Rate the maturity of your employer's playbook for taking agentic use cases from concept to governed production

The mean rating was 3.7 and the median 4.0 (n = 807 respondents). Ratings of 3 or below, the distressed range, made up 48.57% 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 = 0.70, 95% CI 0.58 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 = −0.62, cluster bootstrap 95% CI −0.68 to −0.54, p < 0.001 with standard errors clustered by engagement, n = 756 respondents). After Holm correction, ratings differed by engagement status and lifecycle stage (Welch F 29.95, Holm p < 0.001, omega squared 0.341).

The indicator drew 200 written responses. Of these, 186 were negative and 145 carried the strongest negative score of −10. Every response to C029 named the absence or immaturity of a delivery playbook among the hardest challenges. Responses to C135 described a route from pilot to governed production that each team invented for itself, or a playbook written for staffed work. 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 392 of the 807 answers (48.57%). The engagement mean fell in the distressed range in 60 of 150 engagements.

The primary responses numbered 54, all written in answer to C135, “Add any comments on your employer that the ratings in this section do not capture.” The remaining 146 responses, 73.00% of the total, were secondary and came from 5 other questions. That secondary share ranked 11th 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 14. Negative responses made up 79.63% of primary responses (43 of 54) and 97.95% of secondary responses (143 of 146).

All 139 responses to C029 named the absence or immaturity of a delivery playbook or method, in phrases such as no agentic playbook, improvising, or building the plane in flight. Several added that every rollout started from zero, or that fixes did not transfer between teams running similar agents. The two responses to C055 described reporting that hid the gap: missed milestones rescoped as change requests, and a tracker built on a milestone template borrowed from other engagements that showed progress against playbook steps that did not exist.

In answer to C135, twenty-nine responses said there was no playbook or written path from pilot to production, or that each team or engagement invented its own. One program lead had run many contact center transitions on a playbook that worked and found none here. Another had seen quality assurance for agent output built after the sale on four accounts. Seven responses described a playbook that existed and did not fit: written for a call center two engagements earlier, a pilot template, the staffed-delivery playbook with the word agent inserted, or a document no one on a large account opened. Seven described runbooks, checklists or files the team wrote for itself, in one case at night while the alert backlog grew.

The thirteen positive responses described a playbook that was clear, improving or good enough to carry the team. Several of these respondents named a gap elsewhere, in training, platform or accountability.

Of the 200 responses, 186 (93.00%, 95% CI 89.23% to 96.39%) were negative and 145 (72.50%) carried the strongest negative score of −10. Positive responses numbered 13 (6.50%), and 1 was neutral. The mean written score was −7.9 (95% CI −8.5 to −7.3, n = 200 responses), and the median was −10.0.

Of the 200 responses, 193 came from respondents who also rated the indicator. Among them, the written score rose with the rating (Spearman rho 0.30, 95% CI 0.14 to 0.43, p < 0.001, n = 193 responses). The written responses corroborated the rating. Of the 179 negative responses with a rating, 90 (50.28%) came from respondents whose rating was in the distressed range.

Respondents who wrote about the indicator and gave a rating numbered 177, with a mean rating of 3.7. The 630 respondents who rated the indicator without writing about it had a mean of 3.6. The difference, 0.1 (95% CI −0.3 to 0.4), included zero. Ratings in the distressed range made up 47.46% of the ratings from respondents who wrote (84 of 177) and 48.89% of the ratings from respondents who did not (308 of 630).

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 29.95, Holm p < 0.001, omega squared 0.341). Active engagements in Production rated highest (mean 4.7, n = 101 respondents) and canceled engagements lowest (mean 2.4, n = 304 respondents). Outsourcing model, institution type, process family, class of work, respondent role and vendor type showed no difference.

Written sentiment did not differ by engagement status and lifecycle stage across the 110 engagements with a written response (Kruskal-Wallis H 7.49, df 3, p = 0.058). Of the groups with 10 or more responses, responses from canceled engagements were the most negative (mean −9.1, 76 responses) and responses from active engagements in Production the least negative (mean −5.7, 21 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 2.18, p = 0.140, n = 173 respondents): the mean written score was −8.0 for individual contributors (148 responses) and −7.8 for engagement managers (40 responses). Portfolio directors wrote 12 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 twelve 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 0.58 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 = −0.62, 95% CI −0.66 to −0.57, Holm p < 0.001, n = 756 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 43 other engagements, 40 had no written response addressing the indicator. These two comparisons therefore do not isolate written sentiment. Of the 107 engagements with a negative response, 39 (36.45%) were canceled, against 17 of the 43 other engagements (39.53%). The difference did not reach significance (Fisher exact p = 0.852). Customer satisfaction among active engagements did not differ between the two groups (mean 64.94% on 68 engagements against 61.88% on 26, Welch t 0.97, p = 0.341).

Two correlations test written sentiment directly. Across the 110 engagements with a written response, a more negative engagement mean written score came with cancellation (point-biserial r −0.22, p = 0.023). Across 174 respondents, a more negative respondent mean written score came with higher intent to leave (Spearman rho −0.26, 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, four 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 cancellation and intent to leave did not differ (Holm p = 0.054). The associations with customer satisfaction and intent to leave did not differ (Holm p = 0.136).

Answers clustered within engagements (ICC 0.63). Clustering reduced the 807 respondent answers to the equivalent of about 215 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, 93.00%, ranked 7th from the highest, against a median of 80.82%. Its mean written score, −7.9, ranked 3rd from the most negative, against a median of −4.7.

The indicator most often present in the same responses was C116, “Rate how well your employer is transforming its commercial model as agentic AI reduces the billable human effort it sells today.”, in 22 of the 200 responses (11.00%). C123, “Rate the adequacy of your employer's investment in training its workforce.” (21 responses), C108, “Rate the customer's ability to provision the system access and identities that AI agents require.” (20 responses), C098, “Rate how well the customer's legacy systems support the integration this engagement requires.” (19 responses) and C128, “Rate your employer's ability to price agentic work profitably when billable hours no longer measure the value delivered.” (15 responses) followed.

The most frequent theme was “Practice invented on the live engagement”, in 60 responses (30.00%). The second was “Team or colleagues”, in 8 responses.

Nearly half of all ratings of the maturity of the employer's playbook for taking agentic use cases from concept to governed production fell in the distressed range (392 of 807 answers, 48.57%). 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 = 304 respondents).

The written evidence is extensive and nearly uniform. Respondents described the route from pilot to governed production as work each engagement did for itself, often after the sale and while the service was live. Written sentiment tracked the study measures: 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 (200 responses on 110 engagements). Intervals for written sentiment therefore used a bootstrap by engagement.