Rate the customer organization’s readiness to absorb the changes to daily work that agentic AI introduces

Ratings of the financial institution’s readiness to absorb the changes to daily work that agentic AI introduced (mean 3.5, median 4.0, n = 794 respondents) were associated with all four study measures. Engagements with higher ratings were canceled less often (r = −0.59, 95% CI −0.69 to −0.48, p < 0.001, n = 150 engagements). Higher ratings came with higher customer satisfaction on active engagements (r = 0.73, 95% CI 0.61 to 0.81, p < 0.001, n = 94 active engagements). Ratings rose with the engagement indicator mean, computed with this item left out (r = 0.83, 95% CI 0.78 to 0.88, p < 0.001, n = 150 engagements). Respondents who gave higher ratings reported lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.66 to −0.53, p < 0.001, n = 746 respondents). Ratings differed after Holm control by engagement status and lifecycle stage (Welch F 31.78, Holm p < 0.001, omega squared 0.338) and by respondent role (Welch F 13.65, Holm p < 0.001, omega squared 0.027).

The indicator had 26 written responses coded to it, from 25 of 850 respondents on 23 of 150 engagements. Negative responses numbered 16 of the 26 scored responses (61.54%). The strongest negative score, −10, was carried by 3 of the 26 scored responses (11.54%). Secondary responses, written in answer to questions outside the indicator’s own survey section, numbered 8 of the 26 responses (30.77%). Respondents who wrote rated the indicator higher than respondents who did not (mean 3.7, n = 24 respondents, against mean 3.5, n = 770 respondents), a difference reported with no interval, because fewer than 30 respondents who wrote gave a rating.

The study set mean was 3.5 on 794 answering respondents, with a median of 4.0. 396 answers, 49.87% of the 794, were at the distressed end. A distressed engagement mean was recorded in 65 of 150 engagements.

Of the 26 responses, 18 (69.23%) were primary and 8 (30.77%) were secondary. The secondary responses came from 8 respondents on 6 engagements, in answer to 4 questions outside the indicator’s own survey section (C029 with 4 responses, C055 with 2 responses, C030 with 1 response and C135 with 1 response). One respondent wrote both a primary and a secondary response.

Negative responses made up 7 of the 8 secondary responses, against 9 of the 18 primary responses (50.00%). The difference did not reach significance at 0.05 (Fisher exact p = 0.099, n = 26 responses).

Of the 26 scored responses, 16 (61.54%) were negative, 3 (11.54%) carried the strongest negative score and 10 (38.46%) were positive. The responses had a mean written score of −1.5 (n = 26 responses on 23 engagements). The base of scored responses was under 30, so no interval was reported for the mean.

Respondents who rated the indicator 3 or below wrote 12 of the 16 negative responses with a rating. Written score and rating showed a strong positive correlation that reached significance at 0.05 (Spearman rho 0.52, p = 0.007, n = 25 responses on 22 engagements). The base of responses with a rating was under 30, so no interval was reported for the correlation. Where a written score diverges from the same respondent’s rating, the rating is the measure of the indicator.

Respondents who wrote about the indicator rated it 0.2 points higher than respondents who did not (mean 3.7, n = 24 respondents, against mean 3.5, n = 770 respondents). The base of respondents who wrote was under 30, so no interval was reported for the difference. Ratings of 3 or below came from 13 of the 24 respondents who wrote and gave a rating (54.17%), against 383 of the 770 respondents who did not write and gave a rating (49.74%). Respondents who wrote formed a self-selected group and not a random sample of the answering base, because written responses were voluntary.

Figures are respondent level. Means are on the goodness view. 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. Engagement status and lifecycle stage differed after Holm control (Welch F 31.78, Holm p < 0.001, tested on engagement means). In Descriptive Statistics by Segment, Active, Transition/Cut-Over had the highest mean, 4.3 (n = 227 respondents), and Canceled the lowest, 2.3 (n = 305 respondents). Respondent role differed after Holm control (Welch F 13.65, Holm p < 0.001, tested on respondents). In Descriptive Statistics by Segment, Portfolio Director had the highest mean, 4.9 (n = 31 respondents), and Individual Contributor the lowest, 3.4 (n = 611 respondents). No difference held after Holm control for outsourcing model, institution type, process family, class of work and vendor type.

Scored responses came from 6 engagements in the Active, Build-Out group, 9 engagements in the Active, Transition/Cut-Over group, 1 engagement in the Active, Production group and 7 engagements in the Canceled group. A test of mean written scores across the groups, with each engagement counted once, showed no difference that reached significance at 0.05 (Kruskal-Wallis H 2.47, df 2, p = 0.291, n = 22 engagements). The test compared the Active, Build-Out group, the Active, Transition/Cut-Over group and the Canceled group. It left out the Active, Production group, which held fewer than two engagements. Groups under 10 responses are reported as counts only. Negative responses numbered 2 of 6 in the Active, Build-Out group, 5 of 9 in the Active, Transition/Cut-Over group, 3 of 3 in the Active, Production group and 6 of 8 in the Canceled group.

Across respondent roles, no test was run, because fewer than two roles reached 30 scored responses. Roles under 10 responses are reported as counts only. Among engagement managers, negative responses numbered 3 of 9. Among portfolio directors, negative responses numbered 1 of 2.

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 −0.59 (95% CI −0.69 to −0.48, Holm p < 0.001, n = 150 engagements). Of the 20 tested segment levels, 12 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 0.73 (95% CI 0.61 to 0.81, Holm p < 0.001, n = 94 engagements). Of the 16 tested segment levels, 15 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 0.83 (95% CI 0.78 to 0.88, Holm p < 0.001, n = 150 engagements). Of the 24 tested segment levels, 24 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 −0.60 (95% CI −0.65 to −0.56, Holm p < 0.001, n = 746 respondents). Of the 37 tested segment levels, 35 held after Holm control.

The cancellation contrast compared engagements with a negative written response against all other engagements. The comparison group comprised 136 other engagements, of which 127 had no scored written response coded to the indicator and 9 had only scored responses that were not negative. The contrast therefore did not test written sentiment by itself. Cancellation was recorded for 6 of the 14 engagements with a negative response (42.86%), against 50 of the 136 other engagements (36.76%). The difference did not reach significance at 0.05 (Fisher exact p = 0.773). The customer satisfaction contrast was not computed, because only 8 active engagements had a negative response, fewer than 10. The association of written sentiment itself with the study measures was tested by two correlations. The association of the engagement mean written score with cancellation did not reach significance at 0.05 (r = −0.25, p = 0.245, n = 23 engagements). The association of the respondent mean written score with intent to leave did not reach significance at 0.05 (rho = −0.17, p = 0.410, n = 25 respondents). None of the three outcome comparisons reached significance at 0.05. Both comparisons with cancellation were descriptive, because responses on canceled engagements were written after cancellation. Every comparison rested on a thin base, because fewer than 30 engagements had a scored written response.

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, 5 differed after Holm control.

Clustering reduced the 794 respondent answers to about 225 independent answers. Respondent level intervals therefore used the cluster bootstrap, and respondent level tests on intent to leave used cluster-robust errors.

No measure showed a tested null. 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. This indicator is one of the 100 components, so the engagement indicator mean was recomputed without it, and the correction changed r by 0.00, from 0.84 to 0.83.

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 followed the study’s typical pattern on every measure.

The peer set comprised 71 indicators of Part 2 with 30 or more scored responses. The indicator was outside the peer set, because it had fewer than 30 scored responses. It was added to the ranking only and did not enter the peer median. Its share of negative responses ranked 68 of 72 from the highest, tied with 1 other indicator. Its mean written score (−1.5, n = 26 responses) ranked 70 of 72 from the most negative, tied with 1 other indicator. The peer median of the mean written score was −4.7 across the 71 indicators.

The co-coded indicators tied as most frequent in the responses coded to the indicator were “Rate how well the customer’s data is normalized, with consistent formats and definitions across source systems” (C096, 6 responses) and “Rate how quickly the customer responds when this engagement raises issues that need customer action” (C109, 6 responses). The most frequent theme of the coding rulebook in the same responses was “Respondent proposes a remedy” (5 responses).

Distressed answers made up less than half of the study set (396 of 794 answers, 49.87%). Active, Transition/Cut-Over had the highest mean by lifecycle stage (mean 4.3, n = 227 respondents), and Canceled had the lowest (mean 2.3, n = 305 respondents). Portfolio Director respondents gave the highest ratings by role (mean 4.9, n = 31 respondents), and Individual Contributor respondents gave the lowest (mean 3.4, n = 611 respondents). The associations held under every robustness check run on this item (22 of 22 checks).

The written responses corroborated the rating at respondent level, with a strong positive correlation between written score and rating. Written responses were voluntary. They recorded what the respondents who wrote reported, and how common it was is measured by the ratings. The analysis of the written responses rested on 26 responses, a thin base.

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). Answers clustered within engagements, which reduced the effective number of independent answers (ICC 0.59, 794 answers worth about 225 independent answers).

Shares of written sentiment were shares of responses, not of the study set of respondents. The written score took five fixed values, so its mean and median were coarse. Responses clustered within engagements. With fewer than 30 scored responses, no interval is reported for written sentiment. Coding was performed by AI coders under a fixed rulebook. Agreement on whether a response was coded to the indicator rested on 5 responses second-coded under rulebook 0.5, with the coders agreeing on 2 of 5. Agreement on the score rested on 2 responses second-scored under rulebook 0.5, with the scores agreeing in 1 of 2. These bases of second-coded responses are too small for an indicator-level alpha.