My manager knows how to manage agentic AI outsourcing engagements

The mean rating was 4.8 and the median 5.0 (n = 800 respondents). Ratings of 3 or below, the distressed range, made up 30.13% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.54, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.73, 95% CI 0.62 to 0.81, p < 0.001, n = 94 engagements), a higher engagement indicator mean (r = 0.84, 95% CI 0.78 to 0.88, 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 = 752 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 45.98, Holm p < 0.001, omega squared 0.425), respondent role (Welch F 6.64, Holm p = 0.011, omega squared 0.011) and vendor type (Welch F 3.95, Holm p = 0.011, omega squared 0.019).

A single written response addressed the indicator, from C135, the comment field on the employer, with the strongest negative score of −10. It contrasted the engagement with an earlier account, where the manager had done the job for years and knew when a batch was bad, with one where the people above had never run agent work and pushed instead.

Ratings of 3 or below, the distressed range, accounted for 241 of the 800 answers (30.13%). The engagement mean fell in the distressed range in 28 of 150 engagements.

The single response was secondary. It answered C135, “Add any comments on your employer that the ratings in this section do not capture.”. The respondent compared the engagement with an earlier invoice-processing account that had a real platform, a team to call and a manager who had done the job himself. Here the team had a tool and a chat channel with no owner, training was a recorded webinar from the year before that did not match the tool in use, and the people above had never run such work: they asked how long a task took and put a different number in the deck. The respondent described the team as the first to run agents on live expense data, learning on the financial institution's work, and said the people were fine to work with.

The single response carried −10. With fewer than 10 responses, the distribution is reported as counts only.

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 45.98, Holm p < 0.001, omega squared 0.425). Active engagements in Production rated highest (mean 6.3, n = 98 respondents) and canceled engagements lowest (mean 3.3, n = 300 respondents). Ratings also differed by respondent role (Welch F 6.64, Holm p = 0.011, omega squared 0.011). Portfolio directors rated highest (mean 5.9, n = 30 respondents) and individual contributors lowest (mean 4.7, n = 618 respondents). Ratings also differed by vendor type (Welch F 3.95, Holm p = 0.011, omega squared 0.019). Consulting firms rated highest (mean 5.2, n = 111 respondents) and BPO specialists lowest (mean 4.3, n = 187 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

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 = −0.64, 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 = 0.73, 95% CI 0.62 to 0.81, 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 does not enter the mean.

Higher ratings came with a higher engagement indicator mean (r = 0.84, 95% CI 0.78 to 0.88, 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 = −0.67, 95% CI −0.70 to −0.62, Holm p < 0.001, n = 752 respondents). The association held after Holm correction in thirty-six of the 37 segment levels tested.

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 = 0.763).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 800 respondent answers to the equivalent of about 212 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.

The response also addressed C123 and C124, on the employer's training, C129, on its platforms, and C131 and C132, on its knowledge of agentic AI and its experience running agents in production. It carried four themes, among them “Comparison to a prior engagement” and “Practice invented on the live engagement”.

About three in ten of all ratings of whether the manager knows how to manage agentic AI outsourcing engagements fell in the distressed range (241 of 800 answers, 30.13%). 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 3.3, n = 300 respondents).

The single written response is an anecdote. It places the difference in a manager who had done the work before and could therefore judge it.

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 2 answers, a base of anecdote strength.

Counts of written responses describe the respondents who wrote and not the study set of respondents. With one responses, the written evidence supports counts only and no statistic.