Rate your employer's ability to price agentic work profitably when billable hours no longer measure the value delivered

The mean rating was 3.6 and the median 4.0 (n = 795 respondents). Ratings of 3 or below, the distressed range, made up 48.18% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.56, 95% CI −0.66 to −0.44, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.72, 95% CI 0.60 to 0.80, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.82 to 0.90, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.53, p < 0.001 with standard errors clustered by engagement, n = 748 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 25.03, Holm p < 0.001, omega squared 0.302), respondent role (Welch F 6.07, Holm p = 0.018, omega squared 0.013) and vendor type (Welch F 4.06, Holm p = 0.009, omega squared 0.020).

Of the 137 written responses, all but one were negative. Every response to C029 named the pricing or margin of agentic work among the hardest challenges, as a guess, an unending argument or a price that never matched the effort. Responses to C135 and C055 described engagements still priced on heads and reporting that hid the resulting loss of margin. A more negative written score came with cancellation at engagement level and with higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 383 of the 795 answers (48.18%). The engagement mean fell in the distressed range in 62 of 150 engagements.

The primary responses numbered 15, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 122 responses, 89.05% of the total, were secondary and came from 3 other questions. That secondary share ranked 1st from the highest among the 71 Part 2 indicators with 30 or more written responses, against a median of 18.92%. Respondents who wrote both a primary and a secondary response numbered 6. Negative responses made up 100.00% of primary responses (15 of 15) and 99.18% of secondary responses (121 of 122).

All 115 responses to C029 named the pricing or margin of agentic work. Fifteen called it a guess or a guessing game, and eighteen described an argument or a fight that recurred at each review or renewal. Others said the price never matched the effort, the risk carried or the compliance workload, or that the work was priced against an old FTE baseline.

The six responses to C055 described reporting that hid the cost. A flat rate per endpoint concealed how many certification cycles each processor needed. A handling-time figure cited in pricing reviews blended agent and assisted cases. Pricing disputes sat in a side spreadsheet outside the tracker the steering committee reviewed. Billing calculated from staffing levels before the agent arrived made the invoice look justified after the agent had absorbed most of the work.

In answer to C135, twelve responses said the engagement was still priced or sold on heads, seats or hours. One respondent was asked to add reviewers under a fee fixed on numbers agreed before agents took half the run, and the team absorbed the cost. Another said the work was sold by people who had never priced work that was not measured in hours. The single positive response, to C030, named margin conversations that were becoming more honest.

Of the 137 responses, 136 (99.27%, 95% CI 97.71% to 100.00%) were negative and 44 (32.12%) carried the strongest negative score of −10. Positive responses numbered 1 (0.73%), and no response was neutral. The mean written score was −6.5 (95% CI −7.0 to −6.1, n = 137 responses), and the median was −5.0.

Of the 137 responses, 129 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.23, 95% CI 0.04 to 0.41, p = 0.009, n = 129 responses). The written responses corroborated the rating. Of the 128 negative responses with a rating, 60 (46.88%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 121, with a mean rating of 3.6. The 674 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, 0.0 (95% CI −0.4 to 0.4), included zero. Ratings in the distressed range made up 46.28% of the ratings from respondents who wrote (56 of 121) and 48.52% of the ratings from respondents who did not (327 of 674).

Every figure in the segment tables is at respondent level. Segments with fewer than 30 respondents carry no interval. Segments with fewer than 10 report counts only.

Segment families recorded at engagement level were tested on engagement means. Respondent role and vendor type were tested on respondents. Where an expected count fell below 5, the test of the distressed share used Fisher's exact test for a two-by-two table, marked exact, or a Monte Carlo test, marked simulated. After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 25.03, Holm p < 0.001, omega squared 0.302). Active engagements in Production rated highest (mean 4.6, n = 97 respondents) and canceled engagements lowest (mean 2.5, n = 303 respondents). Ratings also differed by respondent role (Welch F 6.07, Holm p = 0.018, omega squared 0.013). Portfolio directors rated highest (mean 4.7, n = 29 respondents) and individual contributors lowest (mean 3.5, n = 615 respondents). Ratings also differed by vendor type (Welch F 4.06, Holm p = 0.009, omega squared 0.020). Pure-play agentic AI firms rated highest (mean 4.2, n = 72 respondents) and BPO specialists lowest (mean 3.3, n = 184 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment differed by engagement status and lifecycle stage across the 87 engagements with a written response (Kruskal-Wallis H 27.73, df 3, p < 0.001). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −8.3, 45 responses) and responses from active engagements in Production the least negative (mean −5.2, 23 responses).

The role test compared individual contributors and engagement managers, the roles with 30 or more responses. Written sentiment did not differ (Kruskal-Wallis H 0.49, p = 0.484, n = 121 respondents): the mean written score was −6.6 for individual contributors (90 responses) and −6.3 for engagement managers (36 responses). Portfolio directors wrote 11 responses, reported as counts only.

Cancellation rate is the share of engagements canceled by the study date, measured at engagement level (150 engagements, 56 canceled). Canceled engagements were rated after cancellation. The association is therefore a descriptive contrast and not a forecast of cancellation.

Across the study set, higher ratings came with less cancellation (r = −0.56, 95% CI −0.66 to −0.44, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in nine of the 20 segment levels tested.

Customer satisfaction is the percentage recorded against each engagement. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

Across the active engagements, higher ratings came with higher customer satisfaction (r = 0.72, 95% CI 0.60 to 0.80, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in fourteen of the 16 segment levels tested.

The engagement indicator mean is the mean of an engagement's ratings across the rated indicators, measured at engagement level (150 engagements). The indicator is one of the rated indicators. Each comparison therefore uses the mean recomputed without it.

Higher ratings came with a higher engagement indicator mean (r = 0.87, 95% CI 0.82 to 0.90, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-four of the 24 segment levels tested.

Intent to leave is the answer to C019, “How likely are you to leave your current job in the next 12 months?”, rated 1 to 10. It is measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

Respondents who gave higher ratings reported lower intent to leave (r = −0.60, 95% CI −0.65 to −0.55, Holm p < 0.001, n = 748 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 63 other engagements, 63 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 87 engagements with a negative response, 32 (36.78%) were canceled, against 24 of the 63 other engagements (38.10%). The difference did not reach significance (Fisher exact p = 1.000). Customer satisfaction among active engagements did not differ between the two groups (mean 63.09% on 55 engagements against 65.51% on 39, Welch t −0.99, p = 0.323).

Two correlations test written sentiment directly. Across the 87 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.56, p < 0.001). Across 129 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.48, p < 0.001). Of the four comparisons, two reached significance at 0.05.

The four associations were compared in pairs on their engagement-level forms, each oriented so that a higher value is the better outcome, across the 150 engagements with all four measures. After Holm correction, five of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.282).

Answers clustered within engagements (ICC 0.60). Clustering reduced the 795 respondent answers to the equivalent of about 222 independent answers. Respondent-level intervals therefore used the cluster bootstrap. Respondent-level tests of intent to leave used standard errors that allow for clustering by engagement.

No association was equivalent to zero. Each of the four differed from zero. The detection floor shown is the study floor at 150 engagements. Intent to leave was tested on its effective sample size.

Each change was computed before rounding and may differ by 0.01 from the difference between the two printed values.

All 22 checks held. A check holds when its test falls on the same side of 0.05 as the primary test. Where the primary test falls below 0.05, the check must also keep the sign of the primary correlation. The thin-base check removes the engagements in any level of outsourcing model or process family with fewer than 30 engagements. Institution type is not used for that check, because every one of its levels has fewer than 30 engagements.

Each of the four measures was computed from data the indicator does not contain. Each result is therefore a finding and not an arithmetic consequence. The indicator is one of the rated indicators, and the engagement indicator mean was recomputed without it. The correction changed r by 0.00, from 0.87 to 0.87.

Each deviation was computed before rounding and may differ by 0.01 from the difference between the two printed values.

The comparison covered 150 engagements. The indicator followed the study's typical pattern on all four measures, and no deviation held after Holm correction.

Among the 71 Part 2 indicators with 30 or more written responses, the indicator's share of negative responses, 99.27%, ranked 1st from the highest, against a median of 80.82%. Its mean written score, −6.5, ranked 9th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C116, “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today.”, in 29 of the 137 responses (21.17%). C098, “Rate how well the customer's legacy systems support the integration this engagement requires.” (19 responses), C044, “What is the pricing model for this engagement?” (17 responses), C122, “Rate your employer's current supply of qualified agentic AI talent against what its engagements need.” (15 responses) and C126, “Rate the maturity of your employer's playbook for taking agentic use cases from concept to governed production.” (15 responses) followed.

The most frequent theme was “Liability or regulatory accountability unclear”, in 7 responses (5.11%). The second was “Documentation lags the build”, in 5 responses.

Nearly half of all ratings of the employer's ability to price agentic work profitably fell in the distressed range (383 of 795 answers, 48.18%). The rating was associated with all four study measures, and every association held under each of the 22 robustness checks. Canceled engagements rated far below the three active stages (mean 2.5, n = 303 respondents).

The written evidence is nearly uniform. Respondents described pricing built for human effort applied to work in which human effort falls, with the gap absorbed by the delivery team or hidden in the reporting. The written score tracked the study measures directly: a more negative engagement mean came with cancellation, and a more negative respondent mean came with higher intent to leave.

The evidence is the reported experience of the vendor's delivery teams, collected in a single cross-section. The associations reported here are not causal.

Tests by respondent role and by vendor type treated respondents as independent. The section on clustering reports how far clustering within engagements reduced the information in respondent-level figures.

Customer management executives gave 1 answers, a base of anecdote strength.

Shares of written sentiment are shares of responses and not of the study set of respondents. The written score takes five fixed values, so its mean and median are coarse. Responses clustered within engagements (137 responses on 87 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.