Rate how well the engagement manages its dependence on a small number of model and cloud providers

The mean rating was 4.6 and the median 5.0 (n = 802 respondents). Ratings of 3 or below, the distressed range, made up 32.04% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.64, 95% CI −0.73 to −0.53, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.64, 95% CI 0.50 to 0.74, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.86, 95% CI 0.81 to 0.90, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.64, cluster bootstrap 95% CI −0.70 to −0.58, p < 0.001 with standard errors clustered by engagement, n = 755 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 36.32, Holm p < 0.001, omega squared 0.401) and respondent role (Welch F 9.95, Holm p < 0.001, omega squared 0.023).

Of the 26 written responses, nearly all from C198, the comment field on the third-party ecosystem, 19 were negative and 4 positive. Respondents described agents that ran on one model provider, one cloud or one region with no second path configured and no tested plan for an outage. Across engagements, a more negative engagement mean written score came with cancellation, and respondents whose written responses were more negative reported higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 257 of the 802 answers (32.04%). The engagement mean fell in the distressed range in 41 of 150 engagements.

The primary responses numbered 24, all written in answer to C198, “Add any comments on the third-party ecosystem that the ratings in this section do not capture.” The remaining 2 responses, 7.69% 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 70.83% of primary responses (17 of 24) and 2 of the 2 secondary responses.

Seventeen responses said the agents depended on one model provider, one cloud, one region or one account, with no second path configured. Two described the dependence being tested: an account suspended in June, after which the team worked tickets by hand for two days with no other way in, and a regional degradation of about two hours that stopped a provisioning queue, cleared by hand, with no plan on paper for a longer outage. Others said that if the provider were withdrawn they could not say what the team would do, or how long a move would take, and one had been saying so for three quarters. One respondent described nine days spent re-testing onboarding cases against a replacement model version because there was nowhere else to move.

Two responses said the concentration was the engagement's own choice, made because a single model and region could be approved quickly, with a move now a project no one would fund at the proof-of-concept stage. Two responses to C029 named single-provider dependence among the respondent's top three challenges. Of the neutral and positive responses, one said a move to another cloud would take a few days of configuration, one engagement ran two model providers, and others described stable providers while conceding that a second had never been tried.

Of the 26 responses, 19 (73.08%, 95% CI 54.17% to 88.89%) were negative and 11 (42.31%) carried the strongest negative score of −10. Positive responses numbered 4 (15.38%), and 3 were neutral. The mean written score was −5.0 (95% CI −7.0 to −2.7, n = 26 responses), and the median was −5.0.

Of the 26 responses, 25 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.72, 95% CI 0.44 to 0.87, p < 0.001, n = 25 responses). The written responses corroborated the rating. Of the 18 negative responses with a rating, 10 (55.56%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 25, with a mean rating of 4.9. The 777 respondents who rated the indicator without writing about it had a mean of 4.6. The difference, 0.3 (95% CI −0.9 to 1.5), included zero. Ratings in the distressed range made up 40.00% of the ratings from respondents who wrote (10 of 25) and 31.79% of the ratings from respondents who did not (247 of 777).

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 36.32, Holm p < 0.001, omega squared 0.401). Active engagements in Production rated highest (mean 5.7, n = 98 respondents) and canceled engagements lowest (mean 3.1, n = 308 respondents). Ratings also differed by respondent role (Welch F 9.95, Holm p < 0.001, omega squared 0.023). Portfolio directors rated highest (mean 6.1, n = 31 respondents) and individual contributors lowest (mean 4.4, n = 619 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 24 engagements with a written response (Kruskal-Wallis H 4.83, df 3, p = 0.185). Fewer than two groups held 10 or more responses, so the groups are reported as counts only: active engagements in Build-Out (6), active engagements in Transition/Cut-Over (11), active engagements in Production (2) and canceled engagements (7).

No test across respondent roles was run, because fewer than two roles had 30 or more written responses. Individual contributors wrote 12, engagement managers wrote 8 and portfolio directors wrote 6 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 −0.73 to −0.53, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in eleven 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 = 0.64, 95% CI 0.50 to 0.74, Holm p < 0.001, n = 94 engagements). The association held after Holm correction in fourteen 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 0.90, 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 = −0.64, 95% CI −0.68 to −0.60, Holm p < 0.001, n = 755 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 133 other engagements, 126 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 17 engagements with a negative response, 7 (41.18%) were canceled, against 49 of the 133 other engagements (36.84%). The difference did not reach significance (Fisher exact p = 0.792). Customer satisfaction among active engagements did not differ between the two groups (mean 69.20% on 10 engagements against 63.49% on 84, Welch t 1.27, p = 0.232).

Two correlations test written sentiment directly. Across the 24 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.44, p = 0.031). Across 26 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.52, 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 = 0.482).

Answers clustered within engagements (ICC 0.64). Clustering reduced the 802 respondent answers to the equivalent of about 213 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, 73.08%, ranked 53rd from the highest, against a median of 80.82%. Its mean written score, −5.0, ranked 30th from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C179, “Rate the quality of the fallback the engagement has ready when a critical third-party feed, platform, or model stops working.”, in 13 of the 26 responses (50.00%). C181, “Rate the quality of advance notice third-party providers give before changes that can affect the agents on this engagement.” (8 responses), C190, “Rate the engagement's ability to still perform by hand the manual workarounds that once absorbed third-party quirks and breakage.” (6 responses), C178, “Rate how well the agents on this engagement are protected from breaking silently when a third party changes something without warning.” (5 responses) and C194, “Rate how well this engagement could keep operating if it had to switch its primary model or cloud provider.” (5 responses) followed.

The most frequent theme was “Comparison to a prior engagement”, in 4 responses (15.38%). The second was “Third-party work done by hand”, in 3 responses.

About a third of all ratings of how well the engagement managed its dependence on a few model and cloud providers fell in the distressed range (257 of 802 answers, 32.04%). 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 3.1, n = 308 respondents).

The written evidence describes a concentration accepted for speed and never revisited. For a financial institution concerned with continuity through a provider failure, the responses indicate that the fallback in practice was staff working by hand, and that no engagement described a tested move to a second provider. Written sentiment was associated with cancellation and with intent to leave, on a small base.

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 (26 responses on 24 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.