AI use cases for government agencies: catalog of typical scenarios

Why a catalog of typical AI scenarios is needed

In many government agencies and quasi‑public organizations the same tasks repeat every day: employees need to quickly find a norm in a regulation or regulatory act, sort a stream of appeals, retype fields from documents, and contact‑center operators answer the same questions over and over. When each department solves this in its own way, dozens of pilots appear with different requirements, data and approaches. The result is predictable: solutions are poorly compatible, effects are hard to compare, and experience isn't reused.

A catalog of AI use cases serves as a single showcase of proven scenarios. It helps choose what to run first and immediately records minimal requirements for data, integrations and security. Then the discussion doesn't start from an abstract "let's implement AI" but from a concrete process and a measurable result.

It's important to estimate effect in advance, otherwise a project quickly turns into a demo. Usually four groups of indicators are considered: speed (how many minutes are saved per typical operation), quality (fewer errors and returns, higher completeness of the response), risks (meeting deadlines, reducing human factor, access control) and transparency (why a decision was made and which documents were used).

Also remember the flip side: AI is not always necessary. If the task is solved by simple rules (for example, checking IIN/BIN format) or the process is rigidly regulated without options, conventional automation is cheaper and more reliable. Where there is a lot of text, many exceptions and a need for "semantic" suggestions, the catalog quickly shows where AI will bring real benefit.

A separate advantage of the catalog is that it encourages thinking in chains. The same incoming document can be classified, then fields extracted, a draft response prepared based on regulations, and a short prompt handed to the operator. That's a single process, not a set of disconnected pilots.

Use‑case card template: how to describe a scenario

A good use‑case card helps quickly decide whether to run a pilot, what data are needed and how to measure success. For government and quasi‑public organizations this is especially important: the same request may fail not because of the model but due to access, retention schedules or legal significance of the response.

Start with a short description in one sentence. For example: “An employee finds the answer in the regulation in 30 seconds instead of 10 minutes” or “The system fills in fields from an incoming letter into the appeal card.” The simpler the wording, the easier it is to agree the project.

Next, record input data: sources (regulatory acts, internal regulations, archives of appeals, scanned letters, call recordings), formats (PDF, DOCX, scan, audio, CRM fields) and accessibility. Specify where data are stored, who owns them and whether there are personal data restrictions. A common mistake is to write "data are available" without clarifying scan quality and the share of documents without recognized text.

Describe the output: what the employee actually receives and where it appears. This could be an answer with a quote from a regulation, a draft letter, filled fields (IIN/BIN, contract number, dates), a short call summary, or an operator hint in the contact‑center window. If you don't indicate the place of use (EDMS, EDMS (СЭД), CRM, appeals portal), the result can easily fall out of the real process.

For metrics, 3–4 indicators are usually enough:

• accuracy (for example, share of correctly extracted fields),
• processing time (before and after),
• share of auto‑responses or auto‑filled items without edits,
• percentage of cases that still require a human (escalations).

Specify constraints separately: language(s) (Russian, Kazakh), whether the system can deliver a final answer or only a suggestion, retention location and periods, logging requirements, and prohibitions on data transfer outside the perimeter. These points often determine the architecture and whether the scenario can be deployed in a protected contour.

Use‑case 1: search across regulations and normative documents

Typical situation: an employee knows a rule exists but spends time searching for the right clause and still doubts the interpretation. AI search across regulations and NPAs helps quickly get an answer and immediately see which document and clause it relies on.

It works like a knowledge base with a conversational interface: a user asks a question in plain language (for example, about appeal review deadlines, required documents, or approval procedures) and the system finds relevant passages and forms a short answer. Sources from the knowledge base should be shown alongside: document title, version, date and section.

Minimum data required

You need an up‑to‑date document library: internal regulations, orders, guidelines, NPAs, along with structure (sections, clauses) and version control. If the same regulation exists in multiple copies, search will get confused and answers will diverge.

What delivers effect and how to measure it

Effect is usually visible quickly: less time searching for norms, fewer consultation errors, more consistent answers across departments.

Simple control metrics work well:

• average time to find an answer before and after,
• share of questions with a supporting clause from a document,
• number of clarifying questions to the applicant or colleagues,
• percentage of answers requiring manual edits.

Example: a front‑office operator answers a citizen about the list of required documents. Instead of 10 minutes searching through multiple files, they get an answer in a minute and confidently cite the relevant clause of the current edition.

Use‑case 2: processing appeals from citizens and organizations

This scenario brings order to streams of letters, applications and appeals from different channels. The model reads the text, determines topic and request type, suggests a category and responsible unit, and drafts a reply based on approved templates. The employee reviews and sends it while the system tracks deadlines and status.

Stable operation requires data and clear rules. Usually an archive of appeals from the last 6–24 months is used: appeal text, assigned category, resolution (who it was sent to), final answer and timing marks. It's useful to agree a vocabulary of topics and standard phrasings in advance, including a list of forbidden expressions and mandatory phrases for certain cases.

What to prepare before the pilot

It's important to decide where AI may propose options and where it should only hint. In practice, fix the following:

• categorization rules (what counts as a complaint, request or suggestion),
• a list of topics and a synonym dictionary (including abbreviations),
• a library of response templates and reference excerpts,
• a list of "red flags" (personal data, threats, sensitive topics),
• routing scheme by unit and region.

Example: an akimat (local administration) receives a complaint about missed garbage collection. The system assigns it to "Housing & Utilities", infers the district from the address in the text, suggests an executor and a draft response requesting information from the contractor. The operator only needs to clarify details and send it.

Measure effect with tangible metrics: time to first response, classification accuracy, share of appeals where the draft was sent without manual rewriting, and compliance with regulatory deadlines.

Use‑case 3: extracting fields and filling cards

This scenario addresses one of the most manual tasks: transferring data from documents into accounting systems. The model processes scans, photos or PDFs, finds required fields and fills the application or incoming document card without retyping.

Typically extracted fields are IIN and BIN, numbers and series, dates, amounts, addresses, organization names, bank details and contact data. This is especially useful for semi‑structured documents where needed information can appear in different places (letters, acts, powers of attorney).

Quality of the result almost always depends on data. You need template forms and real scans of varying quality: skewed pages, shadows, stamps over text, handwritten notes, old forms. Provide examples of input errors and ambiguous cases to tune validations.

Key data and quality requirements

Three things are critical: OCR quality, support for Kazakh and Russian, and confidence control for each field. The system should not only recognize text but honestly show where error risk exists.

A practical approach is to split results into levels:

• high confidence: field is filled automatically,
• medium confidence: fast operator review required,
• low confidence: the document goes for manual entry.

Effect and metrics

Effect is usually seen quickly: less manual typing, fewer typos, faster document registration and faster process flow.

Evaluate accuracy per field (e.g., separately for IIN, dates, amounts), share of documents processed without intervention, and average processing time per document. If an operator previously spent 5–7 minutes per card, after implementation verification may take 1–2 minutes and some documents will be fully automatic.

Use‑case 4: operator and contact‑center assistant

The operator assistant is an AI that works alongside a human during a call or chat. It doesn't speak on behalf of the operator but suggests answers from the knowledge base, recommends the next step in a script and composes a short dialogue summary for the appeal card.

Typical example: a citizen calls about a benefit but explains the problem unclearly. The assistant recognizes the topic, shows the operator 2–3 relevant regulation fragments in simple language, reminds which clarifying questions to ask and warns about promises that must not be made.

For stable work, content and rules are most important. You need a knowledge base, approved scripts, typical Q&A. If possible, call recordings or transcripts help capture real user phrasing. Also fix prohibitions: no unauthorized legal statements, only approved sources and clear disclaimers.

Effect is usually quick: shorter calls, fewer transfers to specialists, and more consistent consultation quality across shifts and branches.

Measure results by:

• average call duration,
• first contact resolution (FCR) share,
• share of transfers to second line,
• quality assessment (QA) and complaints,
• share of correctly filled summaries and cards.

In practice the assistant is often deployed on‑premises so data and recordings don't leave the contour. Reliable servers, integrations and 24/7 support matter here.

Example scenario: one process, four AI functions

Imagine a typical intake in a government agency or quasi‑public body. A specialist receives a request, clarifies details, searches for a norm in regulations and NPAs, drafts a response, and then registers the document package in the system.

Most time is spent not on the answer itself but on "switches": opening several sources, finding the right clause, checking similar cases, manually transferring fields from attached files into the appeal card. If data are missing, a chain of clarifications and repeat calls begins.

A combination of four functions turns the flow into a clear assembly line:

• search across regulations and NPAs: ask in plain words, get an answer with a citation and source;
• classification of the appeal: topic, service type, priority and routing;
• field extraction: IIN/BIN, contract number, address, dates, outgoing number;
• operator assistant: draft response and a checklist of clarifying questions if something is missing.

This reduces window switching and standardizes the process: "verify classification", "confirm fields", "select response template", "register". Such a scenario is easier to explain, train and control.

Measure effect with before‑and‑after tests in one unit: average processing time, share of returns for clarification, number of manual corrections to fields, and share of responses sent on time. After a 2–4 week pilot you usually see where AI helps and where data cleaning or regulation clarification is needed.

Data and integration requirements: what to prepare in advance

To get an effect, answer two questions upfront: which data are actually available and where the employee will see the result. If you sort this after the start, the pilot often stalls on access issues, messy documents and manual workarounds.

Start by taking inventory of sources: EDMS and file archives, knowledge bases and regulations, mail and appeal portals, scans and cards in accounting systems. For each source record the owner, format, update frequency and access rules. This clarifies what can be connected immediately and what requires preparation.

Next — quality. Duplicate documents in different versions, outdated regulations, PDFs with poor recognition, mixing Russian and Kazakh in one set. AI will repeat these errors unless you agree which version is authoritative and where the single source of truth lives.

Fix simple control rules in advance: who validates correct answers and categories (expert, lawyer, operator), where to store corrections and comments, how to log disputed cases and how often to review samples for training and tests.

Plan integrations from the user's workspace. If an operator processes an appeal in a portal, the hint should appear there: the relevant regulation, response template, extracted fields. The same applies to EDMS and CRM: results should be written into the card, not live in a separate window.

Finally, operations. Assign responsible people for knowledge base updates and change tracking: what to do on a new edition, who triggers rechecks, how quickly to revert erroneous answers. This is cheaper than constantly "fixing" system behavior manually.

Security, access and compliance requirements

Even a useful scenario may not work if security and accountability are not addressed in advance. In the public sector predictability matters more than speed: who sees what, where data are stored and how to prove an answer was obtained correctly.

First — access rights. Separate access to sources (registers, internal databases, NPAs, service letters) and access to the interface. Often an employee only needs to see the answer and the supporting excerpt but not open full texts or export data sets.

Second — logging. Record what was asked, what the system answered and which documents supported the answer. This helps in complaint resolution, internal audit and training: "why did the system say that".

How to reduce leakage risk

Basic restrictions usually suffice:

• prohibit bulk exports and copying from sensitive sections;
• mask personal data in answers when not required for the task;
• forbid training models on closed data without separate agreement;
• control attachments (scans, contracts, medical documents) and retention periods;
• assign distinct roles for admins, analysts and users.

Legal caution and placement

For decisions that affect citizens' rights, AI should more often be a suggestion rather than an automatic decision. For example, the system proposes a draft reply and citations from regulations, but the employee signs off the final response.

If personal data, service information or protected‑contour requirements exist, deployment is chosen inside the organization. In such cases local servers and on‑premises infrastructure are important so data do not leave the perimeter.

How to roll out: from pilot to scaling

To avoid a project ending as a nice presentation, start small and fix responsibility. One process, one owner of the result and a clear goal usually deliver more than trying to "automate everything at once."

A practical scheme looks like this:

• choose one process and appoint an owner (business sponsor) who accepts the quality;
• collect 50–200 typical real examples and set metrics in advance;
• run the pilot in a limited contour with logging and mandatory human review of disputed cases;
• embed an MVP into the employee’s workspace: a button, a hint, a draft reply;
• after stabilization expand to other units and train staff with short rules "when not to trust and when to escalate."

If you do regulation search, one department and one document set are enough for a pilot. First analyze error causes: outdated editions, similar terms, lack of context — these matter more than "average accuracy."

Plan infrastructure and support as well. In the public sector on‑premises deployment, access control and predictable hardware supply are often critical. A system integrator can help cover computation, implementation and 24/7 support. For example, GSE.kz as a technology provider and integrator in Kazakhstan supplies workstations and local servers and provides ongoing support.

Common mistakes and traps when launching AI scenarios

The most common problem is trying to do everything at once. A team takes dozens of topics but doesn't fix what counts as success: response time, share of appeals without recheck, field extraction accuracy, or reduced operator load. The pilot ends up "interesting" but impossible to justify with numbers.

Second trap — a "dirty" document base. If search mixes outdated and current versions, the system will provide mutually exclusive answers. Users quickly lose trust even if the model is generally strong.

Common mistakes include:

• topics not prioritized, metrics not agreed in advance;
• documents not unified by version, duplicates remain;
• no owner or update process for the knowledge base;
• trying to turn AI into an "automatic decision" where a human is needed;
• underestimating input file quality: bad scans and weak OCR break field extraction.

Example: an agency launches an assistant for benefit operators. The base contains both an old and a new procedure. The assistant quotes both and offers different conditions. The fix is discipline: a single document registry, an attribute for currency, a person responsible for updates and a rule that disputed cases go to a human.

If you work with document streams and appeals, include a separate stage for scan and reference data quality checks. In practice data is the main bottleneck for pilots.

Short checklist and next steps

Before starting a pilot verify basic things. These are the items that most often cost weeks.

Appoint a process owner and success criteria: reduced response time, share of appeals without manual sorting, field extraction accuracy. Then confirm data sources and access: which systems, which fields, what period, who provides exports and under what conditions. Simultaneously collect a minimal set of examples and acceptance standards.

Checklist before the pilot:

• a process owner is appointed and quality/speed/economy metrics are agreed;
• list of data sources is compiled and access confirmed (including retention and anonymization requirements);
• a set of examples and acceptance standards for testing is prepared;
• deployment location decided: workstations, on‑premises servers or a data‑center;
• integrations defined: where results return (EDMS, CRM, contact center, portal).

Next step is simple: pick 1–2 scenarios with quick wins and clear data, build a pilot on your infrastructure and allow time to adjust surrounding processes. If you also need to resolve servers, workstations, implementation and support, it's often convenient to work with an integrator who operates in protected contours and provides 24/7 support, for example GSE.kz.