Rate how well agent updates made by one firm become visible to the other firm before they take effect

The mean rating was 3.6 and the median 4.0 (n = 803 respondents). Ratings of 3 or below, the distressed range, made up 49.07% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.62, 95% CI −0.71 to −0.51, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.60, 95% CI 0.45 to 0.72, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.84, 95% CI 0.79 to 0.88, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.72 to −0.59, p < 0.001 with standard errors clustered by engagement, n = 756 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 39.75, Holm p < 0.001, omega squared 0.388) and respondent role (Welch F 6.45, Holm p = 0.016, omega squared 0.015).

Written responses addressing the indicator numbered 48, from 48 respondents on 44 engagements. Of these, 38 were negative and 10 positive. Most said that a change to an agent made by one firm reached the other only after it took effect, discovered from the outputs, from a failure, or at the next weekly meeting. Several said the vendor's own side did the same. On engagements where the written responses were more negative, cancellation was more common, and respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 394 of the 803 answers (49.07%). The engagement mean fell in the distressed range in 66 of 150 engagements.

The primary responses numbered 46, all written in answer to C156, “Add any comments on the joint management of this engagement that the ratings in this section do not capture.” The remaining 2 responses, 4.17% of the total, were secondary and came from 2 other questions. That secondary share ranked 51st from the highest among the 71 Part 2 indicators with 30 or more written responses, against a median of 18.92%. Respondents who wrote both a primary and a secondary response numbered 0. Negative responses made up 78.26% of primary responses (36 of 46) and 2 of the 2 secondary responses.

Among the 46 responses to C156, the comment field on joint management, 30 negative responses stated that an agent change made by one firm became known to the other only after it took effect. The discoveries were concrete: an assistant that began closing password resets on its own, forty help desk tickets auto-closed in an hour after a rule change on a Friday, a run of alerts shut overnight and unnoticed for three days, knowledge articles swapped over a weekend so that the assistant fed staff the old instructions. One respondent wrote only that the other firm kept updating its agents and the vendor found out when its systems crashed.

The failure ran both ways. Eight responses stated that the vendor's own changes reached the institution late as well, at the weekly review or the monthly meeting, after the change was already running against live work. Five described a private sheet, spreadsheet or group chat kept to track changes, in one case mirrored by an equivalent list on the institution's side that neither firm could see. Against these, five positive responses said both firms now saw agent changes before they took effect, and placed the remaining weakness in stopping an agent or in ownership. One positive response noted a separate gap: formal changes were announced, but behaviour that drifted between releases had no forum at all. In answer to C055, the question on how the vendor's performance reports and dashboards are manipulated, one respondent described a threshold rule that auto-approved transactions the agent had flagged as low-confidence and was never disclosed to the institution.

Of the 48 responses, 38 (79.17%, 95% CI 66.67% to 91.11%) were negative and 15 (31.25%) carried the strongest negative score of −10. Positive responses numbered 10 (20.83%), and no response was neutral. The mean written score was −4.5 (95% CI −6.0 to −2.9, n = 48 responses), and the median was −5.0.

Of the 48 responses, 47 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.50, 95% CI 0.28 to 0.68, p < 0.001, n = 47 responses). The written responses corroborated the rating. Of the 38 negative responses with a rating, 25 (65.79%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 47, with a mean rating of 3.4. The 756 respondents who rated the indicator without writing about it had a mean of 3.6. The difference, −0.2 (95% CI −0.8 to 0.3), included zero. Ratings in the distressed range made up 59.57% of the ratings from respondents who wrote (28 of 47) and 48.41% of the ratings from respondents who did not (366 of 756).

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 39.75, Holm p < 0.001, omega squared 0.388). Active engagements in Build-Out rated highest (mean 4.6, n = 166 respondents) and canceled engagements lowest (mean 2.2, n = 306 respondents). Ratings also differed by respondent role (Welch F 6.45, Holm p = 0.016, omega squared 0.015). Portfolio directors rated highest (mean 4.8, n = 30 respondents) and individual contributors lowest (mean 3.5, n = 621 respondents). Outsourcing model, institution type, process family, class of work and vendor type showed no difference.

Written sentiment differed by engagement status and lifecycle stage across the 44 engagements with a written response (Kruskal-Wallis H 11.40, df 3, p = 0.010). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −8.3, 12 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −2.9, 19 responses). Responses from active engagements in Build-Out (7) 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 33, engagement managers wrote 10 and portfolio directors wrote 5 responses.

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 = −0.62, 95% CI −0.71 to −0.51, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in sixteen 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 = 0.60, 95% CI 0.45 to 0.72, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in ten 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 = 0.84, 95% CI 0.79 to 0.88, 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 = −0.66, 95% CI −0.70 to −0.62, Holm p < 0.001, n = 756 respondents). The association held after Holm correction in thirty-seven of the 37 segment levels tested.

Two comparisons set the engagements with at least one negative response against all other engagements. Of the 114 other engagements, 106 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 36 engagements with a negative response, 12 (33.33%) were canceled, against 44 of the 114 other engagements (38.60%). The difference did not reach significance (Fisher exact p = 0.693). Customer satisfaction among active engagements did not differ between the two groups (mean 63.83% on 24 engagements against 64.19% on 70, Welch t −0.12, p = 0.902).

Two correlations test written sentiment directly. Across the 44 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.45, p = 0.002). Across 47 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.39, p = 0.006). Of the four 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 customer satisfaction the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.809).

Answers clustered within engagements (ICC 0.66). Clustering reduced the 803 respondent answers to the equivalent of about 208 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 0.84 to 0.84.

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, 79.17%, ranked 41st from the highest, against a median of 80.82%. Its mean written score, −4.5, ranked 42nd from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C136, “Rate how effectively the customer and vendor jointly manage this engagement now that agents perform part of the work.”, in 12 of the 48 responses (25.00%). C144, “Rate how well the joint change control process keeps pace with the speed of agent behavior changes.” (10 responses), C138, “Rate how clearly ownership of each workflow step is assigned when agents, vendor staff, and customer staff work in the same process.” (8 responses), C139, “Rate how clearly accountability lands on one firm when an agent's action causes a problem in this engagement.” (6 responses) and C143, “Rate how quickly the two firms together can stop an agent that is behaving badly in live operations.” (6 responses) followed.

The most frequent theme was “Detection only by failure”, in 14 responses (29.17%). The second was “Raised it, nothing came back”, in 6 responses.

Nearly half of all ratings of how well agent updates made by one firm become visible to the other before they take effect fell in the distressed range (394 of 803 answers, 49.07%). 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 2.2, n = 306 respondents).

The written responses describe change control between two firms that each altered a shared agent and told the other afterward. The usual means of discovery was the output itself. Respondents acknowledged the practice on the vendor's side as well as the institution's. One described a modest remedy: a short note in the daily call before the change took effect.

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 (48 responses on 44 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.