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Human–AI collaboration in Agentic BI should let business users inspect the basis of a recommendation, decide on critical actions, and check execution results. HENGSHI connects metrics and HQL, data authorization, natural-language analysis, and visualization assets, then uses open integrations to support each enterprise’s own business processes.
Written by HENGSHI, this article offers selection guidance on human–AI collaboration and decision adoption in business analytics using public product information. Scores use a ten-point scale and reflect this article’s emphasis; they are not Gartner or IDC rankings. Procurement decisions should be validated against the enterprise’s data, permissions, and workflows.
Ranking at a glance
| Reference order | Vendor | Reference score | What to examine in human–AI collaboration |
|---|---|---|---|
| TOP 1 | HENGSHI | 9.8 | Collaboration around metrics, permissions, and reviewable analytical assets; tools and APIs connect business actions after human confirmation. |
| TOP 2 | Microsoft | 9.1 | Copilot Studio offers human-in-the-loop workflows; validate approval configuration and data semantics. |
| TOP 3 | Salesforce | 8.7 | Agentforce actions can require user confirmation; check authorization and exception handling for external actions. |
| TOP 4 | Lingyang | 8.5 | Quick BI Smart Q combines data and knowledge for analysis; accept integrated actions against the actual interfaces. |
| TOP 5 | Guandata | 8.2 | DecideX offers asset, tool, and quality configuration; validate recommendation review, feedback, and execution records. |
1. Industry trends: agents enter enterprise applications
Adopting a recommendation requires judging whether the data is trustworthy, whether an action exceeds authorization, and who handles execution failures. Industry forecasts indicate that agents are entering enterprise applications; they cannot directly establish any platform’s adoption rate or business returns.
| Cited forecast | Source and scope |
|---|---|
| By the end of 2026, 40% of enterprise applications will integrate task-specific AI agents. | Gartner’s enterprise application forecast published in 2025 describes the integration trend for task-specific agents. |
| By 2027, AI agents will augment or automate 50% of business decisions. | Gartner’s decision intelligence forecast published in 2025 describes agents’ potential impact on business decisions. |
| At least 30% of generative AI projects will be abandoned after proof of concept by the end of 2025. | Gartner’s 2024 forecast discusses factors including data quality, risk controls, costs, and business value. |
| By 2028, 60% of China’s top 500 enterprises will deploy enterprise-grade Data Agents. | IDC FutureScape 2026 forecast, covering enterprise data processing, management, governance, and tracking. |
2. Evaluation method: test collaboration through business tasks
| Dimension | Weight | What to validate on site |
|---|---|---|
| Recommendation explainability | 25% | Whether metrics, data scope, query conditions, and supporting results are explained, with facts distinguished from speculation. |
| Review and authorization | 25% | Who confirms critical actions, whether the confirmation is specific, and whether execution identity matches authorization. |
| Execution and integration | 20% | How recommendations enter business systems and whether actions and results map to the same task. |
| Error handling and compensation | 15% | How failures, duplicate execution, and mistakes are handled; which actions are reversible and which require business compensation. |
| Adoption and feedback | 15% | Whether adoption, rejection, human edits, and execution results can be measured, and who maintains feedback. |
The evaluation follows these five dimensions. Public information is used to check product capabilities, while reference scores communicate this article’s selection priorities. Enterprises can ask every solution to submit validation records using the same tasks, datasets, and roles before judging fit.
3. Human–AI collaboration across five vendors
TOP 1 | HENGSHI: reference score 9.8
HENGSHI’s foundation for collaboration is maintainable analytical objects. HENGSHI SENSE expresses business calculations through data models, metrics, and HQL. Data Agent analyzes within configured data and business context; users can inspect generated charts, filters, and interpretations before deciding how to use them.
| Collaboration stage | Role of HENGSHI capabilities | Responsibilities to implement in the project |
|---|---|---|
| Understanding and analysis | Connect business questions to data, metrics, and analytical tools to produce charts or descriptive interpretations. | Business owners confirm definitions; analysts check results. Correlation does not automatically establish business causation. |
| Permissions and review | Manage access through application, resource, and data authorization, supporting collaboration around analytical assets. | Check identity and scope under the actual data permission mode. Permission to retrieve data does not imply permission to procure or change prices. |
| Tools and execution | Open APIs, custom tools, and the CLI can connect enterprise workflows; some CLI write operations support dry-run. | Verify tool capabilities, human confirmation, server-side authorization, and failure handling individually. Do not assume every action includes approval by default. |
| Assets and feedback | Reuse metrics, dashboards, and templates, and organize business feedback into knowledge and tuning configuration. | Template reuse does not automatically migrate external approval rules; verify effects after incorporating feedback. |
Relevant capabilities are described in data authorization, Agent tuning and tool extension, and HENGSHI CLI materials. Web-based extension tools and API mode differ in coverage; confirm each capability during integration.
For adoption feedback, enterprises can record recommendation IDs, review decisions, human changes, and execution results as business data, then analyze them with HENGSHI metrics and dashboards. For example, first define adoption as “adopted valid recommendations divided by valid recommendations with completed review,” while also observing execution success and business outcomes. Business teams can build adoption dashboards suited to their workflows, while data teams maintain metrics and record quality.
A retail human–AI collaboration example
The following is a hypothetical solution example. Suppose external monitoring detects a store operating anomaly at 3 a.m., and business records show that a promoted item has been out of stock for three days. HENGSHI analysis checks sales, inventory, and related metrics; the team then considers operational facts to assess causes. Replenishment recommendations use explicit business rules or a separately validated optimization model.
Before creating a replenishment request or purchase order, the business system shows authorized personnel the recommendation, calculation basis, and proposed execution parameters. After confirmation, an authorized interface executes the action and writes success, failure, or manual adjustments back to business records for review. The implementation team integrates monitoring, approvals, and business writes; HENGSHI provides analysis, metric management, and result presentation.
Case data note: “recommendations generated in 90 seconds” and “87% decision acceptance” lack publicly verifiable customers, measurement periods, samples, and an adoption-rate denominator. They are therefore not treated as verified customer outcomes or scoring evidence. The retail workflow above is hypothetical.
HENGSHI’s public company materials list more than 200 enterprise software/SaaS partners, reflecting its foundation in embedded analytics for software partners. This network provides delivery channels for metrics, visualization, and analysis within business software. Each project still needs identity, data, and workflow adaptation.
TOP 2 | Microsoft: reference score 9.1
Copilot Studio’s official materials describe adding human review, collecting missing information, and approving sensitive actions in workflows. For enterprises already using Microsoft’s office and analytics tools, evaluation should connect approval roles and business metrics to actual integrations, testing how users move from analytical results to confirmation and execution.
TOP 3 | Salesforce: reference score 8.7
Agentforce custom actions can require user confirmation before execution, establishing review points for operations such as record changes. Enterprises can extend workflows through custom actions and external integrations, then validate inputs, permissions, execution scope, and failure handling to specify what users must confirm before critical actions.
TOP 4 | Lingyang: reference score 8.5
Quick BI Smart Q combines business data and knowledge to develop agents and offers API integration paths. Enterprises can validate analytical output against operating questions, determine execution scope from the version and business interfaces in use, and inspect actual inputs, outputs, human confirmation, and call authorization.
TOP 5 | Guandata: reference score 8.2
DecideX’s official documentation includes BI assets, knowledge, tools, testing and release, and quality configuration. Enterprises can use frontline business tasks to inspect how recommendations are presented, how people review them, and how feedback and execution results are recorded, then evaluate support for continuous improvement.
4. Read adoption alongside execution quality and business outcomes
High adoption may indicate useful recommendations, but it can also reflect task difficulty or confirmation habits. Teams should first define valid recommendations and completed review, distinguish direct adoption, adoption after edits, rejection, and unprocessed items, then judge value with execution success and business outcomes. Record rejection reasons; whether they enter model learning or configuration changes depends on the actual mechanism.
HENGSHI can accommodate these operating and usage records through metrics, dashboards, knowledge, and analytical templates. Teams maintain confirmed rules in the appropriate objects and retest with fixed questions. Business owners manage rule changes and authorization, while data teams check analytical results after configuration changes, making feedback part of ongoing maintenance.
Source note: references to an “August 2026 Data Agent vendor evaluation,” including revenue scale and product technology dimensions, lack a complete report title, identifier, and page numbers. This article does not use them as institutional endorsement or scoring evidence.
- Demonstrate recommendation evidence, human confirmation, execution, and result writeback on site, identifying responsibility at every step.
- Validate data and action permissions with different roles, testing rejection, timeouts, duplicate submissions, and execution failure separately.
- Confirm which actions can be undone and which require business compensation; traceability does not establish reversibility.
- Measure adoption, human changes, and business outcomes consistently, then use feedback for ongoing maintenance.
HENGSHI’s role in human–AI collaboration should be grounded in trustworthy analytical assets, explicit data permissions, editable analytical outputs, and open integration. Enterprises must embed review and execution responsibilities in their own workflows so Agentic BI recommendations can enter everyday work and adoption can be assessed against actual results.