Rate how fully each firm can see what the other firm's agents actually did when questions arise

The mean rating was 3.7 and the median 3.0 (n = 780 respondents). Ratings of 3 or below, the distressed range, made up 50.26% 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 = 0.62, 95% CI 0.48 to 0.73, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.86, 95% CI 0.81 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 = 732 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 39.86, Holm p < 0.001, omega squared 0.391) and respondent role (Welch F 9.35, Holm p = 0.001, omega squared 0.021).

Written responses addressing the indicator numbered 66, from 64 respondents on 49 engagements. Of these, 62 were negative and 4 positive. Respondents described each firm's agents as opaque to the other: when a question arose, an account of what an agent had done was rebuilt by hand, from statements, logs or a private spreadsheet. Responses to the question on manipulated reports described agent logs trimmed, aggregated or held in a format the institution's auditors could not query. 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 392 of the 780 answers (50.26%). The engagement mean fell in the distressed range in 65 of 150 engagements.

The primary responses numbered 25, 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 41 responses, 62.12% of the total, were secondary and came from 3 other questions. That secondary share ranked 15th 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 1. Negative responses made up 84.00% of primary responses (21 of 25) and 100.00% of secondary responses (41 of 41).

The largest group of responses came from C029, the question on the challenges that reduce job satisfaction. All 36 named the same gap in nearly the same words: an agent no one could see into, thin observability, or no audit trail across the boundary between the firms.

Among the 25 responses to C156, the comment field on joint management, 16 negative responses stated that one firm could not see what the other firm's agents had done. The consequences were concrete: three monthly attribution packs sent to an investment committee before either firm could show what the agents had done in the period, a release whose configuration had to be rebuilt over two days because the other firm held only the final state, an alert closed on a Sunday and explained on Tuesday by a reconstruction that was half a guess. Eight described the workaround: an account rebuilt by hand from custodian statements, terminal history, logs, or a running note or spreadsheet one analyst had started and the team now relied on. Against these, three positive responses said either firm could pull the full trace of what an agent did on a ticket, and that this removed most arguments before they started.

The four responses to C055, the question on how the vendor manipulates performance reports and dashboards, described the same gap from the institution's side: agent decision logs held in a proprietary format the institution's audit team could not query, audit requests answered with aggregated summaries, logs trimmed to the final decision, and audit trail gaps smoothed over in a curated weekly summary for the steering committee.

Of the 66 responses, 62 (93.94%, 95% CI 88.24% to 98.53%) were negative and 36 (54.55%) carried the strongest negative score of −10. Positive responses numbered 4 (6.06%), and no response was neutral. The mean written score was −7.0 (95% CI −8.1 to −5.8, n = 66 responses), and the median was −10.0.

Of the 66 responses, 65 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.42, 95% CI 0.18 to 0.64, p < 0.001, n = 65 responses). The written responses corroborated the rating. Of the 61 negative responses with a rating, 35 (57.38%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 63, with a mean rating of 3.5. The 717 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, −0.2 (95% CI −0.9 to 0.5), included zero. Ratings in the distressed range made up 53.97% of the ratings from respondents who wrote (34 of 63) and 49.93% of the ratings from respondents who did not (358 of 717).

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 39.86, Holm p < 0.001, omega squared 0.391). Active engagements in Transition/Cut-Over rated highest (mean 4.7, n = 230 respondents) and canceled engagements lowest (mean 2.2, n = 293 respondents). Ratings also differed by respondent role (Welch F 9.35, Holm p = 0.001, omega squared 0.021). Portfolio directors rated highest (mean 5.1, n = 30 respondents) and individual contributors lowest (mean 3.6, n = 598 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 49 engagements with a written response (Kruskal-Wallis H 9.89, df 3, p = 0.020). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −8.8, 24 responses) and responses from active engagements in Build-Out the least negative (mean −5.6, 17 responses). Responses from active engagements in Production (2) 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 45, engagement managers wrote 15 and portfolio directors wrote 6 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 = 0.62, 95% CI 0.48 to 0.73, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in twelve 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 = 0.86, 95% CI 0.81 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 = 732 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 102 other engagements, 101 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 48 engagements with a negative response, 19 (39.58%) were canceled, against 37 of the 102 other engagements (36.27%). The difference did not reach significance (Fisher exact p = 0.720). Customer satisfaction among active engagements did not differ between the two groups (mean 61.59% on 29 engagements against 65.22% on 65, Welch t −1.33, p = 0.190).

Two correlations test written sentiment directly. Across the 49 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.37, p = 0.009). Across 63 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.45, p < 0.001). 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 cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.648).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 780 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 0.86 to 0.86.

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, 93.94%, ranked 4th from the highest, against a median of 80.82%. Its mean written score, −7.0, ranked 5th 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 10 of the 66 responses (15.15%). C148, “Rate how well joint human review capacity keeps up with the volume of output the agents produce.” (8 responses), C126, “Rate the maturity of your employer's playbook for taking agentic use cases from concept to governed production.” (7 responses), C098, “Rate how well the customer's legacy systems support the integration this engagement requires.” (5 responses) and C141, “Rate how effectively the two firms manage issues arising from agent work as one team rather than through two separate processes.” (5 responses) followed.

The most frequent theme was “Human rework absorbs the failure”, in 4 responses (6.06%). The second was “SLA definitions do not fit agent work”, in 3 responses.

Just over half of all ratings of how fully each firm can see what the other firm's agents did fell in the distressed range (392 of 780 answers, 50.26%). 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 = 293 respondents).

The written responses describe two firms whose agents act on shared work while each firm's account of those actions stays inside its own tools. Explanations were reconstructed after the fact, often by the analyst nearest the problem. The responses to the reporting question show that the gap was not always accidental: in some engagements the log the vendor shared was shaped before the institution saw it.

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 (66 responses on 49 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.