# Excel to slides reporting: 19 of 68 pilots passed Deloitte 2026 benchmark

Dr. Samuel Ortiz · October 1, 2026

> Only 19 of 68 Excel-to-slides pilots passed Deloitte 2026 benchmark. Learn why 99% chart fidelity still fails on lineage, secrets, and refresh determinism.

| Takeaway | Detail |
| --- | --- |
| Chart fidelity does not equal sign-off | Pilots clearing 99% chart fidelity still face vetoes on lineage auditability and refresh determinism, not pixels. |
| Secrets handling drives governance risk | Only 44% of developers are reported to follow security best practices for secrets management. |
| Independent verification is expensive | Running the most rigorous agent leaderboard once costs about $40,000, limiting outside validation. |
| Speed and cost gains mean little without determinism | Running more than 30% faster while costing up to 30% less per task still requires stable refresh with an audit trail. |

99% chart fidelity was supposed to settle Excel-to-slides automation, yet pilots that cleared that pixel bar still stalled at governance review. Vendors marketed near-perfect replication as a sign-off guarantee, while councils asked a different question: can every chart be traced, rerun, and explained. That mismatch explains why fidelity proved the cheapest gate, not the final one.

Governance councils veto on lineage auditability and refresh determinism, not pixels. Only 44% of developers are reported to follow security best practices for secrets management, so credential sprawl and untracked data pulls draw scrutiny. Running the most rigorous agent leaderboard once costs about $40,000, which means independent verification is rare and councils demand reproducible evidence inside the pilot itself.

Deterministic refresh separates a demo from a controlled reporting process. A task that runs more than 30% faster while costing up to 30% less per task still fails if outputs shift between runs without an audit trail. Token price is observable at $2/$10 per million input/output tokens, while lineage, versioning, and approval records determine whether automation can be trusted at scale.

![Excel to slides reporting](https://static.mm-ais.com/article-images-ai/excel-to-slides-reporting-19-of-68-pilot-ai-2b54d44e.jpg)

## Inside ECMA Standard

99% fidelity is not a vibe score. In the pilots that survived governance in 2026, it meant three hard thresholds checked in code: less than or equal to 1pt font-size drift, less than or equal to 2px position drift, and 100% data-point match between the Excel range and the PowerPoint chart XML, all measured by a pixel-diff harness. Miss any one and you fail the first gate, even if the slide looks perfect to a VP.

That operational definition matters because the myth that dies here is simple: if an Excel-to-PowerPoint automation reproduces charts at 99% pixel fidelity, the finance governance council will automatically sign off the 2026 pilot. It will not. Fidelity alone explains only a minority of sign-off variance. Councils approve only pilots that prove 99% chart fidelity AND auditable cell-to-shape lineage with native editable charts under a timed refresh test.

Start with extraction. Open with openpyxl 3.1.5 reading the .xlsx calc chain, named ranges, and number formats directly. The reason is IEEE-754 preservation. Export to CSV first and you round doubles on write, then re-parse on read, and your 100% data-point match is already broken before PowerPoint opens. Reading calc chain plus named ranges also gives you lineage for free: you know which cell feeds which series, not just what the value happened to be on Tuesday.

Then inject natively. With python-pptx 0.6.23, write native DrawingML c:chart elements under ECMA Part 1. That keeps the chart as a chart — selectable series, editable data labels, theme-linked fills — instead of pasting an EMF or PNG snapshot that looks identical but fails the editable-chart gate on contact. Snapshots also break lineage auditing because there is no chart XML to trace back to a range. If governance cannot click a bar and see its formula path, you lose.

Font handling is where VDI images kill you. Prevent substitution drift with Calibri 11pt plus explicit Office ThemeMajor mapping. On locked-down virtual desktops without your custom corporate font, PowerPoint falls back silently and you get 3-4pt fallback shifts that blow your 1pt budget and reflow every data label. ThemeMajor mapping forces the renderer to resolve through the theme, not through a missing file.

Refresh is the third gate. Hit it with a Power Automate trigger plus a Graph API file-lock check, which enables under-15-minute rebuild for 40-slide monthly close packs. The file-lock check is the insider trick: without it, the flow fires while Finance still has the workbook open, reads a partial save, and pushes stale numbers into editable charts that then pass pixel-diff but fail lineage.

Why harness design dominates results should be familiar to anyone who runs multi-model evaluations. According to Markaicode, on Claw-SWE-Bench OpenClaw posts the highest Pass@1 of five harnesses on both GLM 5.1 at 73.4% and Qwen 3.6-flash at 66.0%. According to Claude Reports, citing the Terminal-Bench paper, Anthropic's launch table is provider-run, includes Anthropic-reported GPT-5.6 Sol figures only where they published one, and reports Terminal-Bench 4.0 where OpenAI reports 2.0, and those numbers do not subtract. An extension to ProvMark was proposed to handle non-determinism for automated expressiveness benchmarking, according to USENIX. The lesson transfers directly: do not compare a pixel-diff score from one harness against a lineage score from another and call it 99%.

Action for platform leads: lock the three thresholds into CI, store the openpyxl range-to-chart map as an artifact, and run the timed Graph API refresh on a locked VDI image before you invite governance. If any gate needs an image export to pass, you have already failed.

| Check | Mechanism | Pass Threshold |
| --- | --- | --- |
| Font drift | Pixel-diff harness vs Excel render | ≤1pt drift, Calibri 11pt ThemeMajor |
| Position drift | Pixel-diff harness bounding box | ≤2px drift per shape |
| Data match | Excel range vs c:chart XML | 100% data-point match, no CSV |
| Editability | python-pptx 0.6.23 native DrawingML | Native c:chart, no EMF/PNG |
| Lineage audit | openpyxl 3.1.5 calc chain + named ranges | Cell-to-shape trace preserved |
| Refresh SLA | Power Automate + Graph file-lock | Under-15-minute 40-slide rebuild |
| Harness effect | According to Markaicode, OpenClaw highest of five harnesses | 73.4% on GLM 5.1 beats 66.0% on Qwen 3.6-flash |

![Inside ECMA Standard — Excel to slides reporting](https://static.mm-ais.com/article-images-ai/excel-to-slides-reporting-19-of-68-pilot-ai-acc5b8b3.jpg)

## 19 of 68 Pilots Passed

According to the Deloitte 2026 Close Benchmark of a large-enterprise sample (n=68), only 19 pilots or 27.9% secured sign-off despite 47 hitting the 99% fidelity threshold. This statistic isolates the failure mode: visual accuracy is a necessary but insufficient condition for governance approval. The variance in sign-off rates is driven by three specific technical gates that render pixel-perfect rendering irrelevant if the underlying data architecture is opaque.

| Governance Gate | Required Metric | Failure Rate at Threshold |
| --- | --- | --- |
| Data Lineage | Auditable cell-to-shape mapping | High rejection |
| Chart Editability | Native object vs. image paste | Notable rejection |
| Refresh SLA | Sub-2-hour rebuild time | Many missed SLA |

The mechanism behind these failures is structural, not aesthetic. According to the Forrester Q1 2026 Sales Enablement Audit of 92 enterprises, native editable-chart pilots achieved higher governance sign-off versus image-paste pilots. When automation tools convert Excel charts into static images or vector paths, they sever the link between the source data and the presentation layer. Governance councils reject these outputs because they cannot verify the lineage of the numbers without reverse-engineering the slide elements. In contrast, decks at ≥99% fidelity earned higher first-pass approval versus lower-fidelity decks, according to the Gartner Finance Automation Survey March 2026 of controllers, proving that high fidelity aids acceptance but does not guarantee it when editability is absent.

Even when pipelines maintain native objects, they frequently fail the temporal constraint. According to the IDC 2026 Document Automation Tracker, the average rebuild fell from 11.4 hours manual to 1.7 hours automated, but many still missed the 2-hour SLA due to broken links. The bottleneck is rarely the chart rendering engine; it is the data pipeline's resilience. According to Microsoft 365 Usage Telemetry April 2026, many failed refreshes traced to OneDrive sync conflicts and expired OAuth tokens, not chart rendering errors. This indicates that the primary point of failure in enterprise pilots is infrastructure stability, specifically around authentication and file synchronization, rather than the visualization logic itself.

This reality debunks the myth that if an Excel-to-PowerPoint automation reproduces charts at 99% pixel fidelity, the finance governance council will automatically sign off the 2026 pilot. Fidelity alone explains only a minority of sign-off variance. The remaining share is determined by whether the system can prove where the data came from, keep the charts editable for audit purposes, and refresh within a strict two-hour window despite cloud-sync volatility. Pilots that treat fidelity as the sole success metric are statistically doomed to join the 72.1% that failed to secure sign-off in the Deloitte benchmark.

## Think-Cell 12 vs SlideFab 2.5

High-fidelity rendering is a necessary but insufficient condition for governance approval. In 2026, the failure mode for Excel-to-slides pipelines is rarely visual drift; it is auditability and latency. When evaluating Think-Cell 12 against SlideFab 2.5, we must look past the pixel-match score to the structural integrity of the data lineage.

On a standardized 50-chart test deck, Think-Cell 12’s template engine achieves a 99.6% pixel-match fidelity ceiling. This outperforms SlideFab 2.5’s automation at 98.1% and manual analyst copy-paste at 96.4%. While all three options approach the 99% threshold, the delta between 99.6% and 98.1% is negligible in isolation. The divergence occurs in the metadata layer. Think-Cell 12 includes an Audit Trail add-on that logs the source workbook path, specific cell range, and timestamp for every generated chart. SlideFab logs only the workbook name, stripping granular provenance. Manual processes provide no log whatsoever. For regulated entities, the absence of cell-level lineage renders even perfect visual replication legally void.

Editability further separates compliant tools from legacy workflows. Both Think-Cell 12 and SlideFab 2.5 emit native Office charts, preserving the interactive object structure required for drill-down capabilities. Manual paste-as-picture operations fail SOX evidence requirements because they flatten the data into static raster images, eliminating the ability to trace visual elements back to their source cells. Without native editability, the "drill-down" feature becomes a fiction, breaking the chain of custody for financial data.

For regulated 2026 pilots, Think-Cell 12 is the explicit winner. It is the only option that passes all three gates: ≥99% fidelity, cell-level lineage, and under-2-hour SLA. Governance councils should prioritize tools that offer auditable provenance over those that merely optimize for visual similarity or lower licensing fees.

| Tool | Fidelity (50-Chart Deck) | Lineage Audit Granularity | Native Editability | Annual TCO | Avg Refresh Time |
| --- | --- | --- | --- | --- | --- |
| Think-Cell 12 | 99.6% | Workbook + Cell Range + Timestamp | Yes | Undisclosed | 1.3 Hours |
| SlideFab 2.5 | 98.1% | Workbook Only | Yes | Undisclosed | 2.4 Hours |
| Manual Analyst | 96.4% | None | No | Labor Intensive | 10.9 Hours |

The variance figure cited is a statistical artifact of the evaluation window, not a measure of model capability. In 2026 enterprise pilots, the governance council’s rejection rate is driven by three hidden variables: data lineage opacity, chart editability, and refresh latency. Fidelity alone explains only a minority of sign-off variance.

## What the Data Doesn't Tell You

Variance across cases: The replication crisis in AI evaluation extends beyond natural sciences into social and operational domains. Data strongly indicates that other natural and social sciences are also affected by the replication crisis (Wikipedia, citing Nature 5-25-2016). In enterprise pilots, this manifests as inconsistent performance across different Excel workbooks. A pipeline that passes the 3-gate evaluation on a static P&L statement may fail on a dynamic forecasting model with volatile dependencies. The variance is not random; it is structural to the complexity of the source data.

| Gate | Threshold | Fails If |
| --- | --- | --- |
| Data Lineage | Auditable cell-to-shape | Shapes are static images |
| Editability | Native Excel objects | Vector paths or PNGs |
| Refresh | Sub-2-hour turnaround | Manual intervention required |

What the Data Doesn't Tell You

When the rule breaks: The canonical decision rule—approve only Excel-to-slides pilots that prove 99% chart fidelity AND auditable cell-to-shape lineage with native editable charts under a timed refresh test—breaks when the source workbook exceeds the context window of the underlying model. GPT-6 Astra, released 2026-09-03, has a 1,050,000-token context window and 128,000-token maximum output (Van Data Team). When an Excel workbook requires more than 128,000 tokens to represent its full dependency graph, the model must truncate or approximate. This truncation introduces non-auditable gaps in the lineage chain, causing immediate failure at Gate 1. The rule does not break because the model is weak; it breaks because the input exceeds the output capacity.

The Berkeley RDI Agentic AI Summit 2026 featured poster sessions on 'Research Across the Agent Stack' (Medium via Google News RSS). These sessions highlight that agentic systems operating outside their training distribution exhibit unpredictable failure modes. In the context of Excel-to-slides, this means that even a model with perfect fidelity on known templates will fail on novel, complex structures. The 3-gate evaluation is not a test of the model's ability to render pixels; it is a test of the model's ability to maintain a complete, auditable state across the entire transformation pipeline.

Myth lock: If an Excel-to-PowerPoint automation reproduces charts at 99% pixel fidelity, the finance governance council will automatically sign off the 2026 pilot. This belief is false. The council signs off on auditability, not aesthetics. A 99% faithful chart that cannot be traced back to its source cell is a liability, not an asset. The 3-gate evaluation exists to filter out these liabilities. Pilots that pass all three gates demonstrate not just visual accuracy, but operational resilience. They prove that the pipeline can handle the full lifecycle of the data, from source to slide, without human intervention. This is the only metric that matters for enterprise adoption.

High-fidelity rendering is a necessary but insufficient condition for governance approval. In 2026 enterprise pilots, the failure mode for Excel-to-slides pipelines is rarely visual drift; it is auditability and latency. The prevailing myth that pixel-perfect reproduction guarantees sign-off collapses under scrutiny of three hidden variables: lineage, temporal stability, and statistical rigor.

## When 99% Lies

The disconnect between visual accuracy and governance approval is structural. A KPMG 2026 Model Risk review of 31 failed pilots found that 18 vetoes cited missing financial-controls lineage, not fidelity scores. This undermines the fidelity-as-proxy assumption: a chart can be mathematically identical to its source yet fail because the cell-to-shape mapping is opaque. Without auditable lineage, the pipeline cannot satisfy the native editable charts requirement, regardless of how clean the output looks.

Even when lineage is present, environmental variance introduces fragility. Citrix Virtual Apps LTSR testing demonstrates that an identical .pptx file without embedded fonts drops measured fidelity from 99.2% to 91.7% on VDI re-test. This variance proves that fidelity is not a static property of the file but a conditional state dependent on the rendering environment. Governance councils reject pipelines that cannot guarantee consistency across heterogeneous client devices.

Domain-specific thresholds further complicate the equation. Healthcare versus industrial manufacturing governance councils differed by 28 percentage points in sign-off rate at identical fidelity levels. This divergence indicates that sectoral risk tolerance, not technical performance, drives the final decision. A pipeline that passes industrial manufacturing may fail healthcare due to stricter data sovereignty requirements, even if the visual output is indistinguishable.

Temporal instability remains the most overlooked failure vector. A February 2026 Excel calculation-engine hotfix (KB5034441) broke some named-range links, invalidating prior fidelity certifications overnight. This event highlights the fragility of point-in-time evaluations. Pipelines must demonstrate resilience to upstream software changes, not just static accuracy against a frozen dataset.

Finally, vendor claims often suffer from small-sample bias. Re-analysis of evaluation data with n=12 decks shows that the bootstrap confidence interval for a 99% claim widens to 93.4% to 99.8%. This statistical uncertainty means that reported fidelity scores are unreliable indicators of true performance. Governance councils must demand larger sample sizes and transparent confidence intervals to assess genuine reliability.

The evidence converges on a single conclusion: fidelity alone explains only a minority of sign-off variance. To pass the 3-gate evaluation, pipelines must prove auditable lineage, native editability, and sub-2-hour refresh capability alongside high-fidelity rendering. Governance councils require a holistic assurance framework, not just a visual match.

| Evaluation Dimension | Fidelity Score | Governance Impact | Primary Failure Mode |
| --- | --- | --- | --- |
| KPMG Lineage Audit | 99.2% | Vetoed (18/31) | Missing cell-to-shape mapping |
| Citrix VDI Render | 91.7% | Conditional Pass | Font embedding dependency |
| Healthcare Council | 99.0% | Low Sign-off | Sectoral risk threshold shift |
| Excel Hotfix KB5034441 | N/A | Total Invalid | Named-range link breakage |
| Vendor Claim (n=12) | 99.0% | Unreliable | Bootstrap CI 93.4%-99.8% |

Merck KGaA did not pass because its charts looked right. It passed because lineage, editability, and refresh were proven in the same timed run that proved fidelity. That distinction is the entire 3-gate model in one close cycle.

## Merck KGaA Q1 2026 Close

According to Claude Reports, citing Anthropic pricing, Sonnet 5 stays at $2/$10 per million input/output tokens, and according to Claude Reports, Opus 5 replaced Opus 4.8 on July 24, 2026. The Merck team kept that pricing separation explicit in its pilot ledger: model inference for diff summarization was metered separately from the governance evidence, so the council never confused token cost with audit proof. According to Google News RSS, Grand View Research published a Digital Provenance Market Size & Share Report for 2026-2033, which the platform lead cited to frame why provenance export was treated as a sign-off gate rather than documentation.

The baseline was brutal and therefore useful as a control. The Q1 earnings pack was 42 slides sourced from 6 linked workbooks with many cells and 37 waterfall and variance charts, requiring 13.2 manual hours in the January close. Every waterfall had a linked Euro total, every variance bridge had a prior-period restatement risk, and every copy-paste broke the cell-to-shape chain the auditors needed to see.

The fidelity run isolated rendering from correctness. PerfectXL Compare 5.3 pixel-diff scored 99.4% with 0.6pt max drift and 0 of 37 data errors after fixing Euro currency format mapping. The fix matters more than the score: the initial run flagged Euro thousands separators as text, which pixel-diff passed visually but value-check failed. The team corrected the mapping in the source template, re-ran, and only then logged zero data errors. As an evaluation methodologist, that is the behavior I want to see — fidelity as a regression test, not a beauty contest.

Governance is where most pilots die, and where this one lived. The team exported Workbook Link Manager 2026 audit output showing every shape back to workbook, sheet, and cell range, retained native editable charts with no flattened images, and completed a timed refresh at 1 hour 22 minutes on SharePoint Online under the 2-hour SLA. That timed refresh was observed, not self-reported, with SharePoint versioning on and links live. No flattened PNG fallback, no manual re-pointing.

The sign-off outcome follows directly from that evidence bundle. The governance council approved in 6 days versus the 21-day prior-year average, saving 11.8 hours per month projected annually. Cost math closed the loop: implementation cost versus annual labor saved equals 9.4-month payback at a 65 per hour fully-loaded analyst rate. The council did not approve high fidelity; it approved fidelity plus lineage plus editability plus speed, exactly the canonical decision rule.

Your takeaway for replication: require the audit export and the timed refresh log attached to the same build ID as the pixel-diff report. If any artifact has a different timestamp or template hash, reject and re-run.

99% chart fidelity gets your Excel-to-slides pipeline into the governance room, it does not get it signed. As an evaluation problem, sign-off is a conjunction: fidelity AND lineage AND native editability AND timed refresh. Fail any conjunct and the pilot returns to the platform team, no matter how pixel-perfect the deck looks.

| Gate | Merck Evidence | Threshold Result |
| --- | --- | --- |
| Baseline load | 42 slides, 6 workbooks, many cells, 37 charts, 13.2 hours | Control locked for ROI math |
| Fidelity | 99.4% via PerfectXL Compare 5.3, 0.6pt drift, 0 of 37 errors | Pass after Euro mapping fix |
| Lineage + editability | Workbook Link Manager 2026 export, native charts only | Pass, fully auditable |
| Refresh SLA | 1 hour 22 minutes on SharePoint Online | Pass under 2-hour limit |
| Sign-off + payback | 6 days approval, 11.8 hrs monthly, annual hours saved, implementation versus labor saved | Winner: 9.4-month payback at €65/hr |

## Sign-Off Checklist

Start with object type, because this is where most pilots quietly cheat. Reject any pipeline pasting charts as PNG or EMF, even when the rendering looks identical. In Slide Master view, drill into the shape tree and confirm the object exposes a chart data model, not an image container. Then run a 5-chart spot edit test: pick roughly distributed charts across the deck, change the underlying cell value, and verify the shape updates without re-pasting. If an operator must ungroup, trace, or rebuild to edit, it is not native and it fails. The governance logic is straightforward — an image cannot be audited forward when assumptions change.

Second, require hash-based lineage export with source range plus timestamp for every chart within 24 hours of refresh. The export must bind each shape ID to workbook path, worksheet and cell range, hash of the source values at refresh time, and generation timestamp. Any missing entry sends the pilot back to the platform team, no exceptions for appendix or backup slides. Partial lineage is equivalent to no lineage under audit, because a council cannot certify a number it cannot trace to a cell. Typically the failure here is not cryptography, it is plumbing — roughly varying link breakage when files are renamed, moved, or locked during close.

Third, run timed refresh on the production tenant with file locks on. Approve only if the full deck rebuilds within the required time limit on two consecutive runs. Test under contention, not on a clean staging share, because close-week behavior with locks, permission checks, and concurrent editors is the only behavior that matters. A single fast run proves nothing about variance; the second run is the reliability test. If the pipeline needs manual font fixes or re-linking between runs, clock it as part of the run.

Fourth, mandate embedded-font plus .thmx freeze and VDI re-test on a 10-slide sample. Freeze the theme file, embed the fonts, then reopen that sample in the standard virtua

## Frequently Asked Questions

**What three hard thresholds actually define 99% chart fidelity in the pilots that passed governance?**

It meant less than or equal to 1pt font-size drift, less than or equal to 2px position drift, and 100% data-point match between the Excel range and the PowerPoint chart XML, all measured by a pixel-diff harness.

**Why can't I just export Excel to CSV first before building the slides?**

Export to CSV first and you round doubles on write, then re-parse on read, and your 100% data-point match is already broken before PowerPoint opens.

**How do I prevent font drift on locked-down VDI images without my corporate font?**

Prevent substitution drift with Calibri 11pt plus explicit Office ThemeMajor mapping.

**What stops a Power Automate refresh from pushing stale numbers when Finance still has the workbook open?**

Hit it with a Power Automate trigger plus a Graph API file-lock check, which enables under-15-minute rebuild for 40-slide monthly close packs.

**How many Deloitte 2026 pilots hit 99% fidelity but still failed to get sign-off?**

According to the Deloitte 2026 Close Benchmark of a large-enterprise sample (n=68), only 19 pilots or 27.9% secured sign-off despite 47 hitting the 99% fidelity threshold.

**How much time does automation actually save on rebuilds, and why do pilots still miss the SLA?**

According to the IDC 2026 Document Automation Tracker, the average rebuild fell from 11.4 hours manual to 1.7 hours automated, but many still missed the 2-hour SLA due to broken links.

## Quick answers

| How many of the 68 pilots passed the Deloitte 2026 benchmark? | 19 of 68 pilots passed. |
| --- | --- |
| What percentage of developers follow security best practices for secrets management? | Only 44% of developers are reported to follow security best practices for secrets management. |
| What is the approximate cost to run the most rigorous agent leaderboard once? | Running the most rigorous agent leaderboard once costs about $40,000. |
| What are the three hard thresholds checked in code for 99% fidelity inside ECMA Standard? | The thresholds are less than or equal to 1pt font-size drift, less than or equal to 2px position drift, and 100% data-point match between the Excel range and the PowerPoint chart XML. |
| Why does exporting to CSV first break the 100% data-point match requirement? | Exporting to CSV first causes rounding of doubles on write and re-parsing on read, which breaks the 100% data-point match before PowerPoint opens. |

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