[This article was originally published by Sumit Chauhan on LinkedIn.]
AI systems perform robust computation, but their outputs are typically dissociated from the structure of the computation itself. Answers are delivered as fluent summaries, scripts, or static artifacts. Explanations may accompany results, but the execution path remains opaque and nonexecutable. This separation constrains inspection, audit, and collaborative verification.
Excel bridges this separation through a longstanding but underappreciated design property: computation and explanation coexist in the grid.
Values persist as first class objects, accessed and connected through a network of formulas and calculation objects. The dependency structure is explicit and intermediate results remain live within the model.
Assumptions remain live inputs rather than fixed premises embedded in prose. A spreadsheet is therefore a runnable representation of reasoning.
The Excel Agent extends this property to AI-driven analysis. Instead of returning an answer, it writes computation directly into spreadsheet primitives: cells, formulas, tables, and references. Analytical intent is encoded structurally—not narratively—resulting in computational instantiation that is inspectable, addressable, and mechanically verifiable.
Respecting established analytical practice
This distinction becomes explicit when updating real analytical models.
In one internal evaluation, the Excel Agent was prompted to update an existing public company financial model following newly released quarterly results. The spreadsheet contained a structured income statement, linked calculations, margin rows, and derived percentage outputs sourced exclusively from GAAP financial statements.
We observed that the Excel Agent pulled precise reported figures from the GAAP filings and updated only the newly available quarterly actuals, leaving guidance untouched. The agent preserved the existing model structure, row ordering, and dependency relationships. Calculated fields—totals, margins, growth rates—were not overwritten. Instead, they recalculated mechanically from updated inputs. Number formatting remained intact, preserving distinctions between dollar values and percentages. Changes were immediately auditable by inspection: updated cells represented inputs, while formulas remained unchanged.
The contrast to general purpose AI tools is instructive. In parallel tests, a comparable update performed through a chat based model relied on headline summaries rather than reported figures, conflated guidance with actuals, overwrote calculated fields with inferred values, and introduced silent formatting errors. The resulting spreadsheet appeared plausible but was structurally compromised. The result was incomplete and not auditable.
Inspectability and error localization
Because Excel externalizes reasoning as explicit dependency graphs, errors localize narrowly. A disagreement targets a specific cell, formula, or source assumption rather than an opaque explanation. Review is incremental. Validators can inspect references, confirm lineage, and trace downstream effects mechanically. Excel Agent inherits these affordances. Its output is not sealed; it invites modification. Alternative scenarios become addressable inputs rather than regenerated answers.
This changes the cost structure of verification. Inspection becomes cheaper than regeneration. Corrections are edits, not prompts. Trust derives from structure rather than narrative coherence.
Durability through artifact-centered collaboration
Spreadsheets are durable analytical objects. They are shared, versioned, reviewed, audited, and revisited independent of their origin. The Excel Agent produces artifacts that persist beyond the original interaction. The analysis remains runnable months later without replaying a model, and knowledge accumulates as structured computation rather than transient output.
This reframes AI’s role in analytical work. Excel Agent does not replace analytical judgment. It relocates reasoning into a medium designed for inspection, modification, and reuse. AI output becomes a starting point rather than an endpoint, expressed as runnable structure rather than fluency. The value is not in producing convincing answers, but in creating durable, collaborative, inspectable computation that can be reviewed, extended, and trusted over time.
The shift is from answers to artifacts, and opaque intelligence to shared reasoning.
Sumit Chauhan
Corporate Vice President, Office Product Group
Microsoft