Transformative AI Solutions for Government & Public Sector

The use of AI in the government sector involves tools designed to help public sector organizations recover information, provide citizen services, process documents, analyze information, automate workflows, manage knowledge, report, and do other proper administrative work.

The suitability of AI for the agency will depend on a number of factors including mission, data, legal considerations, security, accessibility, and risk.

AI can assist with repetitive information and administrative tasks, such as answering frequently asked questions, searching for information, processing documents, summarizing records, routing requests, creating reports, analyzing operational information, and helping to access internal information.

AI should be used as a tool for the government’s employees, not a substitute for taking human responsibility.

Permitting, licensing, transportation, housing, workforce, finance, procurement, records, communications, public administration, education administration, public health administration, citizen services, and other departments are among the potential users.

Yes. There is no need to have a large program of AI in place to start a municipality.

A public information assistant, a document workflow, or internal knowledge assistant may serve as a starting point for a focused use case.

AI can be tested by state agencies for citizen services, document processing, analytics, knowledge management, and administrative workflows, as per state mandates, procurement regulations, security protocols, and system contexts.

Federal projects can have procurement, security, privacy, data, accessibility, and governance requirements; and federal agencies can look at AI for certain applications.

Projects should be assessed to the requirements of the agency and use case.

APIs, databases, integration layers, file exchanges, RPA, or other technical aspects are just some of the ways AI can interact with legacy environments.

The available method varies based on the legacy platform, documentation, permissions, security needs, and integration needs.

A citizen service chatbot is an AI application that facilitates users to locate approved public information, navigate procedures, locate forms, understand general requirements, and identify appropriate next steps.

Yes, in the case of defined information domains.

The chatbot can fetch answers from the approved sources and escalate it accordingly if the question is beyond the scope.

When presented with published information, an assistant can configure it properly and guide citizens in completing the correct form to the appropriate government resource.

Yes. an AI assistant can give conversational navigation and explain where relevant information/forms are located.

Absolutely, multilingual support can be included if it is suitable.

Human translations are required for important public information if it may have a material effect on citizens if there are translation errors.

When a question is asked to the AI in a manner it cannot respond to properly or when human input is needed, escalation can be incorporated into the workflow.

If the proper agency system is available and allows for an authorized integration, then it may be possible.

Individual case information needs to be protected before it is made available, through identity verification, authorization, privacy, data access, and security requirements.

A general citizen chatbot should not be used to decide on eligibility for benefits and other high-impact government services.

Legality, policy, governance, validation, and human oversight are important factors to take into account in any high-impact AI decision-making process.

Examples of potential documents are applications, forms, PDFs, scanned documents, correspondence, permits, licenses, invoices, reports, contracts, grant documents, procurement documents, case files, and other approved administrative documents.

AI document workflows can label, extract, summarize, compare, and organize information from appropriate PDF documents.

AI can extract the defined fields from suitable forms and transform the information into structured data to be validated and processed further.

Yes, in the case of suitable OCR and document processing technologies, they are able to identify the content.

The quality of the written document, the structure of the document, handwriting, image quality, language, and others are all factors affecting performance.

The rules and AI models can be integrated to detect potentially missing fields or supporting documents.

The final decision shall be based on the agency's established procedures.

AI can categorize documents based on specified categories and send them to the correct queues.

It can summarize authorized records where certain employees can have access to them.

Summaries must be read in conjunction with the text and should be seen as a supporting document.

Documents can be categorized as per defined rules, based on extracted information and/or workflow.

Retrieval-augmented generation (RAG) is a technique used by AI systems that enables them to access and retrieve pertinent information from a set of defined knowledge sources before generating a response.

This can be useful in providing a point of reference to applicable policies, procedures, manuals, public documents, FAQs, or other official documents when providing responses for government purposes.

In addition to using a general model's knowledge as a base, a RAG system can be designed around specific sources to find information.

References to the source can be added if the underlying system and content structure support them.

This can assist users to check the information that was utilized to come up with an answer.

Permission-aware retrieval can be designed to provide the user with information based on their permissions.

A knowledge system can include versions of documents and document metadata to enable the correct version to be retrieved.

Content ownership, versioning, expiration, review, replacement, and removal processes shall be in place within knowledge systems.

AI should not assume that it can automatically determine a document is the agency's latest official stance.

Security can be built into architecture, authentication, authorization, data handling, integrations, logging, monitoring, environment separation, and access controls.

The specific controls will vary depending on the environment and requirements of the agency.

Access may be managed with authentication, role-based access, least privilege access concepts, application controls, and system-specific authorization controls.

Data can be segregated, permissions and access can be set, workflow boundaries can be established, etc., to limit access to sensitive data.

The appropriate events can be recorded for audit, troubleshooting, security monitoring, review of operation, etc., depending on the system.

The information stored should be relevant to the system's purpose and requirements.

Retention should be determined in relation to the intent of the application, agency policies, legal requirements, contractual requirements, and the applicable requirements for records management.

Wherever possible, architecture can be designed around separate departments, environments, datasets, permissions, knowledge collections, or applications and where the environment allows for such segmentation.

Requirements are agency and jurisdiction dependent, data dependent, system dependent, user dependent, contract dependent, and use case dependent.

An assistant to answer citizen questions and a system that processes sensitive information have very different requirements.

In the applicable environment, project scope, technology stack, and contract requirements, ZechionAI can design the implementations with applicable agency requirements in mind.

Appropriate control knowledge sources, retrieval, validation, response constraints, monitoring, testing, response escalation to humans, and other controls can be used to limit inappropriate answers in systems.

No responsible AI provider would guarantee that they have no hallucinations.

Whilst AI can assist in specific decision-making processes, high-stakes government decisions must take into account relevant legislation, policy and governance, validation, human oversight, transparency, and accountability.

AI can also misinterpret information, give incomplete answers, and draw wrong conclusions.

When the consequences and accuracy of a judgment are needed, human review can be used.

They should be assessed at a higher level, documented, risk assessed, tested, monitored, legally reviewed, and be subject to human oversight as per the use case.

Evaluation may involve representative test datasets, outcome analysis, error analysis, subgroup testing (if appropriate), human review, documentation, and continuous monitoring.

Depending on the application, the right method needs to be used.

Response quality, error patterns, retrieval, usage, escalation rates, system failures, user feedback, changes in data, and other factors can be considered as aspects of monitoring.

Version control, release records, configuration management, model/prompt changes, knowledge-source updates, testing records, and governance documentation can be used to document changes.

AI can be used to assist with parts of the application processing including classification, extraction, validation, completeness checking, routing, and summarization.

Decisions should be made in accordance with the agency's decision process.

AI can help with document intake, requirement extraction, document completeness, case preparation, case routing, and status communication.

AI and automation can aid in data gathering, verifying data, processing specific measurements, drafting reports, and compiling reports.

Official reporting is still suitable with human intervention.

AI can sort incoming requests or documents and pass them on based on predetermined workflows and access.

Use AI to create drafts, FAQs, summaries, and more.

Official communications shall be made pursuant to the approval process of the organization.

AI can transcribe or summarize meetings and draft action items (where allowed).

When implementing meeting intelligence, organizations will want to take factors such as consent, recording policies, privacy, retention, and relevant requirements into account.

If adequate data exists, then historical information can be used to develop demand forecasts.

AI can generate drafts, FAQs, summaries, etc. of communication.

Official communications should be in accordance with the approval process of the organization.

AI can transcribe or summarize meetings and draft action items, where allowed.

Before implementing meeting intelligence, organizations should think about consent and recording policies, privacy, retention, and applicable requirements.

Where available, historical information can be used to develop demand forecasts.

Weather forecasts are predictions, not promises.

It can be able to look at patterns in the past and other relevant variables to make an estimate of the volume of applications in the future.

Analytics systems can detect behavior that is not in line with expectations.

An indicator of anomaly is not necessarily proof of wrongdoing.

AI can review workload, past demands, capacity, staffing history, and other authorized data to inform scheduling.

ZechionAI can create dashboards to support citizen service, applications, document processing, programs, operations, workforce, and executive reporting.

Depending on the API, permissions, security considerations, data formats, contracts, platform capabilities, etc.

APIs can offer a managed way to integrate AI applications with the systems on which they are supported.

Yes, ZechionAI supports Microsoft 365.

If approved, AI and analytics solutions can leverage a warehouse's data for reporting, analysis, forecasting, and other specified purposes.

The information assistant can be very different from an enterprise document-processing system that integrates with various systems.

Before scaling up a program, a focused use case can be a practical approach for assessing AI.

A pilot can consist of:

Topics for training include: using the system, AI usage guidelines, escalation, limitations, security obligation, and workflow procedures.

Continuous services might involve monitoring, AI engineering, RAG maintenance, integration, data quality monitoring, optimization, documentation, and staff support.

Architectures, workflows, system configurations, AI behavior, integrations, user instructions, governance processes, maintenance resources, and other documentation that suits the project.

Consider the existing process, desired goal, existing data, potential value, risks, legal issues, security, accessibility, human supervision, implementation challenges, and the ability to gauge results.

If there is a reasonable benefit and there is a disproportionate risk, then it may be the wrong solution.