case_studies:reducing_call_center_hold_times_with_governed_knowledge [OKF Expert]

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====== From Long Hold Times to Reliable Answers ======

^ Type ^ Illustrative Composite Case Study ^
^ Audience ^ California government program leaders, constituent-service managers, contact-center supervisors, and procurement teams ^
^ Focus ^ Call-center consistency, knowledge governance, Workflow Audits, OKF Bundles, and responsible AI readiness ^

> **Note:** This is a case study that shows common public-sector operational challenges. It illustrates a practical engagement model and does not identify a specific client, agency or procurement action.

===== The Challenge =====

Daniel Brooks led a constituent-services call center for a California public agency.

His team handled a steady flow of calls from residents seeking help with applications, eligibility questions, required documents, deadlines, policy exceptions, and next steps.

The employees cared about getting people the right answer. The difficulty was that the right answer was rarely located in one place.

Staff searched through policy manuals, internal PDFs, email chains, shared-drive folders, training slides, desk guides, and notes left by former employees. Some documents were current. Others had been replaced but remained in circulation. Some answers depended on program-specific exceptions that were known by supervisors but not clearly documented for frontline staff.

When a caller asked a straightforward question, an employee might find an answer quickly.

When the question involved an exception, a recent policy change, or an unusual circumstance, the employee often had to place the caller on hold, search multiple sources, or ask a supervisor for confirmation.

The effect was visible across the operation:

^ Operational Impacts ^
| Longer call-handling times |
| Inconsistent answers from different representatives |
| Repeated transfers and escalations |
| Supervisor time consumed by routine policy questions |
| Frustrated callers who had already contacted the agency more than once |
| Slower onboarding for newly hired staff |
| Anxiety among employees who wanted to help but were not confident they had the latest guidance |

Daniel’s leadership team had begun exploring AI tools as a possible solution.

But Daniel had a concern.

He did not want to introduce an AI assistant that pulled from outdated policy documents, incomplete guidance, or unofficial staff notes. He knew that a fast answer was not useful if it was incorrect, unsupported, or impossible to verify.

The call center did not simply need an AI tool.

It needed reliable, governed knowledge.

===== The Turning Point =====

Daniel was introduced to OKF Expert through a conversation about improving constituent service without launching a large and expensive technology initiative.

The message was direct:

> Before an agency deploys AI to answer questions, it must know which answers are authoritative, who owns them, how they are maintained, and where staff need human judgment.

OKF Expert proposed a fixed-price Workflow Audit focused on the call center’s highest-volume and highest-friction questions.

The engagement was designed to answer practical questions:

^ Discovery Questions ^
| What questions create the longest hold times? |
| Which topics trigger the most transfers or supervisor escalations? |
| Which policies are most difficult for staff to interpret? |
| Where are employees relying on institutional memory rather than documented guidance? |
| Which documents are outdated, conflicting, incomplete, or difficult to locate? |
| Which answers require human judgment and should not be automated? |
| What knowledge should be organized first to improve service quickly? |

Daniel saw that this was not an abstract technology discussion.

It was a practical operational-improvement project.

===== The Procurement Path =====

OKF Expert provided a one-page, quote-ready scope of work.

The scope clearly defined:

^ Engagement Scope ^
| The call-center workflow to be analyzed |
| The priority call topics to be reviewed |
| The stakeholder interviews and working sessions |
| The documents and knowledge sources to be assessed |
| The deliverables and acceptance criteria |
| The timeline |
| The fixed price |
| The approach to handling agency information |
| The role of human oversight and responsible AI considerations |

Daniel shared the scope with his procurement team and asked:

> “Would this be appropriate for consideration through the SB/DVBE Option? OKF Expert is a California-certified Small Business and Disabled Veteran Business Enterprise, and the engagement is a tightly scoped, fixed-price service.”

Procurement reviewed the request and followed the department’s applicable purchasing procedures, including any required quote and documentation steps.

The engagement was easy to evaluate because it was specific.

It did not ask the agency to commit to an undefined transformation project. It identified a visible problem, proposed measurable deliverables, and provided a clear path from diagnosis to improvement.

===== The Workflow Audit =====

OKF Expert began by examining the journey of a typical constituent call.

The team looked at what happened from the moment a resident asked a question to the point where the employee either resolved the issue, transferred the caller, escalated the matter, or requested additional review.

The audit revealed several patterns.

Some of the agency’s most common questions had answers scattered across multiple documents. Certain internal desk guides had not been updated after policy changes. New employees relied heavily on experienced coworkers for help. Supervisors were repeatedly asked to interpret the same exceptions. In some cases, staff used personal notes because they had no trusted single source for guidance.

The audit also identified an important distinction:

Not every answer should be automated.

Some questions involved judgment, sensitive circumstances, or decisions that required supervisory review. Those matters needed clear escalation rules rather than automated recommendations.

The resulting roadmap separated the call-center knowledge into three categories:

^ Knowledge Type ^ Description ^
| **Direct-answer knowledge** | Questions that could be answered consistently when staff had access to a current, cited, authoritative source. |
| **Guided-process knowledge** | Questions where staff needed a workflow, checklist, or decision path to collect information and determine next steps. |
| **Human-review knowledge** | Questions involving exceptions, discretion, sensitive data, or policy interpretation that required supervisory or specialist involvement. |

That distinction became the foundation for responsible modernization.

===== The OKF Bundle Pilot =====

The agency selected one high-volume service area for an OKF Bundle pilot.

Rather than trying to repair every policy and procedure at once, the pilot focused on the questions that generated the greatest call volume, longest hold times, and most frequent staff escalations.

The OKF Bundle brought together priority content such as:

^ Knowledge Items ^
| Current policies and procedures |
| Approved call scripts and staff guidance |
| Eligibility and documentation requirements |
| Frequently asked questions |
| Exception-handling rules |
| Escalation criteria |
| Relevant forms and submission instructions |
| Source citations |
| Content owners |
| Review dates and maintenance responsibilities |

Each knowledge item was organized so staff could understand:

^ Knowledge Governance Details ^
| What the authoritative answer was |
| Where the answer came from |
| When the content was last reviewed |
| Who was responsible for maintaining it |
| Whether the employee could answer the question directly |
| Whether the question required an escalation or human review |

The result was not simply a better folder structure.

It was an operational knowledge system designed to help people give more consistent, supportable answers.

===== Operational Outcomes =====

The project gave Daniel and his team a clearer path forward.

Supervisors could see where repeated questions signaled a documentation or training problem. Employees had a foundation for locating reliable answers more quickly. New staff had a more structured way to learn the agency’s most important processes. Leadership had a realistic view of where AI could help, and where policy clarification or human oversight remained essential.

The work also shifted the agency’s modernization conversation.

Instead of asking:

> “Can AI answer our calls?”

The agency began asking:

> “Which questions can be answered from approved knowledge, which require a guided workflow, and which must be reviewed by a human being?”

That was a more useful and safer question.

===== Why This Approach Worked =====

The agency did not begin with software.

It began with the service problem.

The initial engagement focused on a real operational burden: callers waiting too long, employees searching for answers, supervisors handling avoidable escalations, and inconsistent guidance creating unnecessary frustration.

OKF Expert helped the agency create a practical progression:

> **Call-Center Workflow Audit → Priority OKF Bundle → Staff Knowledge Improvement → Responsible AI Support**

This allowed the agency to improve service before making a large technology commitment.

===== The Core Lesson =====

For public agencies, faster constituent service does not begin with a chatbot.

It begins with trustworthy knowledge.

When policies, procedures, exception rules, and staff guidance are scattered, outdated, or unclear, employees struggle and residents feel the consequences.

OKF Expert helps agencies organize the information behind their services so it becomes usable, governable, citation-backed, and ready for responsible AI.

> **OKF Expert is a product of eGovernment AI LLC, a California-certified Small Business and Disabled Veteran Business Enterprise. Tightly scoped engagements may be suitable for consideration through the SB/DVBE Option, subject to the agency’s procurement process and applicable requirements.**


===== Related Pages =====

* [[workflow_audit|Workflow Audit]]
* [[okf_bundles|OKF Bundles]]
* [[responsible_ai_readiness|Responsible AI Readiness]]
* [[california_sbdvbe_procurement_pathways|California SB/DVBE Procurement Pathways]]
* [[knowledge_governance|Knowledge Governance]]
* [[public_sector_ai_implementation|Public-Sector AI Implementation]]