flexibly, enabling governance teams to evaluate alternatives before presenting recommendations to leadership rather than presenting a single recommendation that leadership cannot evaluate against alternatives.

Best Practice

Use AI rationalization and scenario modeling in the annual rationalization review cycle to evaluate alternative rationalization priority orderings against the portfolio evidence before finalizing the rationalization roadmap. Standard rationalization modeling queries include: cost-optimized rationalization sequence — given a defined remediation budget, which rationalization programs produce the greatest wasted spend recovery and technology debt reduction in the planning period; risk-optimized rationalization sequence — which rationalization programs produce the greatest reduction in the portfolio security vulnerability score and EOL risk exposure in the planning period; strategic alignment optimization — which combination of adoption and deprecation decisions most rapidly moves the portfolio toward the enterprise architecture target state; and investment return modeling — for each significant technology investment under consideration, model the expected Technology Spread growth, the associated cost trajectory, and the return on investment timeline.

Benefit(s)

AI-assisted rationalization scenario modeling enables the governance team to present leadership with evidence-based rationalization recommendations that have been evaluated against alternatives, rather than single recommendations that leadership must accept or reject without the ability to evaluate what the alternatives would produce. Leadership engagement with technology rationalization decisions improves when the decision options are clearly presented with their respective outcomes rather than presented as a single governance recommendation requiring approval. And the scenario modeling capability enables the governance team to update its recommendations rapidly in response to leadership feedback — adjusting the prioritization criteria or the budget constraint and rerunning the analysis — rather than requiring a separate analysis cycle for each scenario the leadership conversation generates.