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Sample report

What you get back

A confidential, segmented readout of the issues and priorities, plus a drafted intervention plan. Below is an illustrative example for a finance function. Individual answers are never shown; everything is aggregated and gated by a minimum-N threshold.

Synthetic data · illustrative only

Stage one · readout

Issues, prioritised

    Month-end close runs long and crowds out analysis

    highhigh confidence

    The close consistently spills past working day 8, leaving little time for variance analysis before reviews. Manual reconciliations and late intercompany entries are the recurring bottlenecks.

    Dissent · A minority felt the close timeline is acceptable and that the real gap is the quality of commentary, not speed.

    Forecasts are not trusted by the business

    highhigh confidence

    Commercial partners discount the FP&A forecast and keep parallel spreadsheets. Assumptions are not surfaced and reforecasts arrive too late to change decisions.

    Ownership of data quality is unclear

    mediumlow confidence

    No single owner for master data; errors are caught downstream in finance rather than at source, creating rework every cycle.

Your hypotheses vs. the evidence

    confirmedThe close is too slowBorne out strongly across FP&A and controllership.
    refutedThe team lacks technical skillsEvidence points to process and ownership, not capability.
    surpriseBusiness partners distrust the numbersEmerged prominently from internal customers though not initially anticipated.

Scores by segment

Mean 1–5 self-ratings. Small segments and thinly-answered dimensions are withheld to protect individuals.

All respondents

N=24

Accounting & close

  • Close & reportingPeriod-end close2.6

Planning & forecasting

  • Planning & forecastingForecasting2.3

Data, systems & process

  • Data & systemsAutomation2.9

Business partnering

  • Business partneringDecision support3.1

Overall

  • Overall effectiveness today2.8
  • Trust in the numbers2.5

relationship: inside

N=11

Accounting & close

  • Close & reportingPeriod-end close2.9

Planning & forecasting

  • Planning & forecastingForecasting2.6

Overall

  • Overall effectiveness today3

relationship: internal_customer

N=9

Planning & forecasting

  • Planning & forecastingForecasting1.9

Business partnering

  • Business partneringDecision support2.4

Overall

  • Trust in the numbers2

By segment

relationship: inside

N=11
  • ·Close effort is heroic but manual; reconciliations dominate the first week.
  • ·Strong intent to partner, but no time left after the close.
  • “We spend the first eight days just closing the books, so analysis is always rushed.”
  • “People work hard here — the process fights us, not the other way round.”

relationship: internal_customer

N=9
  • ·Forecasts feel like a finance exercise, disconnected from commercial reality.
  • ·Teams keep their own numbers because the official ones arrive late.
  • “By the time the reforecast lands, the decision has already been made.”
  • “I trust my own spreadsheet more than the plan, and that is the problem.”

level: leadership

N=3

Too small to report separately, folded into the overall view to protect confidentiality.

Ask about this readout

Every readout answers back — grounded only in the guarded, aggregated data, never an individual response. Synthetic example:

Stage two · intervention plan

A sequenced programme that frees analytical capacity by shortening the close, then rebuilds forecast credibility with the business, while fixing data ownership at source.

Quick wins

    Pre-close the predictable

    Move standard accruals, recurring journals, and intercompany to a pre-close checklist run before period end, removing the largest first-week bottleneck.

    Measure · Close completed by working day 5 within two cycles.

    Month-end close runs long and crowds out analysis

    Publish forecast assumptions with every reforecast

    Surface the drivers and assumptions alongside the numbers so business partners can challenge inputs, not just outputs — the fastest route to rebuilding trust.

    Measure · Assumption pack attached to 100% of reforecasts; parallel spreadsheets retired in two quarters.

    Forecasts are not trusted by the business

Structural change

    Name a master-data owner and fix errors at source

    Assign clear ownership for master data with source-level validation, ending the downstream rework that finance currently absorbs each cycle.

    Measure · Downstream data corrections down 50% within two quarters.

    Ownership of data quality is unclear

Suggested sequence

  1. 01Stand up the pre-close checklist and measure the next close.
  2. 02Introduce the assumption pack on the following reforecast cycle.
  3. 03Appoint the master-data owner and begin source-level validation.

Run this for your function

Describe your role and audience; the AI designs the assessment, runs the interviews, and returns a readout and plan like this one.

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