Enterprise AI Solutions That Drive Automation, Efficiency, and Real Business Impact

Half of all adult workers in the U.S. now use AI at work in some capacity and individually, they’re seeing real productivity gains. 

But on a broader level, 75% of executives admit their AI strategy is “more for show” than actual guidance, and less than 30% see any ROI from AI initiatives, despite massive investments. 

You've likely already experimented with AI-driven tools. Some teams are probably using them regularly. Maybe you've seen early wins. But results can be inconsistent. Different tools are needed to solve different problems, they often don’t share context or coordinate actions across systems, and plenty of manual work still falls to your employees.

While this isn’t due to a lack of effort, enterprises typically don’t see a positive impact just from adding more and more AI tools. True enterprise-ready platforms need to harness AI through integration and orchestration, allowing AI agents to execute multi-step tasks across connected systems within defined permissions. 

Enterprise AI solutions focus on the specific challenges large organizations face, like handling massive data volumes and prioritizing data security. But not all of them are built with the scale, security, or governance and architectural depth to support what your business needs, so you should choose wisely. 

This guide covers the top enterprise AI solutions available today, how to evaluate them, and how to avoid signing on a tool that just adds complexity to your stack.

What are enterprise AI solutions?

Enterprise AI solutions are the strategic application of artificial intelligence, including machine learning, natural language processing (NLP), and computer vision, across an entire organization to automate complex tasks, optimize workflows, and drive data-driven decision-making at scale across enterprise systems and data sources.

These systems typically combine data access, retrieval, reasoning, and workflow orchestration to move from insight to action.

How enterprise AI differs from consumer and SMB AI

Consumer AI is designed for ease of use and general tasks. It doesn't know your org chart, your existing systems, or your approval workflows. SMB AI tools can be useful, but they aren’t built for the complexity of a global enterprise with thousands of employees, dozens (or even hundreds) of integrated systems, and strict compliance requirements.

Enterprise AI operates differently:

• Contextual awareness: It can understand roles, responsibilities, and access levels, instead of just the request itself, using identity, historical context, and system-level signals.
• System integration: It’s able to connect to and interact with the tools your organization already runs on, from your HRIS to your ITSM and ERP, via APIs and integration layers.
• Governed execution: It can take action within permission-aware environments, respecting who can do what and maintaining an audit trail, aligned with enterprise identity and access controls.

Consumer AI can give you answers. Enterprise AI can support task execution across systems (within defined guardrails).

Core capabilities that define enterprise AI solutions

When evaluating any enterprise AI platform, look for these foundational capabilities:

• Intent understanding: The ability to interpret what someone is actually asking, within the context of the question, and determine the right course of action, using natural language processing and contextual signals.
• Cross-system execution: The ability to integrate and act across multiple enterprise systems from a single interface, coordinating multi-step tasks across APIs and workflows.
• Enterprise search: The ability to find accurate, relevant information across disconnected knowledge sources and use it to drive action, using retrieval across structured and unstructured data with permission-aware access.
• Security and permissions: The ability to operate within your existing access controls, aligned with identity and role-based permissions (e.g., IAM systems).
• Workflow orchestration: The ability to coordinate multi-step tasks across departments without manual handoffs, including task sequencing, system interactions, and conditional logic.

Each of these abilities maps to a real-world business outcome, such as faster resolution, fewer manual touchpoints, reduced workload for IT and HR, or better employee experiences.

Common enterprise use cases for AI solutions

The most impactful enterprise AI use cases typically fall within three core areas:

Enterprise-wide automation and workflow orchestration

AI built for enterprise scale can go well beyond robotic process automation (RPA), which is an older approach that follows static, rule-based scripts. Today's platforms are better equipped to handle dynamic, multi-step workflows that can span systems and teams.

For example, imagine a new employee submitting a single onboarding request. An enterprise AI solution could provision accounts, assign permissions, notify approvers, and confirm completion, coordinating actions across systems like HRIS, IAM, and ITSM platforms, streamlining the whole process without anyone touching a ticket queue. 

Meanwhile, service teams benefit from reduced mean time to resolution (MTTR), fewer manual handoffs, and end-to-end task completion that used to require coordination across IT, HR, and operations.

Knowledge discovery and enterprise search

Scattered knowledge can be one of the most common sources of employee frustration. Policies might live in one system, documentation in another, and institutional knowledge is gated in someone's inbox.

Enterprise search that’s built into an AI platform can do more than just find documents though. It can retrieve accurate information from across your siloed systems and data sources, understand the context of the request, and surface what the employee actually needs, using retrieval across structured and unstructured data sources. 

The best implementations take a step further, moving from search processes to actual action. For instance, an employee might find a policy AND submit the relevant request in a single conversation, with the system triggering downstream workflows where applicable.

Employee support at scale

IT and HR teams often field large volumes of repetitive requests, like password resets, benefits questions, software access, PTO inquiries, and other high-level questions. AI is able to support resolution of many of these requests by triggering workflows and executing predefined actions, not just by giving answers, but by triggering workflows and completing actions when needed.

Proactive support is increasingly becoming a key capability of enterprise AI platforms too, alerting employees to upcoming benefits deadlines or notifying IT of emerging access issues before they turn into bigger problems that impact business operations, based on system signals and predefined conditions.

How to evaluate enterprise AI solutions

With hundreds of enterprise AI vendors competing for attention, the challenge is finding the solution that’s built to work inside your organization's systems, at your scale, with your security requirements.

Platform vs. point solution

A point solution solves one problem for one department. 

A platform has the ability to solve problems across your organization and grow with you, by integrating across systems and supporting multiple workflows.

Tool sprawl can be a major source of operational pain points in large enterprises. When IT, HR, and finance each use different AI tools, employees still have to know which door to knock on. There's no single, connected experience.

When evaluating a solution, ask questions like:

• Can it orchestrate and execute workflows across systems, or does it only surface information?
• Does it connect search, reasoning, and action into a single experience, rather than operating as separate layers?
• Does it support proactive and triggered or system-initiated actions, or only respond when prompted?

Enterprise readiness and security

"Enterprise-ready" is a term that gets used loosely. Here's what it should actually mean:

• Granular access controls that respect your existing permission structures (you shouldn’t have to adjust or adapt your rules to fit into a solution), typically integrated with identity providers (e.g., IAM systems)
• Compliance with relevant data governance and regulatory requirements
• Audit trails and visibility into what the AI has done and on whose behalf
• The ability to operate across sensitive data without creating new risks

Time to value and scalability

Look for pre-built integrations with the systems you already use, low-code configuration for new use cases, and the ability to expand across functions and geographies without significant re-engineering.

It’s also worth asking about adaptability and whether new use cases can be added independently, or if every expansion will require custom development or additional integration work.

Robust enterprise-wide platforms

Now let’s look at the solutions worth shortlisting. These enterprise-wide platforms are capable of integrating, orchestrating, and executing workflows to complete repetitive tasks end-to-end across the organization, rather than operating as standalone tools or isolated capabilities.

Moveworks — Agentic AI that resolves work end-to-end

Moveworks is an agentic AI platform built to be the front door to work. It’s a unified entry point where employees are able to find information, take action, and get support across IT, HR, finance, and operations.

At the center of the platform is the Reasoning Engine, which is the AI that powers every agent, helping it to interpret intent, plan multi-step actions, and execute across systems within defined permissions and system constraints.

Key capabilities:

• AI assistant that can handle complex, multi-step requests across your tech stack
• Enterprise search that can retrieve accurate information from disconnected knowledge sources, using permission-aware retrieval across systems
• Agent Studio for building and deploying custom automations with low-code tools
• Pre-built integrations with ServiceNow, Workday, Salesforce, Jira, Slack, Microsoft Teams, and more
• Multilingual support across 100+ languages for global workforces
• Role-based security and permissions built in

See why 350+ leading enterprises already rely on Moveworks to drive value across the org.

ServiceNow — AI automation for IT and service workflows

ServiceNow is a workflow platform with deep roots in IT service management. Its AI capabilities span incident management, service delivery, and employee self-service, making it well-suited for organizations with complex IT operations that already rely on ServiceNow as their service solution.

Key capabilities:

• Automates IT service workflows across incident, request, and change management (ITSM)
• Supports employee and customer service delivery through workflow automation (CSM, self-service)
• Includes virtual agents for handling routine requests and reducing manual support workload

Microsoft Copilot — AI embedded in productivity workflows

Microsoft Copilot brings generative AI into Word, Excel, PowerPoint, Teams, and Outlook, helping to enhance work where it already happens. It’s best understood as a productivity-layer platform for Microsoft-first setups, with limited cross-system workflow orchestration outside the Microsoft ecosystem.

Key capabilities:

• Embedded in Microsoft 365 apps to support content creation, data analysis, and productivity workflows
• Uses large language models to generate text, summarize meetings, and surface insights
• Enhances workflows within the Microsoft ecosystem, particularly for document and communication tasks

Google Gemini Enterprise — Workspace AI with growing agent capabilities

Google Gemini can bring AI into Gmail, Docs, Sheets, and Meet. Google's Agent Development Kit (ADK) also helps teams to build and deploy custom AI agents on Google's model infrastructure, which is a useful option for engineering teams on a Google-native stack, particularly for building custom agent workflows on Google Cloud.

Key capabilities:

• Integrates AI into Workspace tools like Gmail, Docs, Sheets, and Meet
• Supports custom agent development via Google’s Agent Development Kit (ADK)
• Built on Google Cloud infrastructure for scalable AI application development

Salesforce — AI natively embedded in CRM

Salesforce aims to deliver AI through two offerings: Einstein, for predictive analytics and insights across the CRM, and Agentforce, for AI agents handling predefined workflows. This can be a natural fit for sales- and service-driven enterprises wanting AI deeply embedded in their CRM processes.

Key capabilities:

• Provides predictive analytics and insights directly within CRM workflows (Einstein)
• Enables AI agents to execute tasks using real-time data and predefined integrations (Agentforce)
• Connects to enterprise systems via APIs and MuleSoft for workflow execution

Foundation model / AI infrastructure platforms

Foundational models and AI infrastructure providers power the large language models (LLMs) underlying many enterprise AI solutions. They're foundational, but typically require additional layers to become deployable enterprise tools.

Anthropic (Claude) — Reasoning and safety-oriented models

Anthropic’s Claude is a family of AI models known for strong reasoning and a safety-focused approach. Claude is available through the Anthropic API for custom applications, document analysis, and complex reasoning tasks.

Key capabilities:

• Provides reasoning-focused language models for enterprise use cases
• Supports document analysis, summarization, and complex task reasoning
• Available via API for building custom enterprise AI applications

Best for: Organizations with technical teams building custom AI applications where accuracy and sensitive content handling are priorities.

OpenAI — Frontier models and ChatGPT Enterprise

OpenAI provides enterprise access to GPT-4 and other advanced models through ChatGPT Enterprise and its API, helping to support custom application development and workflow integration.

Key capabilities:

• Provides access to advanced large language models for content generation and automation
• Supports custom AI application development through APIs and enterprise tooling
• Enables integration into workflows across internal tools and systems

Best for: Organizations with strong engineering capabilities looking to build or fine-tune custom AI solutions.

IBM watsonx — AI governance and enterprise model management

IBM's watsonx portfolio covers model development and governance, which can be valuable for regulated industries. It includes watsonx.ai for training and deploying models, watsonx.data for managing AI workloads, and watsonx Assistant for deploying virtual agents without writing code.

Key capabilities:

• Provides tools for building, training, and deploying AI and machine learning models
• Supports data management and scaling AI workloads across enterprise environments
• Includes capabilities for building virtual agents and managing AI governance

Best for: Enterprises in regulated industries where AI governance, observability/explainability, and compliance carry as much weight as capability.

Specialized enterprise AI

These specialized tools help address specific use cases and can be most effective when deployed alongside a broader enterprise AI platform.

Glean — Enterprise search across all workplace data

Glean is an AI-powered enterprise search platform that can connect to your organization's knowledge sources (docs, tickets, emails, wikis, code repositories, etc.) and make them searchable through a single interface. It can also support GenAI applications built on top of that connected knowledge.

Key capabilities:

• Connects enterprise data sources to enable unified search across documents, tickets, and systems
• Uses AI to surface relevant information based on user context and activity
• Supports generative AI applications built on enterprise knowledge

Best for: Knowledge-heavy organizations where employees spend significant time searching for information across disconnected systems.

Kore.ai — Conversational AI and virtual agent building

Kore.ai provides a platform for building and deploying conversational AI experiences, including virtual agents for IT and HR support. Low-code and no-code tools make it accessible to organizations that want to build custom AI assistants without starting from scratch.

Key capabilities:

• Enables development of conversational AI and virtual agents using low-code/no-code tools
• Supports automation of IT and HR workflows through custom bot development
• Integrates with enterprise systems to execute predefined workflows

Best for: Organizations with the internal resources to build and maintain their own conversational AI workflows.

How agentic AI is changing enterprise AI solutions

Most AI tools are reactive, meaning you ask or make a request, and they answer.

Agentic AI works differently. An AI agent is a system that is able to plan, reason, and take action within defined workflows and permissions on behalf of a user or team. It doesn't just respond to a request. It can determine how to fulfill it, coordinate across systems, and support multi-step task execution across systems.

Here's what that looks like in practice:

• Traditional AI: "Here's the PTO policy," delivered with a static document (which could be out of date or irrelevant to the user).
• Agentic AI: Can check your balance, confirm your manager's availability, submit a request in your HRIS, and trigger downstream workflows, all from a single conversation.

It’s a clear shift from AI that supports end-to-end workflow execution on behalf of employees, within defined guardrails.

Real-world enterprise examples include:

• Onboarding workflows that provision access and notify stakeholders automatically
• IT incident triage and resolution that identifies, routes, and triggers appropriate remediation workflows 
• Finance approvals that collect context, route to the right approver, and log the outcome end-to-end

For enterprises, this can mean faster resolution, fewer manual steps, more consistent employee experiences, and IT and HR teams freed up for the work that actually requires human judgment.

Get 100+ more real-world enterprise use cases for AI agents.

Moveworks: An agentic AI platform built for the enterprise

If you're looking for an enterprise-wide solution that can connect search, reasoning, and action into a single employee experience across IT, HR, finance, and operations, Moveworks is built to be the front door to work.

Employees are able to ask a question, submit a request, or kick off a workflow from wherever they already work, whether that’s Slack, Microsoft Teams, or a web browser. Moveworks is designed to coordinate the necessary steps by retrieving information, triggering workflows, and supporting task completion across systems.

What makes this possible:

• Agentic reasoning that can interpret intent and coordinate execution of multi-step tasks across your tech stack
• Enterprise search that is able to retrieve accurate answers from scattered knowledge sources, using permission-aware retrieval across systems
• Hundreds of pre-built integrations with ServiceNow, Workday, Salesforce, Jira, and more for fast time to value
• Agent Studio for building custom automations without heavy engineering lift
• Multilingual, location-aware support for global workforces
• Enterprise-grade security with role-based permissions and compliance controls built in

Broadcom teams see the impact every day. After introducing Moveworks across the enterprise, they’ve achieved an 88% (and climbing) autonomous resolution rate.

With Moveworks, employees can get instant support with automated, end-to-end resolution, while IT and HR teams reclaim time to focus on work that moves the business forward.

See the Moveworks platform in action: Request a demo today.