Rate how well third-party licenses on this engagement cover work done by agents rather than named human users

The mean rating was 4.6 and the median 5.0 (n = 799 respondents). Ratings of 3 or below, the distressed range, made up 31.91% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.63, 95% CI −0.72 to −0.53, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.69, 95% CI 0.57 to 0.78, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.88, 95% CI 0.84 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.64, cluster bootstrap 95% CI −0.70 to −0.58, 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 37.49, Holm p < 0.001, omega squared 0.390) and respondent role (Welch F 11.15, Holm p < 0.001, omega squared 0.022).

All 36 written responses came from C198, the comment field on the third-party ecosystem. Thirty were negative and four positive, and fourteen carried the strongest negative score of −10. Respondents described licenses written per named human user, agents run under one person's seat, and a question about what an agent owed that was raised and never answered. No written outcome test reached significance.

Ratings of 3 or below, the distressed range, accounted for 255 of the 799 answers (31.91%). The engagement mean fell in the distressed range in 34 of 150 engagements.

The primary responses numbered 36, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” No response from any other question addressed the indicator, so every response was primary.

Twelve responses said no one could say what license the agent ran under or what it owed, or that the question had been raised with procurement or the provider and not answered: procurement shrugged and the provider said maybe, nobody at a card processor could say what a named human seat meant for an agent logging in fifty times a night, and one engagement ended before an answer came. Four described the agent run under one person's seat, login or service account, in one case rotated when the vendor asked questions, and in another on a seat that the respondent's legal team had reviewed but that the financial institution's procurement had not, with no one willing to raise it during a renewal.

Four responses described a cost or interruption: a knowledge base that locked the team out for two days after an overnight agent run, a request to buy more seats before another queue could be added, and two warnings of a bill no one had modeled, one expecting a true-up about eighteen months later that no one had budgeted for. The four positive responses included three saying the licenses had been checked or renegotiated to cover machine access, one crediting procurement and noting that this had been the obstacle on every earlier attempt.

Of the 36 responses, 30 (83.33%, 95% CI 70.27% to 94.59%) were negative and 14 (38.89%) carried the strongest negative score of −10. Positive responses numbered 4 (11.11%), and 2 were neutral. The mean written score was −5.4 (95% CI −7.0 to −3.7, n = 36 responses), and the median was −5.0.

Of the 36 responses, 36 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.42, 95% CI 0.10 to 0.68, p = 0.010, n = 36 responses). The written responses corroborated the rating. Of the 30 negative responses with a rating, 16 (53.33%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 36, with a mean rating of 3.8. The 763 respondents who rated the indicator without writing about it had a mean of 4.6. The difference, −0.8 (95% CI −1.6 to −0.1), excluded zero. Ratings in the distressed range made up 44.44% of the ratings from respondents who wrote (16 of 36) and 31.32% of the ratings from respondents who did not (239 of 763).

The respondents who wrote came disproportionately from the distressed end of the scale. The written evidence therefore gives more weight to the respondents who rated the indicator worst.

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 37.49, Holm p < 0.001, omega squared 0.390). Active engagements in Transition/Cut-Over rated highest (mean 5.5, n = 231 respondents) and canceled engagements lowest (mean 3.2, n = 302 respondents). Ratings also differed by respondent role (Welch F 11.15, Holm p < 0.001, omega squared 0.022). Portfolio directors rated highest (mean 5.9, n = 31 respondents) and individual contributors lowest (mean 4.5, n = 617 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 34 engagements with a written response (Kruskal-Wallis H 5.32, df 3, p = 0.150). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (5), active engagements in Transition/Cut-Over (9), active engagements in Production (5) and canceled engagements (17).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 18, engagement managers wrote 10 and portfolio directors wrote 8 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 = −0.63, 95% CI −0.72 to −0.53, 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 = 0.69, 95% CI 0.57 to 0.78, 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 = 0.88, 95% CI 0.84 to 0.91, 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 = −0.64, 95% CI −0.68 to −0.60, Holm p < 0.001, n = 752 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 122 other engagements, 116 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 28 engagements with a negative response, 15 (53.57%) were canceled, against 41 of the 122 other engagements (33.61%). The difference did not reach significance (Fisher exact p = 0.055). Customer satisfaction among active engagements did not differ between the two groups (mean 62.46% on 13 engagements against 64.36% on 81, Welch t −0.59, p = 0.566).

Two correlations test written sentiment directly. Across the 34 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.32, p = 0.064). Across 34 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho −0.28, p = 0.108). Of the four 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, 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 = 0.374).

Answers clustered within engagements (ICC 0.62). Clustering reduced the 799 respondent answers to the equivalent of about 218 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 0.88 to 0.88.

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, 83.33%, ranked 31st from the highest, against a median of 80.82%. Its mean written score, −5.4, ranked 26th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.”, in 8 of the 36 responses (22.22%). C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.” (8 responses), C186, “Rate the availability of true machine interfaces, rather than screens built for humans, across the third-party systems the agents must use.” (8 responses), C179, “Rate the quality of the fallback the engagement has ready when a critical third-party feed, platform, or model stops working.” (6 responses) and C189, “Rate how well third-party rate limits and quotas hold up under the volume the agents on this engagement generate.” (6 responses) followed.

The most frequent theme was “Raised it, nothing came back”, in 5 responses (13.89%). The second was “Detection only by failure”, in 5 responses.

About a third of all ratings of how well third-party licenses covered agent work fell in the distressed range (255 of 799 answers, 31.91%). 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 = 302 respondents).

The written evidence identifies an unpriced liability. Licenses sold per named human user were being consumed by agents, and neither the vendor, the financial institution's procurement nor the providers had settled what was owed. Respondents expected the question to surface as an unbudgeted charge once the work scaled, and the few engagements that had renegotiated licenses for machine access reported the problem resolved.

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 (36 responses on 34 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.