Microsoft Excel’s place in the modern finance stack is not just surviving — it’s thriving, and the latest industry chatter suggests that affection for spreadsheets now spans every generation in corporate finance from Boomers to Zoomers. A recent industry write-up highlighting a Datarails survey reports that more than half of 22–32‑year‑old finance professionals say they “love” Excel, with similarly strong attachment across older cohorts; the same coverage notes that a majority expect Excel to remain as — or more — important over the next decade. Those headline figures are striking, but they beg two questions: what is being measured, and why does a four‑decade‑old tool still command such devotion in an era of cloud collaboration and dedicated FP&A products? The answer is a mix of techni…
Microsoft Excel’s place in the modern finance stack is not just surviving — it’s thriving, and the latest industry chatter suggests that affection for spreadsheets now spans every generation in corporate finance from Boomers to Zoomers. A recent industry write-up highlighting a Datarails survey reports that more than half of 22–32‑year‑old finance professionals say they “love” Excel, with similarly strong attachment across older cohorts; the same coverage notes that a majority expect Excel to remain as — or more — important over the next decade. Those headline figures are striking, but they beg two questions: what is being measured, and why does a four‑decade‑old tool still command such devotion in an era of cloud collaboration and dedicated FP&A products? The answer is a mix of technical durability, workplace culture, and emerging AI features — and each of those has clear upsides and real risks that IT and finance leaders must weigh.
Background
The spreadsheet that would not die
Excel’s origins date back to the mid‑1980s as a graphical spreadsheet first released for the Macintosh in 1985 and later for Windows; its basic tabular model — rows, columns, formulas and cell addresses — rapidly became a lingua franca for business modeling. That design primitive proved remarkably resilient: tables are human‑readable, formulas are composable, and workbooks are portable in ways databases and BI platforms are not. These features explain why Excel remains the default “last mile” for many business processes even when back‑end systems change.
The current moment: surveys, spectacles and sponsorships
The recent attention on Excel’s cross‑generational appeal is tied to a Datarails survey highlighted in technology press and commentary. The same coverage also pointed out the marketing and cultural side of that enthusiasm: Datarails — a vendor that builds Excel‑native FP&A tooling — has been active in Excel communities and sponsorships (including the Microsoft Excel World Championship), which underscores how commercial interests and community events amplify the story of Excel’s revival. Those sponsorships and spectacle help explain why affection for Excel surfaces not only in formal surveys but in public competitions and social content celebrating advanced spreadsheet skill.
What the Datarails‑led narrative actually says — and what it doesn’t
The reported numbers
Coverage summarizing the Datarails report gives several striking statistics:
- 54% of 22–32‑year‑old finance professionals reportedly say they “love” Excel, compared with 39% among an older generation.
- 89% say Excel will be as, or more, important in the next decade.
- 78% would be disinclined to take a job that banned Excel.
- Among users over 51, similarly high attachment rates are reported — including 94% expecting to continue using Excel over the next decade and 96% claiming they would decline a job where Excel was forbidden.
- The coverage also cites an emotional‑attachment figure of 82% reporting high or moderate emotional ties to Excel.
These numbers create a clear narrative: Excel is emotionally and practically central to finance careers across age groups. The headline is simple and compelling — but the underlying context matters. The original survey instrument, sample size, respondent recruitment and question wording are not reproduced in full in the coverage, so the figures should be read as indicative rather than definitive. Where the methodology is not public, caution is required before extrapolating these percentages to the whole finance profession.
Who ran it, and why that matters
Datarails is a commercial vendor whose product strategy is explicitly “Excel‑native” — that is, it wraps enterprise FP&A functionality around Excel rather than replacing it. That positioning creates two facts of relevance:
- Datarails has domain expertise in the Excel‑centric FP&A world and direct access to finance practitioners who use Excel daily.
- The company also benefits commercially from promoting Excel durability and adoption. That does not invalidate the survey, but it does mean the sampling frame and question design may be influenced by product positioning.
In short: the survey results are useful context, but they are not an unbiased academic study; readers and procurement teams should treat the numbers as vendor‑adjacent marketing data unless a full methodology is published. Where methodology is not available, those claims should be treated with caution.
Why Excel still binds generations: technical and cultural drivers
Durable technical primitives
Excel’s staying power comes from a handful of technical strengths that are still hard to beat:
- Universal tabular model: Rows and columns are a universal, human‑interpretable abstraction for structured data.
- Composability: Formulas, named ranges, pivot aggregations and now dynamic arrays let non‑programmers compose logic that would otherwise require code.
- Interoperability: Excel reads and writes CSV/XLSX easily, acts as a staging ground for extracts from ERPs, and is frequently the hand‑off point for consolidations and reporting.
- Performance on local hardware: Desktop Excel can be snappier for many day‑to‑day tasks than web‑first alternatives, depending on the machine and dataset.
These technical attributes mean that Excel is not simply a “legacy convenience” — it is often the fastest route from messy exports to an actionable report, particularly in organizations that lack integrated data pipelines.
Cultural and workflow reasons
Beyond architecture, there are social norms and incentives that keep Excel front and center:
- Training and habit: Finance teams invest time mastering functions and templates; replacing that muscle memory is costly.
- Ownership and gatekeeping: Being “the Excel person” confers influence; teams are often reluctant to decentralize that capability.
- Ad hoc flexibility: Spreadsheets are the place where one‑off analyses, “what if” modeling and bespoke reconciliations happen — tasks that traditional IT projects are poor at supporting quickly.
This combination of human and technical factors is why pivot tables and conditional formatting were cited as the features that bridge generational divides: they provide both speed and signal mastery across cohorts.
The Google Sheets factor: different beasts, different use cases
It is tempting to treat Google Sheets as a direct threat to Excel; in reality the two occupy overlapping but distinct niches:
- Google Sheets is cloud‑first and collaboration‑centric, excelling in realtime co‑editing and lower‑friction sharing for distributed teams.
- Excel is feature‑rich, performance‑tuned (on modern desktop clients) and widely embedded in complex corporate workflows and add‑ins.
For many organizations, the pragmatic balance is mixed: Google Sheets for light, collaborative workflows and quick shared dashboards; Excel for heavy analytics, complex modeling and regulated financial reporting. The choice is not only technical but also organizational: data governance, file size, macros/VBA and existing training are strong determinants in that decision. Where large Excel files or proprietary VBA logic exist, migration to the cloud can be expensive and risky.
The AI turn: Copilot, Agent Mode and what it means for Excel’s future
New capabilities arriving in Excel
Microsoft has been integrating AI into Excel through Copilot and higher‑order features sometimes described as Agent Mode. Those additions aim to:
- Generate or explain formulas from natural language prompts.
- Summarize datasets, highlight outliers and create suggested visualizations.
- Automate multi‑step tasks via agentic workflows that plan, execute, validate and iterate inside a workbook.
Those features promise to democratize certain types of analysis and reduce the barrier to entry for non‑specialists — a potential boon for productivity and accessibility.
Benefits — speed, democratization, and accessibility
- Faster model building and report generation, particularly for routine tasks.
- Reduced dependence on a handful of “power users” for formula crafting.
- Better onboarding for junior analysts who can lean on Copilot to learn idiomatic approaches.
These are real advantages: many routine cleanup, aggregation, and charting tasks are time sinks that can be automated or augmented today.
Risks — skill erosion, hallucination, and auditability
- Skill erosion: If Copilot routinely writes formulas and builds models, core spreadsheet literacy could decline. That becomes an operational risk if AI suggestions are accepted without manual verification.
- Hallucination and incorrect outputs: LLM‑based assistants can produce plausible but wrong formulas or inappropriate aggregations; finance teams must have human‑in‑the‑loop verification for any AI‑produced numbers used in reporting.
- Audit and compliance: Regulated reporting demands traceability. AI‑driven edits must be logged, auditable, and reproducible. Businesses will need policies and tooling that capture agent steps and create verifiable snapshots of workbooks.
- Data governance and privacy: Many AI assistants require cloud connectivity; that creates configurable but nontrivial surface area for data exfiltration or inadvertent exposure. Legal and procurement teams must verify training/data residency clauses and non‑training guarantees where they matter.
These governance and risk dimensions are not hypothetical; internal community discussions and vendor guidance repeatedly highlight the need for tenant controls, DLP rules, and explicit audit trails before deploying agentic Excel features at scale.
Practical guidance for IT, finance and procurement teams
To harness Excel’s benefits while managing the new AI‑driven risks, organizations should consider an explicit rollout plan:
- Pilot on sanitized datasets: validate AI behavior on copies, not production workbooks.
- Define governance and tenant rules: enforce admin opt‑ins, DLP, and non‑training contractual terms for cloud AI where necessary.
- Require audit trails and change logs: retain snapshots and an explanation of each AI edit for reconciliation.
- Pair AI with training: continue to teach core spreadsheet skills so humans can verify AI outputs.
- Measure outcomes: track time saved, error rates and user satisfaction in the pilot versus control groups.
These steps are practical and evidence‑driven: communities around Excel and modern FP&A tools emphasize pilot‑first approaches and strong human oversight as minimum safeguards.
Where vendor incentives and marketing blur the picture — and why that matters
The Datarails report is a useful window into user sentiment, but it sits inside a commercial ecosystem.
- Vendors that build on Excel have an incentive to emphasize Excel longevity; that’s not a conspiracy, it’s a natural market alignment.
- Sponsoring events such as an Excel World Championship and running emotive campaigns increases public visibility and may magnify reported affection.
That doesn’t make the sentiment false — many finance professionals genuinely love the tool — but it does mean readers should apply classic critical appraisal to survey claims: check sample size, recruitment, the precise question framing (what “love” means), and whether the respondent pool skews toward vendor customers or engaged Excel communities. Where methodology is not published, treat the figures as directional rather than absolute.
Anecdotes and unverifiable claims: the Airbus example
Some coverage cites companies such as Airbus as having found Excel hard to replace, with file size presented as a practical reason their teams stayed with Excel. That claim is plausible — large exported files and bespoke models can be migration blockers — but the specific corporate anecdote requires verification. Public corporate statements or case studies from Airbus should be sought before treating the anecdote as a fact about Airbus policy. In the absence of direct, attributable quotes or a published Airbus case study, that example should be flagged as an unconfirmed illustration of the broader point that some enterprises resist migration due to scale and history. Always ask for the source — procurement teams should request customer references or published case studies when a vendor cites a large enterprise as proof.
The talent angle: hiring, retention and the identity of finance work
One striking downstream effect of Excel’s continuing dominance is its role in hiring and identity within finance teams. If 78% of respondents would be disinclined to take a job that forbids Excel (per the coverage), then forbidding Excel becomes an HR risk: you may shape the candidate pool away from experienced finance hires who expect and value Excel competency. Conversely, leaning too heavily on Excel may make a team less attractive to candidates seeking modern BI stacks and collaborative tools. The practical balance for hiring managers is to be explicit in job descriptions about tool expectations and to offer training: preserve what matters from Excel while signaling openness to modern stack components where appropriate. That clarity helps avoid culture clashes between “Excel veterans” and younger hires who may prefer cloud collaboration but still need Excel for complex models.
A realistic prognosis: Excel’s near‑term future
- Excel is not going anywhere. Its combination of usability, ecosystem and extensibility makes it deeply embedded in finance processes.
- AI will augment rather than immediately replace core spreadsheet skills. Copilot and Agent Mode will accelerate common tasks, but they will also require governance and human oversight.
- The migration away from Excel will be incremental and use‑case driven: some teams will adopt cloud‑native tools for collaboration; others will keep Excel for modeling, reconciliations and regulatory work.
- Skill sets will shift: teams that integrate AI responsibly will value a hybrid of spreadsheet literacy and AI‑validation skills.
This is not a technological inevitability but an organizational choice: firms that invest in governance, upskilling, and measured pilots will derive the productivity advantages while containing operational risk.
Recommendations for WindowsForum readers — a compact checklist
- Treat vendor surveys as signals, not mandates: request methodology and sample details before changing policy.
- Pilot AI features on non‑sensitive data first; insist on exportable audit trails for any agentic edits.
- Keep Excel literacy programs active: learning INDEX/MATCH, XLOOKUP, PivotTables and formula auditing will remain valuable.
- Update procurement criteria: include non‑training clauses, data residency, and tenant grounding in AI contracts.
- Map critical spreadsheets: inventory workbooks that feed statutory reports and prioritize their migration only after testing.
These steps are practical, low‑friction ways to keep teams productive while avoiding the classic mistakes of either blindly resisting innovation or naively adopting it without controls.
Conclusion
Excel’s intergenerational appeal is real, but the coverage that celebrates it is only the starting point for a deeper conversation. The technical strengths that have kept Excel central — composability, interoperability and ubiquity — remain intact. New AI features now promise to extend Excel’s usefulness, democratizing complex tasks while introducing governance, audit and skills challenges that could change day‑to‑day finance work. For IT and finance leaders, the right response is neither panic nor passive acceptance. It is disciplined experimentation: pilot AI in Excel with strong safeguards; preserve and teach core spreadsheet literacy; and update procurement and governance to reflect the reality that spreadsheets are now co‑authored by humans and agents. Read the vendor‑adjacent surveys and the spectacle of Excel championships as evidence of enduring interest — but validate claims, demand methodology, and plan operations around audited, reproducible outcomes rather than anecdotes. In that balanced approach lies the best path to preserving Excel’s strengths while avoiding its growing pains in an agentic future.
Source: theregister.com Excel affection spans generations, from Boomers to Zoomers