Financial Institution Readiness

Financial institution readiness received the lowest category mean, with distressed ratings distributed across its constituent indicators. Financial institution readiness covers data, systems, processes, personnel, governance, and the business case. Its category mean was 3.55 across 150 engagements, with 8,219 of 15,966 ratings (51.48%) classified as distressed. These are vendor personnel's assessments of institutional conditions, not independent measures of institutional readiness or control.

The 20 indicators were rated within a narrow range of one another, from a mean of 3.47 to 3.61 (n = 789 to 811 respondents). Every one had about half of its answers in the distressed range. No single part of the financial institution's readiness stood out as the weak point. The delivery teams rated the financial institution's preparation as uniformly poor.

Mean ratings in Table 2.3 are computed across respondents. The category figure of 3.55 is computed across engagements.

Written accounts described vendor personnel correcting input data, documenting workflows, and making decisions where respondents considered institutional guidance inadequate. Some described recurring work and limited disclosure. The open question on the financial institution received comments from 557 of 850 respondents (65.53%). The accounts inform the interpretation of ratings but do not establish that responsibilities transferred formally or uniformly.

“Every file come in different. One branch send payslips as pdf, other one send a photo of the payslip taken on a phone, and the tool cannot read a photo so that file come to me. I do maybe thirty of these a day … Nobody on their side seem to know this is happening.” -Individual Contributor, BPO, Credit, underwriting and suitability

“The agent is fine. It is being fed rubbish and asked to be careful with it…” -Individual Contributor, ITO, Technology engineering and operations

Some respondents distinguished helpful institutional staff from deficiencies in organizational preparation. Their accounts described responsive personnel working with unclear data ownership, restricted system access, or unresolved decision authority. This distinction prevents a low organizational rating from being interpreted automatically as criticism of individual staff.

The category includes three indicators of the financial institution's governance of agentic AI, covering the ownership of AI across its functions, its capacity to oversee agent risk, and its change control. The three are reported in section 6 with the other indicators of the category. They also inform the examination of the controls meant to govern work performed by agents (Part 3).

1 Financial-Institution-Provided Data

Data-quality concerns occurred alongside restricted access, inconsistent definitions, and unclear ownership, each of which was reported to require additional delivery work. Four indicators describe the data the financial institution provided, covering its quality, its accessibility to the engagement, its normalization across source systems, and the governance over it. Each had about half of its answers in the distressed range, from 387 of 795 answers (48.68%) for quality to 413 of 809 (51.05%) for normalization. All four indicators were associated with each core study measure, without exception across the applicable robustness checks.

The written accounts distinguish defective data from inaccessible data, inconsistent definitions, and unclear ownership. Detailed response counts and examples appear under the corresponding indicators below.

“The problem is data quality, it is worst I have seen. My last project was service desk for same type of bank, at least there the user list is clean. Here the joiner leaver file still has people who left in 2019.” -Individual Contributor, ITO, Technology engineering and operations

Respondents described interactions among these problems. Inaccessible systems prompted manual extracts. Inconsistent identifiers required reconciliation, and unclear ownership delayed decisions about valid values. These are reported mechanisms, not a tested causal model of how the four indicators combine.

“The people on their side are not the problem and I want that on record … The same vendor sits in there three times with three tax numbers, spend categories are free text in one region and coded in another, and nobody owns that record. We are asking agents to reason over a file that no human has agreed on.” -Engagement Manager, BPO, Corporate and back-office functions

“in march i needed the sector holdings file from their old research store, it comes only as a weekly pdf dump. so for two days i am typing figures by hand into the model sheet. nobody on their side could say who owns that system” -Individual Contributor, KPO, Corporate and back-office functions

Part 5 examines the reported data alterations, their stated reasons, and their disclosure. Here, the focus is on the data conditions respondents associated with that work.

1.1 Quality of Financial-Institution-Provided Data

Indicator C094 · Rate the quality of the customer’s data across the systems this engagement depends on · Full statistical analysis ›

Respondents described recurring manual correction of financial-institution-provided data before agents could use it, with data-quality ratings lowest on canceled engagements and among individual contributors. The median is the middle value in an ordered distribution. The mean rating was 3.6 and the median 4.0 (n = 795 respondents). Respondents on canceled engagements rated the data lowest (mean 2.4, n = 305 respondents). Among the roles, individual contributors, who handle the data, rated it lowest (mean 3.4, n = 614 respondents).

A confidence interval (CI) expresses uncertainty around an estimate under the stated method. The cluster bootstrap resamples whole engagements to retain the grouping of respondents within engagements. The rating of the quality of the financial institution's data was associated with all four core study measures, and each association held under the applicable robustness checks. Engagements that rated the data higher had a lower cancellation rate (r = −0.60, 95% CI −0.70 to −0.49, n = 150 engagements). They recorded higher customer satisfaction among active engagements (r = 0.68, 95% CI 0.55 to 0.77, n = 94 engagements). The rating was associated with the engagement indicator mean, computed without the indicator, at r = 0.81 (95% CI 0.74 to 0.86, n = 150 engagements). Respondents who rated the data higher reported lower intent to leave (r = −0.61, cluster bootstrap 95% CI −0.67 to −0.53, n = 748 respondents). Distressed ratings came from 387 of the 795 answers (48.68%), and the engagement mean was distressed on 58 of the 150 engagements.

The written responses addressing the quality of the data numbered 582, from 415 respondents on 133 engagements, and 499 of the 582 (85.74%) were negative. In 226 of the 582 responses (38.83%), the response received the strongest negative score. Most came from the questions on altering financial-institution-provided data, where respondents described correcting the financial institution's data by hand before it could be used, in many accounts every cycle or most days. Many of the corrections were reported within the vendor and not to the financial institution.

“CMDB is half empty so my team is filling asset details by hand before we can trust any playbook. It is not difficult work but it is night work, because we can only touch prod inside their window. Two of us are on that shift now instead of building.” -Individual Contributor, ITO, Technology engineering and operations

“Their user directory is old, half the entries have no manager listed and no location… so the agent cannot even tell who is asking for what. For me this means my day is phone calls to verify identity, one by one … Two of us have left already.” -Individual Contributor, CXM, Corporate and back-office functions

“Second week the agent restarted a claims application node at 3am because the asset register shows it as non production, it is very much production. Their own infrastructure team did not know that record was wrong either. Consequently we now verify every entry by hand before the agent is allowed near it” -Engagement Manager, ITO, Technology engineering and operations

“the problem is files come in already broken before we even open them, every single time, i fix it so exception queue does not build up but honestly it never stop, i clean same three field every week and next week same three field broken again” -Individual Contributor, BPO, Corporate and back-office functions

“I dont raise a ticket for every duplicate, there are too many, I just fix it and move on, my lead knows but nobody tells the client, it eats an hour most days and nobody counts that hour anywhere.” -Individual Contributor, KPO, Corporate and back-office functions

Respondents who commented rated data quality lower than those who did not, at means of 3.0 (n = 389) and 4.2 (n = 406), respectively. Some favorable accounts identified other difficulties, including access, ownership, or decision authority. Descriptions of time spent correcting records indicate perceived additional work. The study did not measure its aggregate cost.

1.2 Accessibility of Financial Institution Data to the Engagement

Indicator C095 · Rate how accessible the customer’s data is to this engagement when and where the work requires it · Full statistical analysis ›

Delayed or unavailable access to financial institution data was accompanied by reported reliance on emailed extracts, periodic files, and manual transfer. The mean rating was 3.5 and the median 3.0 (n = 798 respondents). Respondents on canceled engagements rated access lowest (mean 2.4, n = 305 respondents), and individual contributors rated it lower than any other role with 10 or more answers (mean 3.4, n = 618 respondents).

All four core study measures were associated with the accessibility of the financial institution's data, and none of the applicable robustness checks overturned an association. Higher ratings were associated with less cancellation (r = −0.57, 95% CI −0.67 to −0.45, n = 150 engagements). They were associated with higher customer satisfaction among active engagements (r = 0.67, 95% CI 0.54 to 0.77, n = 94 engagements). The engagement indicator mean computed without the indicator was higher where access was rated higher (r = 0.78, 95% CI 0.71 to 0.84, n = 150 engagements). Intent to leave was lower (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.52, n = 749 respondents). Distressed ratings came from 402 of the 798 answers (50.38%), and the engagement mean was distressed on 65 of the 150 engagements.

Data-access comments numbered 190, from 176 respondents on 101 engagements. 163 were negative (85.79%). Respondents described delays in obtaining logins, service accounts, or firewall approvals and interim reliance on emailed extracts, screenshots, and periodic files. Some reported copying data manually because the required system connection had not been approved.

“What they did was give us read only rights on the case system for two weeks, we could see the alert but not close it, so my queue kept growing. i asked twice, no answer. Then one day the access is there, nobody tells us.” -Individual Contributor, ITO, Financial crime and client onboarding

“Last month I wait nine days for a service account on their claims warehouse… On my previous project, a health insurer also, the access came in two days and there is a named person for it. Here nobody owns that.” -Individual Contributor, ITO, Technology engineering and operations

“In March we wait eleven days for a firewall rule so the agents could reach their case system, and the queue builds the whole time. Their monitoring platform is from 2009 and everything we take out of it comes through a nightly file, there is no interface.” -Engagement Manager, BPO, Financial crime and client onboarding

“i put in a request for read access on their HR feed in january and still we do not have it, so every joiner list comes to us as a file by email once a day. And by the time we get it the person already started.” -Individual Contributor, ITO, Technology engineering and operations

“Last week we needed three months of prior notes and their system only gives us export twice a week. So we work from whatever came in the last export and flag the gap in the summary” -Individual Contributor, KPO, Credit, underwriting and suitability

Respondents who commented rated access lower than those who did not, at means of 3.1 (n = 171) and 3.6 (n = 627). Their accounts described manual data transfer while access remained unavailable or delayed.

1.3 Normalization of Financial Institution Data Across Source Systems

Indicator C096 · Rate how well the customer’s data is normalized, with consistent formats and definitions across source systems · Full statistical analysis ›

Inconsistent formats and definitions across the financial institution's source systems required manual reconciliation in respondents' accounts. The mean rating was 3.5 and the median 3.0 (n = 809 respondents). Respondents on canceled engagements rated normalization lowest (mean 2.3, n = 311 respondents), and individual contributors rated it lowest of the roles (mean 3.4, n = 627 respondents).

Every association of the normalization of the financial institution's data with the four core study measures held, in each of the applicable robustness checks. Engagements that rated normalization higher had a lower cancellation rate (r = −0.60, 95% CI −0.69 to −0.49, n = 150 engagements). Their customer satisfaction among active engagements was higher (r = 0.68, 95% CI 0.56 to 0.78, n = 94 engagements). So was the engagement indicator mean computed without the indicator (r = 0.81, 95% CI 0.75 to 0.86, n = 150 engagements). Their respondents reported lower intent to leave (r = −0.63, cluster bootstrap 95% CI −0.69 to −0.56, n = 758 respondents). Distressed ratings came from 413 of the 809 answers (51.05%), and the engagement mean was distressed on 63 of the 150 engagements.

The written responses addressing normalization numbered 199, from 184 respondents on 106 engagements, and 180 of the 199 (90.45%) were negative. They describe the same account holder, vendor, employee, or account recorded under different codes, names, and formats in different systems of the financial institution, and file layouts that changed from one delivery to the next. On several engagements, the differences arose because business units, regions, or merged companies had kept their own codes, and the financial institution had named no owner of a single definition. The delivery team reconciled the records by hand before the agents could use them.

“Last month we tried to build a single joiner record for one branch. Same person appeared three times across their directory, their HR feed and the core entitlement store, each with different identifier and different spelling of name. Nobody owns reconciliation.” -Engagement Manager, ITO, Technology engineering and operations

“Three systems hold job codes and none of them agree, and we spent the first six weeks just proving which one was authoritative. Thus the agent inherited a mess nobody on either side had owned before.” -Engagement Manager, BPO, Corporate and back-office functions

“Here every business unit sends employee data in its own format. One uses employee id, one uses email, one sends a pdf. Nobody on their side thinks that's a problem.” -Individual Contributor, CXM, Corporate and back-office functions

“What i did is keep a small mapping file of how each system writes the trade reference, because settlement side and confirmations side do not write it the same way… one puts the counterparty code in front, the other one puts it at the end. So before the agent picks up the exception queue i run my mapping over the file and make the references match. It take maybe twenty minutes each morning.” -Individual Contributor, BPO, Corporate and back-office functions

“I feel angry writing this again… Same column means one thing in file today and different thing in file next week, client dont check before sending it to us, so I fix number myself before we can use it in system” -Individual Contributor, CXM, Corporate and back-office functions

“Their data is the worst I have seen in nine years, one prospect sits four times in the CRM with four spellings and no client id, so the agent makes four leads and the relationship manager sees the same family twice in a week and stops trusting the list. … There is no name against AI anywhere in that organisation, that is the real gap.” -Engagement Manager, KPO, Client servicing and communications

Some respondents praised institutional staff's responsiveness while criticizing inconsistent data definitions. Commenters rated normalization at 2.8 (n = 178), compared with 3.7 among noncommenters (n = 631). Reported reconciliation of multiple identifiers illustrates a specific burden. It does not establish that every automated process requires the same manual intervention.

1.4 Absence of Ownership and Validation in Financial Institution Data Governance

Indicator C097 · Rate the maturity of the customer’s data governance over the data this engagement uses · Full statistical analysis ›

Unclear ownership and inadequate validation of financial institution data left delivery teams reporting their own checks and decisions about conflicting values. The mean rating was 3.6 and the median 3.0 (n = 797 respondents). Ratings were highest among active engagements in Build-Out (mean 4.4, n = 160 respondents) and lowest among canceled engagements (mean 2.5, n = 305 respondents). Individual contributors gave the lowest ratings of the roles (mean 3.5, n = 614 respondents) and portfolio directors the highest (mean 4.8, n = 31 respondents).

The maturity of the financial institution's data governance showed an association with each of the four core study measures that survived all applicable robustness checks. Engagements with higher ratings had a lower cancellation rate (r = −0.56, 95% CI −0.66 to −0.44, n = 150 engagements). Customer satisfaction among active engagements was higher where governance was rated higher (r = 0.67, 95% CI 0.54 to 0.77, n = 94 engagements). Higher ratings were associated with a higher engagement indicator mean computed without the indicator (r = 0.76, 95% CI 0.68 to 0.82, n = 150 engagements). They were associated with lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.53, n = 750 respondents). Distressed ratings came from 401 of the 797 answers (50.31%), and the engagement mean was distressed on 60 of the 150 engagements.

Data-governance comments numbered 172, from 155 respondents on 99 engagements. 152 were negative (88.37%). Recurring accounts described unclear ownership and inadequate validation before transfer to the vendor. Respondents reported receiving conflicting or absent definitions of fields and performing their own checks before processing.

“The thing a number cannot show is that their data is not so bad, it is the ownership of it which is missing … when we ask who decides what a field is supposed to mean, there is nobody. Two teams give two answers and both are confident.” -Engagement Manager, BPO, Trade, settlement and reconciliation

“on their side the account classification has no owner. every month different person tell us which portfolios sit in which composite, next month it change again, and no one can say who approved it.” -Individual Contributor, KPO, Trade, settlement and reconciliation

“In week three we asked for the data dictionary for the client positions warehouse. What came back was a spreadsheet from 2021 with half of tables missing. Since then we have been mapping fields by reading the source tables ourselves.” -Engagement Manager, ITO, Technology engineering and operations

“In May we found dormant-account flags on core system were three days behind the servicing platform, so agents were writing to customers whose accounts had already been reactivated. Took client eleven days to confirm which system was master.” -Engagement Manager, BPO, Payments, cards and disputes

“But nobody upstream owns the fix, so i keep cleaning the same file every month and it keeps arriving dirty, i if you would flag it each cycle and my lead knows, nothing changes on their side though.” -Individual Contributor, BPO, Trade, settlement and reconciliation

Respondents who commented rated governance at 3.0 (n = 145), compared with 3.7 among noncommenters (n = 652). Some described selecting a value when institutional personnel did not resolve conflicting records. This is an account of decision-making during delivery, not verification that the selected value was correct.

2 Legacy Systems and Technical Infrastructure

Unavailable interfaces and infrastructure limitations were reported to require manual transfer or constrain the environments in which agents operated. Legacy-system integration and infrastructure readiness assess different technical constraints. The first concerns connection to existing systems. The second concerns the environments and capacity needed to operate agentic AI.

Written accounts described unavailable interfaces, manual transfers, and test environments that differed from production. The legacy-system item attracted substantially more comments than infrastructure readiness, so the two bodies of qualitative evidence differ in scope as well as subject.

“Legacy HR systems still fight the agent at every handoff” -Engagement Manager, BPO, Corporate and back-office functions

2.1 Legacy Systems Without an Interface for Agents

Indicator C098 · Rate how well the customer’s legacy systems support the integration this engagement requires · Full statistical analysis ›

Respondents described manual extraction and file transfer where the financial institution's legacy systems lacked the interfaces needed for agent integration. The mean rating was 3.5 and the median 3.0 (n = 801 respondents). Respondents on canceled engagements rated the systems lower than respondents on active engagements in any stage (mean 2.3, n = 308 respondents).

The support the legacy systems gave to integration was associated with cancellation, customer satisfaction, the engagement indicator mean, and intent to leave, and each association held under the applicable robustness checks. Higher ratings were associated with less cancellation (r = −0.58, 95% CI −0.67 to −0.46, n = 150 engagements). Customer satisfaction among active engagements was higher where the systems were rated higher (r = 0.72, 95% CI 0.60 to 0.80, n = 94 engagements). The engagement indicator mean computed without the indicator was also higher (r = 0.82, 95% CI 0.76 to 0.87, n = 150 engagements). Respondents who rated the systems higher reported lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.53, n = 755 respondents). Distressed ratings came from 416 of the 801 answers (51.94%), and the engagement mean was distressed on 65 of the 150 engagements.

The legacy-systems indicator received 231 written responses from 212 respondents on 116 engagements. 216 responses (93.51%) were negative. Within the question asking for the three principal challenges to job satisfaction, 128 answers from 128 respondents were coded to legacy systems. Other accounts described absent programmatic interfaces, nightly file transfers, manual extraction, and integrations affected by provider releases.

“the systems it sits in are very old and there is no way for us to reach them directly, everything comes through a person, and that person is on leave or in another meeting.” -Individual Contributor, BPO, Corporate and back-office functions

“the platform the expenses sit in is very old and there is no clean way in or out, so what reaches us is an extract, and the extract is only as good as whoever set it up years back. And nobody there today knows who set it up or why the columns are in that order.” -Individual Contributor, BPO, Trade, settlement and reconciliation

“Last month we ask for the case API and their SIEM version does not expose it, so agent enrichment runs off a nightly export instead of live.” -Engagement Manager, ITO, Technology engineering and operations

“Legacy platform integration broke constantly and nobody owned the fix” -Individual Contributor, BPO, Trade, settlement and reconciliation

Some respondents distinguished inherited system limitations from the conduct of institutional staff. More negative written sentiment was associated with higher intent to leave and, at engagement level, higher cancellation. The comments described manual transfer where integration was unavailable. They do not establish that no alternative technical solution existed.

2.2 Readiness of Financial Institution Infrastructure for Agentic AI

Indicator C099 · Rate the readiness of the customer’s technical infrastructure to support the agentic AI this engagement deploys · Full statistical analysis ›

Financial institution infrastructure received its lowest ratings on canceled engagements, while written accounts identified mismatched test environments and inadequate operating capacity. The mean rating was 3.5 and the median 3.0 (n = 803 respondents). Respondents on canceled engagements rated the infrastructure lower than respondents on active engagements in any stage (mean 2.3, n = 297 respondents).

Infrastructure readiness was associated with all four core measures and passed the applicable sensitivity checks. Higher ratings were associated with lower cancellation (r = −0.60, 95% CI −0.69 to −0.49, n = 150 engagements). They were associated with higher customer satisfaction among active engagements (r = 0.74, 95% CI 0.63 to 0.82, n = 94 engagements) and a higher engagement indicator mean computed without the infrastructure indicator (r = 0.84, 95% CI 0.79 to 0.88, n = 150 engagements). Respondents who rated infrastructure readiness higher reported lower intent to leave (r = −0.61, cluster bootstrap 95% CI −0.67 to −0.53, n = 755 respondents). Distressed ratings came from 419 of the 803 answers (52.18%), and the engagement mean was distressed on 64 of the 150 engagements.

Few written responses addressed the infrastructure: 23 of 23 respondents on 22 engagements, and 18 of the 23 were negative. The evidence is thin and supports no general statement on its own. The negative responses named test environments that no longer matched production, servers that could not handle the volume, systems that timed out, and environments not ready for cloud services. Each of the five positive responses described the infrastructure as ready and named a different problem.

“The test environment is the thing nobody asks about. It is a copy of prod from about two years ago, and half the deal types we support now do not exist in it. So we build against something that isn't real and find out on release night.” -Individual Contributor, ITO, Technology engineering and operations

“It comes down to one thing, the log sources feeding our triage agent are only about half mapped, so severity keeps coming back wrong on anything touching the acquiring platform.” -Engagement Manager, ITO, Technology engineering and operations

“We were handed a genuinely solid environment to work in, access and identities were provisioned quickly and that is not always our experience. What would help now is one named owner for the custodian data definitions.” -Engagement Manager, BPO, Trade, settlement and reconciliation

The ratings measure perceived infrastructure readiness. They do not estimate the frequency of particular problems such as timeouts or mismatched test environments. The 23 written accounts illustrate experiences within the commenting subset.

Their infrastructure is fine, thats not the issue. What would help is a standing read only account on the uat pricing database, instead of raising a ticket every time we need a sample set and waiting three days. And they want the current workflow automated exactly as it stands, screen order and all.”

INDIVIDUAL CONTRIBUTOR · ITO, Technology engineering and operations

3 Process Documentation, Maturity, and Redesign

Missing documentation and inconsistent working practices left respondents describing uncertainty over which financial institution process an agent should implement. Four indicators describe the financial institution's processes. They cover how completely the processes were documented, how standardized they were, how consistently they were performed across business units and regions, and how willing the financial institution was to redesign them for agentic AI. Each had about half of its answers in the distressed range, from 396 of 789 answers (50.19%) for standardization to 430 of 795 (54.09%) for documentation. Documentation had the highest distressed share of any indicator in the category. The four were associated with every core study measure, and none of the applicable robustness checks overturned an association.

Documentation, standardization, cross-unit consistency, and willingness to redesign are distinct process attributes. The written accounts describe missing or outdated documentation, multiple local variants, and requests to automate existing steps without redesign. A recurring implication is uncertainty over which process definition the agent should implement.

“they told us up front not to change any of the workflow, just make the agent do it the way the underwriters already do it. so my whole week is teaching it to copy a bad process.” -Individual Contributor, ITO, Client servicing and communications

Respondents described observing staff to document workflows, reconciling regional variants, and implementing steps before receiving a definitive institutional decision. Part 5 examines the separately measured process alterations. These accounts suggest possible links between process definition and Manual alteration without establishing causation.

3.1 Process Documentation and Reported Reconstruction

Indicator C100 · Rate how completely the customer’s processes are mapped and documented to the depth agentic AI requires · Full statistical analysis ›

Incomplete or outdated financial institution process documentation left delivery teams reporting reconstruction of workflows from observed practice. The mean rating was 3.5 and the median 3.0 (n = 795 respondents). Respondents on canceled engagements rated documentation lower than respondents on active engagements in any stage (mean 2.4, n = 303 respondents).

The documentation of the financial institution's processes showed an association with each of the four core study measures that survived all applicable robustness checks. Higher ratings were associated with less cancellation (r = −0.56, 95% CI −0.66 to −0.44, n = 150 engagements). They were associated with higher customer satisfaction among active engagements (r = 0.70, 95% CI 0.57 to 0.79, n = 94 engagements). Documentation was associated with a higher engagement indicator mean computed without the indicator (r = 0.81, 95% CI 0.74 to 0.86, n = 150 engagements). Respondents who rated it higher reported lower intent to leave (r = −0.57, cluster bootstrap 95% CI −0.65 to −0.49, n = 748 respondents). Distressed ratings came from 430 of the 795 answers (54.09%), the highest distressed share among the 20 indicators of financial institution readiness, and the engagement mean was distressed on 70 of the 150 engagements.

The written responses addressing documentation numbered 270, from 238 respondents on 121 engagements, and 229 of the 270 (84.81%) were negative. The largest group came from the question on why the respondent had altered the financial institution's processes. Respondents described processes that had never been written down, documents that did not match what the financial institution's staff actually did, and workflows the delivery team rebuilt by watching those staff at work.

“the process doc they handed us was from 2019. it didnt match anything. the desk does it one way, the doc says another, and the two floors dont even do it the same. so we sat with one of their people for a week and wrote down what actually happens. that isnt our job but nobody else was going to.” -Individual Contributor, BPO, Payments, cards and disputes

“In fairness nobody there was hiding, they simply dont have the process written anywhere, so each person answers from memory.” -Individual Contributor, CXM, Corporate and back-office functions

“The signed off process is not what the finance leads actually do day to day… i adjust my workflow to match how they really work, otherwise the model calibration breaks every month.” -Individual Contributor, KPO, Corporate and back-office functions

“Here the reconciliation steps are mostly in peoples heads, we ask and somebody explains it on a call.” -Individual Contributor, BPO, Trade, settlement and reconciliation

“what the numbers don't show is nobody has written down how they actually handle a disputed charge. every senior agent on their side does it slightly diffrent.” -Individual Contributor, CXM, Payments, cards and disputes

Commenters rated documentation at 3.0 (n = 225), compared with 3.7 among noncommenters (n = 570). Some described unanswered clarification requests and reliance on observed practice rather than an approved document. The distinction is between reported operational practice and a formally agreed process specification.

3.2 Process Standardization and Repeatability

Indicator C101 · Rate the maturity of the customer’s processes in scope, meaning how standardized and repeatable they are · Full statistical analysis ›

Respondents described financial institution processes that varied between people and locations rather than operating through standardized, repeatable steps. The mean rating was 3.5 and the median 3.0 (n = 789 respondents). Respondents on canceled engagements rated the processes lower than respondents on active engagements in any stage (mean 2.4, n = 295 respondents).

All four core study measures were associated with the maturity of the financial institution's processes, and the applicable robustness checks left each association intact. Engagements that rated the processes higher had a lower cancellation rate (r = −0.57, 95% CI −0.67 to −0.45, n = 150 engagements). They recorded higher customer satisfaction among active engagements (r = 0.72, 95% CI 0.61 to 0.81, n = 94 engagements). Process maturity was associated with a higher engagement indicator mean computed without the indicator (r = 0.80, 95% CI 0.74 to 0.85, n = 150 engagements). It was associated with lower intent to leave (r = −0.56, cluster bootstrap 95% CI −0.64 to −0.49, n = 742 respondents). Distressed ratings came from 396 of the 789 answers (50.19%), and the engagement mean was distressed on 62 of the 150 engagements.

Process-maturity comments numbered 85, from 79 respondents on 59 engagements. 74 were negative (87.06%). Respondents described variation between people, desks, or regions and unresolved choices about the process to implement. All four comparisons of written sentiment with the study measures reached significance. These analyses concern the commenting subset and do not establish an independent causal contribution.

“Every desk contact on their side confirms trades a different way. Some use the Portal. Some just email a PDF. Some call and read numbers off a screen. And there is no single process document that says which one is correct for which counterparty, so we made our own version months ago and just follow that now.” -Individual Contributor, BPO, Trade, settlement and reconciliation

“each fund still runs its attribution its own way, so what we built for one desk did not carry across to the next. They were never going to redesign how attribution is done — only who does it.” -Portfolio Director, KPO, Trade, settlement and reconciliation

“The problem is every exception gets handled a different way depending which person from Client team responds that day, so I make the call on Process myself and note it internally, revert back if anyone ask.” -Individual Contributor, BPO, Corporate and back-office functions

“On my last account, a card client, the process was written down and every team followed it. Here every desk route their tickets their own way.” -Individual Contributor, CXM, Technology engineering and operations

3.3 Inconsistency of Processes Across Business Units and Regions

Indicator C102 · Rate how consistently the customer performs the same processes across its business units and regions · Full statistical analysis ›

Differences in how financial institution business units and regions performed the same process complicated the definition of a common workflow for agents. The mean rating was 3.6 and the median 3.0 (n = 801 respondents). Respondents on canceled engagements rated consistency lower than respondents on active engagements in any stage (mean 2.4, n = 300 respondents).

The consistency of the financial institution's processes across business units and regions was associated with cancellation, customer satisfaction, the engagement indicator mean, and intent to leave, and each association held under the applicable robustness checks. More consistent processes were associated with less cancellation (r = −0.58, 95% CI −0.68 to −0.47, n = 150 engagements). They were associated with higher customer satisfaction among active engagements (r = 0.74, 95% CI 0.63 to 0.82, n = 94 engagements). The engagement indicator mean computed without the indicator was higher where consistency was rated higher (r = 0.83, 95% CI 0.78 to 0.88, n = 150 engagements). Intent to leave was lower (r = −0.61, cluster bootstrap 95% CI −0.67 to −0.53, n = 755 respondents). Distressed ratings came from 408 of the 801 answers (50.94%), and the engagement mean was distressed on 65 of the 150 engagements.

The written responses addressing consistency numbered 44, from 44 respondents on 35 engagements, all in comment on the financial institution, and 35 of the 44 (79.55%) were negative. They describe the same process performed differently by each region, desk, branch, or business unit. On one engagement, the financial institution had been formed from four companies that never merged their rules.

“the client is four companies that never merged their HR rules. Leave entitlement, asset return, even the shift codes are different in each one, also nobody there could tell us which version was correct. We were asked to automate a process that has four versions and no owner.” -Engagement Manager, CXM, Corporate and back-office functions

“Each region books the same account closure with a different reason code. What I do is keep a small list of which code belongs to which branch group, and I map them before the file goes in, otherwise the agent will raise exception on every one of them.” -Individual Contributor, BPO, Payments, cards and disputes

“New business in the west region orders requirements one way, the east does it different, and the agent has to be told twice. Same product, two workflows.” -Individual Contributor, BPO, Credit, underwriting and suitability

“What has not changed is every region does invoce approval a different way, so rules we set for one business unit do not fit the next one, and nobody wrote any of it down.” -Individual Contributor, BPO, Corporate and back-office functions

No statistically significant association was detected between written sentiment and the study measures on the 44-response base. These accounts concern cross-unit variation specifically, whereas section 3.2 addresses process standardization more broadly. The quantitative outcome associations reported here derive from the ratings.

3.4 Automation of Processes as They Stand Rather Than Redesign

Indicator C103 · Rate the customer’s willingness to redesign processes for agentic AI rather than automate them as they are · Full statistical analysis ›

Respondents described requests to automate existing financial institution workflows without reconsidering their steps or the checks they required. The mean rating was 3.5 and the median 3.0 (n = 804 respondents). Respondents on canceled engagements rated the financial institution's willingness lower than respondents on active engagements in any stage (mean 2.4, n = 307 respondents).

The financial institution's willingness to redesign its processes for agentic AI showed an association with each of the four core study measures that survived all applicable robustness checks. Engagements where the willingness was rated higher had a lower cancellation rate (r = −0.56, 95% CI −0.66 to −0.44, n = 150 engagements). Their customer satisfaction among active engagements was higher (r = 0.70, 95% CI 0.58 to 0.79, n = 94 engagements). Willingness to redesign was associated with a higher engagement indicator mean computed without the indicator (r = 0.82, 95% CI 0.76 to 0.87, n = 150 engagements). Respondents who rated it higher reported lower intent to leave (r = −0.59, cluster bootstrap 95% CI −0.66 to −0.52, n = 755 respondents). Distressed ratings came from 429 of the 804 answers (53.36%), and the engagement mean was distressed on 67 of the 150 engagements.

Redesign comments numbered 43, from 43 respondents on 38 engagements. 34 were negative. Respondents described requests to reproduce existing workflows or accelerate them without reconsidering their steps. Some reported difficulties when human checks were removed. These accounts do not establish that unchanged workflows always fail under automation.

“the client wants the agent to produce the summary in exactly the same shape their underwriters have always done it, box for box, including the sections nobody reads. We ask twice about redesigning the template for a machine and each time we are told to keep it as is. And that one decision costs us more rework than anything else in month one.” -Engagement Manager, KPO, Credit, underwriting and suitability

“They want same close process as before, only faster, no appetite to change any step. Their data itself is fine, we get clean trial balance every month, accordingly I went higher on those. Its the willingness which is missing, not the informations.” -Individual Contributor, BPO, Trade, settlement and reconciliation

“Nobody re-engineered the process before handing it to the agent” -Individual Contributor, CXM, Technology engineering and operations

“A specific case last month, the change sat for two weeks because nobody would take the decision, so we applied it ourselves. Nobody ever updated the process for how an agent actually works, so it falls on us to catch what should be flagged” -Individual Contributor, KPO, Trade, settlement and reconciliation

Written sentiment on redesign was not significantly associated with the study measures on this 43-response base. The accounts suggest that the suitability of retained checks and handoffs should be assessed during redesign. They do not support a universal distinction between the errors people and agents can make.

Two months in, we have opened three of their internal applications, so any judgment on infrastructure is a guess. The form assumes we have seen the systems. Their SMEs do show up when asked, and they will redesign a process rather than lift it as is.”

ENGAGEMENT MANAGER · ITO, Technology engineering and operations

4 Organizational Readiness and Staff Capability

Cooperation from financial institution staff did not necessarily provide the agentic AI knowledge, specialist availability, or changed working practices that delivery required. Three indicators describe the financial institution's people. They cover the organization's readiness to absorb the changes agentic AI makes to daily work, the agentic AI knowledge and skills of its staff on the engagement, and the availability of its subject matter experts. The knowledge of the staff was distressed for 418 of 793 respondents (52.71%) and the availability of experts for 427 of 793 (53.85%). Readiness to absorb change was distressed in 396 of 794 answers (49.87%). The associations of all three with the four core study measures held under each of the applicable robustness checks.

The comments distinguish willingness to cooperate from experience with agentic AI. Some respondents described responsive institutional staff who nevertheless lacked experience operating or overseeing agents. The ratings address capability and readiness. The positive accounts of cooperation remain relevant counterevidence to a general claim of institutional unwillingness.

The expert-availability item concerns timely access to institutional judgment. Accounts of staff shared across assignments, particularly during time-sensitive work, are examined in section 4.3.

“It's that there is nobody on the deal team who can answer a question. I sent a question about which entity a set of payroll files belonged to on a Tuesday, got an answer nine days later, and the answer was a guess.” -Individual Contributor, KPO, Corporate and back-office functions

4.1 Organizational Readiness to Absorb Changes to Daily Work

Indicator C107 · Rate the customer organization’s readiness to absorb the changes to daily work that agentic AI introduces · Full statistical analysis ›

Readiness to absorb changes to daily work received its lowest ratings on canceled engagements, while written accounts distinguished cooperation from actual changes in working practice. The mean rating was 3.5 and the median 4.0 (n = 794 respondents). Respondents on canceled engagements rated the organization's readiness lower than respondents on active engagements in any stage (mean 2.3, n = 305 respondents).

Every association of the organization's readiness to absorb changes to daily work with the four core study measures held in each of the applicable robustness checks. Engagements that rated the organization's readiness higher had a lower cancellation rate (r = −0.59, 95% CI −0.69 to −0.48, n = 150 engagements). Customer satisfaction among their active engagements was higher (r = 0.73, 95% CI 0.61 to 0.81, n = 94 engagements). Readiness was associated with a higher engagement indicator mean computed without the indicator (r = 0.83, 95% CI 0.78 to 0.88, n = 150 engagements). It was associated with lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.66 to −0.53, n = 746 respondents). Distressed ratings came from 396 of the 794 answers (49.87%), and the engagement mean was distressed on 65 of the 150 engagements.

Few written responses addressed the indicator: 26 from 25 respondents on 23 engagements, and the evidence is divided. Negative responses numbered 16 and positive responses 10. The positive responses described staff who were open to the change and placed the difficulty elsewhere. The negative responses described staff who continued working as before, and adoption was reported as complete without being measured.

“their own staff are not ready for any of this, half of them still ask us to mail the same note across because they dont open the tool.” -Individual Contributor, KPO, Corporate and back-office functions

“i felt bad putting one against their people but its not really the people, its that their night shift still works off the old runbook and wont touch the triage queue at all. day team is fine with it. thats the whole of that score.” -Individual Contributor, ITO, Technology engineering and operations

“The vendor reported system uptime as the primary success metric on a tool where the actual risk was answer accuracy, so the dashboard stayed green through months of advisors quietly stopping use of the assistant.” -Individual Contributor, CXM, Client servicing and communications

Written sentiment was not significantly associated with the study measures on the 26-response base. The comments include both favorable and unfavorable experiences of organizational change. The broader readiness ratings cannot determine the prevalence of each specific behavior described in those comments.

4.2 Agentic AI Knowledge of Financial Institution Staff

Indicator C104 · Rate the agentic AI knowledge and skills of the customer staff working with this engagement · Full statistical analysis ›

Respondents reported limits in the agentic AI knowledge and skills of financial institution staff, alongside favorable accounts that identified other delivery constraints. The mean rating was 3.5 and the median 3.0 (n = 793 respondents). Respondents on canceled engagements rated the staff's knowledge lower than respondents on active engagements in any stage (mean 2.3, n = 307 respondents).

All four core study measures were associated with the agentic AI knowledge and skills of financial institution staff, and none of the applicable robustness checks overturned an association. Where the staff's knowledge was rated higher, cancellation was less common (r = −0.59, 95% CI −0.69 to −0.48, n = 150 engagements). Among active engagements, customer satisfaction was higher where the staff's knowledge was rated higher (r = 0.70, 95% CI 0.58 to 0.79, n = 94 engagements). The engagement indicator mean computed without the indicator was higher as well (r = 0.82, 95% CI 0.75 to 0.86, n = 150 engagements). Respondents who rated the staff's knowledge higher reported lower intent to leave (r = −0.60, cluster bootstrap 95% CI −0.67 to −0.53, n = 750 respondents). Distressed ratings came from 418 of the 793 answers (52.71%), and the engagement mean was distressed on 60 of the 150 engagements.

Comments on staff knowledge numbered 36, from 36 respondents on 30 engagements. 24 were negative. Negative accounts described limited experience with agents, while positive accounts credited staff understanding and identified other constraints, including data, access, and ownership.

“their manager is fine, hes responsive, the analysts under him just dont have any experience with this stuff yet.” -Individual Contributor, ITO, Technology engineering and operations

“What they did, please note, is answer us quickly – when we raised the failed release in June their platform team was on the call the same afternoon, which I cannot fault. The difficulty is the people sitting with us have never run a pipeline with an agent in it, so I explain the same rollback twice … ” -Engagement Manager, ITO, Payments, cards and disputes

“Their process documents are actually fine, this i will say. But their people have never worked with an agent before, so every request come back with question. And access to the Config environment took five weeks.” -Individual Contributor, ITO, Client servicing and communications

More negative written sentiment about staff knowledge was associated with higher intent to leave, but no significant association was detected with cancellation or customer satisfaction. Some respondents described additional explanation and checking responsibilities. The study does not establish that staff inexperience caused those responsibilities or subsequent workforce intentions.

4.3 Availability of Financial Institution Subject Matter Experts

Indicator C106 · Rate the availability of the customer’s subject matter experts when this engagement needs them · Full statistical analysis ›

Limited availability of financial institution subject matter experts left some delivery teams reporting process decisions and data correction without the requested guidance. The mean rating was 3.5 and the median 3.0 (n = 793 respondents). Respondents on canceled engagements rated availability lower than respondents on active engagements in any stage (mean 2.3, n = 299 respondents).

The availability of the financial institution's subject matter experts was associated with all four core study measures, and each association held under the applicable robustness checks. Engagements that rated the experts more available had a lower cancellation rate (r = −0.59, 95% CI −0.68 to −0.47, n = 150 engagements). They recorded higher customer satisfaction among active engagements (r = 0.70, 95% CI 0.58 to 0.79, n = 94 engagements). Availability was associated with a higher engagement indicator mean computed without the indicator (r = 0.82, 95% CI 0.77 to 0.87, n = 150 engagements). Respondents who rated the experts more available reported lower intent to leave (r = −0.58, cluster bootstrap 95% CI −0.64 to −0.50, n = 748 respondents). Distressed ratings came from 427 of the 793 answers (53.85%), and the engagement mean was distressed on 69 of the 150 engagements.

Expert-availability comments numbered 220, from 202 respondents on 113 engagements. 184 were negative (83.64%). Respondents described limited specialist capacity shared with other assignments. Some reported making process decisions or correcting data without the requested guidance. Positive accounts described prompt expert responses and located difficulties elsewhere, including data or access.

“There is exactly one person on client side who understands how their shift allowance and arrears rules actually work, she sits in one meeting a week with us and she is covering three other projects … We had a cycle where fourteen queries sat nine days for want of one answer, and then payroll ran anyway because payroll always runs.” -Engagement Manager, BPO, Corporate and back-office functions

“The bankers are never free when a book is live, which is exactly when we need them. So we dont ask in the moment anymore. We keep our own note of how each desk has allocated in the past, deal by deal, and we work from that, then we confirm after pricing rather than before.” -Engagement Manager, KPO, Trade, settlement and reconciliation

“the problem is we wait weeks for one modeler to answer a question, then everything lands at once. my team ends up burning weekends to catch up. and it repeats every quarter” -Individual Contributor, KPO, Credit, underwriting and suitability

“there is one guy who knows the expense policy and he is booked out three weeks. so my team just guesses and eats it when its wrong.” -Individual Contributor, BPO, Corporate and back-office functions

“the problem is a break on one Fund in March, we send the query up on Monday and no informations come back for two weeks, by then NAV is already signed off. My team lead says the SME on Bank side is put on other project.” -Individual Contributor, BPO, Trade, settlement and reconciliation

More negative written sentiment about expert availability was associated with higher engagement cancellation. The accounts describe decisions made without timely specialist input, but they do not independently establish what guidance was available elsewhere or whether the resulting decisions were appropriate.

5 Management of the Financial Institution's Side of the Engagement

Unresolved authority and escalations were reported even where individual financial institution staff responded promptly. Two indicators describe how the financial institution managed its own side of the engagement. They measure the effectiveness of that management and the speed with which the financial institution responded when the engagement raised an issue that needed its action. The management of the engagement was distressed in 398 of 811 answers (49.08%), and the speed of response in 404 of 789 (51.20%). Both indicators held every association with the four core study measures through the applicable robustness checks.

The written accounts distinguish prompt responses from individual staff from decisions requiring formal authority. Respondents described unresolved escalations, conflicting answers, and decisions made by vendor personnel to meet delivery requirements. Some reported that these decisions were not communicated to the financial institution.

“we get an answer same day now, used to be a week. what hasnt changed is who owns it when the tool misroutes something. everybody agrees it needs fixing, nobody signs anything.” -Individual Contributor, BPO, Payments, cards and disputes

The qualitative analyses examine associations between these accounts and outcomes. Their results are reported under the two indicators below. Because ratings and comments come from the same respondent population, agreement between them is not independent-source validation.

5.1 Financial Institution Management and Decision Authority

Indicator C105 · Rate the effectiveness of the customer’s management of its side of this engagement · Full statistical analysis ›

Unclear ownership and decision authority were recurring concerns in accounts of the financial institution's management of its side of the engagement. The mean rating was 3.6 and the median 4.0 (n = 811 respondents). Respondents on canceled engagements rated the financial institution's management lower than respondents on active engagements in any stage (mean 2.4, n = 305 respondents).

The effectiveness of the financial institution's management of its side was associated with all four core study measures, and each association held under the applicable robustness checks. Engagements whose delivery teams rated the financial institution's management of its side higher had a lower cancellation rate (r = −0.56, 95% CI −0.66 to −0.44, n = 150 engagements). They recorded higher customer satisfaction among active engagements (r = 0.72, 95% CI 0.60 to 0.80, n = 94 engagements). Effective management by the financial institution was associated with a higher engagement indicator mean, computed without the management indicator (r = 0.82, 95% CI 0.76 to 0.87, n = 150 engagements). Respondents who rated the management higher reported lower intent to leave (r = −0.59, cluster bootstrap 95% CI −0.66 to −0.51, n = 763 respondents). Distressed ratings came from 398 of the 811 answers (49.08%), and the engagement mean was distressed on 64 of the 150 engagements.

Comments on institutional management numbered 196, from 173 respondents on 104 engagements. 184 were negative (93.88%), including 157 assigned the strongest negative score. Recurring accounts described unclear task ownership or decision authority and vendor personnel making decisions after institutional responses were delayed.

“The people we sat with were fine, they answered on chat, they showed us things when they could. The problem is above them. Nobody there was allowed to say yes to anything. Their engineer told me straight, he cant open a service account for a bot without a sign off that does not exist yet, so we wait. Four weeks we wait and then the project is closed.” -Individual Contributor, ITO, Technology engineering and operations

“In the meantime the product team in London was using one definition of the peer group and the team in Singapore was using another, and both told us theirs was the approved one … Nobody on their side owned it, so we didnt get a decision, only opinions. By the time an answer came the deal review had already passed.” -Engagement Manager, KPO, Corporate and back-office functions

“To be blunt it is a joke, we correct their identity data every single week and nobody above them seems to care, they defer every decision to us and we absorb it, as such I stopped raising it and just fix things myself now.” -Individual Contributor, ITO, Technology engineering and operations

“nobody on their side owns this piece, that is the real problem. on my first desk, card refunds, there was one name on the wall and i could ask her. here i send question and it goes round three teams, nobody answer” -Individual Contributor, BPO, Trade, settlement and reconciliation

Engagements with a negative management comment had a higher observed cancellation rate. More negative respondent sentiment was associated with greater intent to leave, while no significant association was detected with customer satisfaction. Respondents commonly distinguished decision-authority problems from the effort of the institutional staff they worked with.

5.2 Speed of Financial Institution Response to Issues Raised by the Engagement

Indicator C109 · Rate how quickly the customer responds when this engagement raises issues that need customer action · Full statistical analysis ›

Prompt responses from financial institution staff did not always resolve the formal decisions on which delivery depended. The mean rating was 3.5 and the median 3.0 (n = 789 respondents). Respondents on canceled engagements rated the speed of response lower than respondents on active engagements in any stage (mean 2.4, n = 297 respondents).

All four core study measures were associated with the speed of the financial institution's response to issues raised by the engagement, and none of the applicable robustness checks overturned an association. Faster response was associated with less cancellation (r = −0.60, 95% CI −0.69 to −0.48, n = 150 engagements). Faster response by the financial institution was associated with higher customer satisfaction among the active engagements (r = 0.74, 95% CI 0.63 to 0.82, n = 94 engagements). The engagement indicator mean computed without the indicator was higher where response was rated faster (r = 0.84, 95% CI 0.78 to 0.88, n = 150 engagements). Respondents who rated the response faster reported lower intent to leave (r = −0.59, cluster bootstrap 95% CI −0.65 to −0.51, n = 740 respondents). Distressed ratings came from 404 of the 789 answers (51.20%), and the engagement mean was distressed on 64 of the 150 engagements.

The written responses addressing the speed of response numbered 498, from 382 respondents on 142 engagements, and 437 of the 498 (87.75%) were negative. Most came from the questions on manual alteration. There, respondents described questions that sat for days or weeks, answers that arrived after the deadline, and a delivery team that made the decision without the financial institution. The comments on the financial institution were more divided. Many respondents said the people they dealt with answered quickly, and others described waits that stalled the engagement.

“every question we raise on the billing cycle sits for two weeks, and the business case has been rewritten three times in eight weeks.” -Engagement Manager, BPO, Payments, cards and disputes

“I raise it, they say we will discuss about it in the next call. Next call is cancelled. We build the note anyway because the deadline does not move, then I rebuild the same note three times when the list changes again.” -Individual Contributor, KPO, Corporate and back-office functions

“Escalation goes to my lead, my lead sends to vendor manager, vendor manager sends to their side. Answer comes back in three or four days. Agent sends fifty responses in that time. Nothing else from me on it” -Individual Contributor, CXM, Client servicing and communications

“i have not had a full weekend since January. Their environment team answer once a week, Friday evening usually, so my team sit idle four days then works the whole night to make the window… two of us have asked for transfer.” -Individual Contributor, ITO, Technology engineering and operations

“last week ticket needed system access but their Security team wont approve connection, again, same as every time, and i had to enter data by hand instead, like always.” -Individual Contributor, ITO, Technology engineering and operations

“We asked three times for a methodology note and got a call instead, forty minutes, two of their people disagreeing with each other while we listened. So we built to what we heard, and then the review came back saying the outputs did not look like what they expected – which is fair, except nobody had told us what they expected.” -Engagement Manager, KPO, Corporate and back-office functions

Written sentiment about response speed was associated with cancellation, customer satisfaction, and intent to leave. The comments distinguish interpersonal responsiveness from formal authorization: some staff answered promptly while escalated decisions remained unresolved. Reports of vendor decisions made without subsequent disclosure are examined in Part 5.

6 Governance of Agentic AI Within the Financial Institution

Diffuse ownership, limited agent-risk oversight, and change approvals mismatched to operational needs were distinct reported weaknesses in the financial institution's governance. Three indicators describe how the financial institution governed agent work on its own side. They cover the assignment of ownership and accountability for AI, the capacity to oversee the risks of agents performing the engagement's tasks, and the capacity of change control to handle the changes the engagement required. About half of the answers on each were distressed: 417 of 802 answers (52.00%) for ownership of AI, 417 of 797 (52.32%) for oversight of agent risk, and 411 of 800 (51.38%) for change control. None of the applicable robustness checks overturned an association of the three with the four core study measures. Diffuse ownership of AI had the lowest mean rating of the 20 indicators in the category, 3.47 (n = 802 respondents).

The comments described unclear ownership across institutional functions, oversight frameworks respondents considered unsuitable for agents, and approval schedules that did not match reported operational changes. Their limited volume constrains generalization, and favorable accounts of established risk functions are retained below.

“Moreover their governance model assumes changes happen at release pace, not the pace an agent generates cases, so we adapted the workflow ourselves.” -Individual Contributor, KPO, Financial crime and client onboarding

The three indicators record the financial institution's own controls over work performed by agents. With the joint controls of co-management, they inform the examination of how the controls meant to govern agent work were bypassed or absent (Part 3).

6.1 Diffuse Ownership of AI Across Financial Institution Functions

Indicator C112 · Rate how clearly ownership and accountability for AI are assigned inside the customer organization · Full statistical analysis ›

The assignment of AI ownership and accountability received the lowest mean rating among the financial institution readiness indicators. The mean rating was 3.47, the lowest of the 20 indicators of financial institution readiness, and the median 3.0 (n = 802 respondents). Respondents on canceled engagements rated the assignment of ownership lower than respondents on active engagements in any stage (mean 2.3, n = 306 respondents).

Every association of the assignment of ownership for AI inside the financial institution with the four core study measures held, in each of the applicable robustness checks. Clearer ownership of AI at the financial institution was associated with less cancellation (r = −0.57, 95% CI −0.67 to −0.45, n = 150 engagements). It was associated with higher customer satisfaction among active engagements (r = 0.70, 95% CI 0.59 to 0.79, n = 94 engagements). Engagements where ownership of AI was rated clearer had a higher engagement indicator mean, computed without the ownership indicator (r = 0.81, 95% CI 0.75 to 0.86, n = 150 engagements). Intent to leave was lower among respondents who saw clearer ownership of AI (r = −0.59, cluster bootstrap 95% CI −0.66 to −0.52, n = 755 respondents). Distressed ratings came from 417 of the 802 answers (52.00%), and the engagement mean was distressed on 67 of the 150 engagements.

Ownership comments numbered 46, from 46 respondents on 40 engagements. 41 were negative. Recurring descriptions included responsibilities divided among functions, referrals between teams, and delayed decisions about permitted agent actions. The accounts concern perceived ambiguity rather than a verified inventory of institutional responsibilities.

“there is nobody on their side who owns AI. Risk own the watchlist, technology own the platform, and when the agent flags an obligor wrongly, both of them say it is the other one. Their credit analysts do turn up when we ask, that is fair, but they cannot approve anything.” -Engagement Manager, KPO, Credit, underwriting and suitability

“the client does not have anybody who owns what the agent is allowed to do. thats the whole problem right there … i flagged it same day. it took eleven days to get a yes or no on whether we keep repairing them or return the whole batch. eleven days of me sitting on a queue and aging it.” -Individual Contributor, BPO, Payments, cards and disputes

“I came away impressed, and the softer answers here come down to one thing. Their desk leads are strong and the endpoint data is clean. But nobody on their side owns which ticket categories an agent may close unsupervised, and that call keeps moving between people” -Portfolio Director, ITO, Technology engineering and operations

“My last engagement was a smaller bank and their finance team gives us one owner for the model, one person to ask. Here nobody owns it. I send a question on the entity heirarchy and it goes round three teams and comes back to me.” -Individual Contributor, KPO, Corporate and back-office functions

No statistically significant association was detected between ownership sentiment and the study measures. Some respondents praised institutional staff while reporting unresolved authority across functions. This distinction concerns organizational decision rights, not a demonstrated lack of individual competence.

6.2 Financial Institution Oversight of Agent Risk

Indicator C111 · Rate the customer’s capacity to govern and oversee the risks of AI agents doing this work · Full statistical analysis ›

Respondents identified gaps in the financial institution's oversight of agent risk, including delayed review and arrangements they considered inadequately adapted to agentic work. The mean rating was 3.5 and the median 3.0 (n = 797 respondents). Respondents on canceled engagements rated the financial institution's capacity lower than respondents on active engagements in any stage (mean 2.3, n = 306 respondents).

The financial institution's capacity to oversee the risks of AI agents showed an association with each of the four core study measures that survived all applicable robustness checks. Cancellation was less common on engagements that rated the oversight capacity higher (r = −0.57, 95% CI −0.67 to −0.45, n = 150 engagements). Among active engagements, it was associated with higher customer satisfaction (r = 0.75, 95% CI 0.64 to 0.83, n = 94 engagements). Oversight capacity was associated with a higher engagement indicator mean, computed without the oversight indicator (r = 0.83, 95% CI 0.77 to 0.88, n = 150 engagements). Respondents who rated the oversight higher reported lower intent to leave (r = −0.61, cluster bootstrap 95% CI −0.67 to −0.54, n = 750 respondents). Distressed ratings came from 417 of the 797 answers (52.32%), and the engagement mean was distressed on 71 of the 150 engagements.

The written responses addressing oversight of agent risk numbered 45, from 44 respondents on 38 engagements, and 37 of the 45 were negative. Most came from the question on the challenges that most reduced job satisfaction, where respondents listed challenges in a few words each. They named the oversight the financial institution lacked, model-risk review that arrived late or never closed, audit frameworks that did not exist for agents, and vendor-risk terms not written for this form of work. A small number described an institution whose risk function was strong, or whose AI oversight forum existed before the engagement began.

“nobody on their side is really watching what agent does with it” -Individual Contributor, CXM, Corporate and back-office functions

“Asking how quickly they govern AI risk when nobody has been named to govern it is not a question anyone here can answer.” -Engagement Manager, CXM, Corporate and back-office functions

“Client could not audit the agent's recon decisions and we had no answer” -Individual Contributor, BPO, Trade, settlement and reconciliation

“Model-risk sign-off lags the build” -Individual Contributor, ITO, Technology engineering and operations

Written sentiment about agent-risk oversight was not significantly associated with the study measures. The comments identify functions respondents considered absent or inadequate, including review and audit arrangements. The study did not inspect those functions directly. The ratings measure perceived oversight capacity.

6.3 Financial Institution Change Control and Release Requirements

Indicator C110 · Rate how well the customer’s change control process handles the changes this engagement requires · Full statistical analysis ›

Financial institution change control did not consistently match the frequency of changes required in delivery, according to respondents' accounts. The mean rating was 3.6 and the median 3.0 (n = 800 respondents). Respondents on canceled engagements rated the financial institution's change control lower than respondents on active engagements in any stage (mean 2.4, n = 304 respondents).

The financial institution's change control, as rated by the delivery team, was associated with cancellation, customer satisfaction, the engagement indicator mean, and intent to leave, and each association held under the applicable robustness checks. Engagements that rated the financial institution's change control higher had a lower cancellation rate (r = −0.59, 95% CI −0.69 to −0.48, n = 150 engagements). Their customer satisfaction among active engagements was higher (r = 0.75, 95% CI 0.64 to 0.83, n = 94 engagements). So was their engagement indicator mean computed without the indicator (r = 0.84, 95% CI 0.79 to 0.89, n = 150 engagements). Their respondents reported lower intent to leave (r = −0.62, cluster bootstrap 95% CI −0.69 to −0.55, n = 751 respondents). Distressed ratings came from 411 of the 800 answers (51.38%), and the engagement mean was distressed on 62 of the 150 engagements.

The written responses addressing change control numbered 85, from 80 respondents on 64 engagements, and 69 of the 85 were negative. The central complaint was pace. Respondents described change processes and boards built for a change a month, or meeting weekly or monthly, against work that changed several times a day, and changes made without waiting for approval as a result. The positive responses described a quick change process and placed the difficulty elsewhere.

“it is sign-off where we lose everything. A fix we write in two days sits four weeks in their change queue.” -Engagement Manager, BPO, Trade, settlement and reconciliation

“What they did not have was a change process that could absorb a rule set moving weekly rather than quarterly, and I don't think that is a failing so much as an artifact of how regulatory systems are governed everywhere. Their own model validation group has a mandate written for annual releases.” -Portfolio Director, ITO, Regulatory, audit, tax and legal

“the problem is the way client set up change process only expect maybe one update a month, but we adjust ticket routing rule several time a day just to keep queue moving, so i just update it myself when it need fixing” -Individual Contributor, CXM, Client servicing and communications

“My team sits late every second Thursday waiting on their approval, the window opens at 11pm and we are still online at 2. Two people already ask for shift change because of it” -Individual Contributor, ITO, Technology engineering and operations

“A composite construction decision sat waiting on client sign-off for eleven days — the change board only meet monthly, as such we made the call ourselves and moved it through.” -Engagement Manager, KPO, Trade, settlement and reconciliation

No significant association was detected between change-control sentiment and the study measures. Some respondents described implementing changes before approval or outside the formal process. Such practices could make change records incomplete, but the study did not compare those records with actual system changes.

7 Provisioning of System Access and Agent Identities

Delayed access was reported to be supplemented by manual transfer and borrowed or shared credentials, raising questions about attribution and segregation of duties. Access provisioning concerns the identities, permissions, and connections needed for agent operation. Written accounts describe manual entry, file transfers, and borrowed or shared credentials when approved access was delayed. These arrangements can weaken attribution and segregation of duties. Their actual control consequences depend on the surrounding access and logging architecture.

7.1 Ability to Provision Access and Identities for Agents

Indicator C108 · Rate the customer’s ability to provision the system access and identities that AI agents require · Full statistical analysis ›

Delays in provisioning the system access and identities required by agents were accompanied by reported manual transfer and use of borrowed or shared credentials. The mean rating was 3.5 and the median 3.0 (n = 801 respondents). Respondents on canceled engagements rated provisioning lower than respondents on active engagements in any stage (mean 2.4, n = 307 respondents).

The applicable robustness checks left intact each association between the provisioning of system access and agent identities and the four core study measures. Cancellation was less common where provisioning was rated higher (r = −0.58, 95% CI −0.67 to −0.46, n = 150 engagements). Among active engagements, better provisioning was associated with higher customer satisfaction (r = 0.69, 95% CI 0.57 to 0.79, n = 94 engagements). The engagement indicator mean, computed without the provisioning indicator, was higher on engagements that rated provisioning higher (r = 0.80, 95% CI 0.74 to 0.85, n = 150 engagements). Respondents who rated provisioning higher reported lower intent to leave (r = −0.59, cluster bootstrap 95% CI −0.66 to −0.51, n = 751 respondents). Distressed ratings came from 410 of the 801 answers (51.19%), and the engagement mean was distressed on 59 of the 150 engagements.

The written responses addressing access and identities numbered 437, from 348 respondents on 135 engagements, and 403 of the 437 (92.22%) were negative. They describe access requests that waited weeks or months and agents with no identity of their own. Delivery teams worked from emailed files, typed data in by hand, or entered systems through borrowed and shared logins.

“Their identity team treats a service account for an agent exactly like a request for a new joiner, same form, same four week SLA, and nobody has explained to them that it is a different animal. So we are building dashboards off extract files that somebody mails us on Fridays.” -Engagement Manager, KPO, Corporate and back-office functions

“We waited five weeks for processor sandbox credentials and in the end we got a shared login that three teams were using, which is its own problem … Every screen wants a human login. So we sat a human in front of it and called it agentic.” -Individual Contributor, ITO, Payments, cards and disputes

“i send request, it goes to a team mailbox, three approvers are named and each one thinks other two will do it. so nothing move for four five days.” -Individual Contributor, ITO, Technology engineering and operations

“access provisioning never finish before we go live so the agents patch around missing permissions constantly, happens most days.” -Engagement Manager, CXM, Corporate and back-office functions

“Access provisioning for the agent took weeks instead of days” -Individual Contributor, ITO, Technology engineering and operations

Negative access comments were associated with higher engagement cancellation and lower customer satisfaction. More negative respondent sentiment was associated with higher intent to leave. Accounts of borrowed or shared credentials raise a separate attribution concern. They may limit identification of the person or system performing an action, but the study did not establish whether other records permitted that identification.

8 Stability of the Business Case

Revisions to scope or measured-return assumptions after delivery began were reported to require rework. Business-case stability concerns whether the scope and definition of expected return remained sufficiently consistent to guide delivery.

8.1 Changes to the Business Case and Return Definition

Indicator C113 · Rate the stability of the customer’s business case and ROI definition for this engagement · Full statistical analysis ›

Changes to the financial institution's business case and definition of measured return were reported as sources of rework after delivery began. The mean rating was 3.5 and the median 3.0 (n = 794 respondents). Respondents on canceled engagements rated the stability of the case lower than respondents on active engagements in any stage (mean 2.3, n = 298 respondents).

The stability of the business case showed an association with each of the four core study measures that survived all applicable robustness checks. A more stable case and definition of return were associated with less cancellation (r = −0.57, 95% CI −0.67 to −0.46, n = 150 engagements). Customer satisfaction among active engagements was higher where the case was rated more stable (r = 0.72, 95% CI 0.60 to 0.80, n = 94 engagements). Business-case stability was associated with a higher engagement indicator mean, computed without the stability indicator (r = 0.82, 95% CI 0.76 to 0.87, n = 150 engagements). Intent to leave was lower among respondents who rated the case more stable (r = −0.59, cluster bootstrap 95% CI −0.65 to −0.51, n = 746 respondents). Distressed ratings came from 420 of the 794 answers (52.90%), and the engagement mean was distressed on 67 of the 150 engagements.

Business-case comments numbered 34, from 33 respondents on 25 engagements. 30 were negative. Respondents described repeated revisions to scope or return assumptions after delivery began. Four positive accounts described clear objectives from the outset.

“We ask, it goes to their platform team, then to their security, then it stops. The business case also changes twice in that period, so we are never sure what we are building towards.” -Engagement Manager, ITO, Technology engineering and operations

“Here it is a ticket for every access and the ROI story changes each quarter, consequently i stopped asking” -Individual Contributor, ITO, Technology engineering and operations

“Scope on their side moves after work is already underway. What that costs my team is not the rework itself, it is that I must ask same four people to redo something they finished on Friday, and I am running out of ways to ask it” -Engagement Manager, KPO, Regulatory, audit, tax and legal

“Business case kept shifting to justify a bad bet” -Individual Contributor, KPO, Corporate and back-office functions

No significant association was detected between business-case sentiment and the study measures. The accounts describe reported rework following scope or assumption changes but do not establish why those changes occurred or whether they were justified.

We have had no trouble at all getting to their data. It is well structured and the definitions hold across their card and merchant systems, which is why data readiness scored high from me. The lower marks are narrower than they look. Their business sponsors are still moving the goalposts on what the benefit case is meant to be, and the people who own the metrics have not sat down with us to agree the definitions behind them. And their staff are new to working this way. That is a starting position, not a criticism of them.”

ENGAGEMENT MANAGER · KPO, Corporate and back-office functions