Rate the depth of your employer's knowledge of the financial industry

The mean rating was 3.6 and the median 4.0 (n = 802 respondents). Ratings of 3 or below, the distressed range, made up 48.88% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.56, 95% CI −0.66 to −0.44, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.71, 95% CI 0.59 to 0.79, 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 = −0.60, cluster bootstrap 95% CI −0.68 to −0.52, 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 25.03, Holm p < 0.001, omega squared 0.304), respondent role (Welch F 6.76, Holm p = 0.012, omega squared 0.015) and vendor type (Welch F 3.81, Holm p = 0.012, omega squared 0.017).

All 64 written responses came from C135, the comment field on the employer's capability. Forty-five were negative and eighteen positive. The negative responses described delivery staff who did not know the financial business they served and learned it on live work, often from the financial institution's own documents. The positive responses held that domain knowledge was sound and placed the gap in running agents. A more negative written score came with higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 392 of the 802 answers (48.88%). The engagement mean fell in the distressed range in 60 of 150 engagements.

The primary responses numbered 64, all written in answer to C135, “Add any comments on your employer 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 45 negative responses, forty-two described thin or absent knowledge of the financial domain. Respondents named the business each time: settlements, payroll for a bank, credit risk, insurance endorsements, a trading floor help desk, private capital fees, fund back offices, deposits, mortgages, trusts and wealth management. One respondent's colleagues could not explain a basis trade. Another found that the firm's solution specialist did not know that a dealing floor resets trader passwords through a separate process.

Eight responses said the vendor's staff learned the domain from the financial institution's own documents or people: runbooks, test notes, policy packs, old models, or a compliance team the respondent was told to lean on. Six responses said the firm's depth in the relevant domain sat with a handful of people, in one case a single person who was not on the engagement. Several respondents described informal remedies: a new analyst paired with one of two experienced colleagues for a full deal cycle, a notes file of terms carried from a previous account.

The eighteen positive responses held that the firm knew the business, in cards and payments, funds, insurance, audit, credit or servicing, and placed the gap elsewhere: in running agents at scale or in live production, in a method for checking output that varies between runs, in the playbook from prototype to governed production, or in the commercial model.

Of the 64 responses, 45 (70.31%, 95% CI 58.82% to 81.25%) were negative and 24 (37.50%) carried the strongest negative score of −10. Positive responses numbered 18 (28.13%), and 1 was neutral. The mean written score was −3.5 (95% CI −5.1 to −1.8, n = 64 responses), and the median was −5.0.

Of the 64 responses, 64 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.55, 95% CI 0.36 to 0.71, p < 0.001, n = 64 responses). The written responses corroborated the rating. Of the 45 negative responses with a rating, 30 (66.67%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 64, with a mean rating of 3.7. The 738 respondents who rated the indicator without writing about it had a mean of 3.6. The difference, 0.0 (95% CI −0.5 to 0.6), included zero. Ratings in the distressed range made up 54.69% of the ratings from respondents who wrote (35 of 64) and 48.37% of the ratings from respondents who did not (357 of 738).

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 25.03, Holm p < 0.001, omega squared 0.304). Active engagements in Production rated highest (mean 4.6, n = 99 respondents) and canceled engagements lowest (mean 2.4, n = 302 respondents). Ratings also differed by respondent role (Welch F 6.76, Holm p = 0.012, omega squared 0.015). Portfolio directors rated highest (mean 4.6, n = 31 respondents) and individual contributors lowest (mean 3.5, n = 619 respondents). Ratings also differed by vendor type (Welch F 3.81, Holm p = 0.012, omega squared 0.017). Pure-play agentic AI firms rated highest (mean 4.2, n = 68 respondents) and BPO specialists lowest (mean 3.4, n = 184 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 53 engagements with a written response (Kruskal-Wallis H 6.81, df 3, p = 0.078). Of the groups with 10 or more responses, responses from active engagements in Transition/Cut-Over were the most negative (mean −5.0, 20 responses) and responses from canceled engagements the least negative (mean −4.6, 27 responses). Responses from active engagements in Build-Out (9) and 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 39, engagement managers wrote 20 and portfolio directors wrote 5 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 = −0.56, 95% CI −0.66 to −0.44, Holm p < 0.001, n = 150 engagements). The association held after Holm correction in ten 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 = 0.71, 95% CI 0.59 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 = 0.85, 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 = −0.60, 95% CI −0.65 to −0.56, 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 110 other engagements, 97 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 40 engagements with a negative response, 18 (45.00%) were canceled, against 38 of the 110 other engagements (34.55%). The difference did not reach significance (Fisher exact p = 0.257). Customer satisfaction among active engagements did not differ between the two groups (mean 65.91% on 22 engagements against 63.54% on 72, Welch t 0.85, p = 0.400).

Two correlations test written sentiment directly. Across the 53 engagements with a written response, the engagement mean written score was not associated with cancellation (point-biserial r −0.16, p = 0.255). Across 62 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.31, p = 0.014). 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 cancellation 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 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 0.85 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, 70.31%, ranked 60th from the highest, against a median of 80.82%. Its mean written score, −3.5, ranked 54th 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 64 responses (35.94%). C125, “Rate how well your employer's training programs build financial industry knowledge.” (13 responses), C129, “Rate how well the technical platforms your employer builds or licenses are optimized for agentic AI outsourcing.” (9 responses), C116, “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today.” (7 responses) and C132, “Rate the depth of your employer's experience running agentic AI systems in live production, beyond pilots.” (7 responses) followed.

The most frequent theme was “Practice invented on the live engagement”, in 8 responses (12.50%). The second was “Positive with an embedded negative”, in 8 responses.

Nearly half of all ratings of the depth of the employer's knowledge of the financial industry fell in the distressed range (392 of 802 answers, 48.88%). 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 2.4, n = 302 respondents).

The written evidence divides along the line between domain and agents. Respondents who rated the firm low described staff placed on banking work without banking knowledge, who then learned it from the financial institution. Respondents who rated it high described the domain as covered and the agent operation as the weaker side. Written sentiment was associated with intent to leave and not with the engagement-level measures.

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 (64 responses on 53 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.