Generative AI has rapidly entered the enterprise mainstream in the last few years . Organizations worldwide are investing heavily in enterprise AI tools – global spending on generative AI jumped to $13.8 billion in 2024 (up from $2.3B in 2023), and the market is projected to reach $803 billion by 2033. This explosive growth signals that AI is transitioning from experimental to essential for businesses.

Surveys of IT leaders show optimism about AI: 72% of decision-makers expect broader adoption of generative AI tools in the near future. Large enterprises (1,000+ employees) on average doubled their GenAI investments from 2023 to 2024. Clearly, enterprise leaders are betting big on AI to drive productivity and innovation.

The surge in enterprise AI adoption is focused on practical outcomes. Customer support is a leading area – 49% of generative AI projects target customer service, with issue resolution alone accounting for 35%. Other popular use cases include marketing content generation, IT automation, and R&D assistance. These trends highlight why having the right AI tools for business is now mission critical.

With so many AI solutions emerging, IT leaders and business decision makers must identify which enterprise AI tools deliver real value and integrate well into their operations. Below we explore 10 of the most-used generative AI tools in enterprises today. For each, we’ll outline what it is, why it’s popular, and how it’s being used to transform business workflows.

This list is based on current usage data and industry surveys, to help you prioritize tools that are already proving their worth in organizations.

1. ChatGPT by OpenAI – AI Chatbot for All-Purpose Enterprise Use

ChatGPT, launched by OpenAI in late 2022, is the AI chatbot that catalyzed mainstream adoption of generative AI. Built on the GPT-3.5 and GPT-4 large language models, it quickly became the most widely used generative AI tool across enterprises. According to industry surveys, approximately 62% of enterprises report active use, with another 28% currently testing it. Its rise to prominence is largely due to its advanced ability to understand and generate natural language with human-like fluency making it a flexible tool for a wide range of knowledge work.

Enterprise Use Cases

ChatGPT’s versatility has led to its integration across nearly every business function:

• IT Support: Companies deploy ChatGPT-powered chatbots to handle Tier-1 support tickets, answering frequently asked questions around the clock and reducing strain on internal helpdesks.

• Marketing & Sales: Teams use ChatGPT to generate first drafts of product descriptions, blog content, email campaigns, and social media posts accelerating content production while keeping brand voice intact.

• Operations & Admin: Repetitive tasks like summarizing long documents, extracting key points from customer feedback, or drafting responses to standard inquiries are now handled by ChatGPT-based workflows.

• Knowledge Management: By embedding ChatGPT into enterprise systems, organizations enable faster retrieval of SOPs, policy information, or product documentation.

In short, any department reliant on written communication, customer engagement, or knowledge access can leverage ChatGPT to improve efficiency and reduce turnaround time.

Why It’s So Popular in the Enterprise

ChatGPT’s appeal lies in its low barrier to entry and broad applicability. Unlike traditional enterprise software that often requires extensive training or integration work, ChatGPT is intuitive any employee can begin using it with just a few natural-language prompts.

Its adaptability is further enhanced through ChatGPT Enterprise, which offers:

• Enhanced data privacy and security

• Longer context windows for complex workflows

• Admin controls for IT and compliance teams

• Higher throughput and priority access to OpenAI models

These features make it especially attractive for organizations with stringent data governance requirements or high-volume AI usage.

Limitations & Considerations

Despite its strengths, ChatGPT is not without limitations. It can occasionally produce inaccurate, outdated, or misleading outputs commonly referred to as “hallucinations.” It also does not truly understand emotional context or intent, which means its responses can come across as impersonal or generic in sensitive communications.

Key considerations for enterprise use include:

• Human Oversight: Most companies implement content review workflows to validate AI-generated material before it’s published or sent externally.

• Security & Compliance: To protect proprietary information, organizations are advised to use the enterprise version or API rather than the public ChatGPT web interface.

• Data Privacy: ChatGPT Enterprise ensures customer data is not used for model training and complies with industry data protection standards.

Discover the Top 10 Alternatives to ChatGPT

2. Microsoft 365 Copilot – Productivity AI Integrated into Office Suite

Microsoft 365 Copilot is an AI-powered assistant built directly into Microsoft’s suite of workplace tools including Word, Excel, PowerPoint, Outlook, and Teams. Launched in 2023 and powered by OpenAI’s GPT-4, Copilot has quickly become the second most widely adopted enterprise generative AI solution, with 52% of enterprises already using it and 35% actively evaluating it.

What sets Copilot apart is its deep integration within tools employees already use daily, allowing them to generate content, analyze data, and streamline communication without ever leaving the Microsoft 365 environment.

Enterprise Use Cases

Microsoft 365 Copilot is designed to enhance everyday workflows with minimal disruption. Its core strength lies in how naturally it integrates into existing applications:

• In Word: Instantly draft documents, reports, or proposals using natural language commands.

• In Excel: Generate formulas, charts, and data visualizations by describing what you want in plain English.

• In PowerPoint: Create presentations from outlines or documents, complete with design recommendations.

• In Outlook: Summarize long email threads, suggest responses, or organize your inbox.

• In Teams: Automatically generate meeting summaries, action items, and conversation recaps.

Because it operates within the secure, compliance-ready Microsoft 365 ecosystem, Copilot adheres to existing enterprise security standards, privacy controls, and data governance policies. This built-in trust factor is a key differentiator for IT decision-makers evaluating generative AI tools.

Why Enterprises Embrace Microsoft Copilot

The enterprise appeal of Copilot comes down to productivity, context-awareness, and ease of deployment:

• Time Savings: Employees can hand off repetitive tasks like formatting a presentation, summarizing a document, or analyzing a dataset so they can focus on higher-impact work.

• Contextual Intelligence: Copilot leverages the context from your documents, messages, and meetings (with appropriate permissions) to generate more relevant and personalized outputs.

• Frictionless Adoption: For companies already using Microsoft 365, enabling Copilot requires no overhaul or new platform. It's a native extension of tools employees already know reducing onboarding time and change resistance.

This combination of accessibility and enterprise-readiness has made Copilot a fast favorite among digital transformation leaders looking to scale AI without disrupting existing workflows.

Usage Tips & Considerations

While Microsoft Copilot is powerful, optimal results depend on user input and oversight. Here are a few best practices for enterprise rollout:

• Prompt Crafting: Train teams to give Copilot clear, specific prompts (e.g., “Summarize this email thread into 3 key decisions and next steps”) to improve relevance and reduce editing time.

• Quality Control: AI-generated content should be reviewed to ensure accuracy, especially in sensitive areas like HR, legal, or finance where tone and nuance matter.

• Departmental Pilots: Start with limited rollouts in content-heavy teams such as finance, HR, or marketing to gather feedback and refine usage patterns before scaling organization-wide.

By working inside the Microsoft tools employees already rely on, Copilot makes enterprise AI not just accessible but instantly useful.

Read the comprehensive comparison between Microsoft Copilot Studio vs StackAI

3. Google Workspace Gemini (Bard) – Google’s Multimodal AI in the Enterprise

Google Gemini, formerly known as Bard, is Google’s enterprise-grade generative AI offering, launched in 2023 as a direct competitor to Microsoft Copilot and OpenAI’s ChatGPT. Now embedded across Google Workspace apps like Gmail, Docs, Slides, Sheets, and Meet, Gemini enables employees to interact with AI directly within their daily workflows.

Adoption is rapidly rising: about 40% of enterprises currently use Gemini, and another 39% are testing it, making it one of the most widely evaluated GenAI platforms in the business ecosystem. One of Gemini’s biggest differentiators is its multimodal capability meaning it can process not only text, but also images, and in some cases, audio and video inputs.

Enterprise Use Cases

For organizations running on Google Workspace, Gemini acts as a true productivity enhancer across departments:

• In Gmail and Docs: Gemini serves as an advanced writing assistant, helping users draft emails, reports, proposals, or summaries based on short prompts think of it as Smart Compose, elevated by generative AI.

• In Google Slides: Teams can auto-generate entire presentations from a rough outline or ask Gemini to create slide visuals or image-based backgrounds using AI-generated graphics.

• In Google Sheets: With the Spreadsheet Analyzer, data professionals can ask plain-language questions about their datasets (e.g., “What are the top sales trends this quarter?”) and receive instant, visual insights.

• In Google Meet: AI-enhanced features include generating custom background visuals and improving meeting audio/video quality in real time.

Across the board, Gemini acts as an AI-powered co-worker embedded directly into Google’s suite, allowing teams to collaborate, write, analyze, and present with less manual effort.

Why Enterprises Choose Gemini

Gemini’s core strength is native integration into the Google Workspace ecosystem. For organizations already standardized on Gmail, Drive, and other Google tools, setup is minimal, and usage feels familiar drastically reducing onboarding time.

Key differentiators include:

• Multimodal Input & Output: Gemini was one of the first enterprise AI tools to support text, image, and (in some cases) audio input/output in a single prompt. This opens new workflows for creative, marketing, and product teams.

• Visual & Creative Utility: Teams can ask Gemini to generate images, charts, or visual storyboards directly inside Docs or Slides something particularly valuable for content and design workflows.

• Cloud-Native Efficiency: Everything lives inside the Google Workspace environment, benefiting from the same security, access control, and compliance infrastructure already trusted by IT.

For data teams, marketers, and content creators working in the Google ecosystem, Gemini provides an AI-first interface that feels like an extension of your team.

Limitations & Considerations

While Gemini shines inside the Google ecosystem, its utility diminishes outside of it. If your organization uses a mixed stack or isn’t deeply invested in Google tools Gemini’s integrations may feel limited compared to more platform-agnostic AI solutions.

Other important considerations:

• Accuracy & Oversight: Like all generative AI models, Gemini can hallucinate or misinterpret information. Human validation remains critical, especially for external-facing or data-sensitive outputs.

• Data Governance: Gemini retains data within your Workspace domain, which appeals to privacy-conscious IT leaders. Still, organizations should train employees to avoid entering sensitive information into AI prompts and enforce appropriate governance policies.

• Limited Customization: While Gemini is rapidly improving, its customization and fine-tuning options are more constrained compared to open developer ecosystems or API-first platforms.

4. Meta AI (Llama) – Virtual Assistant for Social and Customer Engagement

Meta AI is Meta’s (formerly Facebook) generative AI assistant, built on the Llama large language model and embedded across the company’s suite of platforms including Facebook, Instagram, WhatsApp, and Messenger. Launched as part of Meta’s broader AI initiative, the assistant is designed to support customer interaction and automation within social and messaging environments.

As of 2024, 40% of enterprises report using Meta AI in some capacity, with an additional 36% currently piloting or evaluating it. Unlike traditional workplace tools, Meta AI is purpose-built for customer-facing engagement offering businesses a scalable way to automate support, personalize messaging, and analyze interactions where their customers already are.

Enterprise Use Cases

Meta AI is most commonly used by enterprises to automate and enhance customer engagement across social and messaging channels. Key applications include:

• Customer Support Chatbots: Businesses set up AI-powered assistants on Facebook Pages, WhatsApp for Business, or Instagram DMs to provide 24/7 responses to common inquiries, product lookups, or order tracking requests.

• E-Commerce Automation: Meta AI can assist with conversational commerce guiding users through product catalogs, checkout steps, or personalized recommendations within chat interfaces.

• Community Moderation: In large Facebook Groups or Messenger threads, Meta AI can monitor and respond to frequently asked questions, easing the load on community managers.

• Social Listening & Insights: For marketing teams, Meta AI can summarize high-volume social conversations, flag trending topics, or extract customer sentiment for campaign planning.

• Multimodal Interaction: It can suggest image edits or filters, translate real-time messages, and adapt to emoji-heavy or casual language making it ideal for brand-consumer interactions in dynamic social spaces.

In essence, Meta AI acts as a frontline digital rep, helping businesses stay responsive and relevant across platforms where billions of users spend time every day.

Why Enterprises Use Meta AI

Meta AI’s primary advantage is simple: it meets customers where they already are. With native integration into the world’s most-used social and messaging platforms, it allows businesses to deliver seamless, on-brand experiences without asking customers to switch apps or channels.

Additional strengths include:

• Conversational Intelligence: Trained on casual, emoji-rich, and multilingual conversations, Meta AI understands the tone and context of social interactions better than most enterprise chatbots.

• Real-Time Engagement: Brands can respond instantly to questions or feedback, fostering stronger relationships and reducing friction in support and sales.

• Data-Driven Insights: When configured properly (and with user consent), Meta AI can analyze engagement trends and feedback logs, revealing opportunities for product improvement or campaign targeting.

These capabilities make Meta AI especially valuable for retail, hospitality, e-commerce, and global brands that rely on direct customer interaction through social platforms.

Privacy & Ethical Considerations

With broad reach comes a high bar for responsibility. Enterprises deploying Meta AI must ensure:

• Clear Data Governance: AI interactions may involve personal or sensitive customer data. Businesses must configure data access controls and comply with privacy regulations like GDPR or CCPA.

• Transparency in Automation: Customers should always know when they’re interacting with a bot, and businesses must have a human fallback in place for complex or high-stakes conversations.

• Bias & Accuracy Oversight: Like all LLMs, Meta AI may occasionally misinterpret inputs or respond with incomplete or biased information. Continuous monitoring and human-in-the-loop practices are essential.

When used thoughtfully, Meta AI enables companies to scale personalized engagement and real-time service across the social channels that dominate modern digital life bridging the gap between brand presence and meaningful customer experience.

5. DALL-E 3 (OpenAI) – Image Generation for Creative and Marketing Teams

DALL·E 3, released by OpenAI in late 2023, is the latest evolution of the company's text-to-image model designed to generate original visuals, artwork, and illustrations from natural language prompts. Unlike text-based AI tools such as ChatGPT or Copilot, DALL·E 3 focuses entirely on visual content creation.

Adoption is growing rapidly: approximately 30% of enterprises report using DALL·E 3, while 35% are currently testing it for use in marketing, product design, and content development. One of DALL·E 3’s most significant advancements over previous versions (DALL·E 1 and 2) are its enhanced image realism and seamless integration within ChatGPT, making it accessible to non-designers through conversational prompts.

Enterprise Use Cases

DALL·E 3 is transforming how creative, and product teams generate visual content at scale. Common enterprise applications include:

• Marketing & Design: Create high-quality ad banners, email visuals, blog illustrations, and branded social media assets without the need for manual design work in early stages.

• E-Commerce & Product Imagery: Quickly produce product mockups or stylized visuals showing items in different settings, seasons, or demographic contexts for A/B testing and campaign variants.

• R&D and Product Development: Generate concept art, UI mockups, or product design ideas from a brief description, helping teams visualize and iterate on ideas faster.

• Training & Education: Develop custom graphics or illustrations for presentations, onboarding manuals, explainer slides, or even animated video storyboards.

In essence, DALL·E 3 acts as a creative co-pilot, empowering teams to go from prompt to draft image in seconds dramatically speeding up ideation and early-stage design.

Benefits of DALL·E 3 for Enterprise

What makes DALL·E 3 especially valuable is its blend of creativity and accessibility:

• Integrated with ChatGPT: Users can interact with ChatGPT conversationally and simply say, “generate an image of a team collaborating on a futuristic dashboard,” and get a usable output instantly no special tools or design knowledge required.

• Rapid Prototyping: Ideal for brainstorming, DALL·E enables fast creation of mood boards, concepts, and campaign variations without needing to wait on lengthy design cycles.

• Hyper-Personalization: Enterprises can generate dozens of image variants tailored to different customer personas, markets, or channels enabling dynamic and targeted content strategies at scale.

• Time & Cost Efficiency: By handling the early visual drafts, DALL·E shortens production cycles and reduces reliance on external creative vendors for every iteration.

Limitations & Considerations

While DALL·E 3 is powerful, there are a few limitations to keep in mind:

• Visual Fidelity: Outputs may lack photorealism or consistency, especially in complex scenes or with realistic human faces. These limitations mean that professional designers may still be needed to refine final visuals for high-stakes campaigns.

• AI Aesthetic: Some images still carry a distinctive AI-generated "look" which can be useful for stylized art but may not suit every brand or use case.

• Usage Policies & Rights: DALL·E is governed by OpenAI’s content and commercial use policies. While images can generally be used for business under OpenAI’s terms, companies should review licensing guidelines to avoid legal ambiguity.

• Ethical Use & Guardrails: Content filters are in place to prevent misuse (e.g., creating harmful or deceptive images), and enterprises must ensure internal usage complies with both OpenAI's policies and company brand standards.

Ultimately, DALL·E 3 is best used as a starting point for visual ideation. It unlocks creativity at scale while allowing human designers to focus on polish, branding, and final execution.

6. Runway Gen-2 – AI Video Generator for Multimedia Content

Runway ML’s Gen-2 model, released in 2023, is pushing the frontier of generative AI into the world of video. Unlike text or image generation tools, Runway Gen-2 enables users to create short, high-concept video clips from simple inputs such as a text prompt, image, or even another video. This represents a significant leap forward for content creation, giving businesses the ability to produce dynamic video assets without cameras, actors, or studios.

Enterprise adoption is gaining momentum: about 25% of companies currently use Runway’s video generation tools, with another 31% in the evaluation phase. For organizations aiming to scale visual storytelling, Runway offers an AI-powered alternative to traditional video workflows.

What Runway Gen-2 Can Do

Runway Gen-2 turns creative ideas into motion. Here’s how:

• Text-to-Video: Users can input a prompt like “10-second video of a futuristic city skyline at dawn,” and Gen-2 will generate a short-animated sequence matching the request.

• Image & Video Input: Upload an image to animate it or apply a stylized transformation to existing footage like converting real video into an illustrated or cinematic look.

• Extended Clips: Compared to Gen-1, Gen-2 supports longer clips (up to ~18 seconds), camera direction control, and the ability to stitch together multiple clips into a longer sequence.

• Style Transfer & Visual Effects: Creative professionals can apply artistic effects, change scenes’ moods, or visualize abstract concepts without the need for manual editing.

These capabilities turn Runway Gen-2 into a lightweight virtual video studio, particularly valuable for fast-paced teams who need visuals on demand.

Enterprise Use Cases

Businesses are beginning to tap into Runway’s generative video capabilities for:

• Marketing & Social Content: Create ad spots or social videos without the need for production crews ideal for launching fast campaigns or testing creative ideas at scale.

• Design & Creative Storyboarding: Replace static storyboards with moving video prototypes to communicate ideas more effectively.

• Media & Entertainment: Experiment with AI-powered special effects, scene previews, or mood-based concept visuals before committing to full-scale production.

• Internal Comms & HR: Develop engaging internal messages, onboarding content, or leadership updates without hiring a video team.

Whether for external brand campaigns or internal storytelling, Runway Gen-2 democratizes video creation, making it accessible to non-editors and non-animators across departments.

Challenges & Considerations

As promising as AI-generated video is, there are still important limitations to consider:

• Output Quality: While Gen-2 can produce compelling clips, they often lack the polish of professional-grade videos. Low resolution, visual artifacts, or unrealistic motion can occur especially in more complex scenes.

• Prompt Experimentation Required: Users often need to iterate on prompts and combine multiple outputs to get usable results. This experimentation can take time.

• Compute Requirements: Video generation is resource intensive. To use Runway effectively, enterprises will need access to powerful GPUs or cloud infrastructure, which could lead to additional costs.

• Governance & Compliance: Although sensitive data use is limited (most prompts are creative), IT teams should still ensure cloud-based usage aligns with corporate data policies.

For these reasons, it's smart for enterprises to start with small pilot projects. For example, a design team could experiment by creating a short promotional clip or visual prototype testing both the workflow and the final output quality before wider adoption.

7. Stable Diffusion – Open-Source Image Generator for Custom Enterprise Needs

Stable Diffusion is a powerful open-source text-to-image model developed by Stability AI and released in 2022. It allows users to generate high-resolution images from natural language prompts, and unlike most commercial alternatives, it can run locally on standard hardware. This makes it especially appealing for businesses that want more control over their AI infrastructure.

Roughly 24% of enterprises have already adopted Stable Diffusion in some way, and another third are actively testing it. Its growing popularity stems from its flexibility, affordability, and the ability to customize the model without relying on external APIs or paying per image, aside from compute costs.

Why Enterprises Use Stable Diffusion

The biggest reason companies turn to Stable Diffusion is the level of control it offers. Since both the model weights and source code are open, teams can deploy it privately on internal servers and avoid sending data to third-party providers. This is a major advantage for industries where privacy, compliance, or security concerns prevent the use of cloud-based tools.

Stable Diffusion can also be fine-tuned. For example:

• A retail brand can train the model on its product catalog to generate new colorways or design variations.

• An architecture firm might generate early-stage building concepts for client presentations.

• AI research teams can create synthetic training data to augment real-world datasets.

Creative teams use it to generate marketing images, concept art, product mockups, or mood boards. And because it works on typical high-end PCs with good GPUs, there's no need to invest heavily in cloud resources just to get started.

Key Features and Capabilities

Stable Diffusion uses a latent diffusion approach, which makes it more efficient than many earlier image generation models. Some of its most useful features for enterprise teams include:

• Image-to-Image Transformation: You can start with an existing image and apply changes or stylizations using text prompts.

• Inpainting: This feature lets users modify or fill in specific parts of an image based on contextual instructions.

• Batch Generation: Teams can produce hundreds of variations quickly, making it ideal for content testing or design iteration.

• Animation Experiments: Some users have explored creating short animated loops for storyboarding or visual experimentation.

• Domain-Specific Use Cases: Stable Diffusion has been used in areas like non-diagnostic medical visualization, industrial design concepts, and more.

With the help of community plugins and open-source add-ons, enterprises can unlock even more functionality, including greater prompt control and refined output quality.

Considerations Before Adoption

While Stable Diffusion offers clear benefits, it also requires more technical oversight than plug-and-play commercial tools. There is no built-in support team, so organizations may need in-house AI experts or third-party partners to help with setup, customization, and optimization.

Output quality can vary, especially with complex prompts. Out-of-the-box, the visuals may look less polished compared to something like DALL·E or Midjourney. However, many users see significant improvements after fine-tuning the model or using specialized community tools like ControlNet or LoRA.

Legal and ethical use is another consideration. Because the model was trained on publicly available internet images, there have been concerns around copyright and likeness. Businesses should establish internal guidelines to ensure that generated images do not violate usage rights or resemble real individuals or brands without permission.

In short, Stable Diffusion gives enterprises full creative and operational control over their generative AI workflows, with a cost structure and flexibility that’s hard to match. It’s especially well-suited for teams that want to customize models, protect data, and experiment with large volumes of AI-generated content.

8. Midjourney – High-Quality AI Art Generator via Discord

Midjourney is one of the most visually striking AI image-generation tools available today. Launched as an open beta in 2022, it has built a strong reputation for producing high-quality, stylized, and often artistic visuals. What makes Midjourney stand out is its unique interface: users interact with it primarily through a Discord bot or, more recently, a dedicated web app.

Despite this unconventional approach, Midjourney has seen significant enterprise interest. Around 23% of organizations report using it, and another 32% are currently testing it across marketing, media, and design workflows. Creative teams consistently praise the model for its ability to generate visually polished and imaginative images with little effort.

Enterprise Use Cases

Midjourney serves as a powerful visual ideation tool for creative professionals. Enterprises use it to accelerate workflows across design, content creation, and marketing. Examples include:

• Advertising and Marketing: Teams generate campaign visuals, product mockups, and ad creatives without needing to start from scratch.

• Branding and Concept Art: Design teams use Midjourney to brainstorm logo variations, create mood boards, or visualize product packaging.

• Entertainment and Gaming: Storyboard scenes, generate scenery, or bring early-stage character concepts to life with rich detail.

• Fashion and Retail: Brands explore apparel design directions or create lifestyle visuals for product pages and digital catalogs.

• Internal Content Creation: Some organizations use Midjourney to build custom imagery for websites, presentations, or social posts, replacing generic stock photos with original artwork.

In short, Midjourney acts like a digital artist that turns text prompts into detailed, creative images helping teams move from idea to visual in minutes.

Why Creative Teams Prefer Midjourney

Midjourney is widely recognized for the aesthetic quality of its outputs. With the right prompt structure, it can produce images that are nearly photorealistic or highly stylized, depending on the desired look. The results often require very little post-editing, which helps teams save time during the design process.

Other notable strengths include:

• Consistent Style Rendering: Midjourney excels at generating visuals in a cohesive artistic style, which is especially useful for brands that need consistent creative direction.

• Vibrant Community: The Discord-based community provides a constant stream of prompt ideas, use cases, and inspiration. This ecosystem is valuable for enterprise users who want to learn quickly and iterate on prompt techniques.

• Frequent Model Updates: The tool is updated regularly, with newer versions (such as v5 and v6) offering more realism, detail, and creative flexibility.

Licensing and Access Considerations

Using Midjourney in a business context comes with a few things to keep in mind:

• Subscription Required: Access is based on a paid plan, and enterprise users must work within either the Discord interface or the web app. IT teams may need to review and approve Discord usage if it's not already supported.

• Commercial Rights: Paid subscribers are granted commercial use rights, meaning businesses can legally use the generated images in marketing campaigns, product visuals, or public content.

• Copyright Limitations: According to Midjourney’s terms, AI-generated images are not eligible for copyright unless they are substantially modified by a human. This means unedited outputs could be considered public domain. Enterprises that want image exclusivity should have a designer adjust or build upon the generated content.

• Security and Governance: Because generation occurs in the cloud, and often through a social platform, companies should avoid using sensitive data in prompts and ensure any internal use aligns with corporate security protocols.

For creative teams that value style, speed, and artistic control, Midjourney offers a fast and flexible way to produce visually compelling assets with minimal overhead.

9. Perplexity AI – AI-Powered Answer Engine for Enterprise Knowledge

Perplexity AI is a generative AI search engine and chatbot that blends large language models with real-time internet search and optional internal data sources. Launched in 2022, its public-facing version functions like an enhanced Q&A assistant users can ask questions and get clear, conversational answers backed by cited sources.

While originally designed for general web use, Perplexity is quickly gaining ground in the enterprise world. Roughly 21% of organizations have already adopted it, with another 33% actively evaluating its potential. Companies are turning to Perplexity for its ability to act as a smart research assistant, one that pulls from both public and private data to provide trustworthy, well-organized responses.

Enterprise Use Cases

Enterprises are finding value in Perplexity across several functional areas:

• Internal Knowledge Management: Teams connect Perplexity to internal documentation, wikis, and databases. Employees can then ask natural-language questions like “What’s the PTO policy?” or “How do I submit travel expenses?” and get quick, cited answers pulled directly from internal content. This reduces time spent searching across folders or systems.

• Customer Support Enablement: Support agents can use Perplexity to instantly reference product manuals, ticket history, or FAQs while assisting customers. This improves both response times and consistency.

• Research and Insights: Analysts and strategists use Perplexity to gather real-time data, such as stock prices, peer company comparisons, or summaries of the latest industry news. Because the tool incorporates live web data, it’s much more dynamic than static AI chatbots.

• Productivity on the Go: Perplexity also offers a mobile AI assistant, making it easy for professionals to run quick queries while in meetings, during travel, or between tasks.

Whether it's helping a new hire navigate onboarding or enabling leadership to gather competitive insights quickly, Perplexity serves as a powerful tool for enterprise knowledge retrieval.

What Sets Perplexity Apart

Perplexity’s main strength is its ability to combine the reasoning of a large language model with real-time search capabilities. This means it can pull from the most current sources, including your company’s intranet or shared drive, and still explain the results in a conversational format.

Key advantages include:

• Up-to-Date Information: Unlike static AI models, Perplexity can reference the latest documents or online content, making it useful for fast-moving departments like finance or legal.

• Cited Sources for Transparency: Responses often include source links, allowing users to verify the origin of the information an essential feature for compliance-heavy industries.

• Secure, Flexible Deployment: Perplexity is hosted on Microsoft Azure and offers model options for paid users. IT leaders can choose between faster performance or more advanced outputs, depending on internal needs and security protocols.

These features make it ideal for use cases where accuracy, transparency, and access to live data are critical.

Considerations Before Deployment

To use Perplexity effectively within an enterprise, there are a few things to keep in mind:

• Access Controls: If connected to internal data, it’s important to configure strict permissions around what the AI can access and who can query it. This helps prevent unintentional exposure of confidential information.

• Training Users on Prompts: Like all LLM-based tools, the quality of the output depends on the quality of the input. Teams should receive basic training on how to ask clear and specific questions.

• Response Auditing: While Perplexity is generally accurate, it can occasionally pull in outdated or off-topic sources. Regular reviews of its outputs especially in sensitive departments help maintain quality and reliability.

Used thoughtfully, Perplexity AI can become an always-on research concierge, reducing search time, improving access to knowledge, and freeing up teams to focus on higher-value work.

10. Claude by Anthropic – Safe Conversational AI for Business

Claude is a conversational AI developed by Anthropic, a company founded by former OpenAI researchers and launched in 2023. Positioned as an alternative or complement to ChatGPT, Claude was built with a unique emphasis on AI safety and ethical alignment. It’s powered by a training methodology called Constitutional AI, which guides the model to follow a set of predefined principles and avoid generating harmful or inappropriate content.

As of now, about 19% of enterprises report using Claude, and another 34% are actively evaluating it. Its structured, safety-first approach has made it especially attractive to organizations that prioritize brand integrity, regulatory compliance, and trustworthy AI behavior.

Enterprise Applications

Claude can be deployed across many of the same use cases as other large language models, including:

• Customer Support Automation: Businesses use Claude to handle common questions in customer service chats, particularly in sensitive industries like healthcare, insurance, and HR where accuracy and tone matter.

• Document Summarization and Analysis: With support for large context windows (up to ~100,000 tokens), Claude can process and summarize long documents such as contracts, technical specs, policy guides, or regulatory filings.

• Internal Knowledge Retrieval: Claude helps employees query company FAQs, onboarding documents, or operational procedures in plain language, saving time and reducing friction in daily tasks.

• Content Creation and Decision Support: Teams use Claude for drafting reports, explaining complex topics, or brainstorming ideas, especially in regulated environments where precision and clarity are essential.

Its ability to understand and respond thoughtfully to nuanced, high-stakes prompts make Claude well-suited for legal teams, compliance officers, and enterprise users who want a more responsible approach to AI assistance.

What Makes Claude Stand Out

Claude’s standout feature is its Constitutional AI training method, which gives it a built-in set of ethical guidelines. This makes it more likely to reject unsafe or inappropriate prompts and less likely to generate biased or problematic responses.

Additional enterprise benefits include:

• Well-Structured, Detailed Answers: Claude often delivers thorough, step-by-step responses, making it ideal for problem-solving, troubleshooting, or walking users through complex reasoning.

• Privacy and Deployment Options: Anthropic offers Claude via API and through Claude Enterprise plans, which include strong privacy protections and support for long-form input processing. This makes it a secure choice for handling proprietary or sensitive data.

• Risk Reduction: Claude’s design philosophy prioritizes safety and reliability, helping reduce the risk of brand-damaging outputs or AI “hallucinations” that could lead to customer confusion or internal errors.

For organizations concerned about AI alignment and long-term accountability, Claude represents a thoughtful, reliable solution.

Things to Consider

While Claude brings a lot to the table, there are a few practical considerations:

• Verbosity: Claude is known for being detailed, sometimes too much so. Teams may need to prompt it to be brief or concise, especially when working under time pressure or in chat-based environments.

• Ecosystem Limitations: Compared to ChatGPT, Claude currently has fewer third-party integrations, plugins, or extensions. For companies that rely heavily on custom tools or external APIs, this may require supplemental platforms.

• Knowledge Coverage: Claude may have a more limited knowledge base compared to other LLMs, depending on the use case. Enterprises should assess whether the model’s current capabilities align with their domain-specific needs.

In many cases, companies find success by using Claude alongside other AI tools, depending on the task. Claude excels where responsibility, clarity, and high-context reasoning are required, making it a strong addition to any enterprise AI stack.

Enterprise AI Tools Feature Matrix

Here’s a quick comparison of the top generative AI tools used in enterprises today. Each tool excels in different areas, from conversational support to creative generation to productivity automation. Use this matrix to identify which solutions align best with your team's needs.Ask ChatGPT

Enterprise AI Agents for Every Job

Generative AI has transformed how businesses operate. Tools like ChatGPT and Copilot help teams draft content, summarize data, and automate repetitive tasks. But they still rely on humans to initiate every action. You write a prompt, wait for a response, then decide what to do next. The real evolution is no longer just about generating content. It's about completing work from start to finish, without manual follow-up.

This is where autonomous agents come in. These AI agents don't wait for prompts. They follow instructions, make decisions, and carry out multi-step tasks on their own. They're able to read documents, interact with tools, analyze data, and complete actions based on logic and context. This shift represents a major milestone in the evolution of AI. Enterprises are no longer just using AI to assist humans. They're beginning to deploy AI agents that operate independently within real business workflows.

With StackAI, you can build and launch these enterprise-grade agents without writing a single line of code. These agents plug into your existing tools like Google Sheets, Slack, Salesforce, Notion, Airtable, and internal APIs. They can process structured and unstructured data, follow decision paths, and take action based on rules you define.

For example:

• A finance agent can review uploaded receipts, compare them to policy guidelines, and submit categorized entries into your accounting system.

• A customer support agent can scan emails or tickets, retrieve knowledge base content, generate a helpful reply, and follow up when needed.

• A sales reporting agent can pull data from Salesforce, identify deal risks, write a weekly report, and format it as a presentation.

• A research agent can monitor industry news sources, summarize trends, and send curated insights to stakeholders every day.

You control what the agent sees, what it can do, and how it behaves. You can monitor performance, apply data privacy settings, and adapt logic to match your processes. StackAI makes it possible to automate entire workflows in a way that is secure, scalable, and tailored to your enterprise.

This isn’t about replacing humans. It’s about giving your team intelligent systems that take work off their plate and move faster than any manual process ever could.

See What AI Agents Can Do for Your Business. Curious how StackAI can work for your team? Book a demo to explore real use cases or sign up for free to start building your first AI agent today.