What is an AI workflow? Examples and Best Practices

AI is showing up in every corner of the enterprise, but the companies getting real outcomes aren’t just “adding a chatbot.” They’re building AI workflows: repeatable, observable processes where AI helps move work forward (often end-to-end), with the right data, systems, and people in the loop.

An AI workflow is a sequence of steps that uses artificial intelligence to automate, optimize, or support decisions inside a business process. An AI workflow uses AI-powered technologies to streamline tasks and activities within an organization. This guide walks through what AI workflows are, how they work, real examples, and best practices—plus what to watch out for when you operationalize them.

Why AI workflows matter now

Most business processes are already “workflows,” even if they don’t live in a workflow tool: onboarding a customer, triaging a support request, approving spend, closing the month, refreshing dashboards, responding to security alerts, etc.

The challenge is that modern workflows are messy:

• Work is spread across dozens (or hundreds) of apps.
• Data is fragmented.
• Exceptions are constant (and rules don’t cover the weird stuff).
• Decisions require context that lives in documents, tickets, emails, and dashboards—not just in structured fields.

That’s where AI adds leverage. AI workflows can:

• Reduce manual effort by handling repetitive work (or drafting the first pass).
• Improve decision speed by summarizing context, detecting patterns, and proposing actions.
• Increase consistency with embedded policies, guardrails, and approvals.
• Scale expertise by packaging best practices into repeatable automations.

What an AI workflow is (and what it isn’t)

AI workflow vs. traditional workflow automation

Traditional workflow automation usually relies on rules and deterministic logic (if X happens, then do Y). AI workflows can still include rules, but they also incorporate AI for tasks that are hard to fully specify in advance—like understanding natural language requests, classifying free-text, extracting entities, spotting anomalies, or recommending the “next best action.” AI workflows can learn and adapt based on data rather than only rigid rules.

AI workflow vs. ML pipeline

An ML pipeline is usually focused on model development and deployment (data prep → training → evaluation → inference). An AI workflow is broader: it’s the business process that surrounds and uses AI. In other words, the ML pipeline might be one step inside the workflow.

AI workflow vs. RPA

Robotic process automation (RPA) automates repetitive UI or rules-based tasks. AI workflows often combine RPA-style actions with AI that can handle ambiguity and exceptions. Moveworks explicitly calls out how AI-driven automation differs from fixed-rule automation by learning from experience and context.

AI workflow vs. “AI agent”

You’ll hear these together a lot. A practical way to separate them:

• Workflow = the orchestration and guardrails (the process map).
• Agent = the AI-powered worker inside one or more steps (reasoning + tools).

Some platforms embed agents as steps inside workflows. For example, Domo’s documentation explains that an AI agent embedded in a workflow step can analyze inputs, make context-aware decisions, and execute actions to reduce manual effort.

How an AI workflow works

Most AI workflows follow a repeatable pattern:

1. Trigger
   
   • A ticket arrives, a row changes in a dataset, a threshold is crossed, a form is submitted, a schedule hits 9 a.m., etc.
     
2. Context assembly
   
   • Pull relevant structured data (CRM fields, order history, HR system data).
   • Retrieve unstructured context (policy docs, call transcripts, emails, knowledge base).
   • Add user/org context (role, region, permissions).
     
3. AI step(s)
   
   • Classify, extract, summarize, predict, generate, recommend, or decide.
   • Optional: call tools/functions (send message, update a record, create a ticket).
     
4. Deterministic logic + guardrails
   
   • Validate outputs.
   • Route by confidence, risk, value, or policy.
   • Enforce constraints (PII rules, approval thresholds).
     
5. Human-in-the-loop (when needed)
   
   • Review, approve, edit, or override.
   • Provide feedback that improves prompts, routing, or training data.
     
6. Action + system updates
   
   • Create/update records in systems of record.
   • Notify stakeholders.
   • Write back outcomes so the workflow can be measured.
     
7. Monitoring + learning
   
   • Track cycle time, error rates, deflection, rework, ROI.
   • Iterate prompts, retrieval sources, and decision rules.

Domo positions workflow automation as orchestrating processes across systems, integrating via APIs, and enhancing workflows with AI to detect anomalies, trigger alerts, and monitor performance—while balancing automation with human oversight.

Core components of AI workflows

While implementations vary, most AI workflows rely on a few common building blocks:

Data and integration layer

AI is only as helpful as the context it can access securely. IBM calls out APIs as a key component because they enable systems to communicate and exchange data/functions.

What this means in practice:

• Connectors to systems of record (CRM, ERP, HRIS, ITSM).
• Event triggers and webhooks.
• Read/write access patterns with permissions.

Orchestration layer

This is the “map” of your business process:

• Steps, branching logic, retries, and exception handling.
• State management (where is this request in the process?).
• SLA timing (wait steps, timers, escalations).

AI capabilities

Common AI tasks inside workflows include:

• NLP understanding: intent detection, entity extraction, routing.
• Summarization: compressing threads, tickets, calls into decisions.
• Classification: priority, sentiment, category, compliance risk.
• Generation: drafting emails, responses, briefs, status updates.
• Forecasting/anomaly detection: spotting unusual patterns in data.
• Recommendation: next best action, suggested assignee, suggested offer.

Governance and safety

You need guardrails around:

• Data access and least privilege.
• Logging and audit trails.
• Prompt hygiene and output validation.
• Human approval gates for high-risk actions.

Measurement

If you can’t measure it, you can’t scale it:

• Time-to-resolution, cycle time, and throughput.
• Quality scores (CSAT, accuracy, compliance).
• Automation rate vs. assisted rate vs. escalated rate.
• Cost savings and productivity gains.

AI workflow examples 

Below are practical, high-impact examples you can use as inspiration. A helpful litmus test: if it has a trigger, a repeatable set of steps, and clear outcomes, it’s a strong workflow candidate.

Employee onboarding (HR + IT)

Goal: reduce time-to-productivity and eliminate access errors.

Trigger: HR creates a new hire record.

Workflow steps:

• Pull role, department, start date, location, manager.
• AI checks onboarding checklist based on role and region (policy + compliance differences).
• Automatically provision standard access (email, SSO groups, core apps).
• AI drafts a personalized onboarding plan (first-week schedule, training, key docs).
• Human approval gate for elevated permissions.
• Notify manager and new hire with next steps.

Where AI helps most:

• Translating role info into required access bundles.
• Summarizing policy differences (regional labor rules, security constraints).
• Drafting comms and schedules.

Moveworks gives a similar “new contractor” example: AI workflow automation can provision access and update systems like Workday or Salesforce end-to-end.

Customer support triage and resolution

Goal: faster first response, better routing, higher deflection.

Trigger: new ticket/chat/email arrives.

Workflow steps:

• AI classifies issue type, urgency, sentiment, and product area.
• Retrieve relevant knowledge articles and similar past tickets.
• Draft a suggested response + troubleshooting steps.
• If confidence is high, auto-send response; if medium, route to agent with draft; if low or high-risk, escalate.
• Update CRM with issue category, root cause, and resolution.

Where AI helps most:

• Summarization and context retrieval.
• Drafting replies and recommended steps.
• Detecting compliance-sensitive content.

Marketing operations: Campaign performance “autopilot”

Goal: faster insight-to-action loops.

Trigger: daily schedule or KPI threshold (e.g., CPL spikes 25% WoW).

Workflow steps:

• Pull spend, conversion, attribution, and creative performance.
• AI summarizes what changed, why it likely changed, and what to do next.
• Create tasks: pause underperforming ads, alert channel owner, request creative refresh.
• Human review before budget-impacting actions.
• Log actions and outcomes for learning.

Where AI helps most:

• Explaining “why” behind KPI changes (with retrieved context like audience shifts, creative fatigue).
• Generating stakeholder-ready summaries.

Finance: Invoice exceptions and approvals

Goal: reduce exceptions, shorten approval cycles.

Trigger: invoice received or flagged.

Workflow steps:

• Extract invoice fields (vendor, amount, PO, dates).
• Match to PO and receiving data.
• AI flags anomalies (duplicate invoice patterns, unusual amounts).
• Route for approval based on policy; request missing info automatically.
• Update AP system and notify stakeholders.

Where AI helps most:

• Extracting/normalizing messy invoice data.
• Detecting unusual patterns and explaining flags.
• Drafting clarification requests.

Sales: Lead qualification and routing

Goal: get the right leads to the right reps faster.

Trigger: inbound lead form submission.

Workflow steps:

• Enrich lead with firmographic data.
• AI scores and explains qualification (ICP match + intent signals).
• Route to rep; draft outreach email and call notes.
• If unqualified, route to nurture sequence.
• Capture outcomes (converted, disqualified reason) to refine scoring.

Domo’s AI Agent Task example in its support docs includes a lead qualification scenario, with instructions for assigning leads and sending notifications.

Common AI workflow patterns you can reuse

If you’re designing a library of workflows, these patterns show up everywhere:

• Triage → route → draft → approve → act
• Detect anomaly → explain → recommend → approve → remediate
• Extract → validate → enrich → update system of record
• Summarize → decide gate → notify stakeholders
• Self-serve request → fulfill → confirm → log

The magic is rarely in a single model call. It’s in how you orchestrate steps, tool calls, and human review.

Best practices for designing AI workflows

Start with a measurable business process

Pick workflows with:

• High volume (lots of repetition),
• High cost (manual effort is expensive),
• High friction (handoffs + delays),
• Clear success metrics (cycle time, accuracy, cost).

Define success upfront:

• “Reduce ticket triage time by 30%”
• “Cut onboarding access errors by 50%”
• “Increase first-contact resolution by 10%”

Map the workflow before adding AI

Document:

• Triggers
• Inputs/outputs per step
• Systems touched
• Decision points
• Exceptions and escalation paths
• Approval requirements

Then decide where AI adds leverage:

• Understanding messy inputs
• Making probabilistic decisions
• Generating drafts/summaries
• Detecting patterns humans miss

Treat context as a first-class product

AI workflows fail more often from missing context than from “bad models.”

Practical tips:

• Standardize the inputs (normalize fields, define schemas).
• Use retrieval from approved knowledge sources for policies and product info.
• Pass only what the step needs (minimize noise and sensitive data).

Add guardrails, not hope

Good guardrails include:

• Confidence thresholds and routing rules
• Output validation (schema checks, banned phrases, policy constraints)
• “Read-only” mode for early deployments
• Human approvals for irreversible actions (e.g., sending an external email, changing payroll, deleting records)

Maintaining human oversight for critical tasks as part of AI-powered automation.

Design human-in-the-loop experiences people will actually use

If review is painful, it becomes a bottleneck.

Make it easy to:

• See the evidence (what data and docs were used)
• Edit the output
• Approve/reject with a reason
• Provide lightweight feedback (“wrong category,” “missing detail”)

Build for exceptions from day one

Plan for:

• Missing fields
• Conflicting data between systems
• API downtime
• Ambiguous requests
• Low confidence AI outputs

Your workflow should degrade gracefully:

• Retry
• Ask a clarifying question
• Escalate to a queue with context attached

Log everything you’ll wish you had later

Capture:

• Inputs
• AI outputs (and versions/prompts)
• Actions taken
• Approval decisions
• Final outcomes

This powers debugging, compliance, and continuous improvement.

Iterate like a product team

Launch small:

• One workflow
• One team
• One set of metrics
• Two-week iteration cycles

Then scale:

• Convert what works into templates
• Create governance standards
• Expand connectors and reuse components

Best practices for prompts and AI steps

Even if you’re not writing the implementation, these design principles will improve outcomes:

• Be explicit about the role (“You are a support triage assistant…”).
• Give the model a checklist (“Follow these steps in order…”).
• Constrain the output format (JSON schema, bullet list, table).
• Separate instructions from data (clear delimiters).
• Ask for citations to internal sources when using retrieval (e.g., “Include the policy section title you used.”).
• Use structured outputs for downstream actions (routing, record updates, notifications).

Domo’s agent configuration guidance also leans on clear instructions and structured outputs, describing how outputs can be defined so downstream steps can use them.

Pitfalls to avoid

“We automated a bad process”

AI won’t save a workflow that’s fundamentally broken. If approvals are unclear or ownership is fuzzy, fix that first.

“AI everywhere” syndrome

Too many AI steps make workflows expensive and brittle. Use AI where it changes the outcome—not where it’s merely novel.

No ownership

AI workflows need a product owner: someone accountable for accuracy, performance, and iteration.

Missing change management

People need to trust the workflow. Train users, publish clear guidelines, and roll out progressively.

Lack of governance

Without logging, access controls, and approvals, you’ll stall at security review. Build in governance from the start.

Where Domo fits in an AI workflow strategy

Domo positions Workflows as a way to orchestrate complex processes into automated workflows via a low-code experience, integrate systems through APIs and third-party apps, and “enhance workflows with AI” for monitoring, anomaly detection, alerts, and performance optimization.

In practice, that “fit” often looks like:

• Orchestration: mapping business processes into steps and decision points.
• Integration: connecting datasets, apps, and APIs so workflows can both read and act.
• Intelligent automation: embedding AI/agents where judgment, summarization, or classification is needed.
• Operational visibility: monitoring execution outcomes and bottlenecks over time.

And if you’re using an “agent step” approach, Domo’s documentation frames agents as being able to analyze inputs, make context-aware decisions, and execute actions inside a workflow step.

Implementation checklist

If you want a simple blueprint to follow, use this checklist:

1. Pick a workflow
   
   • High volume, measurable, clear owner
     
2. Define success metrics
   
   • Cycle time, accuracy, cost, satisfaction
     
3. Map the process
   
   • Triggers → steps → exceptions → approvals → outputs
     
4. Identify AI leverage points
   
   • Triage, extraction, summarization, recommendation, drafting
     
5. Lock down data + access
   
   • Least privilege, approved knowledge sources, auditing
     
6. Design guardrails
   
   • Confidence routing, validations, human approvals
     
7. Pilot + iterate
   
   • Small rollout, weekly review, refine prompts and rules
     
8. Scale
   
   • Templates, governance, reusable components, monitoring

Wrap-up

An AI workflow isn’t just automation with a new label. It’s a modern way to run business processes where AI contributes real “work”: understanding, deciding, drafting, detecting, and sometimes acting—within a governed, measurable system.

If you build around a clear process, strong context, guardrails, and iteration, AI workflows can move from “cool demo” to dependable operational advantage.