Rate your employer's overall qualification to deliver elite agentic AI outsourcing services to banks

The mean rating was 3.6 and the median 4.0 (n = 803 respondents). Ratings of 3 or below, the distressed range, made up 47.95% 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 = −0.58, cluster bootstrap 95% CI −0.65 to −0.50, p < 0.001 with standard errors clustered by engagement, n = 757 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 24.26, Holm p < 0.001, omega squared 0.302), respondent role (Welch F 9.84, Holm p < 0.001, omega squared 0.020) and vendor type (Welch F 3.90, Holm p = 0.010, omega squared 0.017).

Few written responses addressed the indicator: 28, nearly all from C135, the comment field on the employer's capability. Seventeen were negative and nine positive. The negative responses described a firm that had sold agentic delivery before anyone in it had run such work, with teams learning on live engagements. Most positive responses named a weakness alongside the rating. 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 385 of the 803 answers (47.95%). The engagement mean fell in the distressed range in 67 of 150 engagements.

The primary responses numbered 26, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 2 responses, 7.14% of the total, were secondary and came from 2 other questions. Respondents who wrote both a primary and a secondary response numbered 1. Negative responses made up 57.69% of primary responses (15 of 26) and 2 of the 2 secondary responses.

Ten responses said that no one in the firm had run this work before, or that the firm had sold the capability and was building it on live work. One respondent described a firm selling something it had not built yet. Another said the firm sold agent desk support to a trading bank and then looked for someone who knew how to run it. A third had seen quality assurance for agent output built after the sale on four accounts, each team inventing its own answer.

Seven responses described their own lead or team as capable or hard-working and placed the gap elsewhere, in the firm above them or in what it had built. Nine responses described training that was absent, shallow or missing the domain: a playbook two engagements old written for a call center, two slides and a recording from another project, a course on the old ticket tool. Responses to C177 and C216 placed the shortfall on the employer's side, behind tooling that worked, or with a third party outside its control.

Of the nine positive responses, eight named a weakness alongside the rating: training, banking or domain knowledge, live production experience, or the sequence in which capability was built. One positive response reported no issues.

Of the 28 responses, 17 (60.71%, 95% CI 42.28% to 78.57%) were negative and 2 (7.14%) carried the strongest negative score of −10. Positive responses numbered 9 (32.14%), and 2 were neutral. The mean written score was −1.6 (95% CI −3.6 to 0.6, n = 28 responses), and the median was −5.0.

Of the 28 responses, 28 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.51, 95% CI 0.10 to 0.84, p = 0.005, n = 28 responses). The written responses corroborated the rating. Of the 17 negative responses with a rating, 14 (82.35%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 27, with a mean rating of 3.0. The 776 respondents who rated the indicator without writing about it had a mean of 3.6. The difference, −0.6 (95% CI −1.3 to 0.1), included zero. Ratings in the distressed range made up 59.26% of the ratings from respondents who wrote (16 of 27) and 47.55% of the ratings from respondents who did not (369 of 776).

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 24.26, Holm p < 0.001, omega squared 0.302). Active engagements in Production rated highest (mean 4.5, n = 97 respondents) and canceled engagements lowest (mean 2.5, n = 306 respondents). Ratings also differed by respondent role (Welch F 9.84, Holm p < 0.001, omega squared 0.020). Portfolio directors rated highest (mean 4.6, n = 30 respondents) and individual contributors lowest (mean 3.5, n = 621 respondents). Ratings also differed by vendor type (Welch F 3.90, Holm p = 0.010, omega squared 0.017). Pure-play agentic AI firms rated highest (mean 4.2, n = 70 respondents) and IT services majors lowest (mean 3.4, n = 300 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 26 engagements with a written response (Kruskal-Wallis H 6.90, df 3, p = 0.075). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −5.0, 10 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −1.0, 10 responses). Responses from active engagements in Build-Out (5) and active engagements in Production (3) are reported as counts only.

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 16, engagement managers wrote 8 and portfolio directors wrote 4 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 twelve 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 fifteen 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 = −0.58, 95% CI −0.62 to −0.53, Holm p < 0.001, n = 757 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 135 other engagements, 124 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 15 engagements with a negative response, 8 (53.33%) were canceled, against 48 of the 135 other engagements (35.56%). The difference did not reach significance (Fisher exact p = 0.260). 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 26 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.49, p = 0.012). Across 26 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.44, p = 0.025). Of the three 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 = 0.335).

Answers clustered within engagements (ICC 0.59). Clustering reduced the 803 respondent answers to the equivalent of about 224 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, 60.71%, ranked 70th from the highest, against a median of 80.82%. Its mean written score, −1.6, ranked 70th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C123, “Rate the adequacy of your employer's investment in training its workforce.”, in 9 of the 28 responses (32.14%). C132, “Rate the depth of your employer's experience running agentic AI systems in live production, beyond pilots.” (7 responses), C120, “Rate the depth of your employer's knowledge of the financial industry.” (6 responses), C122, “Rate your employer's current supply of qualified agentic AI talent against what its engagements need.” (6 responses) and C080, “My manager is effective.” (5 responses) followed.

The most frequent theme was “Team or colleagues”, in 9 responses (32.14%). The second was “Positive with an embedded negative”, in 6 responses.

Nearly half of all ratings of the employer's qualification to deliver elite agentic outsourcing services to banks fell in the distressed range (385 of 803 answers, 47.95%). 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 = 306 respondents).

The written evidence is small. It describes capable delivery teams inside a firm that sold agentic work ahead of the experience to run it, so that the experience was acquired on the engagements themselves. 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 (28 responses on 26 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.