Rate how well the technical platforms your employer builds or licenses are optimized for agentic AI outsourcing

The mean rating was 3.7 and the median 4.0 (n = 794 respondents). Ratings of 3 or below, the distressed range, made up 48.36% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.57, 95% CI −0.67 to −0.45, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.70, 95% CI 0.57 to 0.79, 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 = −0.59, cluster bootstrap 95% CI −0.66 to −0.51, p < 0.001 with standard errors clustered by engagement, n = 743 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 26.27, Holm p < 0.001, omega squared 0.318), respondent role (Welch F 7.77, Holm p = 0.005, omega squared 0.017) and vendor type (Welch F 3.64, Holm p = 0.017, omega squared 0.017).

Of the 61 written responses, nearly all from C135, 38 were negative and 21 positive. The negative responses described platforms built for other work, such as ticket handling or contact centers, and teams that moved the work into spreadsheets, mailboxes and personal scripts. The positive responses found the platform sound or improved and placed the gap elsewhere. Written sentiment was not associated with the study measures.

Ratings of 3 or below, the distressed range, accounted for 384 of the 794 answers (48.36%). The engagement mean fell in the distressed range in 63 of 150 engagements.

The primary responses numbered 55, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 6 responses, 9.84% of the total, were secondary and came from 3 other questions. That secondary share ranked 44th 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 58.18% of primary responses (32 of 55) and 6 of the 6 secondary responses.

Fourteen responses said the platform was built for other work or was old: ticket handling, a generic help desk, contact center work, chat support with collections added on top, a staffed service desk whose reports counted only tickets a person touched, or a quality control system built for end-of-day batches. One respondent said a modeling platform the firm had built years earlier failed with every agent interaction. All four responses to C029 named the vendor's own platforms as not ready for agents.

Nine responses described moving the work off the platform: research synthesis in spreadsheets and mail, a shared mailbox and one spreadsheet, personal scripts shared in a team chat, test suites run from a personal laptop after a license lapsed unnoticed, reconciliation run on the financial institution's tooling because the vendor's could not hold the volume, and files split by hand each morning. Three responses said a proper sandbox had arrived only recently, after testing against live extracts. One respondent said the sales team had presented a development kit as built for AI before anyone checked, and the team validated it after the contracts were signed.

The twenty-one positive responses found the platform, sandbox or pipelines sound or improved, and most placed the gap elsewhere: in staffing, domain knowledge, training, accountability or the route from pilot to production.

Of the 61 responses, 38 (62.30%, 95% CI 50.00% to 74.55%) were negative and 17 (27.87%) carried the strongest negative score of −10. Positive responses numbered 21 (34.43%), and 2 were neutral. The mean written score was −2.8 (95% CI −4.4 to −1.2, n = 61 responses), and the median was −5.0.

Of the 61 responses, 61 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.57, 95% CI 0.39 to 0.73, p < 0.001, n = 61 responses). The written responses corroborated the rating. Of the 38 negative responses with a rating, 31 (81.58%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 60, with a mean rating of 3.4. The 734 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, −0.3 (95% CI −0.9 to 0.3), included zero. Ratings in the distressed range made up 61.67% of the ratings from respondents who wrote (37 of 60) and 47.28% of the ratings from respondents who did not (347 of 734).

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 26.27, Holm p < 0.001, omega squared 0.318). Active engagements in Production rated highest (mean 4.8, n = 94 respondents) and canceled engagements lowest (mean 2.5, n = 298 respondents). Ratings also differed by respondent role (Welch F 7.77, Holm p = 0.005, omega squared 0.017). Portfolio directors rated highest (mean 4.7, n = 30 respondents) and individual contributors lowest (mean 3.6, n = 612 respondents). Ratings also differed by vendor type (Welch F 3.64, Holm p = 0.017, omega squared 0.017). Pure-play agentic AI firms rated highest (mean 4.1, n = 70 respondents) and IT services majors lowest (mean 3.5, n = 293 respondents). Outsourcing model, institution type, process family and class of work showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 48 engagements with a written response (Kruskal-Wallis H 5.14, df 3, p = 0.162). Of the groups with 10 or more responses, responses from active engagements in Build-Out were the most negative (mean −4.4, 17 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −1.3, 19 responses). Responses from active engagements in Production (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 38, engagement managers wrote 20 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 = −0.57, 95% CI −0.67 to −0.45, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in thirteen 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 = 0.70, 95% CI 0.57 to 0.79, 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 = −0.59, 95% CI −0.63 to −0.54, Holm p < 0.001, n = 743 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 117 other engagements, 102 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 33 engagements with a negative response, 11 (33.33%) were canceled, against 45 of the 117 other engagements (38.46%). The difference did not reach significance (Fisher exact p = 0.686). Customer satisfaction among active engagements did not differ between the two groups (mean 62.36% on 22 engagements against 64.63% on 72, Welch t −0.93, p = 0.360).

Two correlations test written sentiment directly. Across the 48 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.15, p = 0.306). Across 58 respondents, the respondent mean written score was not associated with intent to leave (Spearman rho −0.16, p = 0.227). Of the four 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 cancellation the weakest. The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.274).

Answers clustered within engagements (ICC 0.59). Clustering reduced the 794 respondent answers to the equivalent of about 224 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, 62.30%, ranked 67th from the highest, against a median of 80.82%. Its mean written score, −2.8, ranked 63rd from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C123, “Rate the adequacy of your employer's investment in training its workforce.”, in 23 of the 61 responses (37.70%). C120, “Rate the depth of your employer's knowledge of the financial industry.” (9 responses), C126, “Rate the maturity of your employer's playbook for taking agentic use cases from concept to governed production.” (9 responses), C122, “Rate your employer's current supply of qualified agentic AI talent against what its engagements need.” (7 responses) and C044, “What is the pricing model for this engagement?” (6 responses) followed.

The most frequent theme was “Team or colleagues”, in 10 responses (16.39%). The second was “Raised it, nothing came back”, in 6 responses.

Nearly half of all ratings of how well the employer's technical platforms are optimized for agentic outsourcing fell in the distressed range (384 of 794 answers, 48.36%). 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 2.5, n = 298 respondents).

The written evidence divides. Where the platform had been built for the work, respondents rated it well and named other gaps. Where it had been built for something else, the work moved into tools the team assembled for itself. Written sentiment was not associated with cancellation, customer satisfaction or intent to leave.

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