Business AI adoption, from choosing applications to everyday operations

Our Services

Business AI adoption services

Describe the current task, input data and completion criteria before choosing a service. A project can combine services, but responsibilities and acceptance criteria must be agreed for each part.

• Business AI adoption and transformation consulting

  Review departmental tasks, data and systems. Compare improvement value, implementation cost and the consequences of errors to form a phased plan. Suitable when you have not chosen an application, or already have tools but no clear implementation path.

• AI workflow automation and agent implementation

  Document repetitive tasks, decision rules and exception handling, then define authorized system actions. Steps involving writes, messages or approvals need clear human authorization, logs and failure handling.

• Company AI knowledge bases and RAG

  Organize document versions, categories and access rights so answers can retrieve relevant information and cite sources. Test retrieval accuracy, fidelity to the source and responses when information is unavailable.

• AI customer service implementation

  Define answers using approved service information, with identity checks, case lookups and handover to staff. Complaints, refunds and contractual commitments must follow the company’s authorization rules.

• AI system integration and custom development

  Check interfaces, field mappings and permissions before integrating AI into business workflows. Specify which read, write, testing and maintenance tasks the engagement covers.

• AI governance and usage-policy consulting

  Define permitted uses, data classification, tool review, output checking and incident reporting, so teams know who may use AI, which data it may handle and who is responsible when problems occur.

Our Approach

Turn use cases into testable tasks

Start with frequent tasks that have clear data sources and a person responsible for acceptance:

• Internal knowledge search:

  Staff search policy documents within their access rights, with source references and a request for clarification when information is insufficient.

• Document processing:

  Extract agreed fields, check formatting and send uncertain content to staff for review.

• Customer support:

  Answer general questions from approved information and hand over individual cases, complaints or out-of-scope requests.

• System operations:

  Send prepared data into a defined workflow, retaining necessary authorization, logs and exception handling.

Priorities

Assess five conditions first

1. Business value:

   How often does the problem occur, how much time does it take, and how will you verify improvement?

2. Data readiness:

   Can the data be used lawfully, is it complete enough, and who maintains it?

3. Technical feasibility:

   Do existing systems provide interfaces, and does the environment allow integration?

4. Risks and responsibilities:

   What could an error affect, and which steps require a human decision?

5. Ongoing costs:

   Beyond implementation, what platform, usage, maintenance and review costs will arise?

Our Approach

Plan data security and human oversight together

AI risks depend on its purpose, data and permitted actions. Identify users, accessible data and people who could be affected by errors, then assign testing, approvals and incident responsibilities. This applies across brands and deployment models.

Review supplier terms, retention and deletion, model-training use and access controls. When connecting internal systems, separate read and write permissions and restrict actions to the task.

Check AI outputs according to their intended use. External commitments, data changes and high-impact decisions need explicit authorization and human control. If errors occur, functions must be stoppable, records traceable and designated staff able to take over.

Our Approach

Further implementation and staff development

For an AI management system, you can also discuss ISO/IEC 42001 consulting; to help managers and staff understand applications and operations, consider Business AI application training。

Project Planning

Estimating time and cost

Confirm the first-phase task, user numbers, data sources, connected systems and acceptable error handling before scheduling interviews, pilots, acceptance and handover. Document preparation, system interfaces, security review and internal decisions can all affect timing. A model name is not enough to price an undefined project.

Separate one-off implementation from ongoing costs. Implementation may include data preparation, workflow design, development and testing. Ongoing expenses may include platform licences, model calls, search, storage, monitoring and maintenance. Training, production deployment and later changes are specified in the scope.

Preparation

What to prepare before implementation

Provide process steps, user roles, workload, common exceptions and the problem to improve. Use de-identified document or form samples and describe update frequency, data ownership and current permissions.

For integration, prepare system names, interface documentation, test-environment details and internal contacts. Access must still be agreed with your company and system provider. Production passwords or keys are not needed for the initial data review.