Rate how well today's joint governance model fits work performed by agents rather than work performed by people

The mean rating was 3.6 and the median 4.0 (n = 803 respondents). Ratings of 3 or below, the distressed range, made up 49.19% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.72 to −0.53, 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 = 0.85, 95% CI 0.80 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 = 758 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 44.82, Holm p < 0.001, omega squared 0.401) and respondent role (Welch F 9.93, Holm p < 0.001, omega squared 0.028).

Few written responses addressed the indicator: 10, nearly all from C156, the comment field on joint management, and all but one negative. The most common account was a governance model drawn up for people doing the work by hand and left in place after agents took over much of it. Respondents described decisions that no one on either side could authorise and agent changes that both firms learned of afterward. Written sentiment showed no association with the study measures, though the base is too small for that test to carry weight.

Ratings of 3 or below, the distressed range, accounted for 395 of the 803 answers (49.19%). The engagement mean fell in the distressed range in 67 of 150 engagements.

The primary responses numbered 9, 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 1 responses, 10.00% of the total, were secondary and came from 1 other questions. Respondents who wrote both a primary and a secondary response numbered 0. Negative responses made up 8 of the 9 primary responses and 1 of the 1 secondary responses.

Of the 10 responses, six said the joint model was written for people. One described a model designed for a large team doing manual matching, with agents placed underneath it and no line of it rewritten. Another said the governance assumed that a person made each offer and could be asked afterward why. A third said the forum met monthly, a sensible interval when a change meant a release, while the agent's behaviour drifted between meetings.

Five responses described decisions that stalled. Two managers from the two firms agreed what should happen with unmatched cash and knew that neither could authorise it. A routing rule could not be changed locally because it was a joint decision, and the joint decision took longer than the service level it was breaking. Cash breaks went up one side and came back down the other with the same question. Four responses said that agent updates surfaced after the fact, through categories that looked different in the morning or a forecast that moved with no joint record of which side had changed it.

Five responses attributed the low ratings to the design of the model and described the people on both sides as capable or diligent. One respondent added that the human review point on arrangement offers had become an approval step, with the reviewer releasing nearly the whole queue. The single positive response rated the frame itself as better than most at this stage and placed the weakness in the running of it: two intake processes and agent updates that surfaced late.

Of the 10 responses, 9 (90.00%, 95% CI 66.67% to 100.00%) were negative and 3 (30.00%) carried the strongest negative score of −10. Positive responses numbered 1 (10.00%), and no response was neutral. The mean written score was −5.5 (95% CI −7.5 to −2.5, n = 10 responses), and the median was −5.0.

Of the 10 responses, 10 came from respondents who also rated the indicator. Among them, the written score did not track the rating (Spearman rho 0.40, 95% CI −0.43 to 0.87, p = 0.253, n = 10 responses). Of the 9 negative responses with a rating, 6 came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 10, with a mean rating of 3.4. The 793 respondents who rated the indicator without writing about it had a mean of 3.6. The difference, −0.2 (95% CI −2.0 to 2.1), included zero. Ratings in the distressed range made up 60.00% of the ratings from respondents who wrote (6 of 10) and 49.05% of the ratings from respondents who did not (389 of 793).

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 44.82, Holm p < 0.001, omega squared 0.401). Active engagements in Build-Out rated highest (mean 4.6, n = 164 respondents) and canceled engagements lowest (mean 2.2, n = 301 respondents). Ratings also differed by respondent role (Welch F 9.93, Holm p < 0.001, omega squared 0.028). Portfolio directors rated highest (mean 5.2, 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 did not differ by engagement status and lifecycle stage across the 9 engagements with a written response (Kruskal-Wallis H 0.94, df 2, p = 0.625). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (2), active engagements in Transition/Cut-Over (3) and canceled engagements (5).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 4, engagement managers wrote 3 and portfolio directors wrote 3 responses, reported as counts only.

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 = −0.64, 95% CI −0.72 to −0.53, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in fifteen 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 eight 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 0.80 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 = 758 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 142 other engagements, 141 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 8 engagements with a negative response, 4 were canceled, against 52 of the 142 other engagements (36.62%). The difference did not reach significance (Fisher exact p = 0.472). The comparison of customer satisfaction was not computed, because one of the two groups held too few active engagements.

Two correlations test written sentiment directly. The correlation of the engagement mean written score with cancellation was not computed, because fewer than ten engagements (9) held a written response. Across 10 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho −0.45, p = 0.191). Of the two comparisons, none 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 = 0.794).

Answers clustered within engagements (ICC 0.66). Clustering reduced the 803 respondent answers to the equivalent of about 206 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 0.85.

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, 90.00%, ranked 13th from the highest, against a median of 80.82%. Its mean written score, −5.5, ranked 25th 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 3 of the 10 responses (30.00%). C141, “Rate how effectively the two firms manage issues arising from agent work as one team rather than through two separate processes.” (3 responses), C153, “Rate how closely the speed of joint decision making matches the speed at which the agents act.” (3 responses), C080, “My manager is effective.” (2 responses) and C144, “Rate how well the joint change control process keeps pace with the speed of agent behavior changes.” (2 responses) followed.

The most frequent theme was “Raised it, nothing came back”, in 2 responses (20.00%). The second was “Comparison to a prior engagement”, in 2 responses.

Nearly half of all ratings of how well the joint governance model fits work performed by agents fell in the distressed range (395 of 803 answers, 49.19%). 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 = 301 respondents).

The written evidence is small. It describes a governance model inherited from manual work and applied unchanged to agents, with authority to decide placed above the people who met each day. In the interval, the agent continued on its existing basis and the open breaks and exceptions waited.

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 (10 responses on 9 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.

The written responses number fewer than 30, so their shares and the written-sentiment tests rest on a small base.