How many agentic AI engagements does your employer run for banks today?

Respondents reported how many agentic AI engagements their employer ran for financial institutions, with the mean close to the median (mean 24.6, median 25.0, n = 328 respondents). The association with cancellation was equivalent to zero (TOST p < 0.001). The association with customer satisfaction on active engagements was equivalent to zero (TOST p 0.018). The association with the engagement indicator mean was equivalent to zero (TOST p < 0.001). The association with intent to leave was equivalent to zero (TOST p 0.001). The count did not differ after Holm control by any segment family (Holm p 0.683 or higher in every family).

The study set mean was 24.6 on 328 answering respondents, with a median of 25.0.

Figures are respondent level. Means are on the scale as recorded. A level under 30 prints no interval and a level under 10 prints counts only.

Engagement families were tested on engagement means, and respondent role and vendor type were tested on respondents. A distressed p marked exact is a Fisher exact test for a two by two table, and one marked simulated is a Monte Carlo test run until its p is stable to the third decimal, both used where an expected count is under 5. No difference held after Holm control for outsourcing model, institution type, process family, class of work, engagement status and lifecycle stage, respondent role and vendor type.

The share of engagements canceled on the study date, measured at engagement level (base 150 engagements, 56 canceled). The association is a descriptive contrast, inflated because canceled engagements were rated after cancellation, and is not predictive.

The study set r was 0.02 (95% CI −0.14 to 0.18, Holm p 1.000, n = 150 engagements). The association did not hold after Holm control. Equivalence Tests of Null Results found it equivalent to zero inside the bound. Of the 20 tested segment levels, 0 held after Holm control.

The satisfaction score recorded against each engagement on a 100-point scale. The primary estimate uses the 94 active engagements. A descriptive contrast uses all 150 engagements.

The study set r was 0.09 (95% CI −0.11 to 0.29, Holm p 1.000, n = 94 engagements). The association did not hold after Holm control. Equivalence Tests of Null Results found it equivalent to zero inside the bound. Of the 16 tested segment levels, 0 held after Holm control.

The mean of an engagement’s 100 indicator ratings, measured at engagement level (base 150 engagements). Where this indicator is one of the 100, the mean is recomputed without it.

The study set r was 0.03 (95% CI −0.13 to 0.19, Holm p 1.000, n = 150 engagements). The association did not hold after Holm control. Equivalence Tests of Null Results found it equivalent to zero inside the bound. Of the 24 tested segment levels, 0 held after Holm control.

The respondent’s stated likelihood of leaving the current job within 12 months, rated 1 to 10 (C019). Measured at respondent level on up to 798 respondents, with the engagement-level form reported alongside.

The study set r was −0.05 (95% CI −0.16 to 0.06, Holm p 1.000, n = 312 respondents). The association did not hold after Holm control. Equivalence Tests of Null Results found it equivalent to zero inside the bound. Of the 37 tested segment levels, 0 held after Holm control.

Pairs were compared on the engagement level form of every measure, each turned so that a higher value was the better outcome, on the 150 engagements where the indicator and all four measures existed. Of the 6 pairs, 0 differed after Holm control.

Clustering reduced the 328 respondent answers to about 150 independent answers. Respondent level intervals therefore used the cluster bootstrap, and respondent level tests on intent to leave used cluster-robust errors.

Tested null, equivalent to zero: cancellation rate, customer satisfaction, engagement indicator mean and intent to leave. No result was inconclusive. The detection floor column is the study floor at 150 engagements. Intent to leave was tested on its effective n.

Each change was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

Every check held. A check holds when its test lands on the same side of 0.05 as the primary test and, where the primary test is below 0.05, its r keeps the primary sign. Thin-base rule: engagements in any level under 30 engagements of outsourcing model or process family removed. Institution type is not used, because every one of its levels is under 30.

Each of the four measures was computed from data this indicator does not contain, so each result is a finding rather than an arithmetic consequence.

Each deviation was computed before rounding, so it can differ by 0.01 from the difference of the two printed values.

The comparison ran on 150 engagements. The indicator departed from the study’s typical pattern on cancellation rate, customer satisfaction, engagement indicator mean and intent to leave.

The results held under every robustness check run on this item (22 of 22 checks). The number of agentic AI engagements the employer ran for financial institutions showed no association with the four study measures.

The evidence is reported experience: what the delivery team said about its own engagement, gathered in one cross-section. The figures showed associations, not causes. Indicators about the financial institution were reported at second hand by vendor staff. Cancellation is a descriptive contrast, because canceled engagements were rated after they ended.

Tests across respondent role and vendor type were run at respondent level and treated respondents as independent. The section Clustering and Effective Sample Size states whether, and by how much, clustering within engagements reduced the information in respondent level figures.

Customer Management Executive answers were at anecdote strength (n = 2).