Marcus Buckmann, Galina Potjagailo and Shobhit Rawat
What can artificial intelligence reveal about firms’ pricing decisions from text-based business intelligence? We use a large language model (LLM) to quantify pricing narratives from reports documenting discussions during the Bank of England’s company visits. The LLM is prompted to derive scores for output-price changes and decompose them into determinants, including direct and indirect input costs, labour costs and demand. The resulting measures recover economically meaningful signals from firms’ narratives over time, from weak demand following the global financial crisis (GFC) to broad cost pressures and subsequent effects through wage and price setting during the recent inflation surge. The latest narratives point to marked cost increases following Middle East conflict, but so far less broad and persistent than in 2022.
Inside firms’ pricing narratives: exploiting text sections from business intelligence reports
Inflation data tell us how aggregate prices are changing but reveal little about the factors shaping firms’ pricing decisions and the mechanisms driving inflation. To shed light on these narratives ‘on the ground’, Bank of England staff regularly meet companies across the United Kingdom, documenting discussions in company visit reports. Alongside manually assigned company visit scores, a typical report contains information on the firm, such as its location, employment and turnover, as well as detailed qualitative notes summarising staff discussions with the contact on demand, costs, wages, margins, investment and prices. This business intelligence provides a direct qualitative view of how firms perceive economic conditions, form expectations and explain their decisions. Yet these text-based reports have remained largely underexplored as a source for systematically quantifying such narratives over time.
In ongoing research, we use an LLM (GPT‑5.4) to analyse these text sections. Our data set contains 66,820 reports from January 2005 to September 2026, averaging around 260 per month, although volumes have declined somewhat over time (to 237 per month in 2026). To manage costs, we analyse a random one third sample up to 2025 and the full sample for 2026. The text sections have become substantially longer over time, with median length increasing from 585 words in 2005 to 1,254 words recently, allowing a more nuanced quantification of pricing narratives in the later part of the sample. The reports cover 2,000–3,000 firms per year. Manufacturers and medium-sized firms are somewhat over-represented relative to the UK business population.
Using an LLM to derive scores and decompositions of pricing determinants
For each report, the LLM quantifies reported and expected output-price changes over the past and next three to 12 months relative to normal conditions. This mirrors the Bank’s existing practice of manually scoring firms’ assessments but applies it systematically across the text. Importantly, rather than simply tracking whether prices are rising or falling, the LLM decomposition maps the factors cited by contacts to a set of economic determinants. This allows us to trace the evolution of firms’ pricing narratives over time, akin to recent work on aggregate inflation narratives from newspaper articles. Analysis at the Reserve Bank of Australia instead quantifies business intelligence into stand-alone scores using dictionary methods.
We instruct the LLM to separate the following six determinants in our preferred specification: direct energy-related costs, input costs that pass-through indirectly via supply chains such as via energy being used to produce inputs, labour costs, demand, profit-margin adjustment, reflecting the extent to which firms absorb or pass through higher costs, and strategic complementarities, which capture how firms adjust prices in response to competitors facing similar pressures. Further, any unexplained component is assigned to an unattributed category.
The LLM analyses the full text of each report and is instructed to assign contributions only when a factor is explicitly linked to firms’ pricing decisions. For example, wage developments contribute to the labour costs component only when described as influencing price-setting and receive a zero contribution otherwise. Reports with insufficient pricing information are excluded. Averaging these report-level scores and determinant contributions produces aggregate and sector-level monthly scores and decompositions.
The LLM recovers intuitive inflation narratives.
Chart 1 shows the LLM score for realised output prices (black line), demeaned over the sample period and rescaled to CPI inflation. It tracks CPI inflation (dotted blue) closely, albeit with greater month-to-month volatility, despite a larger unexplained component following the GFC and during the recent inflation episode. The LLM score is also highly correlated with Bank staff-assigned company visit scores (yellow), but occasionally turns sooner, such as in 2016 and 2021, and unwinds more quickly thereafter, most notably in 2023.
Chart 1: LLM score of business contacts’ output price changes over the past three to 12 months, against manual Bank staff (Agents’) scores and CPI inflation
Notes: LLM output-price scores (black line), demeaned and rescaled to CPI inflation (dotted line, mean of 2.9%, max: 11.1% in October 2022). Aggregated across all reports used. Original scale for LLM score: [-100;100], Bank staff (Agents’) scores: [-5;5].
The LLM decomposes the score into determinants, shown in Chart 2, overall capturing an economically meaningful inflation anatomy over time. During the GFC, the LLM decomposition points to price pressures dominated by weak demand, followed by downward pressure from strategic pricing and unattributed factors, although this weakness was less evident in CPI inflation. Labour costs also weighed on prices in the subsequent recovery. In 2014–15 and during the Covid-19 pandemic, lower global oil and commodity prices were reflected in weaker indirect cost pressures, though less so than in CPI inflation. Following the Brexit referendum, firms’ narratives instead pointed to rising non-labour costs and a larger unattributed component, potentially reflecting heightened uncertainty.
Chart 2: LLM decomposition of business contacts’ output price changes over past three to 12 months
Pricing narratives during the 2021–23 inflation and recently
In 2021, firms reported broad-based direct and indirect cost pressures, supported by strong demand and smaller contributions from margins and strategic pricing. During 2022–23, labour costs and price-setting channels became increasingly important, particularly among service providers, as demand weakened. By 2024–25, labour costs had emerged as the dominant upward driver, coinciding with announced increases in the National Living Wage and employers’ National Insurance contributions. To get a more nuanced picture, in Chart 3 we also separately aggregate results by goods producers and service providers, rescaled to goods and services CPI inflation, respectively.
Chart 3: LLM decomposition of output price changes, by goods and services producers, from 2021
Notes: Demeaned and rescaled LLM output-price scores (black line) and decompositions (bars), aggregated separately by goods and services producers. Demeaned and rescaled based on goods inflation (mean: 2.3%) and services inflation (mean: 3.6%), respectively.
The effects from the Middle East conflict on realised pricing narratives have so far remained relatively subdued. A temporary rise in direct cost pressures was followed by some indirect cost pressures in recent months. However, these effects remain much more muted than in 2021–23 and have been accompanied by weak labour cost pressures and subdued demand. For services firms, the decomposition also suggests sustained price pressures from strategic pricing and profit margins, pointing to some continued ability to pass through higher costs into prices. However, compared with 2022–23, weaker wage pressures may provide greater scope for firms to absorb part of these pressures rather than pass them through.
Past versus expected narratives
Around turning points, what businesses expect may be more informative than what they report about the recent past. We therefore separately quantify expected output-price changes and their determinants over the next three to 12 months. Chart 4 compares past and expected contributions: both tend to move closely together, possibly reflecting both the difficulty of fully separating past and future developments within the reports and contacts’ tendency to extrapolate current conditions. The largest divergences emerged around major shocks. In early 2021, expected price pressures strengthened ahead of realised pressures, particularly through direct and indirect costs and profit margins. Since the start of the Middle East conflict, direct cost pressures have risen more sharply in expectations than in realised narratives and remain elevated, while a temporary rise in expected indirect costs appears to be feeding into realised pressures, albeit much less than in 2022–23. In the future, we aim to explore whether expected pricing narratives contain leading information for inflation.
Chart 4: Comparing contributions to past and expected output-price changes
Notes: Selected LLM decomposition contributions for past three to 12 months (solid) and next three to 12 months (dashed) narratives, 2020–26, aggregated across all reports, no rescaling, 12-month one-sided moving average.
Validating the LLM determinants against external series
Finally, the LLM-derived pricing determinants closely resemble established macroeconomic indicators. Chart 5 shows, for example, that the direct cost component tracks UK CPI energy inflation, while the demand component aligns well with a Bank of England staff estimate of the UK output gap, using an approach based on a state-space model similar to those developed for the Euro area. Given the uncertainty surrounding output-gap estimates in real time, the LLM-derived measure of firms’ demand pressures may provide a useful complementary indicator. This suggests that the decomposition recovers economically meaningful signals from firms’ narratives despite not being designed to directly identify aggregate structural inflation drivers.
Chart 5: Selected LLM decomposition contributions compared against external series
Notes: LLM direct-energy (left) and demand (right) contributions from decomposition of realised output-price changes over past three to 12 months. The LLM contributions are rescaled to the corresponding dashed-line external series. The output gap is a Bank of England staff estimate from a state-space model estimated over 1988 Q1 to 2026 Q3 and is linearly interpolated from quarterly to monthly frequency for plotting.
Sources: Bank of England staff calculations, ONS and authors’ calculations.
From qualitative intelligence to quantitative evidence for policy
We show how LLMs can transform narrative business intelligence into quantitative indicators while preserving meaningful distinctions between pricing determinants. The approach complements Bank staff’s qualitative assessments by providing a consistent framework for comparing narratives across firms, sectors and time. Beyond tracking whether firms report prices rising or falling, it helps uncover the drivers behind those changes and distinguish inflation episodes by their narrative anatomy. In the current conjuncture, for example, it suggests that cost pressures following the Middle East conflict remain concentrated in direct costs, with limited evidence of the broader propagation mechanisms seen during the 2021–23 inflation surge. More generally, it opens new possibilities for turning qualitative business intelligence into timely quantitative evidence for economic analysis and policy.
Marcus Buckmann works in the Bank’s Advanced Analytics Division, Galina Potjagailo works in the Bank’s Monetary Policy Innovation Lab and Shobhit Rawat works in the Bank’s Agency Network.
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