Rate your employer's ability to manage agentic AI outsourcing engagements end to end

The mean rating was 3.7 and the median 4.0 (n = 787 respondents). Ratings of 3 or below, the distressed range, made up 46.25% 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 0.61 to 0.81, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.86, 95% CI 0.81 to 0.90, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.61, cluster bootstrap 95% CI −0.68 to −0.53, p < 0.001 with standard errors clustered by engagement, n = 738 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 25.84, Holm p < 0.001, omega squared 0.299) and vendor type (Welch F 3.31, Holm p = 0.040, omega squared 0.013).

Few written responses addressed the indicator: 17, nearly all from C135, the comment field on the employer's capability. Eleven were negative and six positive. The negative responses described teams placed on engagements without the preparation to run them, and an absence of anyone who owned the engagement end to end. Despite the small base, a more negative written score came with cancellation and with higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 364 of the 787 answers (46.25%). The engagement mean fell in the distressed range in 65 of 150 engagements.

The primary responses numbered 16, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 1 responses, 5.88% of the total, were secondary and came from 1 other questions. Respondents who wrote both a primary and a secondary response numbered 0. Negative responses made up 68.75% of primary responses (11 of 16) and 0 of the 1 secondary responses.

Six negative responses described teams placed on the engagement without the time, plan, experience or domain knowledge to run it. A screening queue for KYC work was stood up in three weeks. A solution specialist set a ticket agent live on joiner requests on a Friday afternoon and then left on holiday, and by Monday dozens of tickets sat with the wrong approvers. Two new staff joined a settlement shift after an afternoon of training, and the respondent redid their exception files before the cutoff while reports up the line showed the queue as green.

Five responses said that no one owned the engagement end to end or would answer for an agent's error. One respondent asked twice who would carry the cost if an agent pushed a bad deployment into the financial institution's live environment, and received a workshop invitation each time. Another asked that one person own the whole engagement in place of four leads each owning a piece.

The six positive responses included one, to C030, that described the engagement as a model for agentic delivery. The five positive responses to C135 each qualified the rating with a gap: no settlements knowledge, no one senior to explain the agent's behaviour, thin industry depth, engineers taken from other accounts to staff each new win, or an unanswered question of who pays for a bad deployment.

Of the 17 responses, 11 (64.71%, 95% CI 41.18% to 88.24%) were negative and 4 (23.53%) carried the strongest negative score of −10. Positive responses numbered 6 (35.29%), and no response was neutral. The mean written score was −2.4 (95% CI −5.3 to 0.9, n = 17 responses), and the median was −5.0.

Of the 17 responses, 17 came from respondents who also rated the indicator. Among them, the written score did not track the rating (Spearman rho 0.43, 95% CI −0.03 to 0.79, p = 0.081, n = 17 responses). Of the 11 negative responses with a rating, 9 (81.82%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 17, with a mean rating of 3.2. The 770 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, −0.5 (95% CI −1.3 to 0.5), included zero. Ratings in the distressed range made up 64.71% of the ratings from respondents who wrote (11 of 17) and 45.84% of the ratings from respondents who did not (353 of 770).

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 25.84, Holm p < 0.001, omega squared 0.299). Active engagements in Production rated highest (mean 4.6, n = 94 respondents) and canceled engagements lowest (mean 2.5, n = 294 respondents). Ratings also differed by vendor type (Welch F 3.31, Holm p = 0.040, omega squared 0.013). Pure-play agentic AI firms rated highest (mean 4.3, n = 69 respondents) and BPO specialists lowest (mean 3.5, n = 179 respondents). Outsourcing model, institution type, process family, class of work and respondent role showed no difference.

Written sentiment differed by engagement status and lifecycle stage across the 17 engagements with a written response (Kruskal-Wallis H 7.31, df 2, p = 0.026). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Transition/Cut-Over (4), active engagements in Production (3) and canceled engagements (10).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 8, engagement managers wrote 6 and portfolio directors wrote 3 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 0.61 to 0.81, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in eleven 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 = 0.86, 95% CI 0.81 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 = −0.61, 95% CI −0.65 to −0.56, Holm p < 0.001, n = 738 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 139 other engagements, 133 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 11 engagements with a negative response, 9 (81.82%) were canceled, against 47 of the 139 other engagements (33.81%). The difference reached significance (Fisher exact p = 0.002). 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 17 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.68, p = 0.003). Across 17 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.59, p = 0.013). Of the three comparisons, all 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 = 0.700).

Answers clustered within engagements (ICC 0.61). Clustering reduced the 787 respondent answers to the equivalent of about 219 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 0.86 to 0.86.

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, 64.71%, ranked 67th from the highest, against a median of 80.82%. Its mean written score, −2.4, ranked 66th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C122, “Rate your employer's current supply of qualified agentic AI talent against what its engagements need.”, in 4 of the 17 responses (23.53%). C116, “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today.” (3 responses), C120, “Rate the depth of your employer's knowledge of the financial industry.” (3 responses), C123, “Rate the adequacy of your employer's investment in training its workforce.” (3 responses) and C132, “Rate the depth of your employer's experience running agentic AI systems in live production, beyond pilots.” (3 responses) followed.

The most frequent theme was “Comparison to a prior engagement”, in 3 responses (17.65%). The second was “Raised it, nothing came back”, in 2 responses.

Nearly half of all ratings of the employer's ability to manage agentic outsourcing engagements end to end fell in the distressed range (364 of 787 answers, 46.25%). 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 = 294 respondents).

The written evidence is small and divided. The negative responses describe engagements started without preparation and run without a single owner. The positive responses describe engagements that ran well with a named gap. Even on this base, written sentiment was associated with cancellation and with intent to leave in the same direction as the rating.

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.

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 (17 responses on 17 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.