Rate your employer's current supply of qualified agentic AI talent against what its engagements need

The mean rating was 3.7 and the median 4.0 (n = 802 respondents). Ratings of 3 or below, the distressed range, made up 47.63% of the answers. The rating was associated with all four study measures. Higher ratings came with less cancellation (r = −0.59, 95% CI −0.68 to −0.47, p < 0.001, n = 150 engagements), higher customer satisfaction among active engagements (r = 0.71, 95% CI 0.59 to 0.80, p < 0.001, n = 94 engagements), a higher engagement indicator mean computed without the indicator (r = 0.87, 95% CI 0.83 to 0.91, p < 0.001, n = 150 engagements) and lower intent to leave (r = −0.62, cluster bootstrap 95% CI −0.68 to −0.54, p < 0.001 with standard errors clustered by engagement, n = 754 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 29.83, Holm p < 0.001, omega squared 0.340) and respondent role (Welch F 6.75, Holm p = 0.012, omega squared 0.014).

Of the 158 written responses, 144 were negative. Every response to C029 named a shortage of agentic talent among the hardest challenges. Responses to C135 described teams staffed with people who had never done the work, slow replacement, and knowledge held by two or three people shared across accounts. A more negative written score came with cancellation at engagement level and with higher intent to leave.

Ratings of 3 or below, the distressed range, accounted for 382 of the 802 answers (47.63%). The engagement mean fell in the distressed range in 58 of 150 engagements.

The primary responses numbered 67, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 91 responses, 57.59% of the total, were secondary and came from 6 other questions. That secondary share ranked 17th 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 7. Negative responses made up 80.60% of primary responses (54 of 67) and 98.90% of secondary responses (90 of 91).

All 86 responses to C029 named a shortage of agentic talent: a bench thinner than demand, no backup when a skilled person leaves, every account competing for the same few people. The single response to C055 described contractors brought in to fill the gap and left off the staffing dashboard, so that utilization looked full while the delivery team was thin.

In answer to C135 and other questions, seventeen responses described teams staffed with people who had never done agentic work, or the work in question, before. One respondent asked for two analysts experienced in entitlement reviews and received two who had completed the internal AI course and had never seen a bank access review. Another was given three analysts for a workstream that needed six, two of whom had never seen a data room. Eight responses described replacements that took nearly six weeks or two months, roles open since January, or requests for experienced staff that went unmet.

Eleven responses said the knowledge the engagement needed sat with two to four people, often shared across accounts. When one automation lead went on leave, testing paused until he returned. When a configuration drifted, hours passed before one of the few people who knew the equipment joined the call. The fourteen positive responses described staffing that had improved, with specialists assigned the same day or replacements found inside a fortnight, and most placed the weakness elsewhere.

Of the 158 responses, 144 (91.14%, 95% CI 86.33% to 95.51%) were negative and 49 (31.01%) carried the strongest negative score of −10. Positive responses numbered 14 (8.86%), and no response was neutral. The mean written score was −5.6 (95% CI −6.3 to −4.9, n = 158 responses), and the median was −5.0.

Of the 158 responses, 151 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.41, 95% CI 0.27 to 0.54, p < 0.001, n = 151 responses). The written responses corroborated the rating. Of the 137 negative responses with a rating, 75 (54.74%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 143, with a mean rating of 3.7. The 659 respondents who rated the indicator without writing about it had a mean of 3.7. The difference, 0.0 (95% CI −0.4 to 0.4), included zero. Ratings in the distressed range made up 51.05% of the ratings from respondents who wrote (73 of 143) and 46.89% of the ratings from respondents who did not (309 of 659).

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 29.83, Holm p < 0.001, omega squared 0.340). Active engagements in Production rated highest (mean 4.6, n = 93 respondents) and canceled engagements lowest (mean 2.5, n = 304 respondents). Ratings also differed by respondent role (Welch F 6.75, Holm p = 0.012, omega squared 0.014). Portfolio directors rated highest (mean 4.6, n = 31 respondents) and individual contributors lowest (mean 3.6, n = 620 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 94 engagements with a written response (Kruskal-Wallis H 13.64, df 3, p = 0.003). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −7.2, 58 responses) and responses from active engagements in Transition/Cut-Over the least negative (mean −4.3, 49 responses).

The role test compared individual contributors and engagement managers, the roles with 30 or more responses. Written sentiment did not differ (Kruskal-Wallis H 2.22, p = 0.136, n = 141 respondents): the mean written score was −6.0 for individual contributors (112 responses) and −4.5 for engagement managers (37 responses). Portfolio directors wrote 8 and customer management executives wrote 1 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 = −0.59, 95% CI −0.68 to −0.47, 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 = 0.71, 95% CI 0.59 to 0.80, 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 = 0.87, 95% CI 0.83 to 0.91, 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 = −0.62, 95% CI −0.66 to −0.57, Holm p < 0.001, n = 754 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 61 other engagements, 56 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 89 engagements with a negative response, 34 (38.20%) were canceled, against 22 of the 61 other engagements (36.07%). The difference did not reach significance (Fisher exact p = 0.864). Customer satisfaction among active engagements did not differ between the two groups (mean 65.49% on 55 engagements against 62.13% on 39, Welch t 1.42, p = 0.160).

Two correlations test written sentiment directly. Across the 94 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.36, p < 0.001). Across 144 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.39, 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 = 0.362).

Answers clustered within engagements (ICC 0.63). Clustering reduced the 802 respondent answers to the equivalent of about 216 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 0.87 to 0.87.

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, 91.14%, ranked 10th from the highest, against a median of 80.82%. Its mean written score, −5.6, ranked 22nd 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 25 of the 158 responses (15.82%). C116, “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today.” (18 responses), C127, “Rate your employer's quality assurance capability for agents whose outputs are not identical every run.” (15 responses), C128, “Rate your employer's ability to price agentic work profitably when billable hours no longer measure the value delivered.” (15 responses) and C108, “Rate the customer's ability to provision the system access and identities that AI agents require.” (12 responses) followed.

The most frequent theme was “Comparison to a prior engagement”, in 10 responses (6.33%). The second was “Team or colleagues”, in 9 responses.

Nearly half of all ratings of the employer's supply of qualified agentic talent fell in the distressed range (382 of 802 answers, 47.63%). 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 = 304 respondents).

The written evidence describes a supply problem with three forms: people placed on agentic work without having done it, gaps left open for weeks, and expertise concentrated in a few individuals whose absence stops the work. Written sentiment tracked the study measures directly: a more negative engagement mean came with cancellation, and a more negative respondent mean came with higher 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 1 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 (158 responses on 94 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.