AI Agents in Business Intelligence: Practical Guide and Use Cases

min read

Monday, April 13, 2026

AI Agents in Business Intelligence: Practical Guide and Use Cases

AI agents represent the next evolution of business intelligence, combining autonomous data monitoring with goal-oriented reasoning and action-taking capabilities. This guide covers the fundamentals of AI agents, their integration with existing BI stacks, practical use cases across retail, healthcare, finance, and manufacturing, and a framework for getting started with implementation.

Key takeaways

Here are the big points to keep in mind as you read:

AI agents go further than traditional BI by proactively analyzing data, surfacing insights, and triggering actions without manual intervention, helping teams move from reactive reporting to proactive intelligence
Successful implementation starts with high-impact use cases, clean data foundations, and clear success metrics rather than attempting organization-wide rollouts
Integration with existing BI stacks requires attention to semantic layer grounding, data governance, and natural language interfaces to prevent metric inconsistencies and maintain trust
Organizations across industries are using AI agents for fraud detection, inventory optimization, churn prevention, and predictive maintenance, with measurable improvements in speed-to-insight and decision quality

What is business intelligence?

BI refers to the processes, tools, and technologies used to collect, organize, and visualize data to support operational and strategic decision-making. At its core, BI answers questions like: What happened? What's happening right now? Where are we off track?

BI platforms centralize data from across the business (sales, finance, supply chain, marketing) and make it accessible through dashboards, reports, and key performance indicators (KPIs). Teams use BI to monitor performance, identify trends, and act quicker when something changes.

A retail manager might use BI to track in-store sales by product and location. A finance director could compare budget vs actual spend across departments. Both get visibility. Neither gets told what to do next.

Key components of BI include:

Data integration and extract, transform, load (ETL)
Centralized dashboards
KPI and metric tracking
Role-based reporting
Real-time alerts
Visualizations and drilldowns

BI is essential for establishing a shared view of business performance. But while it tells you what's happening, it rarely tells you what to do next.

What is an AI agent?

An AI agent for business intelligence is an autonomous software system that continuously monitors your data, reasons about business goals, and takes action (such as generating insights, triggering alerts, or updating workflows) without requiring manual prompts or predefined rules.

This definition matters because it separates AI agents from simpler tools. A BI dashboard displays data. A chatbot answers questions when asked. An AI agent proactively identifies that revenue dropped 12 percent in the Northeast region, investigates whether the cause is pricing, volume, or product mix, and notifies the regional sales director with a recommended action.

In the context of business intelligence, AI agents don't just passively display information. They actively support decision-making and execution. That might include analyzing complex data sets, summarizing insights, generating recommendations, or triggering downstream workflows.

Think of them as digital team members that work 24/7 to help humans make more informed decisions.

Where traditional automation tools follow rigid instructions, AI agents learn from historical data, adjust to new patterns, and interact with people through natural language. This makes them especially valuable in dynamic environments where conditions change quickly and decisions can't be hard-coded in advance. However, this flexibility introduces risk. Agents that learn from poor-quality historical data will reinforce bad patterns rather than improve over time, which is why data quality audits should precede any agent deployment.

AI agents typically share these traits:

Operate autonomously or semi-autonomously
Continuously learn and improve
Interpret structured and unstructured data
Interact via natural language
Execute predefined or dynamic tasks

Some agents are narrow and task-specific, like a forecasting agent for sales projections. Others are more general-purpose and can support multiple workflows across departments, from customer support to financial reporting.

How AI agents work: sense, reason, act, and learn

AI agents operate through a continuous four-step cycle that mirrors how a skilled analyst approaches problems.

The cycle works like this:

Observe: The agent monitors data streams, dashboards, and connected systems for changes, anomalies, or patterns. For example, it might detect that earnings before interest, taxes, depreciation, and amortization (EBITDA) missed plan by 400 basis points in Q2.
Interpret: Using machine learning and contextual understanding, the agent analyzes what the observation means. It might decompose the EBITDA miss into contributing factors: pricing compression in one product line, volume shortfall in another, and unfavorable product mix shifts.
Decide: Based on its analysis and predefined goals, the agent determines the appropriate response. This could range from generating a summary report to recommending specific interventions to escalating for human review.
Act: The agent executes the decision by taking concrete steps, creating a Jira ticket for the finance team, sending a Slack alert to the chief financial officer (CFO), updating a customer relationship management (CRM) record, or drafting an email to regional managers.

Not all actions are created equal. For routine tasks like generating daily summaries or flagging minor anomalies, agents can operate autonomously. For high-stakes decisions (financial reporting, compliance-related actions, or customer-facing communications) agents should surface recommendations for human approval before proceeding. This human-in-the-loop design ensures accountability while still capturing the time savings from automation.

The learning component runs continuously in the background. When a sales director dismisses an alert as irrelevant, the agent adjusts its sensitivity. When a recommended action leads to improved outcomes, the agent reinforces that pattern.

AI agents vs chatbots vs traditional automation

People apply the term "AI agent" loosely, which creates confusion when evaluating tools. Understanding the distinctions helps you choose the right approach for different problems.

Capability	Traditional Automation (Robotic Process Automation, or RPA, Alerts)	BI Copilots	Chatbots	AI Agents
Trigger	Scheduled or rule-based	Person prompt	Person prompt	Autonomous + person prompt
Reasoning	None (if-then logic)	Query interpretation	Intent matching	Goal-oriented reasoning
Learning	None	Limited	Conversation context	Continuous improvement
Data scope	Single source	Connected sources	Knowledge base	Multi-source with context
Actions	Predefined only	Query and visualize	Respond and route	Analyze, recommend, execute
Autonomy	Low	Low	Low	High (with guardrails)

‍

Traditional automation excels at repetitive, predictable tasks. If inventory drops below 100 units, send an email. If a report is due Monday, generate it Sunday night. Reliable but brittle. They can't adapt when conditions change.

BI copilots help people interact with data through natural language. You can ask "What were sales last quarter?" and get a chart. They're valuable for democratizing data access but still require humans to know what questions to ask.

Chatbots handle conversational interactions, typically routing requests or answering FAQs. Reactive by design. Limited to their training data.

AI agents combine the best of these approaches while adding autonomous reasoning. They don't wait for questions. They proactively identify what matters. They don't just retrieve data. They analyze it, form hypotheses, and recommend actions. And they don't just follow rules. They learn from outcomes and adapt.

The decision tree is straightforward. If you need to automate a predictable, rule-based task, traditional automation works fine. If you need to help people explore data, a copilot adds value. If you need a system that can independently monitor, analyze, and act on your data while keeping humans informed, you need an AI agent.

How AI agents and BI work together

BI provides the data. AI agents put that data to work.

Traditional BI platforms offer insights, but they often require people to find, interpret, and act on those insights manually. This process can be time-consuming and reactive, especially in fast-paced environments. AI agents fill the gap by proactively surfacing relevant information, interpreting patterns, and even initiating next steps without needing constant human direction.

AI agents can operate continuously in the background, monitoring KPIs, identifying anomalies, and delivering insights in real time. They bridge the gap between data visibility and decision execution.

The integration follows the observe-interpret-decide-act pattern:

Monitor data streams for anomalies or trends (observe)
Summarize complex data sets in plain language (interpret)
Recommend actions based on patterns or predictions (decide)
Automate repetitive tasks like report generation or categorization (act)
Enable conversational queries and answers (interact)

Instead of logging into a dashboard to check on customer retention, a CX-focused AI agent might notify you when churn risk spikes and suggest targeted interventions. The agent doesn't just flag the problem. It analyzes contributing factors, identifies which customer segments are most at risk, and recommends specific outreach strategies.

Teams even integrate some agents into communication tools like Slack or Microsoft Teams, allowing people to interact with BI data through chat, get automatic alerts, and ask follow-up questions on the fly.

When agents surface recommendations that require judgment (renegotiating a vendor contract, adjusting pricing strategy, or escalating a compliance concern) they route those decisions to the appropriate human approver. This human-in-the-loop design maintains accountability while still capturing efficiency gains.

Rather than replacing BI, AI agents enhance it.

How AI agents integrate with your BI stack

Understanding how AI agents connect to your existing infrastructure helps you evaluate implementation requirements and avoid common pitfalls.

Data sources and connectivity

AI agents need access to the same data your BI platform uses, plus the ability to act on it in real time. This means connecting to cloud applications, databases, data warehouses, and operational systems across your organization.

The quality of these connections matters. Agents that rely on batch-updated data can only act on yesterday's information. Agents with real-time connectivity can respond to changes as they happen, flagging a supply chain disruption within minutes rather than discovering it in tomorrow's report.

Modern BI platforms like Domo offer 1,000+ prebuilt connectors that handle authentication, schema mapping, and data transformation automatically. The Adrenaline engine enables sub-second query performance, which means agents can analyze large datasets and return insights without the latency that makes real-time decision-making impractical.

It also helps when your integration layer does a little housekeeping. AI-ready data preparation can validate, enrich, and standardize incoming data before an agent touches it, so the agent isn't making recommendations off a messy CRM export. Content certification adds another layer of confidence by designating which datasets are approved for decision-making and automation.

For data engineers, this matters because governed integration is possible without building custom pipelines for each data source.

Unstructured data and retrieval-augmented generation (RAG) grounding

Not everything an agent needs lives in neat tables. Policy documents, contracts, support transcripts, PDFs, and internal wikis often contain the context people need to interpret BI results.

This is where RAG comes in. RAG lets an agent pull relevant passages from approved documents and datasets, then use that material to generate an answer or recommendation grounded in your source content. In Domo, that can include governed datasets plus FileSets and unstructured documents, so an agent can explain a KPI change and also cite the policy change that caused it.

Governance, security, and trust

Enterprise deployment requires strong controls that prevent agents from accessing unauthorized data, generating inconsistent metrics, or taking actions without appropriate oversight.

A governance framework for AI agents should include these controls:

Least-privilege role-based access control (RBAC) for agent identities: Agents should have dedicated service accounts with access limited to the specific data and actions they need. No agent should have broader access than the humans it serves.
Row-level and column-level security: Agents must respect the same data access rules as people. If a regional manager can only see their region's data, the agent serving them should have the same restriction.
Personally identifiable information (PII) masking in non-production environments: When testing or developing agents, sensitive data should be masked or anonymized to prevent accidental exposure.
Prompt injection defenses: Input validation and output guardrails prevent malicious prompts from manipulating agent behavior or extracting unauthorized information.
Immutable audit logs: Every agent action (data accessed, queries run, recommendations made, actions taken) should be logged with tamper-proof retention for compliance and troubleshooting.
Human-in-the-loop approval workflows: High-stakes actions (financial transactions, compliance reporting, customer communications) should require human approval before execution.

One critical but often overlooked control is semantic layer grounding. Agents that query raw database tables can generate inconsistent metrics, calculating revenue differently than your official reports, for example. By requiring agents to query a governed semantic layer or metrics store, you ensure they use the same definitions and calculations as your BI dashboards. This prevents the "my numbers don't match your numbers" problem that erodes trust in AI-generated insights. And honestly, this is the part most guides skip over. Teams assume that connecting an agent to "clean" data is sufficient, but without explicit semantic layer grounding, agents may still derive their own metric calculations that diverge from organizational standards.

Centralized agent management to reduce tool sprawl

One more practical point: agents multiply quickly. A sales agent here, a finance agent there, and suddenly you're maintaining a small zoo of bots across disconnected tools.

A centralized management layer helps IT and BI leaders keep oversight as adoption grows by keeping key pieces in one place:

Where agent identities, permissions, and audit logs are managed
How tools and actions are allowlisted (and parameter-validated)
How agents are packaged and distributed (for example, as governed apps across the organization)

This is the difference between "cool demo" and "governed intelligence at scale."

Benefits of AI agents for BI

The real power of AI agents in BI lies in their ability to reduce the gap between insight and action. Traditional BI tools offer visibility, but it's often up to the person to interpret the data, uncover what matters, and determine next steps. AI agents change that dynamic. They act as real-time collaborators, analyzing trends, detecting risks, and delivering suggestions, so your team can focus less on gathering insights and more on using them.

Proactive insights

AI agents identify issues and opportunities without needing a prompt. Instead of waiting for someone to notice a dip in performance, they spot anomalies, forecast emerging patterns, and alert the right people at the right time.

Time savings

Instead of manually slicing and dicing reports, teams can ask agents questions in plain language and get quick, contextual answers. AI agents also automate repetitive work like tagging transactions, generating summaries, or scheduling reports. This frees up analysts and business teams to focus on more strategic work, like planning and optimization.

For BI leaders, this translates to scaling analytics without scaling headcount.

Improved decision quality

AI agents can bring in multiple data sources, analyze scenarios, and recommend the most data-backed course of action. Whether it's selecting a vendor, prioritizing leads, or optimizing delivery routes, agents reduce guesswork and help standardize decision-making across the organization.

Scalability

As companies grow, so do their data and reporting needs. Manually managing reports and dashboards for every region, product, or business unit becomes unsustainable. AI agents scale efficiently, monitoring thousands of metrics simultaneously and serving insights across teams, without adding operational overhead.

Increased data literacy

Natural language interfaces and self-service capabilities make data more accessible to non-technical people. AI agents eliminate the need to learn structured query language (SQL) or understand how to build dashboards. Instead, people can simply ask questions like, "How did sales perform last week?" or "Which region is under budget?" and get clear, useful responses.

For business teams, this means getting answers without waiting on analysts or IT.

Use cases of AI agents in BI by industry

AI agents are already making an impact across industries, even as the technology continues to evolve. From streamlining logistics to improving patient outcomes, these digital assistants are helping teams move quicker, make more informed decisions, and scale more efficiently.

The following examples illustrate how teams can embed AI agents into BI environments to solve high-value problems. Each includes the agent's goal, data inputs, actions taken, and safeguards applied.

Retail: inventory optimization agent

A national retailer deployed an inventory optimization agent to reduce stockouts while minimizing excess inventory costs.

Agent goal: Maintain optimal inventory levels across 200+ store locations while reducing carrying costs
Data inputs: Point-of-sale transactions (real-time), warehouse inventory levels, supplier lead times, weather forecasts, promotional calendars, and historical sales patterns
Actions taken: The agent detected that a promoted product was selling 340 percent above forecast in the Southwest region. It automatically generated purchase orders for additional inventory, adjusted allocation algorithms to redirect stock from lower-velocity stores, and notified the merchandising team with a summary of actions taken.
Safeguards applied: Purchase orders exceeding $50,000 required manager approval. The agent operated within predefined inventory budget constraints and logged all decisions for audit review.

The result: 23 percent reduction in stockouts and 15 percent decrease in excess inventory within six months. These improvements directly impact both revenue (fewer lost sales) and margin (lower carrying costs), two metrics that matter most when justifying AI agent investments to leadership.

Healthcare: patient readmission risk agent

A regional health system implemented a readmission risk agent to improve patient outcomes and reduce costs associated with 30-day readmissions.

Agent goal: Identify patients at high risk of readmission and recommend personalized intervention strategies
Data inputs: Electronic health records, lab results, medication adherence data, social determinants of health, prior admission history, and post-discharge follow-up records
Actions taken: The agent flagged a patient with congestive heart failure whose medication adherence had dropped and whose recent lab values indicated fluid retention. It generated a care plan recommendation, scheduled a follow-up call with the care management team, and updated the patient's risk score in the electronic health record (EHR).
Safeguards applied: All clinical recommendations required review by a licensed care manager before patient contact. The agent could not modify treatment plans directly, only surface recommendations.

The result: 18 percent reduction in 30-day readmissions for high-risk patient populations.

Financial services: fraud detection agent

A mid-size bank deployed a fraud detection agent to identify suspicious transactions while reducing false positives that frustrated legitimate customers.

Agent goal: Detect fraudulent transactions in real time while maintaining customer experience
Data inputs: Transaction records, customer behavior patterns, device fingerprints, geolocation data, merchant category codes, and historical fraud cases
Actions taken: The agent identified a pattern of small test transactions followed by a large purchase from an unusual location. It placed a temporary hold on the account, sent a verification request to the customer's mobile app, and created a case for the fraud investigation team with supporting evidence.
Safeguards applied: Account holds exceeding 24 hours required human review. The agent could not close accounts or reverse transactions, only flag and hold.

The result: 35 percent improvement in fraud detection rates with 40 percent fewer false positives.

Manufacturing: predictive maintenance agent

A manufacturing company implemented a maintenance prediction agent to shift from reactive repairs to proactive maintenance scheduling.

Agent goal: Predict equipment failures before they occur and optimize maintenance schedules
Data inputs: Internet of Things (IoT) sensor data (vibration, temperature, pressure), maintenance logs, equipment age and specifications, production schedules, and spare parts inventory
Actions taken: The agent detected abnormal vibration patterns in a critical production line motor that matched historical failure signatures. It calculated a 78 percent probability of failure within 14 days, generated a maintenance work order, checked spare parts availability, and recommended scheduling the repair during a planned production pause.
Safeguards applied: Work orders for critical equipment required supervisor approval. The agent could not shut down production lines, only recommend maintenance windows.

The result: 45 percent reduction in unplanned downtime and 20 percent decrease in maintenance costs.

Software as a service (SaaS): churn prevention agent

A B2B software company deployed a churn prevention agent to identify at-risk accounts before they canceled.

Agent goal: Identify accounts showing churn signals and recommend retention strategies
Data inputs: Product usage metrics, support ticket history, Net Promoter Score (NPS) scores, contract renewal dates, billing history, and customer success interaction logs
Actions taken: The agent identified an enterprise account whose product usage had declined 60 percent over three months, with increasing support tickets about a specific feature. It calculated a high churn probability, recommended a personalized outreach strategy focused on the problematic feature, and scheduled a check-in for the customer success manager.
Safeguards applied: Discount offers exceeding 15 percent required sales leadership approval. The agent could not modify contracts, only recommend retention actions.

The result: 28 percent improvement in at-risk account retention rates.

Logistics: delivery optimization agent

A logistics company implemented a delivery optimization agent to improve on-time delivery rates and reduce transportation costs.

Agent goal: Optimize delivery routes and carrier selection based on real-time conditions
Data inputs: Carrier performance data, delivery service-level agreements (SLAs), traffic patterns, weather conditions, fuel costs, and customer delivery preferences
Actions taken: The agent detected that a primary carrier was experiencing delays due to weather in the Midwest. It automatically rerouted affected shipments to alternative carriers, updated delivery estimates for customers, and flagged contracts with underperforming carriers for renegotiation review.
Safeguards applied: Carrier changes for shipments exceeding $10,000 required logistics manager approval. The agent could not renegotiate contracts, only flag opportunities.

The result: 12 percent improvement in on-time delivery rates and 8 percent reduction in transportation costs.

These examples share a common pattern: agents that observe data continuously, interpret patterns using historical context, decide on appropriate responses, and act within defined guardrails.

AI agent tools and platforms for BI

The market for AI agent platforms is evolving rapidly. Understanding what to look for helps you evaluate options and avoid tools that don't fit your needs.

What to look for in an AI agent platform

When evaluating platforms, focus on these criteria:

Governance controls: Does the platform support RBAC, row-level security, and audit logging out of the box? Can you define approval workflows for high-stakes actions? Governance isn't optional for enterprise deployment.
Semantic layer support: Can agents query governed metrics and definitions rather than raw tables? This prevents metric inconsistencies and reduces hallucination risk.
Human-in-the-loop design: Does the platform make it easy to route decisions to human approvers? Can you configure different autonomy levels for different action types?
Data connectivity: How many prebuilt connectors does the platform offer? Can it connect to your existing data sources without custom pipeline work?
Unstructured data support: Can agents use RAG to pull from approved documents, FileSets, and knowledge bases, not just database tables?
Centralized agent management: Can you manage multiple agents without adding tool sprawl across disconnected systems?
Large language model (LLM) flexibility: Can you choose between different language models (proprietary, open-source, or custom-trained)? This matters for cost, performance, and data privacy.
Low-code workflow design: Can business teams configure agents without engineering support? Self-service capabilities accelerate adoption.
Prebuilt agent templates: Does the platform offer industry-specific or function-specific templates? Starting from a template is quicker than building from scratch.
Multi-channel distribution: Can agents deliver insights through desktop, mobile, Slack, Teams, and embedded applications? Meeting people where they work increases adoption.

The build vs buy decision depends on your specific situation. Building custom agents makes sense if you have unique requirements that no platform addresses, strong AI and machine learning (ML) engineering capabilities, and time to invest in development. Buying a platform makes sense if you want shorter time-to-value, prefer governed infrastructure over custom code, and need to scale across multiple use cases without proportional engineering investment.

How Domo approaches AI agents for BI

Domo's approach to AI agents centers on two principles: speed-to-value and human-in-the-loop design.

Agent Catalyst provides the infrastructure for deploying AI agents that connect to your existing Domo environment. Rather than building agents from scratch, organizations can start with prebuilt AI Agent Templates designed for specific use cases, including retail promotion effectiveness, risk and fraud analysis, manufacturing transformation, and competitive research.

AgentGuide offers structured AI roadmaps that help organizations identify high-value use cases and sequence their agent deployments. This addresses a common challenge: knowing where to start.

Domo Workflows enables multi-step orchestration, so agents can execute complex processes that span multiple systems and require conditional logic. Combined with Domo's 1,000+ prebuilt connectors and sub-second query performance, this means agents can act on current data rather than batch-delayed reports.

Under the hood, DomoGPT provides a secure LLM foundation for agent experiences, and Agent Catalyst can also support third-party and custom models when teams need more flexibility in experimentation and deployment.

The human-in-the-loop philosophy runs throughout. Agents work alongside humans rather than replacing them. High-stakes decisions route to human approvers. All actions are logged for audit and review.

In practice, many teams package agents as Domo apps so teams can deploy the same governed agent across departments with consistent access controls and auditing. That's a big deal for IT and BI leaders trying to scale adoption without losing oversight.

Watch the demo to see how AI agents work within the Domo platform.

Challenges and best practices for AI agents in BI

Deploying AI agents successfully requires anticipating common pitfalls and building appropriate safeguards. The following challenges appear consistently across implementations, along with proven mitigations.

Challenge	Risk	Mitigation
Data quality issues	Agents produce inaccurate insights based on incomplete, outdated, or inconsistent data	Implement content certification and AI-ready data preparation; establish data quality SLAs before agent deployment
Metric hallucination	Agents calculate metrics differently than official reports, eroding trust	Ground agents in a semantic layer or metrics store; require agents to query governed definitions rather than raw tables
Unrestricted tool access	Agents access systems or take actions outside their intended scope	Implement tool allowlisting with parameter validation; define explicit boundaries for each agent's capabilities
Prompt injection	Malicious inputs manipulate agent behavior or extract unauthorized information	Apply input/output validation and guardrails; sanitize inputs from people before processing
Organizational resistance	Teams distrust AI-driven recommendations or feel threatened by automation	Design for human-in-the-loop approval; provide explainability for agent decisions; involve stakeholders in agent design
Scope creep	Agents expand past their original purpose without appropriate governance	Define clear agent charters with explicit boundaries; review and approve scope changes through governance processes
Tool sprawl	Multiple disconnected agent tools create gaps in governance, logging, and access controls	Centralize agent management, auditing, and policy controls within a single governed environment where possible

‍

Successful implementations share these best practices:

Start with low-risk, high-value use cases that build confidence before expanding to more sensitive areas
Establish clear ownership for each agent, including who approves changes and who reviews performance
Build feedback loops so agents improve based on person input and outcome data
Document agent behavior so stakeholders understand what agents do and why
Plan for failure modes, including how to disable agents quickly if they malfunction

How to get started with AI agents for BI

You do not need to overhaul your entire BI stack to benefit from AI agents. Here's a step-by-step approach to introducing them effectively.

Identify high-impact use cases

Start with problems that are frequent, repetitive, and data-driven.

For example:

"We spend hours each week building the same reports"
"We miss customer churn signals until it's too late"
"We don't have visibility into key metrics in real time"

Look for tasks where AI could save time, improve accuracy, or surface insights sooner. The best starting points combine high business value with low implementation risk. Teams often select use cases based on what's technically interesting rather than what delivers measurable business impact. Start with the business problem, not the technology.

Audit your data readiness

AI agents are only as effective as the data they access. Ensure you have:

Integrated and cleaned data sources
Standardized formats and taxonomies
Clear governance and access controls
Documented metric definitions

If your data is not centralized or reliable, fix that first. A solid BI foundation is essential for AI success.

Data quality deserves particular attention. Agents that operate on inconsistent or incomplete data will produce unreliable outputs, which erodes trust and undermines adoption. Content certification and AI-ready data preparation tools help ensure agents operate on clean, trusted inputs rather than raw or inconsistent source data.

Choose the right tools

Some BI platforms now offer native AI agent capabilities (e.g., chat-based interfaces, automated alerts, smart recommendations). Others integrate with third-party agent frameworks.

Evaluate tools based on:

Compatibility with your existing stack
Ease of customization
People experience (especially for non-technical teams)
Support for governance and data privacy
Prebuilt templates for your industry or use cases
Flexibility in LLM options

The build vs buy question matters here. If the platform provides prebuilt agent templates for your industry, governed data access without custom pipeline work, and human-in-the-loop controls out of the box, buying likely delivers shorter time-to-value than building. If your requirements are highly specialized and you have strong AI/ML engineering capabilities, building may make sense.

If you're a line-of-business leader and that whole paragraph made your eyes glaze over, you're not alone. Guided tools like AgentGuide and executive workshops can help translate "we want AI-driven BI" into a phased roadmap tied to measurable outcomes.

Don't chase hype.

Define clear tasks and goals

AI agents work best when they have well-scoped roles. For example:

"Summarize daily sales trends and anomalies"
"Flag invoices over $50K for manual review"
"Answer questions about monthly marketing performance"

Avoid vague or overly broad instructions. Start small, then scale as confidence grows.

A quick role-based starting point

If you're wondering who should do what, this cheat sheet helps teams get moving without stepping on each other's toes:

IT/data leaders: Start with governance, audit logs, and approval workflows so agents can operate safely at scale
BI/analytics leaders: Start with semantic layer definitions and a short list of certified KPIs agents are allowed to use
Data engineers: Start by connecting governed datasets (and approved documents, if you need RAG) to reduce custom pipeline work
AI/ML engineers: Start by choosing LLM options and guardrails, then run controlled experiments before going broad
Business teams: Start with always-on KPI monitoring, scheduled summaries, and workflow-triggering alerts in the tools you already use

Measure success and iterate

AI agents improve with use, but they need feedback and structured evaluation. Track how often suggestions are used, how accurate they are, and where people get stuck.

An Analytics Agent Scorecard provides a structured measurement approach:

Metric-definition adherence: Does the agent reference governed metrics or raw tables?
SQL/tool-call validity: Are queries well-formed and within allowlisted parameters?
Citation/lineage to source tables: Can outputs be traced back to verified data?
Stability across reruns: Does the agent produce consistent results on the same inputs?
Action-safety checks: Are high-risk actions gated by human approval?

Pair these technical metrics with business KPIs: time saved on manual reporting, speed of insight-to-action, reduction in missed anomalies, and improvements in forecast accuracy.

Involve both technical teams and business stakeholders in testing and adoption.

The future of AI agents in business intelligence

The trajectory of AI agents in BI points toward increasing autonomy, but with carefully designed guardrails. Organizations are moving through a maturity model that balances capability with control.

The progression typically follows these stages:

Inform: Agents surface insights and anomalies for human review. No autonomous action.
Recommend: Agents analyze situations and suggest specific actions. Humans decide whether to proceed.
Draft: Agents prepare outputs (reports, emails, work orders) for human approval before distribution.
Execute: Agents take defined actions autonomously within strict boundaries, with human oversight for exceptions.

Each stage requires progressively stronger governance. Inform-level agents need accurate data and clear presentation. Execute-level agents need strong access controls, audit logging, approval workflows, and rollback capabilities.

The organizations moving fastest are those that treat governance as an enabler rather than a constraint. Strong controls build the trust needed to expand agent autonomy over time.

Multimodal capabilities are expanding what agents can analyze. Agents that can interpret charts, documents, and images alongside structured data will handle a broader range of business questions. Voice interfaces will make agent interaction more natural for frontline workers.

The integration between agents and operational systems will deepen. Today's agents primarily analyze and recommend. Tomorrow's agents will orchestrate complex workflows across CRM, enterprise resource planning (ERP), supply chain, and customer service systems.

Your next step with AI agents in BI

BI helps you see what's happening in your business. AI agents help you do something about it.

Together, they represent the next evolution of decision intelligence: systems that don't just inform, they assist, act, and adapt.

While still early, AI agents are already making business intelligence quicker to act on, more accessible, and easier to operationalize. As platforms mature and use cases expand, these digital coworkers will become essential to how we manage performance, optimize operations, and uncover new opportunities.

Whether you're just starting with BI or have a mature data stack, now's the time to explore what AI agents can add. Start with a single task, measure results, and scale thoughtfully.

Your dashboards are about to get a whole lot more action-oriented.

See Domo in action

Watch Demos

Start Domo for free

Free Trial

Frequently asked questions

What is an AI agent in business intelligence?

An AI agent for business intelligence is an autonomous software system that continuously monitors your data, reasons about business goals, and takes action without requiring manual prompts. Unlike dashboards that display information or chatbots that answer questions when asked, AI agents proactively identify anomalies, analyze root causes, and trigger workflows or recommendations. They combine the analytical capabilities of BI with the autonomous action-taking of intelligent automation, while keeping humans in the loop for high-stakes decisions.

How are AI agents different from BI copilots or chatbots?

BI copilots help people query data through natural language, you ask a question, and they return a visualization or answer. Chatbots handle conversational interactions, typically routing requests or answering FAQs. AI agents go further by operating autonomously: they monitor data continuously, identify issues without being prompted, reason across multiple data sources, and take action within defined guardrails. The key distinction is autonomy. Copilots and chatbots are reactive; agents are proactive.

What are the risks of deploying AI agents for BI?

The primary risks include data quality issues causing inaccurate insights, metric hallucination when agents calculate KPIs differently than official reports, prompt injection attacks that manipulate agent behavior, and unrestricted tool access that allows agents to take unintended actions. Mitigations include grounding agents in a semantic layer, implementing tool allowlisting with parameter validation, applying input/output validation, and designing human-in-the-loop approval workflows for high-stakes decisions. Organizations should also establish clear agent charters and audit logging.

How do I measure ROI from AI agents in BI?

Measure both technical performance and business outcomes. Technical metrics include metric-definition adherence, SQL validity, output stability across reruns, and action-safety compliance. Business metrics include time saved on manual reporting, speed of insight-to-action, reduction in missed anomalies, improvement in forecast accuracy, and impact on downstream KPIs like revenue, cost, or customer retention. Start by establishing baselines before deployment, then track improvements over time. The Analytics Agent Scorecard framework provides a structured approach to evaluation.

Where should I start with AI agents for BI?

Start with a use case that is frequent, repetitive, data-driven, and low-risk. Good candidates include automated report generation,anomaly detectionin operational metrics, or answering common questions about business performance. Audit your data readiness first, agents are only as good as the data they access. Choose a platform that offers prebuilt templates, governance controls, and human-in-the-loop design. Define clear success metrics before deployment, and plan to iterate based on person feedback and outcome data.

Explore all

Domo transforms the way these companies manage business.