The executive imperative Legacy BI estates are expensive to maintain and difficult to modernize because dashboards contain more than visuals: they encode data relationships, calculations, filters, parameters, security assumptions, and years of business knowledge. Manual reconstruction cannot deliver portfolio-scale change at the speed most enterprises now require.

This whitepaper presents a governed, accelerator-led approach to BI modernization. The model combines deterministic metadata extraction, a canonical representation of source assets, retrieval-augmented AI translation, target-native artifact generation, automated checks, and human approval at defined control points.

The approach is grounded in two Apexon initiatives: a Tableau-to-Power BI accelerator developed and demonstrated for a large financial services organization, and an earlier QlikView-to-Amazon QuickSight accelerator presented to a client. The Tableau demonstration received very positive feedback, particularly for the speed of first-pass conversion, the visibility of each migration stage, and the ability to review and amend generated output before packaging.

Modern target formats make this engineering approach increasingly practical. Power BI Project (PBIP), TMDL, and PBIR expose semantic-model and report definitions as structured files, while Amazon QuickSight provides APIs and asset-bundle operations for programmatic asset management and promotion. [1] [2] [3] [5]

Central Thesis Agentic AI does not remove the migration team. It removes repetitive engineering burden so practitioners can concentrate on semantic correctness, security, performance, usability, and business trust.

Post-trade reconciliation in global equities and derivatives is a major pain point for large banks. Every day, thousands of trade breaks occur (due to mismatched amounts, cutoff times, pricing errors, FX conversions, etc.), and most are still resolved by hand. These manual workflows are slow, error-prone, and costly: for example, a 2% trade-fail rate (typical in OTC derivatives) can cost the industry about $3 billion per year1. But can LLMs and Generative AI come to the rescue, to automatically reconcile these breaks and save significant manual cost? Simply dumping raw trade data into an LLM is impractical – given that trades of large investment banks run into millions and exception in tens or even hundreds of thousands, the context window and per-token costs would explode, and accuracy demands (~98–99% in trading) are very high2. Instead, agentic AI – i.e. systems of specialized AI agents – can be used to automate reconciliation at scale. This approach combines targeted tools, vector retrieval, and rules-based logic so the heavy lifting is done by smaller agents, not by one brute-force LLM. In this paper we

Trade reconciliation compares multiple systems’ records (front-office trade capture, exchange/clearing reports, custodians, etc.) to ensure all details agree. The classic reconciliation process involves six steps: data loading, matching, break classification, investigation, resolution, and reporting. In practice, large firms still see thousands of daily exceptions. Common break types include: