How FP&A problems show up in surveys, hiring, and public curiosity

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Percentages are taken from the November 2024 results article summarising the FP&A Trends Survey. [1] Full survey programme context: [2].

Before you read the first pair of charts: the FP&A Trends write-up quantifies a persistent capacity trap—only about one-third of practitioner time lands on insight work, while the rest is absorbed by data wrangling—alongside hard forecasting constraints (a majority struggling beyond six months; many needing more than ten days to close a forecast cycle) and a non-trivial share still flagging poor data quality. Those patterns are drawn from the same survey recap we cite throughout. [1]

Takeaway: the donut encodes the “35% / remainder” time split from the survey narrative; the horizontal bars put concrete percentages on horizon, cycle-time, and data-quality pain called out alongside it—useful when you explain why dashboard productisation or reconciliation work (as in Cases 01 and 03) matters upstream of review. [1]

Planning & scenarios: the article contrasts adoption of rolling forecasts versus static methods and spells out scenario-latency extremes (a minority running scenarios inside a day versus another cohort unable to run them at all), while still reporting heavy reliance on Excel and very low shares for fully automated driver models or AI in planning. The next two panels visualise that bundle of statistics. [1]

Takeaway: nearly half of respondents report rolling forecasts—yet nearly as many remain on static methods; scenario capability is bifurcated; Excel still dominates and AI adoption in planning is a single-digit share. Together, that frames why integrated planning and xP&A investments remain topical. [1]

Culture, tools, and CFO technology expectations: a webinar poll excerpted in the same ecosystem of articles surfaces tool and culture barriers to data-driven decisions (insufficient analytics tooling is the largest bucket). Separately, McKinsey’s public CFO/gen-AI graphic reports very high expectations that gen AI will trim manual analysis and lift productivity—useful as a cross-check to the low single-digit AI-in-planning adoption in the FP&A survey. Barriers / poll themes: [1] · CFO expectations chart: [3]

Takeaway: the doughnut mirrors the “lack of tools / culture / skills / leadership” split from the poll narrative; the McKinsey bars summarise external sentiment on gen AI’s promised upside—pair the two when arguing that tooling execution lags executive optimism. [1] [3]

LinkedIn ranks emerging skills using three pillars—skill acquisition, hiring success, and growth in paid job postings—comparing calendar 2024 to 2023. The US list and job-function callouts below come from that methodology write-up. [4]

Workforce signal adjacent to FP&A: even outside finance job families, AI literacy and execution skills dominate the US “Skills on the Rise” ranking; the finance row in the same article still spotlights substantive investment and retirement-planning themes—helpful when you connect capital-markets literacy to enterprise planning roles. [4]

Takeaway: the bar ordering mirrors LinkedIn’s published ordering for the United States (AI literacy first, followed by conflict mitigation, adaptability, process optimization, and innovative thinking)—we encode order as weights for display only; consult LinkedIn’s primary list for verbatim ordering and any future updates. [4]

Finance callouts & illustrative search demand: the short finance-themed panel highlights skills explicitly named under the Finance job family in LinkedIn’s overview. The following “search-topic” chart is not an official statistic—it is an illustrative relative index so you have a talking-point graphic before you build your own compare in Google’s tool. Finance list: [4] · Trends methodology / explorer: [5]

Takeaway: side-by-side, the panels separate documented labour-market naming (LinkedIn) from self-serve public curiosity you can re-estimate by exporting queries in Google Trends—when presenting externally, label the right-hand chart as illustrative only. [4] [5]

Illustrative only (repeat): bar heights show a plausible relative ordering for generic finance queries (normalized 0–100 within the set). They are not live API pulls. Compare at Google Trends → Explore. [5]

This matrix is editorial, not sourced from a third-party dataset—it maps each portal simulation to the survey themes discussed above (data/reporting load, forecast stress, cost control, consolidation, cross-functional planning). Use it as a teaching aid alongside the cited survey lines. Survey context: [1]