Rate how effectively the two firms manage issues arising from agent work as one team rather than through two separate processes

The mean rating was 3.7 and the median 4.0 (n = 799 respondents). Ratings of 3 or below, the distressed range, made up 48.94% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.61, 95% CI −0.70 to −0.50, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.61, 95% CI 0.47 to 0.72, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.85, 95% CI 0.79 to 0.89, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.66, cluster bootstrap 95% CI −0.72 to −0.60, p < 0.001 with standard errors clustered by engagement, n = 749 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 39.93, Holm p < 0.001, omega squared 0.372) and respondent role (Welch F 8.92, Holm p = 0.002, omega squared 0.019).

Written responses addressing the indicator numbered 54, all from C156, the comment field on joint management. Of these, 39 were negative and 15 positive. Nearly half described two issue trackers, one for each firm, that no one joined, so that the same problem had two statuses, was discussed in two calls and was sometimes fixed in neither. Positive responses described a joint call or a single shared list that worked, and placed the remaining weakness in review, in stopping the agent, or in a joint arrangement that depended on one person. Respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 391 of the 799 answers (48.94%). The engagement mean fell in the distressed range in 63 of 150 engagements.

The primary responses numbered 54, 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.” No response from any other question addressed the indicator, so every response was primary.

Of the 54 responses, 26 negative responses stated that each firm logged issues in its own tracker, queue or log and the two were never joined. Respondents described what followed: a wrong data vintage that each firm believed the other was fixing for three weeks, an agent that wrongly placed holds on around forty accounts while the institution's servicing team ran a separate investigation and the two joined up the next day, and fortnightly meetings whose first half hour went to agreeing what had happened. One respondent put the overlap between the two logs at about half. Five described a private sheet kept by the respondent's team as the working record of what the agent got wrong, read by no one at the institution, and in one case described as the real control in place of the meeting.

Against these, thirteen positive responses said a joint call, a shared list or a single queue worked: issues closed in the call, a joint triage call where the institution's lead read every flagged classification, a shared bridge for severe incidents. Several still named a gap: review that could not keep pace, a pause that waited for the weekly call, or no measure of the mistakes the agent actually makes. One respondent wrote that the arrangement was a person, not a process, and might not hold if she moved on.

Of the 54 responses, 39 (72.22%, 95% CI 59.62% to 83.64%) were negative and 19 (35.19%) carried the strongest negative score of −10. Positive responses numbered 15 (27.78%), and no response was neutral. The mean written score was −4.0 (95% CI −5.6 to −2.3, n = 54 responses), and the median was −5.0.

Of the 54 responses, 51 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.67, 95% CI 0.47 to 0.82, p < 0.001, n = 51 responses). The written responses corroborated the rating. Of the 37 negative responses with a rating, 23 (62.16%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 51, with a mean rating of 3.5. The 748 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, −0.2 (95% CI −0.8 to 0.3), included zero. Ratings in the distressed range made up 52.94% of the ratings from respondents who wrote (27 of 51) and 48.66% of the ratings from respondents who did not (364 of 748).

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 39.93, Holm p < 0.001, omega squared 0.372). Active engagements in Build-Out rated highest (mean 4.8, n = 162 respondents) and canceled engagements lowest (mean 2.3, n = 302 respondents). Ratings also differed by respondent role (Welch F 8.92, Holm p = 0.002, omega squared 0.019). Portfolio directors rated highest (mean 4.9, n = 31 respondents) and individual contributors lowest (mean 3.6, n = 616 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 50 engagements with a written response (Kruskal-Wallis H 4.64, df 3, p = 0.200). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −5.7, 21 responses) and responses from active engagements in Build-Out the least negative (mean −1.4, 11 responses). Responses from active engagements in Production (8) 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 34, engagement managers wrote 17 and portfolio directors wrote 3 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 = −0.61, 95% CI −0.70 to −0.50, 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 = 0.61, 95% CI 0.47 to 0.72, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in eleven 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 = 0.85, 95% CI 0.79 to 0.89, 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 = 749 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 115 other engagements, 100 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 35 engagements with a negative response, 14 (40.00%) were canceled, against 42 of the 115 other engagements (36.52%). The difference did not reach significance (Fisher exact p = 0.842). Customer satisfaction among active engagements did not differ between the two groups (mean 65.29% on 21 engagements against 63.75% on 73, Welch t 0.58, p = 0.565).

Two correlations test written sentiment directly. Across the 50 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.21, p = 0.144). Across 51 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.34, p = 0.016). Of the four comparisons, one 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 = 0.276).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 799 respondent answers to the equivalent of about 211 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 0.85 to 0.85.

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, 72.22%, ranked 54th from the highest, against a median of 80.82%. Its mean written score, −4.0, ranked 49th 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 16 of the 54 responses (29.63%). C144, “Rate how well the joint change control process keeps pace with the speed of agent behavior changes.” (8 responses), C147, “Rate how well human review points are placed across the two firms at the moments where agent errors would matter most.” (8 responses), C148, “Rate how well joint human review capacity keeps up with the volume of output the agents produce.” (8 responses) and C157, “Rate the factual reliability of the AI tools' outputs on this engagement before any human review.” (8 responses) followed.

The most frequent theme was “Comparison to a prior engagement”, in 8 responses (14.81%). The second was “Respondent proposes a remedy”, in 8 responses.

Nearly half of all ratings of how effectively the two firms manage issues as one team fell in the distressed range (391 of 799 answers, 48.94%). 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 2.3, n = 302 respondents).

The written responses reduce joint issue management to one artefact: whether the two firms keep one list of what went wrong. Where they kept two, the same issue was handled twice and owned by neither, and in some engagements the vendor's team kept a third, private list to protect itself. Where they kept one, the conversation moved on to review and to the speed of stopping an agent.

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 (54 responses on 50 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.