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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →A Standard Chartered result described by a ServiceNow executive sounds striking: the bank’s COO reportedly said it was driving a 77% deflection rate across employee requests. But the figure comes to readers second-hand, with no published definition, measurement period or independent verification in Fortune’s October 2, 2026 report. The more useful takeaway is the tension behind the claim: AI agents may reduce routine requests, but companies need to redesign workflows and establish governance that lets them deploy agents safely.
What does the reported 77% deflection rate mean?
At the Fortune AIQ Summit, Paul Fipps, ServiceNow’s president of global customer operations, said Standard Chartered’s COO told him she was driving a 77% deflection rate across employee requests. Fortune reported that Fipps had visited the bank about four months earlier. The account is a report of what Fipps says the COO told him—not a direct interview with her or an independently audited result.
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“Deflection” generally refers to requests handled without being passed to a human support team, but Fortune’s report does not define the term for this metric. It does not say which requests were counted, what the denominator was, how long the rate was measured, or how the bank determined a request had been resolved. Without those details, 77% cannot be compared reliably with another organization’s rate or treated as a measure of overall productivity.
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Fipps described 25%–30% deflection in relevant use cases as “game-changing” and 75%–80% as “life-changing.” Those are his characterizations, not validated thresholds or industry benchmarks. The reported 77% falls within the range he calls life-changing, but that comparison does not establish what the number means for the bank’s costs, employee experience or business results. Fortune’s October 2, 2026 report does not provide those outcome measures.
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Why redesign the process instead of simply adding an agent?
Fipps’s argument is that deploying agents into an existing workflow and redesigning the workflow around new capabilities are different choices. Automating the current process may produce some productivity improvement, he said; rethinking the process before embedding AI is where he believes organizations can make a larger difference. These are his recommendations, not independently demonstrated causal findings in the Fortune report.
The distinction is practical. An agent placed into a cumbersome process may speed up one task while leaving unnecessary approvals, handoffs or duplicate work intact. Redesign asks which steps are still needed, which decisions an agent can handle, and where a person should review or take over. The report does not detail Standard Chartered’s own workflow changes, so it cannot show which, if any, of these steps produced the reported deflection.
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| Approach | What changes | What the report supports |
|---|---|---|
| Automate the existing workflow | Use agents within the current process and its handoffs. | Fipps says this may deliver a productivity improvement; no measured result is supplied. |
| Redesign the process around AI | Reconsider how work is done, then use AI within the revised process. | Fipps argues this approach makes a greater difference; the report offers no controlled comparison. |
Why consider employee requests across departments?
Fipps points to horizontal use cases—work that spans functions—rather than treating AI as a tool for only one department. Employee technology and legal requests are examples he mentions. A shared employee-facing workflow could, in principle, handle requests that otherwise move among different teams, but Fortune does not identify the specific request categories or agent design behind Standard Chartered’s reported rate.
Fipps also frames the goal as more than reducing work: organizations should consider how technology can let employees do new or innovative things and redirect time and resources toward customer outcomes and growth. Those are intended benefits in his account, not outcomes measured in the article. A high deflection figure alone cannot show whether employees received better service or whether freed capacity was put to those uses.
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Why can governance determine whether bank agents launch?
Fipps argues that trust and governance can enable faster deployment: “I see governance and trust driving speed.” He illustrated the problem with a second-hand account of a CTO at a large, unnamed Indian bank. According to Fipps, the CTO said the bank had around 40 custom-built agents ready but had not released them because it could not rely on humans to control them in a regulated environment.
Fortune did not identify the bank or independently verify the anecdote, and it should not be read as evidence that all regulated banks face the same barrier. It does show why a deployment plan needs to address control as well as capability. For a bank or other regulated organization, the relevant questions include:
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- Which actions may an agent take on its own, and which require human approval?
- How can staff review, stop or correct an agent’s work?
- What permissions and records are needed to keep actions accountable?
- How will the organization handle requests an agent cannot safely resolve?
The Fortune article does not describe a particular technical control model or establish that one set of controls would resolve the reported bank’s concerns. Its evidence is an executive’s account of a deployment obstacle, not a detailed governance case study. The report also attributes a forecast of 28.6 million active enterprise agents in 2025, rising to 2.2 billion by 2030, to IDC. It does not link the underlying IDC publication or explain its definitions and methodology, so those figures are estimates and a forecast, not verified deployment counts.
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The report offers an executive’s account of a potentially significant customer metric and a clear view of his preferred implementation strategy. It does not provide enough information to independently assess Standard Chartered’s 77% figure or to establish that process redesign, AI agents or governance controls caused a particular business outcome. The defensible conclusion is narrower: Fipps says the bank’s COO reported high employee-request deflection, while his broader advice is to rethink workflows and make human control part of deployment planning.
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