My manager knows agentic AI well

The mean rating was 4.7 and the median 5.0 (n = 803 respondents). Ratings of 3 or below, the distressed range, made up 30.14% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.65, 95% CI −0.73 to −0.54, 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 (r = 0.82, 95% CI 0.76 to 0.87, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.67, cluster bootstrap 95% CI −0.72 to −0.61, p < 0.001 with standard errors clustered by engagement, n = 751 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 45.00, Holm p < 0.001, omega squared 0.412) and respondent role (Welch F 16.41, Holm p < 0.001, omega squared 0.027).

All 10 written responses were negative, nine from C135, the comment field on the employer, and one from C177, on the AI tools. Eight said no one above the respondent knew the agents or had run them, and four described a question passed from lead to someone else that never came back. No written outcome test reached significance.

Ratings of 3 or below, the distressed range, accounted for 242 of the 803 answers (30.14%). The engagement mean fell in the distressed range in 29 of 150 engagements.

All 10 responses were secondary. Each answered a question outside the indicator's survey section and addressed the indicator in its content. They came from 2 questions.

Eight responses said no one above the respondent, in the line or in the company, knew the agents or had run them in production: one lead learned the work from the vendor's documents, one team solved problems from search, and one management chain swung between treating AI as overpromised and as underrated. One response came from a lead whose own line management had never used the tools, and whose best people asked what they were learning.

Four responses described a question, such as what a confidence number meant or why the cost of a run varied, passed from the lead to someone else and never answered. Three said the status reported upward showed the work as handled or green when the respondent saw otherwise. One respondent with more AI experience than the management team wrote a proposal that went unread.

Of the 10 responses, 10 (100.00%, 95% CI 100.00% to 100.00%) were negative and 3 (30.00%) carried the strongest negative score of −10. Positive responses numbered 0 (0.00%), and no response was neutral. The mean written score was −6.5 (95% CI −8.0 to −5.0, n = 10 responses), and the median was −5.0.

Of the 10 responses, 9 came from respondents who also rated the indicator, too few for a correlation. Of the 9 negative responses with a rating, 6 came from respondents whose rating was in the distressed range.

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 45.00, Holm p < 0.001, omega squared 0.412). Active engagements in Production rated highest (mean 6.0, n = 99 respondents) and canceled engagements lowest (mean 3.2, n = 303 respondents). Ratings also differed by respondent role (Welch F 16.41, Holm p < 0.001, omega squared 0.027). Portfolio directors rated highest (mean 6.1, n = 31 respondents) and individual contributors lowest (mean 4.5, n = 621 respondents). Outsourcing model, institution type, process family, class of work and vendor type showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 9 engagements with a written response (Kruskal-Wallis H 1.31, df 2, p = 0.518). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (2), active engagements in Transition/Cut-Over (2), active engagements in Production (1) and canceled engagements (5).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 8 and engagement managers wrote 2 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 = −0.65, 95% CI −0.73 to −0.54, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in seventeen 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 thirteen 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 does not enter the mean.

Higher ratings came with a higher engagement indicator mean (r = 0.82, 95% CI 0.76 to 0.87, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in twenty-three 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 = −0.67, 95% CI −0.70 to −0.63, Holm p < 0.001, n = 751 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 140 other engagements, 140 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 10 engagements with a negative response, 5 (50.00%) were canceled, against 51 of the 140 other engagements (36.43%). The difference did not reach significance (Fisher exact p = 0.502). The comparison of customer satisfaction was not computed, because one of the two groups held too few active engagements.

Two correlations test written sentiment directly. Across the 10 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r 0.22, p = 0.545). Across 10 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho 0.23, p = 0.521). Of the three comparisons, none 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, three of the six pairs differed. The association with the engagement indicator mean was the strongest and the association with cancellation the weakest.

Answers clustered within engagements (ICC 0.65). Clustering reduced the 803 respondent answers to the equivalent of about 211 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.

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 11 Part 4 indicators with 30 or more written responses, the indicator's share of negative responses, 100.00%, ranked 1st from the highest, against a median of 33.33%. Its mean written score, −6.5, ranked 1st from the most negative, against a median of 0.8.

The indicator most often present in the same responses was C123, “Rate the adequacy of your employer's investment in training its workforce.”, in 6 of the 10 responses (60.00%). C131, “Rate the depth of your employer's knowledge of agentic AI.” (4 responses), C017, “Where did you gain your AI knowledge?” (2 responses), C020, “How happy is your extended team with the current engagement?” (2 responses) and C157, “Rate the factual reliability of the AI tools' outputs on this engagement before any human review.” (2 responses) followed.

The most frequent theme was “Raised it, nothing came back”, in 3 responses (30.00%). The second was “Practice invented on the live engagement”, in 3 responses.

About three in ten of all ratings of whether the manager knows agentic AI well fell in the distressed range (242 of 803 answers, 30.14%). 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 3.2, n = 303 respondents).

The written evidence describes a chain of management that could not answer the questions the agents raised, so that questions went up and did not come back and the respondents learned on live work. No written outcome test reached significance.

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 (10 responses on 10 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.

The written responses number fewer than 30, so their shares and the written-sentiment tests rest on a small base.