Table of contents
Highlights
- The best generative AI tools for enterprises go beyond content creation to reason and take action across complex tech stacks.
- Foundation models can require significant resourcing, but they often require additional security, governance, and grounding to support reliable AI deployments in enterprise environments.
- Narrow single-use AI tools deliver value for specific tasks or applications, often create fragmented experiences, and don’t scale well across the enterprise.
- Enterprises are increasingly prioritizing unified AI platforms that connect systems, data, and workflows in one trusted experience.
- Key evaluation criteria include deep integrations, trustworthy outputs, responsible AI practices, contextual awareness, and the ability to take action across applications — not just response quality.
- Choosing the right generative AI solution depends on how well it supports real-world productivity at enterprise scale.
- Moveworks stands apart as a unified AI platform that combines generative AI, enterprise search, and agentic workflows in one experience, enabling enterprises to be able to execute work end-to-end across their entire tech stack.
Generative AI is everywhere in the enterprise — but most of it still isn’t getting work done.
Teams are generating content faster than ever, but real work still gets stuck in tickets, approvals, and disconnected systems. The result: more output, but not necessarily more progress.
Most generative AI tools excel at generating content. Fewer are built to take actions like routing a request, provisioning system access, or completing a multi-step workflow with little to no human intervention. For enterprise leaders, that distinction matters.
This guide breaks down the leading generative AI tools by category, what each does well, and how to evaluate them for enterprise use — with a focus on which tools can drive real productivity across systems, not just produce content.
What we mean by "generative AI tools" for the enterprise
Generative AI tools are AI-powered applications, often built on large language models (LLMs), that create new text, summaries, images, drafts, and other content based on prompts and business inputs. In an enterprise context, they're typically designed to be secure, governable, and integrated with company systems.
That said, not all generative AI tools are the same. They generally fall into one of six categories:
- Foundation model providers: The underlying AI models (like GPT-4 or Claude) that power applications and can be accessed via API
- Application-layer copilots: AI embedded directly into productivity suites like Microsoft 365 or Google Workspace
- Enterprise search platforms: Tools that use AI to surface and make sense of knowledge bases across company systems
- Revenue-specific AI systems: CRM-native AI built to automate sales, marketing, and customer service workflows
- Creative acceleration tools: AI built for media, design, and content production
- Unified enterprise AI platforms: Platforms that combine search, automation, and agentic AI in a single experience across the enterprise
Each tool belongs to a primary category, which ultimately makes it easier to find the right fit for your business and use cases.
The shift from standalone AI tools to unified platforms
For a lot of enterprises, AI adoption started with a few specific, well-chosen tools. A writing assistant here, a code helper there. That approach made sense when experimenting.
But as AI use has expanded, so have the challenges. Teams now largely manage a growing collection of point solutions, and employees often context-switch between multiple tools just to complete a single task. Each platform may work well on its own, but none of them talk to each other.
The next stage of enterprise AI maturity is consolidation: moving toward platforms that orchestrate workflows across the tech stack vs. just handling individual tasks. Instead of "Which tool does this one thing?”, enterprises should ask, "Which platform can reason across our systems and take action end-to-end?"
What makes a generative AI tool "the best" for enterprise use
In the enterprise, "best" doesn't mean “most impressive demo.” It means operational impact.
A tool that generates polished content — but can't integrate with your ITSM platform, enforce data governance, or scale across departments — will hit its ceiling pretty quickly.
Modern enterprise generative AI should:
- Support action and workflow execution, not just content generation
- Integrate securely across ITSM, HRIS, ERP, CRM, and identity systems
- Operate within governance, compliance, and auditability requirements
- Protect enterprise data with strong privacy and security controls
- Scale across applications, departments, and geographies
- Deliver measurable productivity gains with fast time to value
How these generative AI tools compare
Most generative AI solutions fit squarely into one category, and that category shapes what they're able to do for your enterprise.
It's also worth distinguishing between generative AI and agentic AI. Generative AI can produce content in response to a prompt. Agentic AI goes further. Instead of just generating responses, it can reason through ambiguous requests, plan multi-step workflows, and take action across systems to complete work end-to-end — not just return an answer.
This is a key distinction from traditional automation approaches like RPA, which rely on predefined scripts, or copilots that operate within a single application. Agentic systems are designed to adapt, orchestrate, and execute across complex enterprise environments.
There are some trade-offs between categories, though:
Category | Core strength | Key limitation |
Foundation model providers | Cutting-edge language capabilities | Require additional orchestration, governance, and integration to deploy at scale |
Application-layer copilots | Embedded in familiar tools | Strongest inside their own ecosystems, limited across heterogeneous environments |
Enterprise search platforms | Knowledge discovery and retrieval | Primarily surface information, limited action-taking capability |
Revenue-specific AI | Deep CRM automation | Focused on revenue workflows, limited reach across other departments |
Creative tools | Media and content generation | Not really designed for enterprise-wide automation |
Unified enterprise AI platforms | Cross-system orchestration and action | Higher initial implementation effort |
The right category for your enterprise depends on your primary use case, your integration requirements, and how far you want to take AI beyond content generation.
At a glance: The top generative AI tools for enterprise
Tool | Best For | Primary Feature |
Moveworks | Enterprise-wide automation and employee support | Agentic AI platform that reasons across systems, takes action end to end, and works inside Slack, Teams, and other tools employees already use |
Microsoft Copilot Studio | Building AI agents within the Microsoft ecosystem | Low-code agent builder with deep native integration across Microsoft 365, Teams, and Power Platform |
Glean | Enterprise knowledge discovery and search | Connects 100+ apps into a unified knowledge hub with real-time indexing across company systems |
OpenAI | Custom AI application development | Leading-edge language model capabilities with flexible API access for technical teams |
Claude | Long-context reasoning and document-heavy workflows | Designed with safety and interpretability in mind; strong performance on compliance-oriented and text-intensive tasks |
Gemini | Productivity within the Google ecosystem | Multimodal AI natively integrated across Google Workspace, Search, and more |
Salesforce Agentforce | Sales, marketing, and customer service automation | CRM-native AI agents built directly into Salesforce workflows |
RunwayML | AI-powered video and media production | Purpose-built creative tools for video generation, editing, and visual storytelling |
Adobe Firefly | Generative AI for design teams | Built on commercially licensed training data; embedded across the entire Creative Cloud suite |
Midjourney | AI image generation and visual experimentation | High-quality image output from text prompts; strong creative range across artistic styles |
GitHub Copilot | Developer productivity and code generation | In-IDE AI assistance with real-time suggestions and multi-language support |
Enterprise automation and agentic AI workflows
This category goes furthest in terms of AI capability — not just generating a response, but reasoning across systems and completing tasks on behalf of employees (and pulling them in when needed).
1. Moveworks — The agentic front door to work
Moveworks is a unified AI platform built to automate work and boost productivity across the enterprise, from IT and HR to finance, sales, and more.
What sets Moveworks apart is its advanced Reasoning Engine that can understand employee intent, break down complex requests, and coordinate multi-step actions across existing enterprise systems.
Employees interact with the platform through a centralized AI Assistant that’s available in Slack, Microsoft Teams, and other channels they already use. Meanwhile, the larger Moveworks system works behind the scenes via deep, secure connections across other tools like ServiceNow, Workday, Okta, and Salesforce.
Movework’s key capabilities include:
- Pre-built integrations with 100+ enterprise systems for fast deployment
- Multilingual support across 100+ languages for global teams
- Agent Studio, a low-code environment for building and deploying custom AI agents
- An AI Agent Marketplace with ready-to-use plugins for common workflows
- Enterprise-grade governance, role-based permissions, and security controls
Explore the Moveworks platform and see it in action.
2. Microsoft Copilot Studio — Build AI agents inside Microsoft workflows
Microsoft Copilot Studio is a low-code agent-building platform within the Microsoft ecosystem, including Teams, Power Platform, and Microsoft Graph. It's a natural fit for organizations standardized on Microsoft that want to bring AI into existing workflows, with strong native governance and integration controls.
Enterprises operating across other, non-Microsoft environments may find it less flexible for cross-platform orchestration.
Enterprise search and knowledge platforms
These tools use AI to help employees find and connect with information across company systems to reduce time spent searching and, ideally, improve answer quality.
3. Glean — Enterprise search across company knowledge
Glean is a search-first platform designed for knowledge discovery, summarization, and information retrieval across tools like Google Drive, Slack, Confluence, and Salesforce.
It's strong at helping employees find information quickly, especially in organizations with disconnected knowledge. But compared to agentic platforms, Glean is primarily focused on retrieval rather than taking autonomous action across workflows.
Foundation model providers
Foundation models are the AI models that power most GenAI applications. Enterprises can typically access them directly via API or through hosted products, but deploying them at scale may require additional orchestration, governance, and integration work.
4. OpenAI — Foundation models powering generative AI
OpenAI can provide enterprise access to GPT-4 and other advanced language models through ChatGPT Enterprise and its API. For organizations with strong technical teams, it offers cutting-edge capabilities and flexibility for building custom AI solutions.
That said, moving from a model integration to a production-ready enterprise deployment can take a big investment in orchestration, security, and governance.
5. Claude — AI built for reasoning and long-context tasks
Claude, developed by Anthropic, is known for long-context reasoning, document analysis, and compliance-oriented use cases. It's also designed with safety and interpretability in mind.
While it’s a strong solution for text-heavy workflows, like other foundation models, it requires enterprise infrastructure for system-level action and scaled deployment.
6. Gemini — Multimodal AI within Google Workspace
Gemini is Google's AI model, integrated across Google Workspace and Search. It can support multimodal inputs like text, images, and more, and it works well for content creation, research, and productivity within the Google ecosystem.
That said, enterprises that rely heavily on non-Google systems may need additional tooling for broader automation.
Revenue and CRM AI
Revenue teams generate a lot of data, and generative AI can help turn it into action, automating follow-ups, surfacing insights from customer interactions, and keeping sales and service workflows moving without manual effort.
7. Salesforce Agentforce — AI agents for revenue workflows
Salesforce Agentforce brings AI agents into CRM-native workflows, automating tasks across sales, marketing, and customer service within the Salesforce platform. It's a strong fit for revenue teams already in Salesforce, and a useful complement to broader enterprise-wide AI platforms, but use cases may be limited outside revenue-centric workflows.
Creative AI tools
These tools are built for media, design, and marketing teams that need to accelerate creative content production. They can deliver real value for specific teams, but they're not especially designed for enterprise-wide automation.
8. RunwayML — AI-powered video and media creation
Runway is a creative acceleration platform for video generation, editing, and media production, bringing together top AI models to support the full content creation workflow. It’s best suited for marketing teams working on campaigns, branded content, and visual storytelling.
9. Adobe Firefly — Generative AI for creative teams
Adobe Firefly is part of the Creative Cloud suite and built on commercially licensed training data, which is an important consideration for enterprises with IP and compliance requirements. It's a strong choice for design teams already working within the Adobe ecosystem.
10. Midjourney — High-quality AI image generation
Midjourney is known for producing high-quality images from text prompts, letting creators easily explore new concepts and ideas. It's a great application for design inspiration and visual experimentation, but it's not built for governed enterprise workflows or automation at scale.
Developer productivity AI
Generative AI can help developers move faster by handling the repetitive parts of coding, like suggestions, completions, and reviews. Engineering teams can then focus on bigger issues that need more human creativity and problem-solving.
11. GitHub Copilot — AI pair programmer for developers
GitHub Copilot integrates directly into development environments to accelerate coding with AI-assisted suggestions and code generation. It can deliver pretty immediate value for engineering teams. However, it's designed more for developer workflows than cross-departmental enterprise automation.
How enterprise teams actually use generative AI tools
In enterprise organizations, generative AI tends to show up most in a few high-impact areas:
- Employee support and IT automation: Resolving common requests like password resets, access provisioning, and benefits questions without a ticket
- Knowledge retrieval: Surfacing answers from across different systems so employees spend less time searching
- Content and communications: Drafting, summarizing, and editing across marketing, HR, and operations teams
- Development acceleration: Helping engineering teams write, review, and debug code faster
The biggest productivity gains typically show up in reduced resolution times, fewer manual handoffs, and faster completion of everyday workflows — especially when AI moves beyond a single use case and starts orchestrating work across systems.
How to choose the right enterprise generative AI tool
Clarify your primary business objective
Different roles call for different tools:
- CIO / IT leader: Governance, cross-system orchestration, and scalable employee support
- CHRO / HR leader: Workflow automation and employee experience
- Revenue leader: CRM-native AI for sales and service
- Engineering leader: Development acceleration
- Creative teams: Media and content generation
Apply enterprise evaluation criteria
Once you know what you're solving for, evaluate tools across the following framework:
Use case and scope: Does it handle your primary need and scale across departments over time?
Integration depth: How well does it connect with your existing tech stack, including legacy systems?
Governance and security: Does it support role-based permissions, auditability, and compliance?
Trust and reliability: Are outputs grounded and backed by responsible AI practices?
Action capability: Does it generate content only, or is it able to take action across systems?
Time to value: How quickly can your team see results without heavy custom development?
Why unified AI platforms are the new enterprise standard
Point solutions have a ceiling. They might work well within a single application, but they don't really communicate with each other — and they only support one segment of an overall workflow.
In other words, point solutions create fragmentation. Unified AI platforms reduce complexity.
Unlike point solutions, which operate in isolation, or copilots that stay confined to a single ecosystem, unified platforms are designed to coordinate work across systems — connecting data, workflows, and actions into a single execution layer. They can act as a single intelligent layer across your whole tech stack, orchestrating workflows rather than supporting isolated tasks. Instead of just generating responses, it’s able to understand intent, coordinate across systems, and complete workflows end to end.
For enterprises dealing with sprawling SaaS environments and knowledge management that‘s anything but managed, that can mean much less complexity. A unified AI platform can connect what you already have instead of replacing it, scaling with your business while working with existing resources.
Moveworks: A unified AI platform built for the enterprise
There are many generative AI tools available, but most focus on a single task — generating content, answering questions, or assisting within a specific application.
Moveworks is designed differently. It acts as a unified AI platform that connects generative AI, enterprise search, and automation, enabling organizations to move beyond answers and actually execute work across systems.
Thanks to a powerful Reasoning Engine, Moveworks’ AI is able to understand intent, reason across systems, and complete multi-step workflows securely at scale. By combining enterprise search with action, Moveworks helps employees not only find information, but also complete tasks across systems with minimal manual handoffs.
When trust and governance are central to responsible AI deployment, organizations can’t afford to invest in a solution that isn’t enterprise-ready. They don’t need another tool that surfaces information — they need an AI that can take action.
Explore the Moveworks platform to see what enterprise scale really looks like.
Frequently Asked Questions
ROI is typically measured through operational metrics such as reduced resolution times, lower support volumes, increased employee productivity, and faster workflow completion. Mature organizations also track adoption rates, task automation coverage, and time saved across departments. Over time, enterprises may tie AI impact to cost avoidance and improved employee experience rather than direct revenue alone.
Successful teams involve security, legal, and IT stakeholders early in the evaluation process to define guardrails around data access and usage. Many enterprises favor solutions that support role-based permissions, auditability, and responsible AI practices. This approach allows organizations to innovate without compromising regulatory or privacy requirements.
Generative AI is most effective when it complements existing systems rather than replacing them. Instead of acting as a system of record, AI acts as an intelligent layer that connects tools, surfaces insights, and automates actions. This enables organizations to modernize workflows without disrupting core infrastructure.
Scaling generative AI typically requires cross-functional collaboration between IT, operations, security, and business leaders. While some tools are designed for low-code or no-code use, enterprises still need governance models, change management, and enablement programs to drive adoption. The most successful deployments treat AI as an organizational capability, not a one-off tool.
