Table of contents
Highlights
- Enterprise AI is the strategic deployment of AI technologies, including agentic AI, across an organization's systems to automate work, ground decisions in company data, and drive measurable outcomes at scale.
- Unlike basic or narrow AI tools, enterprise AI is built to operate across departments, integrate with existing business systems, and support complex, large-scale workflows securely.
- Agentic AI can reason, plan, and execute multi-step tasks within defined guardrails. It represents the current evolution of enterprise AI, moving beyond conversational copilots.
- Successful enterprise AI implementation depends on strong governance, including role-based access controls, data compliance, and architecture designed so AI agents operate within authorized boundaries.
- Moveworks delivers enterprise AI through a unified platform, including its Reasoning Engine, AI Assistant, and Agent Studio, that combines agentic automation with enterprise-grade governance and out-of-the-box integrations across systems like ServiceNow, Workday, and Okta.
Enterprise AI is the strategic use of AI technologies across your company's systems to automate work, accelerate decision-making, and improve business outcomes. What that looks like in practice has shifted considerably in the past couple of years.
Not long ago, enterprise AI meant advanced search or virtual assistants that helped employees draft emails, retrieve answers quickly, or complete simple tasks. That framing is now dated. Today's enterprise AI centers on agentic systems that can plan and execute multi-step actions across your connected tech stack without manual hand-holding at every step.
If you're evaluating where enterprise AI fits in your organization's roadmap, this guide covers what it is, how it works, where it tends to deliver the most value, and what a successful implementation tends to look like.
What is enterprise AI?
Enterprise AI is the strategic deployment of AI technologies, including agentic AI, machine learning, and large language models (LLMs), across your organization's systems. The goal is to automate complex, execution-focused work, detect trends faster, ground decisions in company-specific data, and drive measurable business outcomes at scale.
Agentic AI plays a central role in how enterprise AI operates today. These tools go beyond answering questions or surfacing information. They're designed to assess a situation, plan a multi-step path through it, and work through that path within defined guardrails, adapting as needed to accomplish their goal.
They can connect to the systems you already use, while still respecting access controls and governance boundaries.
How is enterprise AI different from basic or narrow AI?
Basic AI handles single tasks reasonably well. Image recognition, product recommendations, standard keyword search — these are the kinds of things basic AI tools do effectively.
Narrow AI is trained for a specific function but tends to break down when a request falls outside its defined scope. Think about a helpdesk tool that handles a standard password reset when an employee gets locked out of Workday. If that same employee needs a reset for a DevOps platform that isn't in the training data, the tool stalls, even though the two requests are nearly identical.
Where narrow tools stop at the answer, enterprise AI is built for a different operating environment. It's designed to reason through a new situation, connect to multiple systems, handle exceptions, and adapt when inputs or conditions change, all while staying within the governance frameworks that regulated industries require.
What is agentic AI? And how does it fit into enterprise AI?
Agentic AI refers to AI systems that can reason, plan, and execute multi-step tasks across enterprise applications, typically with minimal human intervention (outside of any human-in-the-loop checkpoints you build into the process).
Earlier AI implementations centered on copilots and assistants with conversational interfaces, tools that helped employees write drafts or find information within a single application. Current enterprise AI focuses on agents designed to take action across connected systems.
In short, generative AI creates content in response to a prompt, copilots put that capability behind a conversational interface, and agentic AI is designed to act on it:
What it does | Enterprise example | |
Copilots | Conversational interface within a single tool | Drafts a message or answers a policy question inside that tool |
Agentic AI | Designed to reason, plan, and execute multi-step workflows across systems within defined guardrails | Processes a leave request end to end, checking employee eligibility, updating Workday, and notifying the manager |
Moveworks delivers enterprise-ready agentic AI with two core tools:
The Reasoning Engine is the orchestration layer designed to understand intent and complete multi-step actions across your connected platforms, adapting when conditions change.
Agent Studio is the low-code platform where teams can build and deploy agents to manage repeatable workflows at scale for specific use cases, while respecting data governance and security requirements.
Core components of an enterprise AI platform
When evaluating enterprise AI platforms, a few components tend to determine whether the software can work at scale:
- Reasoning engines and LLMs: The orchestration and intelligence layer designed to interpret intent, plan complex workflows, and adapt when conditions change. Look for reasoning engines grounded in your organization's specific data and policies.
- Knowledge grounding and retrieval-augmented generation (RAG): RAG connects AI to your internal systems, including wikis, ITSM platforms (ServiceNow), identity providers (Okta), CRMs, and HRIS platforms (Workday). When an employee asks about their PTO balance, for example, a RAG-connected agent can check your HR platform and return accurate, specific information.
- Orchestration and agent frameworks: Frameworks are designed to coordinate agents across connected tools to complete workflows end to end without requiring manual handoffs at each step (like routing a software request through approval and provisioning in one pass).
- Integrations and connectors: Connectors link the systems in your tech stack, so agents can take action across multiple platforms.
- Governance and access controls: Role-based permissions, approval gates, and audit trails need to be built into the platform architecture, so agents and the people using them can only access what you've authorized.
Enterprise AI use cases by department
Enterprise AI tends to create the most immediate value in workflows that are high-volume, repeatable, and easy to measure. That's where impact often shows up fastest, and governance is easiest to enforce.
In IT, popular real-world use cases include:
- Password resets, access requests, and software provisioning based on onboarding or offboarding workflows
- Guided, policy-based ticket resolution across platforms like ServiceNow or Jira
- Proactive issue alerts that notify and, where possible, resolve problems before employees file tickets, such as flagging and addressing a VPN outage as it's detected
In HR, popular use cases include:
- Onboarding workflows that streamline documentation completion and initial required training
- Benefits updates and management
- Leave request processing
- Policy question answering through a conversational interface
- Automated updates across Workday and identity systems without manual handoffs
- Predictive analytics, such as identifying potential employee retention risks — subject to applicable privacy regulations and employee notification requirements
In finance, common applications include:
- Invoice approvals
- Reimbursement status checks
- Out-of-policy spend flagging and review before approval or escalation
- Reconciliation and month-end close support
- Audit trail logging
How to implement enterprise AI
Successful AI adoption at the enterprise level starts with clarity. You need to understand what kind of value AI can deliver and what governance is required to make the output trustworthy. A phased approach tends to work better than trying to deploy everything at once.
A practical rollout moves through five steps:
- Assess readiness. Evaluate your current data infrastructure and integrations. Enterprise AI needs clean, accessible data and connected systems. If your data is siloed or inconsistent, fix that foundation first before trying to layer AI on top.
- Prioritize use cases. Start with high-volume, well-defined workflows where success is measurable and the impact of potential errors is limited, such as IT ticket deflection or HR self-service.
- Set up governed integration. Connect AI to your systems of record through integrations that understand permission rules and operate within clearly defined boundaries.
- Plan for adoption. Be transparent with your teams about what agents can do and where human judgment is still needed. Build feedback loops, so employees can flag issues after deployment. For teams that are hesitant about AI adoption, structured change management can help.
- Iterate and expand. Adjust your approach based on early results from pilot programs. A strong foundation built on proven use cases makes scaling to more complex, cross-functional workflows more manageable.
Governance and security in enterprise AI
Governance has to be built into an enterprise AI platform from the start, and this becomes especially important as organizations adopt agentic AI that can access sensitive employee data and take actions with less direct human involvement, within defined guardrails.
AI agents can now submit requests, trigger approval workflows, and update employee records. Ungoverned access in these contexts can create real security and operational risk.
Role-based permissions, approval gates, and audit logs are the baseline for any responsible enterprise deployment. Cost controls belong in the same conversation, because agents that call third-party APIs on every request can quickly drive up usage spend.
Moveworks was built with security and governance as foundational requirements, complete with:
- FedRAMP Moderate authorization, which positions the platform for federal government and regulated industry deployments
- Governed integrations with ServiceNow, Workday, SAP, Okta, and Microsoft, designed to operate within each organization's existing access controls
- Privacy-aware architecture designed to support organizations operating under data privacy requirements like GDPR and CCPA, with the understanding that organizations remain responsible for their own regulatory obligations
What ROI can enterprises expect from AI?
The returns you see from agentic AI will depend on what you deploy it for and how mature your implementation is, but the early data from organizations that have moved agents into production is encouraging.
A PwC study found that 66% of surveyed organizations that adopted AI agents reported increased productivity, and 57% reported cost savings.
Moveworks customers have seen outcomes in line with that trend. Procore saves nearly 4,000 hours of operator time per quarter across HR, IT, payroll, and workplace experience. Toyota reported cutting mean time to resolution on IT issues from days to minutes, freeing up its team members for higher-value work.
These outcomes share a common thread: They started with well-scoped, high-volume use cases before expanding to more complex workflows.
Choosing the right enterprise AI partner
What "enterprise AI" means today is different from what it meant two years ago, which makes choosing the right platform more consequential than it might have been in earlier buying cycles. A point solution that handles a handful of niche tasks is a limited investment, and it's likely to lack the governance and integration depth you'll need as your use cases grow.
What you're looking for is a platform that can take action across your entire tech stack while staying within the governance boundaries you've defined. Native integrations and strong API capabilities matter here, because they determine whether the AI can work within your existing workflows or needs you to rebuild around it.
Moveworks offers a unified platform for enterprise AI that includes:
- An agentic AI Assistant: The conversational layer where employees request work across Slack, Teams, or the web
- An advanced Reasoning Engine: The orchestration layer designed to plan and execute actions across connected systems
- Agent Studio: Where teams build secure, repeatable workflows for their specific use cases, with pre-built integrations to widely used applications
Databricks, Broadcom, and Hearst have used Moveworks to move from basic AI automation to enterprise-wide agentic systems designed to work across departments, complete complex workflows, and adapt when conditions change.
Explore the Moveworks platform to see what enterprise AI looks like in production.
Frequently asked questions
Generative AI (GenAI) focuses on creating new content, such as text, summaries, and code, in response to a prompt. Agentic AI goes further by reasoning through multi-step problems and executing actions across connected systems within defined guardrails. In practice, an enterprise might use GenAI to draft a policy summary, but use agentic AI to process an employee's leave request end to end, checking eligibility, updating systems, and notifying stakeholders within defined governance boundaries, with human-in-the-loop checkpoints where needed.
Governed AI means every action an AI system takes, not just every answer it gives, is subject to role-based permissions, audit trails, and compliance controls. This becomes more important with agentic AI than with earlier conversational tools, because agents take real actions like submitting requests or updating records. Ungoverned actions in those contexts carry higher operational and security risk than an ungoverned answer ever would.
Timelines vary based on use case complexity and how ready your existing data infrastructure is, but organizations that start with well-scoped, high-volume use cases like IT ticket deflection or HR self-service tend to see measurable impact earlier than those that try to deploy complex, cross-functional workflows from day one.
Modern enterprise AI platforms are designed to integrate with existing systems of record like ServiceNow and Workday, not replace them. Enterprises typically layer AI on top of what they already have, allowing it to read from and take governed actions within those existing systems.
Enterprise AI evaluations usually involve a cross-functional group. IT and security teams assess integration and governance requirements. HR or business unit leaders define use cases and success metrics. Procurement or legal teams evaluate compliance and vendor risk. It's a broader evaluation process than earlier point-solution purchases tended to require.
