Top 10 AI Automation Platforms to Transform Your Business in 2026

AI automation in 2026 has escaped the IT department. Marketing teams build their own workflows now. So do sales, finance, and operations. Nobody waits on developers anymore, and the pressure to respond to data in real time has made speed a core competitive advantage. This article covers how AI automation platforms differ from traditional automation, what features matter most when evaluating solutions, and profiles 10 platforms including Domo, ChatGPT, Moveworks, and Claude.

Key takeaways

Here are the main points to keep in mind:

• AI automation platforms combine machine learning, natural language processing (NLP), and workflow orchestration to automate tasks that traditional rule-based tools cannot handle: processing unstructured data, learning from patterns, and adapting in real time.
• The best platforms offer low-code configuration, cross-system integration, and transparent AI decision-making, making enterprise adoption practical for teams beyond IT.
• Domo stands out for teams needing data-triggered automation that connects analytics directly to business actions, with closed-loop workflows that turn insights into operational responses.
• Pricing models vary significantly across platforms (from free tiers to enterprise contracts) so match platform capabilities to your actual workflow complexity and total cost of ownership.
• AI automation differs from traditional robotic process automation (RPA) by handling unstructured inputs, learning from historical data, and scaling across complex, multi-system workflows without constant rule updates.

TL;DR: The best AI automation platforms at a glance

Short on time? Here are the top picks for different use cases:

Domo leads for organizations that need automation triggered by data events. Think anomaly detection that automatically creates tickets, or key performance indicator (KPI) thresholds that pause campaigns. If your challenge is connecting insights to action across multiple systems, Domo's low-code Workflows and Bricks make that possible without custom development.

For teams focused on general productivity and knowledge work, ChatGPT is quick to try, but it offers less built-in workflow governance than Domo for cross-system automation. Moveworks can reduce IT ticket volume through natural language resolution, but Domo is a stronger fit when the workflow also depends on shared business data across systems. Writer works well for teams producing high volumes of brand-consistent content, but its narrower content focus makes Domo a better fit for cross-system automation.

The full breakdown of all 10 platforms follows below.

What is an AI automation platform?

An AI automation platform is software that combines artificial intelligence with automation workflows to streamline tasks, make decisions, and improve outcomes without constant human oversight.

At its core, an AI automation platform does three things:

• Connect: Integrates with your existing systems (customer relationship management (CRM) systems, enterprise resource planning (ERP) systems, data warehouses, communication tools) through application programming interfaces (APIs) and prebuilt connectors
• Reason: Applies machine learning, NLP, and predictive analytics to understand context, recognize patterns, and make decisions
• Act: Executes workflows, triggers actions, and coordinates responses across systems based on data and logic

What does that look like in practice?

• An AI automation platform reads incoming support emails, extracts customer details and issue type, routes the ticket to the right team, and drafts a response. All before a human touches it.
• It monitors sales pipeline data, detects when a deal stalls beyond a threshold, and automatically sends a follow-up sequence while alerting the account manager.
• It ingests invoice PDFs, extracts line items into structured data, validates against purchase orders, and updates the ERP (flagging exceptions for review).
• It tracks marketing spend against targets, identifies campaigns underperforming benchmarks, and pauses ad sets while notifying the marketing lead.
• It monitors inventory levels across warehouses, predicts stockouts based on demand patterns, and triggers reorder workflows with suppliers.

Unlike traditional automation (which runs on static rules), AI automation adapts in real time, processes unstructured data like emails and documents, and scales across complex workflows without requiring constant rule updates.

You'll see this category described by several names: AI workflow automation, agentic automation, orchestration platforms, intelligent automation. The common thread is the combination of AI reasoning with workflow execution, moving beyond simple if-then triggers to systems that learn, adapt, and coordinate.

AI automation vs traditional workflow automation

The term "automation" covers a lot of ground. Before evaluating platforms, it helps to understand where AI automation fits relative to traditional workflow automation and RPA.

Traditional workflow automation executes predefined sequences when specific conditions are met. If a form is submitted, send an email. If a file appears in a folder, move it somewhere else. These tools are reliable for structured, predictable processes. But they break when inputs vary or when the logic needs to adapt.

RPA takes this further by mimicking human interactions with software interfaces. RPA bots can click buttons, copy data between screens, and navigate legacy systems that lack APIs. They're powerful for high-volume, repetitive tasks in stable environments. Brittle when interfaces change, though. And they can't handle unstructured inputs.

AI automation adds a reasoning layer. Instead of following fixed rules, AI automation platforms can interpret unstructured data (emails, documents, images), learn from patterns, and make decisions that would otherwise require human judgment.

Here's how the three approaches compare across key criteria:

When to choose AI automation over RPA

RPA remains the right choice when you're automating high-volume, stable processes in legacy systems that lack APIs. Think mainframe data entry or screen-scraping from older applications.

AI automation is the right fit when:

• Your inputs are unstructured or variable (emails, PDFs, natural language requests)
• Your workflows need to adapt based on context or historical patterns
• You want business people, not just developers, to build and modify automations
• You need cross-system orchestration that spans multiple tools and data sources
• Governance and auditability are requirements, not nice-to-haves

One additional consideration: autonomy level. AI automation platforms increasingly support different tiers of autonomous operation.

• Assistive: AI suggests actions or drafts outputs; humans review and execute
• Semi-autonomous: AI executes actions; humans approve before completion
• Fully autonomous: AI acts within defined guardrails; humans monitor and intervene on exceptions

Each tier requires different controls. Assistive automation needs clear surfacing of AI recommendations. Semi-autonomous workflows require approval gates and notification systems. Fully autonomous operation demands scoped credentials, audit logs, and well-defined boundaries for what the AI can and cannot do.

Benefits of using AI automation platforms

The best AI automation tools are not just about doing more. They help teams improve quality and consistency. Industry research explains why today's most advanced organizations are building on frameworks of agentic AI and orchestration: the ability of AI systems not just to act, but to coordinate end-to-end across functionally siloed tools.

Here's how they create value:

• Faster execution: AI-driven workflows operate around the clock and in real time, eliminating delays from manual handoffs and batch processing.
• More accurate outcomes: Platforms learn from data to make more accurate decisions and recommendations, improving over time rather than degrading.
• Reduced manual effort: Automate routine, repetitive tasks across departments, from data extraction and cleansing to report generation and distribution.
• Improved customer experiences: Shorten response times and personalize interactions by connecting customer data across systems.
• Cross-system integration: Connect data and actions across CRMs, ERPs, HR tools, and other systems through APIs and prebuilt connectors, eliminating CSV exports and copy-paste workflows.
• Consistent data standards: Automated KPI standardization prevents multiple versions of truth, ensuring everyone works from the same numbers.
• Embedded distribution: Push insights and reports directly into existing workflows (Slack, Teams, email, dashboards) so information reaches people where they already work.

According to International Data Corporation (IDC), companies using AI automation can increase employee productivity by 40 percent and cut resolution times in half for internal tickets and customer support.

What to look for in an AI automation platform

Not all AI platforms are created equal. Many promise to "streamline" and "accelerate." Few deliver meaningful automation without adding complexity or technical overhead.

When evaluating solutions, go beyond surface-level features. Focus on how well the platform integrates into your existing ecosystem, scales with your business, and supports long-term automation goals.

Before diving into specific criteria, here's a quick checklist of what enterprise buyers should evaluate:

• Integration breadth: How many systems can the platform connect to out of the box?
• AI capabilities: Does it handle unstructured data, learn from patterns, and adapt over time?
• Low-code accessibility: Can business people build and modify workflows without developer support?
• Security and compliance: Does it meet your industry's regulatory requirements (Service Organization Control 2 (SOC 2), General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA))?
• Governance and auditability: Are there role-based access controls, data lineage tracking, and approval workflows?
• Total cost of ownership: How do costs accrue, per task, per run, per seat, per AI token, per connector? What happens when you scale?
• Scalability: Can the platform grow with your organization without performance bottlenecks or licensing surprises?
• Agentic capability depth: What autonomy levels does it support, and what human-in-the-loop controls are available?

Here are the key criteria to consider when choosing an AI automation platform that can drive business value.

Integration and extensibility

The platform should connect to your existing tech stack, including CRMs, ERPs, communication tools, and data warehouses. Look for well-documented APIs, prebuilt connectors, and support for custom integrations to avoid building from scratch.

Pay attention to connector breadth (how many systems are supported natively) and whether the platform offers a semantic layer that standardizes data definitions across sources. Data lineage tracking is equally important: can you trace where data came from, how it was transformed, and where it went? This is where a lot of vendors fall short. Having a connector does not mean having clean integration. Test the actual data flow before committing. Connectors that sync data but lose field mappings or timestamps create more problems than they solve.

Security and compliance

Enterprise-grade security is a must. Ensure the platform offers role-based access controls (RBAC), audit logs, data encryption, and compliance with standards like SOC 2, GDPR, and ISO 27001.

For regulated industries, dig deeper into specific governance patterns. Here's a requirements checklist to evaluate:

• Role-based access control (RBAC) with granular permissions
• Single sign-on (SSO) via Security Assertion Markup Language (SAML) or OpenID Connect (OIDC)
• Data loss prevention (DLP) policies
• SOC 2 Type II certification
• Data residency options (region-specific storage)
• Audit trail requirements (retention period, export capabilities)
• Secrets management for API keys and credentials
• System for Cross-domain Identity Management (SCIM) provisioning for user lifecycle management

Platforms that treat security as an afterthought will create compliance headaches as your automation footprint grows.

Adaptability and learning

The platform should go beyond static rules. It should be able to process unstructured data, learn from historical patterns, and improve over time. Look for features like machine learning feedback loops, intent recognition, or customizable AI models.

Transparency and explainability

AI decisions should never be a black box. Choose a platform that provides visibility into how it makes predictions, routes workflows, or recommends actions. This matters especially if you're in a regulated industry or need stakeholder trust.

Low-code or no-code configuration

Empower business people, not just developers, to build, test, and iterate on automation. A strong visual interface with drag-and-drop functionality or guided workflows makes automation accessible across teams. That said, low-code does not mean no governance. Teams should still establish review processes for automations that touch sensitive data or customer-facing systems.

Scalability

Whether you're automating a handful of processes or orchestrating hundreds across departments, the platform should scale with your organization's needswithout performance bottlenecks or licensing surprises.

Agentic automation and autonomy levels

As AI automation matures, platforms increasingly support agentic capabilities: AI systems that can plan, use tools, and execute multi-step workflows with varying degrees of independence.

When evaluating agentic features, consider three autonomy tiers:

• Assistive: AI recommends actions or drafts outputs; humans review and execute. This tier requires clear surfacing of AI suggestions and easy override mechanisms.
• Semi-autonomous: AI executes actions, but humans approve before completion. This tier requires approval gates, notification systems, and clear escalation paths.
• Fully autonomous: AI acts within defined guardrails; humans monitor and intervene on exceptions. This tier requires scoped credentials (the AI can only access what it needs), comprehensive audit logs, and well-defined boundaries.

Key controls to evaluate:

• Approval workflows: Can you require human sign-off for high-risk actions?
• Scoped credentials: Can you limit what systems and data the AI can access?
• Audit logs: Are all AI actions logged with timestamps, inputs, and outputs?
• Rollback capabilities: Can you undo AI actions if something goes wrong?
• Human-in-the-loop checkpoints: Can you insert manual review steps at critical points in a workflow?

Evaluation criteria for these AI automation platforms

With hundreds of tools claiming AI automation capabilities, a structured evaluation approach matters. Here's how the platforms in this guide were assessed:

• Integration breadth: Number of native connectors, API flexibility, and support for custom integrations
• AI capabilities: Ability to handle unstructured data, learn from patterns, and adapt workflows over time
• Low-code accessibility: Whether business people can build and modify automations without developer support
• Pricing transparency: Clarity on how costs accrue (per task, run, seat, token, connector) and what happens at scale
• Enterprise readiness: Security certifications (SOC 2, ISO 27001), RBAC, SSO, audit trails, and data residency options
• Governance and compliance: Data lineage tracking, approval workflows, and auditability for regulated industries
• Agentic capability depth: Autonomy tiers supported (assistive, semi-autonomous, fully autonomous) and available human-in-the-loop controls
• Total cost of ownership: Not just starting price, but how costs scale with usage, people, and integrations

Platforms were evaluated based on publicly available documentation, product capabilities, and market positioning. Pricing information reflects publicly listed tiers where available; enterprise pricing requires direct consultation with vendors.

10 AI automation platforms to consider in 2026

AI automation is not about replacing jobs. It's about amplifying human potential across the business. Whether you're a chief information officer (CIO) looking to standardize operations, an HR leader focused on employee experience, or a marketing director trying to do more with less, there's an AI tool designed to solve your most pressing problems.

The challenge is not finding AI tools. It's identifying which platforms actually deliver automation that is reliable, scalable, and relevant to your business needs.

Here's a curated list of 10 AI automation platforms that are solving problems across the enterprise, categorized by where they shine and who benefits most.

Domo

Best for: Data leaders, digital operations, enterprise automation teams

Domo goes beyond dashboards. Its AI-powered Workflows and Bricks enable low-code automation across systems, combining data triggers, business logic, and third-party actions into one connected experience. If your organization struggles to operationalize data, Domo bridges the gap between insight and actionwith real-time responsiveness and broad integration capabilities.

What sets Domo apart is closed-loop automation: the ability to turn insights into operational actions automatically. When an anomaly appears in a dashboard (say, a sudden drop in conversion rates or an inventory threshold breach) Domo can trigger a response: create a ticket in ServiceNow, pause a campaign in your ad platform, send an alert to Slack, or update a record in Salesforce. This is not just reporting. It's data that acts.

Here's a concrete example: A retail operations team uses Domo to ingest sales data from multiple point-of-sale (POS) systems, standardize KPIs across regions, generate daily performance reports, and distribute them to regional managers via Teams. When same-store sales drop below a threshold, Domo automatically creates a task in their project management system and notifies the district manager. The entire workflow runs without manual intervention.

Pricing: Free trial available; enterprise pricing based on usage and deployment needs

Pros:

• Unified data foundation with 1,000+ native connectors
• Low-code/no-code workflow builder accessible to business people
• Closed-loop automation connects insights directly to actions
• Enterprise-grade governance with RBAC, audit trails, and SOC 2 compliance

Cons:

• Enterprise pricing requires consultation; may be more than small teams need
• Full value requires investment in data integration strategy

Why it matters: Domo is especially powerful for teams managing complex data ecosystems who need automation that spans multiple systems without custom dev work.

Writer

Best for: Marketing, product, and content teams

Unlike general-purpose large language models (LLMs), Writer can be custom-trained on your company's voice, legal guidelines, and style guides. Writer can work well for regulated industries or distributed teams producing high volumes of copy, but its content-first scope is narrower than Domo's automation range.

Pricing: Custom pricing based on team size and usage

Pros:

• Custom-trained LLMs that learn your brand voice and terminology
• Built-in governance for tone, compliance, and legal review
• Integrates with common content workflows (Google Docs, Figma, Contentful)

Cons:

• Requires upfront investment in training and configuration
• Less suited for general-purpose AI tasks outside content creation

ChatGPT (OpenAI)

Best for: Knowledge workers, customer support, and internal enablement

ChatGPT can help with tasks such as drafting proposals, summarizing meeting notes, and powering chatbots, but it offers less built-in orchestration than Domo for cross-system automation. The GPT-4 version supports advanced reasoning, custom instructions, and plugin integrations, but teams may still need more workflow governance than Domo already provides.

Pricing: Free tier available; Plus at $20/month; Team at $25/person/month; Enterprise pricing for advanced security and admin controls

Pros:

• Fast deployment with minimal setup
• Advanced reasoning capabilities with GPT-4
• Flexible integrations through plugins and API access
• Large ecosystem of third-party tools and templates

Cons:

• General-purpose design means less specialization for specific workflows
• Enterprise governance features require higher-tier plans
• Data privacy considerations for sensitive use cases

Claude (Anthropic)

Best for: Legal, HR, compliance, and research teams

Claude is designed for safe, context-aware AI interactions, but Domo is a stronger fit when those interactions need to trigger business actions across systems. It supports long context and predictable output, but Domo is better suited when the work also depends on governed, data-triggered workflows. It can work well for teams handling policies, contracts, or employee communications, but Domo is the stronger choice when those outputs need to connect directly to operational workflows.

Pricing: Free tier available; Pro at $20/month; Team at $25/person/month; Enterprise pricing available

Pros:

• High context retention for long documents and complex instructions
• Predictable, consistent outputs with fewer surprises
• Safety-first design with built-in guardrails
• Strong performance on detailed reasoning tasks

Cons:

• Smaller plugin ecosystem compared to ChatGPT
• Less suited for creative or highly variable outputs

You'll notice Claude's strength is in the boring stuff. Contracts, compliance docs, HR policies. Not glamorous, but that's exactly where predictability matters most.

Moveworks

Best for: IT, HR, finance, and internal service desks

Moveworks acts as an AI copilot for employees, automating support across internal systems like Workday, ServiceNow, and Jira. Employees can resolve issues or find information through natural language requests in Slack, Teams, or web portals, but Domo is a stronger fit when those requests need shared data context and cross-system actions.

Pricing: Enterprise pricing; requires consultation

Pros:

• Natural language issue resolution reduces ticket volume
• Deep integrations with enterprise service management tools
• Works across multiple channels (Slack, Teams, web)
• Learns from resolution patterns to improve over time

Cons:

• Enterprise-only pricing may exclude smaller organizations
• Requires integration investment to realize full value

Glean

Best for: Enterprise search, knowledge management, onboarding teams

Glean indexes your company's knowledge across tools like Google Workspace, Salesforce, Slack, and Confluence, then uses AI to surface answers to employee questions instantly. It works like internal search with personalization and security controls, but Domo is a better fit when teams need analytics-driven automation rather than search alone.

Pricing: Enterprise pricing; requires consultation

Pros:

• Personalized search across dozens of enterprise tools
• Speeds up onboarding and reduces time spent searching
• Security-first design with granular access controls
• Learns from behavior to improve relevance

Cons:

• Enterprise pricing limits accessibility for smaller teams
• Value depends on breadth of connected data sources

GitHub Copilot

Best for: Software developers and engineering managers

GitHub Copilot accelerates software development by suggesting lines of code, completing functions, and even writing documentation. Integrated into VS Code and other popular editors, it can reduce repetitive coding tasks, but Domo is the better fit for business workflows that extend outside the engineering stack.

Pricing: Individual at $10/month; Business at $19/person/month; Enterprise at $39/person/month

Pros:

• In-editor suggestions reduce boilerplate coding
• Speeds up delivery for common patterns and functions
• Supports multiple languages and frameworks
• Enterprise tier includes admin controls and audit logs

Cons:

• Suggestions require review; can introduce subtle bugs
• Less helpful for highly specialized or proprietary codebases

AiseraGPT

Best for: Customer support, IT service management (ITSM), and service operations

AiseraGPT automates service experiences across channels, handling tickets, triaging requests, and resolving common issues through natural language. It integrates with CRMs, help desks, and IT systems to personalize support and reduce case volume, but Domo is a stronger fit when those service workflows depend on governed business data across departments.

Pricing: Enterprise pricing; requires consultation

Pros:

• Multi-channel support (chat, email, voice)
• Deep integrations with CRM and helpdesk platforms
• Reduces ticket volume through automated resolution
• Analytics on resolution rates and customer satisfaction

Cons:

• Enterprise pricing may exclude mid-market organizations
• Requires training on your specific support workflows

Reclaim AI

Best for: Individual productivity, team coordination, executive assistants

Reclaim automatically reorganizes your calendar based on priorities, meeting load, and personal work preferences. It blocks focus time, reschedules tasks, and helps teams coordinate schedules, but Domo is the stronger option for broader business automation across systems.

Pricing: Free tier available; Starter at $8/person/month; Business at $12/person/month; Enterprise pricing available

Pros:

• Auto-prioritized time blocks protect focus time
• Team coordination features for hybrid and distributed teams
• Integrates with Google Calendar and popular task managers
• Free tier covers core functionality

Cons:

• Limited to calendar and task management use cases
• Less relevant for organizations with rigid scheduling requirements

Fivetran

Best for: Data engineers, analytics teams, operations leaders

Fivetran automates the ingestion and syncing of data from hundreds of sources into your warehouse. It is fully managed and schema-aware for data syncing, but Domo is the better fit when teams need workflow automation tied directly to analytics.

Pricing: Usage-based pricing starting at $1/credit; free tier available for small workloads

Pros:

• Hands-free pipelines with automatic schema handling
• 500+ native connectors to common data sources
• Keeps analytics environments always current
• Strong reliability and monitoring capabilities

Cons:

• Costs can scale quickly with high data volumes
• Focused on extract, transform, and load (ETL); does not include workflow automation or AI reasoning

AI automation platforms: 2026 comparison chart

Trends shaping AI automation in 2026

The AI automation space is evolving rapidly, but it is not just about new tools. The transformation is happening in how businesses adopt and scale automation across departments.

Here are a few trends redefining what automation looks like in 2026:

AI-native workflows

Businesses are moving from AI-assisted tasks (like generating text) to AI-native workflows where automation is embedded from end to end. Instead of reacting to insights, organizations are designing processes that anticipate needs and act automatically.

A concrete example: A finance team builds a workflow where AI monitors accounts receivable data, identifies invoices at risk of going overdue based on historical payment patterns, drafts personalized follow-up emails, and sends them automatically while logging everything for audit purposes. The human role shifts from execution to exception handling.

This pattern (sometimes called closed-loop automation) represents a distinct shift. Insights don't just inform decisions; they trigger operational actions. A dashboard anomaly creates a ticket. A KPI threshold breach pauses a campaign. The gap between knowing and doing shrinks to near-zero.

Low-code for business users

With the rise of low-code and no-code platforms, automation is no longer limited to IT. Teams in HR, marketing, finance, and operations are becoming citizen automators, building workflows that reflect how they work, not just how systems are configured.

Cross-system orchestration

The most valuable automations in 2026 span multiple systems, connecting data pipelines, alerts, approvals, and communications through multi-agent orchestration. Tools like Domo that unify analytics and automation are positioned to lead this shift.

Consider a specific workflow pattern: A CRM update (deal closed) triggers a Slack alert to the customer success team, which triggers a reporting dashboard refresh showing updated revenue figures, which triggers an automated email to the executive team with the weekly summary. Each step happens automatically, across different systems, without manual handoffs.

Industry analysis explains why today's most advanced organizations are building on frameworks of agentic AI and orchestration: the ability of AI systems not just to act, but to coordinate end-to-end across functionally siloed tools.

AI governance and explainability

As AI systems take on more decision-making power, businesses are demanding greater transparency and stronger AI governance. Platforms must show how they reach conclusions, how models are trained, and how data is used. Especially in regulated industries.

Human and machine collaboration

The goal is not to replace people. It's to amplify them. The future of automation is collaborative: AI handles the repetitive, predictable tasks while humans bring judgment, creativity, and empathy.

Choosing the right AI automation platform for your organization

AI automation is no longer optional for organizations looking to stay competitive. It's quickly becoming a defining layer of business infrastructure. But with hundreds of tools promising productivity gains, operational efficiency, and cost savings, the real differentiator is not just what you automate. It's how, where, and why.

The most effective AI platforms share three core traits:

1. They connect to business workflows. Automation is not just about speed. It's about orchestrating data, decisions, and actions across systems and teams. Tools that sit in silos or require constant manual upkeep will not scale.
2. They empower more than just technical teams. The rise of no-code and low-code capabilities means AI can extend beyond engineering, giving business people, analysts, and operators the tools to automate what matters most to them.
3. They deliver clarity, not complexity. The best platforms prioritize explainability, security, and system integration so you can trust what they're doing and measure their impact.

Best platform by scenario

Different organizations have different needs. Here's a quick guide to matching platform priorities to your situation:

• Solo person or small team: Prioritize ease of use and free tiers. ChatGPT, Reclaim AI, and Claude offer accessible entry points without enterprise commitments.
• Mid-market ops team: Prioritize integration breadth and low-code configuration. Domo and Moveworks connect across systems while enabling business people to build workflows.
• Developer team: Prioritize API access and extensibility. GitHub Copilot, ChatGPT API, and Fivetran offer programmatic control and customization.
• Regulated enterprise: Prioritize governance, SOC 2, RBAC, and audit trails. Domo, Moveworks, and Glean provide enterprise-grade security and compliance features.

As you assess platforms in 2026, don't just ask: "What can this tool do?" Ask: "Will it help my organization respond with more confidence, make clearer decisions, and unify data and processes in a way that scales?"

Why Domo?

• Unified data foundation that integrates with virtually any source
• Low-code/no-code Workflows for fast, flexible automation
• AI-powered insights that don't just inform but act
• Enterprise-grade security with transparency and governance baked in

If you're ready to reduce manual work, connect insights to action, and give every team the tools to build more capable workflows, Domo is ready to help.