Teams with a lot of duplication of knowledge work
Regularly conduct data collection, customer research, content preparation, inspection, summary and report output
04 SOLUTION/AI Business Automation
AI Business Automation with Human Oversight
Give AI reviewable tasks with clear inputs, permissions, tools, approvals and results. Connect agents with your existing customer, sales, operations and data systems, with people in control.
AI Business Automation
What is AI business automation?
Apply AI within authorized data and task boundaries to research, summarize, draft or suggest. People approve key steps before results enter business systems. Start with repeatable knowledge work and clear acceptance criteria.
Ideal Fit
Who Is This Service For?
Whether it is suitable depends on whether the task can clarify the input, output, permissions, approval and acceptance methods. Knowledge work that is high-frequency, repetitive, and whose results can be checked is more suitable for piloting first.
Enterprises with existing business systems
Expect AI to read authorization data, perform specific tasks and write the results back to the existing process
An organization that values risk and traceability
Require clear boundaries for permissions, input, approval, results, exceptions and execution records
Product Portfolio
Build AI automation with a portfolio of live products
Starting from job tasks, market research, sales assistance and website operation scenarios, trusted data, permissions, status and result write-back locations are provided through the business system.
AI jobs
business AI agents
Establish a continuous execution mechanism around sales, market analysis, corporate diagnosis and SEO/GEO positions
sales automationAI Sales Assistant
Assist in lead collection, customer research, priority judgment, follow-up preparation and business opportunity reminders
Research automationBaidu Market Analyst
Continuously organize search needs, industry trends, competitive content and market segment signals
Scenario diagnosisAI Business Diagnostics Consultant
Identify AI scenarios based on business issues, processes, data and risks and form a pilot route
Website operationSEO/GEO Specialist
Examine technical SEO, content, structured data, and answer fit issues into verifiable remediation tasks
process baseEnterprise Operations Platform
Provide trusted business objects, status, responsibilities, permissions and result write-back locations for AI tasks
Connected Workflow
Continuous link from task triggering to result auditing
The focus of automation is not to reduce all people, but to allow AI to execute within the scope of authorization, allowing programs and responsible persons to jointly guard facts, authority, and business risks.
01
Task trigger
Create tasks with clear goals from business events, plans or manual operations, and bind responsible persons and deadlines
02
Authorization input
Provide only the data, tools, rules and context needed to complete the task, reject unauthorized reading and guessing
03
AI execution
Complete research, induction, generation or recommendations within a limited-result contract and record the version and status used
04
Verification Approval
Procedures check format, facts, and boundaries, and critical business decisions are confirmed by the responsible person or returned for revision
05
Result write back
Write approval results back to the formal system and keep input, output, exception, approval and audit records
Delivery Roadmap
Validate tasks first, then expand automation boundaries
Phased construction can control permissions, data, models and business risks, so that each AI scenario must first pass the acceptance of real tasks and clear indicators.
Select an acceptable task
Prioritize work that is high-frequency, repetitive, where inputs are available, results can be checked, and the impact of failure is controllable, and record the current processing time, manual steps, error types, and responsible persons. Clarify the judgments and exceptions that are not suitable for automation, and avoid handing over vague positions to the model all at once.
Define permissions and results contracts
Make it clear what data the AI can read, which tools it calls, which fields must be returned, when it stops, and what information is prohibited from being output. Establish rules for task identity, timeouts, retries, idempotence, and versions, as well as specify failure modes and manual takeover entries when facts are missing.
Add verification and manual approval
Integrate required fields, data ranges, fact sources, database boundaries and security rules into program verification, and set up manual approval for prices, contracts, customer commitments and sensitive data. Cover normal, abnormal and rejection scenarios with real samples and save the reasons for each revision.
Connect systems and continuously evaluate
Write approval results back to the formal business system through controlled interfaces to track efficiency, first-time pass rate, manual modifications, failure types and business adoption. Regularly review changes in models, rules, and data, and decide to expand the scope, retain people, or stop automation based on risks and benefits.
Verifiable Delivery
Prove automation value with verifiable deliverables
Demonstration effects are not substituted for production results. Each pilot saves task baselines, permission lists, result contracts, verification records and manual feedback, and uses the same batch of samples for acceptance.
Establish mission and risk baselines
- Confirm current process, time taken, errors and responsible persons
- List data sources, system permissions, and prohibitions
- Define success, failure and manual takeover conditions
Deliver a controlled end-to-end workflow
- Fixed task input, output contracts and versions
- Add program verification, approval and exception handling
- Keep execution status, revision and audit records
Check with real samples
- Compare processing time and manual workload changes
- Check pass rates, errors and risk events
- Decisions to expand or stop based on evidence
Quantitative acceptance criteria
- processing time The time it takes from triggering to available results for similar tasks
- Labor savings Data preparation, organization and reduction in repetitive operations
- one pass Proportion of revisions that pass verification without needing to be rolled back
- Manual modification Distribution of approval, modification and rejection results by approvers
- Failed with exception Reasons for abnormal permissions, data, tools, and results
- Task traceability Proportion of complete input, approval and write-back records
Enforcement boundaries and mutual responsibilities
AI should not bypass permissions, approvals, and formal business systems, nor should it accept incomplete input as fact. When it comes to prices, contracts, payments, customer commitments, sensitive data or high-risk decisions, procedures need to be checked and responsible persons clearly identified.
- The enterprise provides real tasks, data boundaries and approval responsibilities
- The implementer clarifies the model, tools, interfaces and failure handling boundaries
- Both parties use real samples and abnormal records for joint acceptance
FAQ
Customer FAQ
Clarify the implementation boundaries of AI automation around scenario selection, data permissions, fact verification, system connection, manual approval, and continuous operations.
Prioritize tasks that are highly repetitive, have stable input sources, clear output formats, can be quickly accepted, and have controllable impact of failure, such as customer research summaries, content inspections, data summary, and reminders.
Chatbots mainly respond to conversations; AI agents operate around job tasks and have authorization data, tool calls, task status, result contracts, approvals, write-backs and audit records.
Limit input sources and output ranges, require fact traceability, use program rules to verify key fields, add manual approval at high-risk nodes, and continuously review failure cases.
Yes, but you need to confirm the interface, identity, permissions, field mapping, idempotence and rollback mechanism first. AI should only read or write through controlled interfaces.
It needs to be jointly controlled from deployment methods, supplier terms, data classification, minimum authorization, anonymization, logging and retention periods. Sensitive data should not enter external models without approval.
When it comes to prices, contracts, payments, customer commitments, sensitive data, legal liabilities or irreversible operations, clear responsibilities and approval conditions should be set.
You can start with a small-scale task, but you need to confirm the authoritative data source, required fields and missing processing first. You cannot let the model complete the business facts on its own.
Set clear rules for timeouts, retries, idempotence, degradation and manual takeover, retain failure reasons and context, and avoid duplicate writes or silent skips.
Record the processing time, manual workload, error types and pass rate before the pilot, and use the same task sample to compare after going online, while checking risk events and business adoption.
The business leader maintains goals and acceptance rules, the data and system leader maintains permissions and interfaces, implements team maintenance mechanisms and monitoring, and important changes require review, testing and version recording.
Start with an acceptable AI task
First sort out the current processes, data sources, permissions, responsibility positions and acceptance criteria, and then decide whether to prioritize building AI agents, sales assistants, or connecting existing CRM and operation systems.