7 Best Enterprise AI Platforms in 2026 | Tested & Reviewed

Enterprises everywhere are moving from automation tools to autonomous AI solutions that combine AI search, AI Agents, workflow orchestration, AI governance, and integrations to improve how work gets done across customer experience (CX), employee experience (EX), and business operations. 

By 2027, Gartner predicts one-third of enterprise AI implementations will combine autonomous agents with different skills to manage complex tasks within application and data environments.

But adopting enterprise AI isn’t just about proving a single use case. The real challenge is scaling AI safely across the business, keeping performance consistent, integrating with core systems, governing access and risk, and making costs predictable as usage grows. 

That’s why enterprise AI platforms are becoming essential. They provide the enterprise foundation, such as connectors, orchestration, AI governance, security, observability, and lifecycle controls, so teams can move from pilots to production across multiple workflows. 

With dozens of platforms now emerging, the question becomes: which enterprise AI platforms are best suited for large-scale AI adoption in 2026 and beyond?

In this guide, we break down the top 7 enterprise AI platforms for 2026, covering where each one fits best, what it does well, and what to watch for, so you can choose the right long-term platform for your organization.

Let’s dive in.

What enterprises need to know (TL;DR)

Before diving into the enterprise AI platforms, here are the key takeaways:

• Building AI solutions in-house hits a ceiling quickly. Creating one prototype is easy; however, running governed, observable, reliable AI solutions in production is where most internal teams stall. 
• Not all platforms are built the same. Some enterprise AI platforms specialize in a single function, while others offer the breadth and scalability for enterprise-wide deployment. Match the platform to your use case.
• Look for enterprise-grade capabilities. Prioritize platforms with multi-agent orchestration, model and cloud flexibility, deep system integrations, strong AI governance, and a track record with large global enterprises.

What is an enterprise AI platform?

An enterprise AI platform is a software foundation that helps organizations build, connect, govern, and scale AI across the business. It typically brings together the building blocks enterprises need in one place, such as model support, data and knowledge access (including RAG), integrations/connectors, workflow orchestration, security controls, and AI governance. 

In practice, these enterprise AI platforms let teams deploy AI for use cases such as CX, EX, and business operations, while keeping deployments compliant and manageable as adoption grows.

Think of an enterprise AI platform as the control plane for enterprise AI that standardizes how AI is deployed, how it connects to systems, how risk is managed, and how performance is measured, so organizations can scale beyond pilots without re-engineering everything each time. In practice, an enterprise AI platform allows organizations to deploy multiple specialized AI solutions, whether for sales operations, IT support, customer service, finance, HR, or supply chain management

As enterprise AI matures, platforms are increasingly supporting agentic AI. These are the AI systems that can reason, take action, and coordinate tasks across applications. Agentic AI extends traditional enterprise AI by enabling autonomous, goal-driven workflows with built-in governance and observability.

Learn more about how you can build and deploy AI solutions at scale.

What are the benefits of enterprise AI platforms?

As enterprises build AI solutions internally, many teams find that a single pilot is easy, but running production-grade AI solutions across teams, systems, and geographies is where things break. Enterprise AI platforms solve this by providing the shared foundation for integration, governance, scalability, and operations.

Below are the key benefits of using an enterprise AI platform instead of building everything in-house. 

1. Deep integration with core business systems 

Instead of stitching together custom APIs each time, platforms offer mature integration frameworks so AI can read and act across CRM, ERP, ITSM, HRIS, data warehouses, and collaboration tools, enabling end-to-end workflows across departments.

2. Built-in enterprise AI governance, auditability, and compliance 

When enterprises build AI solutions themselves, AI governance often becomes an afterthought. Platforms centralize enterprise controls such as RBAC, audit logs, policy enforcement, data isolation, and guardrails. Whereas DIY solutions rarely have full auditability or compliance support (GDPR, HIPAA, ISO), which limits safe deployment in production.

3. Reliable knowledge access with enterprise + agentic RAG 

Enterprise AI platforms operationalize retrieval through secure knowledge access, grounded responses, and permission-aware retrieval. With agentic patterns, AI can retrieve, reason, and take action (e.g., retrieve a policy, validate a case state, then trigger the right workflow) while keeping decisions traceable.

4. Scalability without re-engineering 

What’s hard isn’t building one solution, but it’s supporting many solutions at high volume. Enterprise platforms are designed for concurrency, performance, and lifecycle management, so organizations can scale usage, regions, channels, and use cases without rebuilding infrastructure.

5. Faster deployment and lower operational overhead 

Building enterprise AI systems manually requires engineering effort, extensive testing, and constant maintenance as business systems and APIs evolve. Enterprise AI platforms bundle capabilities, such as tooling, connectors, agent orchestration, guardrails, and deployment controls out of the box. This accelerates time-to-value by enabling teams to deploy AI solutions quickly, often with minimal configuration. Non-tech users can even build domain-specific AI agents without relying solely on developer resources. 

6. Stronger security posture and automated risk management 

DIY AI solutions typically lack centralized controls for identity, access, auditing, and behavioral monitoring. Enterprise AI platforms embed security automation, anomaly detection, and policy enforcement into every step an AI solution takes. They can support use cases like: 

• Fraud detection 
• Finance & compliance workflows 
• Secure supply chain operations 
• Identity-sensitive actions

Where security for DIY AI solutions becomes complex, expensive, and fragmented, enterprise AI platforms standardize it. 

Watch this webinar to learn how enterprise AI is reshaping enterprise workflows.

Top 7 enterprise AI platforms in 2026 & beyond

Now that we’ve covered what enterprise AI platforms are and why they’re essential for scaling AI across a business, the next step is understanding which solutions are leading the market. 

Below are the top 7 enterprise AI platforms that stand out in 2026 and beyond, along with a breakdown of where they excel, the problems they solve, and the use cases they’re best suited for.

1. Kore.ai: Ideal for enterprises operationalizing AI agents at scale across CX, EX, and business processes

‍

Kore.ai is an enterprise AI platform that helps enterprises quickly design, deploy, manage, and scale AI solutions and agents across the business. Unlike other platforms that cater to narrow use cases, Kore.ai offers the full range of AI solutions for workplace productivity, customer service, or process orchestration, giving enterprises the freedom to build agents for virtually any scenario. It brings together agent orchestration, enterprise search/RAG, workflow automation, integrations, and AI governance in a single control plane.

At the center of Kore.ai’s enterprise AI platform is its multi-agent orchestration engine. Acting as a control layer across the enterprise tech stack, it enables multiple AI agents to collaborate, hand off context, and execute tasks with differing levels of autonomy, from simple assistive copilots to fully autonomous task-executing agents.

Kore.ai’s enterprise AI platform is built with accessibility in mind. With no‑code and pro‑code development options, both business teams and IT teams can get AI solutions up and running in no time. To accelerate that further, Kore.ai offers an Agent Marketplace with 300+ pre-built AI agents and templates, so organizations can start from proven building blocks instead of reinventing common workflows.

Under the hood, the platform stays intentionally flexible. Its model-agnostic, cloud-agnostic, and data-agnostic architecture lets enterprises choose any LLM they prefer, including bringing their own in-house models, running them in any environment, and connecting to virtually any data source. Simply put, this flexibility ensures businesses are never locked into a single ecosystem.

Kore.ai’s pricing is equally flexible and scalable. It provides options for session-based, usage-based, and per-seat-based models. For larger deployments, Kore.ai offers tiered volume pricing, plus the flexibility to move up or down tiers as usage changes. For teams that want to start smaller and scale later, pay-as-you-go plans are also available without requiring bulk commitments.

Where Kore.ai truly comes into its own, though, is in its AI governance-first approach. The platform includes a comprehensive AI governance dashboard that provides full visibility into every agent’s decisions, actions, and performance. Enterprises can trace interactions, monitor agent reasoning, manage security guardrails, enforce role-based access controls (RBACs), and review detailed audit logs to ensure compliance and responsible AI behavior at every step.

Kore.ai’s leadership in the enterprise AI landscape is widely recognized across the industry. The platform has been named a Leader in the  Gartner® Magic Quadrant™ for Conversational AI Platforms, 2025, for the third consecutive year. According to Gartner, “Kore.ai delivers a feature-rich platform that stands out for its comprehensive and well-balanced capabilities.” 

Similarly, Kore.ai has been named a Leader in The Forrester Wave™: Cognitive Search Platforms, Q4 2025. According to Forrester, “Kore has been able to capitalize on the rush to deploy 'ChatGPT for the enterprise' over the past few years.”

Kore.ai has also been named a Leader in the Everest Group’s Conversational AI & AI Agents in CXM Products PEAK Matrix® Assessment 2025. According to Everest Group, “Kore.ai supports advanced multi-agent orchestration through features such as agent collaboration, short- and long-term agent memory, agent tool access, and intelligent delegation.”

Kore.ai is trusted by over 400 Fortune 2000 companies, delivering more than $1Bn in cost savings. They have proven across industries like finance, healthcare, technology, manufacturing, telecom, and retail, with deep expertise in complex workflows. 

Key features of Kore.ai:

Some of the notable features of Kore.ai’s enterprise AI platform are:

• Multi-agent orchestration enables enterprises to design AI agents that collaborate seamlessly, share context, and execute complex workflows across customer, employee, and operations use cases.
• Enterprise-grade context, tools, and memory support both enterprise and agentic RAG, letting agents securely retrieve, reason, and act on real-time knowledge via APIs. Agents can chain retrieval with tools to complete multi-step tasks, while retaining context across interactions.
• 250+ enterprise-grade, plug-and-play integrations give agents direct access to systems like CRM, ITSM, HRIS, ERP, and data lakes. Teams can also add custom integrations or bring in any system they need, without heavy engineering.
• No-code + pro-code development framework lets business teams build visual workflows and agent logic without writing code, while developers extend functionality with APIs, custom skills, and deeper integrations. 
• Comprehensive AI governance and observability provide full lifecycle oversight with end-to-end tracing, audit logs, and real-time monitoring so enterprises can deploy AI safely, transparently, and at scale. 
• Configurable guardrails and RBAC enforce organizational policies, restrict unsafe behaviors, and manage permissions through structured role-based access control to uphold security and compliance. 
• Agent marketplace with 300+ pre-built AI agents allows enterprises to build and deploy agents up to 10 times faster and start generating ROI from the get-go.

Pros of Kore.ai: 

• End-to-end enterprise AI lifecycle controls (build, deploy, monitor, improve)
• Enterprise-scale multi-agent orchestration 
• Best-in-class governance and observability
• Highly flexible, model- and cloud-agnostic architecture
• Agentic RAG with tool-use memory
• Flexible pricing. (Request-based, session-based, per-seat, or pay-as-you-go pricing structures)
• Deep integration ecosystem with 250+ plug-and-play enterprise connectors 
• Agent marketplace with 300+ pre-built AI agents
• Proven scalability, trusted by 400+ Fortune 2000 enterprises
• Consistently recognized as a leader by third-party analysts

Cons of Kore.ai: 

• Not suited for small and medium businesses
• The wide product suite may overwhelm teams without a clear onboarding plan and defined starting points.
• Given the number of integrations, some documentation, especially for newer connectors, is still catching up.
• With a broad template library, finding the right template can take a few extra clicks.

Overall verdict:

Enterprises looking to start small yet rapidly scale enterprise AI across CX, EX, and business processes will find Kore.ai as a trusted partner of choice. With advanced multi-agent orchestration, cloud and model-agnostic architecture, and a strong focus on AI governance, Kore.ai empowers organizations to deploy AI safely and responsibly at scale. Proven in large enterprise environments worldwide, it delivers the flexibility and full control leaders demand, while driving significantly lower total cost of ownership (TCO). 

Learn how Kore.ai can help you build and scale enterprise-grade AI solutions.

2. Glean: Ideal for knowledge discovery and search 

Glean is an “AI for work” platform built to improve enterprise knowledge discovery by unifying information scattered across emails, documents, chats, applications, and knowledge bases. By integrating with a wide range of business tools, it creates a single search experience that helps employees find relevant information quickly and work more efficiently. 

The platform offers contextual search, role-aware recommendations, natural-language question answering, and an assistant that can surface insights across the entire organization. What sets Glean apart is its intent understanding and permission-aware retrieval, which helps employees access the right information without compromising security.

That said, Glean remains more focused on knowledge discovery than on full end-to-end enterprise automation. In The Forrester Wave™: Cognitive Search Platforms, Q4 2025, Forrester noted that there are a few areas where Glean is still expanding. Specifically, the report points out that Glean’s connector ecosystem is narrower than some competitors, and that its tools for enriching indexed content with added context are comparatively lighter today. 

Forrester also reports that Glean has a smaller catalog of pre-built apps and packaged solutions, which may matter for organizations that want more out-of-the-box automation. Finally, the report notes that Glean needs to strengthen its scaling efficiency for broader and more specialized use cases.

Glean follows a user-based subscription pricing model, with plans that scale based on the number of users and required features. Enterprise tiers are available for organizations needing custom integrations, enhanced administration, or advanced security and compliance controls.

Key features of Glean:

• Unified enterprise search across documents, messages, tickets, and tools.
• A knowledge graph that maps people, content, activity, and permissions to deliver personalized, context-aware search results. 
• AI Assistant that answers questions in natural language and triggers workflow actions based on retrieved insights. 
• Multimodal ingestion, allowing the platform to index structured and unstructured data, from files and emails to databases and legacy systems. 
• Permissions-aware search and governance, so users only see information they are authorized to access.

Pros of Glean: 

• Strong enterprise search capabilities 
• AI assistance for summarization and Q&A
• Enterprise-grade security with compliance controls

Cons of Glean:

• Best for knowledge discovery, not full workflow automation or complex multi-step orchestration 
• The connector set is narrower than many of its competitors
• Fewer capabilities for enriching indexed data with further context
• Limited set of pre-built apps and off-the-shelf solutions
• Scaling efficiency for broader, specialized use cases is still improving

Overall verdict:

Though Glean is still developing agentic features, its core strength lies in search and information access, not in scaling AI agents across end-to-end enterprise use cases. If you’re looking for a platform that centralizes enterprise knowledge and makes information discovery easier, but not expected to have strong agentic capabilities to run complex workflows, Glean is a competent and well-designed solution.

3. Sierra AI: Ideal for customer service operations 

Founded in 2023, Sierra is a relatively young enterprise AI vendor that has gained visibility thanks to its well-known founders. The platform is designed to help organizations deploy AI-driven customer support that can handle transactional workflows such as refunds, account updates, subscription changes, and other routine service requests across digital channels. Its architecture supports integrations with CRM, ERP, and CX systems through tools such as Agent Studio and the Agent SDK.

From an enterprise AI perspective, Sierra is purpose-built for CX rather than enterprise-wide automation. Its capabilities are strongest in customer service scenarios, but it offers limited support for broader enterprise workflows such as IT support, HR operations, finance processes, or cross-department orchestration. Additionally, Sierraʼs single-agent model handles all tasks, which may make it harder to segment work across multiple specialized agents in very large or multi-domain deployments.

Sierra’s pricing model is also a consideration for enterprise adoption. It charges its customers on an “outcome-based” pricing model, which means you’re billed based on specific business results achieved by the agents. On paper, this sounds great because you pay for success, but in practice, there’s no clear definition of what an “outcome” is, and defining it can be tricky. This may lead to month-to-month cost fluctuations, billing disputes, and make budgeting more challenging.

Key features of Sierra AI:

• Goal-oriented agents that pursue specific business outcomes, such as retaining a customer or resolving a billing issue, rather than simply responding to queries. 
• Agent Data Platform (ADP) that stores long-term customer context for personalization and continuity. 
• Supports many LLMs, including models from OpenAI, Anthropic, and Meta. 
• Brand-level customization to match a company’s tone of voice, policies, decision logic, and operational workflows.

Pros of Sierra AI: 

• Strong focus on fully autonomous customer service 
• Multi-LLM support

Cons of Sierra AI:

• Complicated “outcome-based” pricing model
• Primarily focused on customer service use cases
• Single-agent model may be less suited to multi-domain deployments
• Enterprise success and stability are yet to be proven

Overall verdict:

Sierra is a good option for organizations that want to introduce autonomous customer service, especially for workflows like refunds, account updates, and subscription changes. It’s particularly well-suited to small and mid-sized teams that are comfortable adopting a newer platform and don’t require extensive enterprise-scale maturity. 

4. Moveworks: Ideal for employee support automation

Moveworks is an enterprise AI assistant platform built to modernize employee support. By integrating with core IT and HR business systems, it gives employees a single entry point to get help and complete routine tasks, without navigating multiple tools or raising support tickets. 

The platform delivers capabilities such as natural-language understanding, autonomous task execution,  and contextual reasoning. What sets Moveworks apart is its ability to interpret complex employee requests and execute that process across connected systems.

However, Moveworks remains firmly centred on employee support automation, with limited reach into customer-facing, back-office, or wider enterprise workflows. Even within employee support, Forrester, in its Forrester Wave™: Cognitive Search Platforms, Q4 2025, notes that its ability to understand and extract meaning from images and other visual formats is still evolving, which may affect how fully certain IT or HR issues can be resolved end-to-end without human help.

From a strategic perspective, enterprises should also consider the platform’s longer-term trajectory. Following ServiceNow’s acquisition (agreed to acquire) of Moveworks on March 10, 2025, organizations may want to monitor how the product roadmap, platform positioning, and commercial packaging evolve over time, especially for teams planning multi-year enterprise AI initiatives.

Key features of Moveworks: 

• Agentic reasoning engine that interprets employee intent and coordinates actions across IT and HR systems 
• Cross-system orchestration through a library of enterprise integrations 
• Contextual intelligence using signals such as role, department, location, and interaction history 
• Out-of-the-box deployment designed to accelerate time-to-value for employee support use cases

Pros of Moveworks: 

• Strong HR and IT support capabilities
• Proven with large companies 
• Strong set of data connectors

Cons of Moveworks:

• Less suited for customer-facing or back-end workflows
• Visual understanding is still maturing
• Customers cannot bring their own LLMs
• Roadmap may be uncertain post-acquisition

Overall verdict:

Moveworks is a strong fit for organizations that want to modernize HR and IT employee support with a ready-to-deploy AI assistant. It works best for enterprises that want focused EX automation and rapid deployment, but it may be less suitable for organizations planning broader, multi-domain enterprise AI initiatives that span CX, business processes, and complex cross-functional workflows.

‍

5. Decagon: Ideal for customer support automation

Founded in 2023, Decagon is an emerging customer support AI platform. It delivers fast, human-like interactions across chat, email, and voice, enabling organizations to resolve customer inquiries with speed and consistency. 

‍

One of Decagon’s distinguishing elements is its use of Agent Operating Procedures (AOPs), which allow support teams to define agent logic in natural language. This enables a blend of flexibility and control, where operational teams can outline the agent’s behavior while developers manage integrations, workflows, and guardrails. In an enterprise AI context, Decagon fits best as a CX-focused automation layer rather than a broad, cross-functional enterprise AI platform.

That said, Decagon is built around a single primary agent rather than a multi-agent system. This can be challenging as workflows become more complex; a single agent may need to handle many responsibilities at once, which can make it harder to split work into parallel tasks or scale consistently across functions and channels.

The platform is also currently more oriented toward technical teams than non-technical builders, with fewer no-code options like visual, drag-and-drop tools for citizen developers. Further, in many environments, Decagon is used alongside existing helpdesk or contact-center platforms rather than as a full CCaaS replacement, which can influence how organizations design their overall CX stack.

Key features of Decagon:

• Agent Operating Procedures (AOPs) that allow support teams to describe agent behavior in natural language. 
• Omnichannel customer resolution across chat, email, voice, and SMS 
• Integrations with CRM and support platforms such as Zendesk and Salesforce, along with API access to back-office systems. 
• Watchtower is a monitor and analytics tool that tracks both AI and human interactions.
• Model-agnostic architecture that supports multiple LLM providers.

Pros of Decagon: 

• Flexible agent design 
• Strong omnichannel support 
• Model-agnostic architecture
• Suitable for small and medium businesses 

Cons of Decagon:

• Built on a single agent
• Lacks multi-agent coordination or cross-platform collaboration
• Limited visual and drag-and-drop tools
• Typically used alongside existing CCaaS / helpdesk platforms 
• Not suitable for heavily-regulated large enterprises 
• Currently runs on cloud only, not on-premises or hybrid deployments 

Overall verdict:

Decagon is a good fit for small and medium-sized organizations that want to design a customer support agent using natural language while still maintaining developer-level control behind the scenes. Whereas for larger, heavily regulated enterprises, the lack of multi-agent coordination can make it harder to modularize complex CX workflows and maintain consistent oversight as automation expands.

6. Aisera: Ideal for service desk operations 

Aisera is an enterprise AI platform designed to automate service interactions and operational workflows across IT, HR, customer support, and other internal service functions. 

Aisera’s value centers on its ability to streamline high-volume service environments. Its pre-built workflows (“Hyperflows”) and domain-specific templates allow organizations to automate ticket resolution, handle employee queries, and improve customer support without relying solely on human agents. 

Though Aisera delivers strong out-of-the-box value for service desk automation, organizations should plan for a more time-intensive implementation. This is because configuration, tuning, and integrations often require sustained effort, and deeper tailoring may also depend on Aisera’s managed services team rather than being fully self-serve, which can extend onboarding timelines.

Also, Aisera tends to be most effective in service desk automation, particularly within IT and HR. While it does extend into CX and operational scenarios, those broader use cases are still developing compared to its core service workflows. 

Key features of Aisera:

• AI Agent System provides purpose-built agents that can coordinate decisions, orchestrate processes, and execute tasks across IT, HR, and customer support. 
• Strong security and governance ensure data protection and compliance with industry regulations.
• Agent Studio enables teams to create AI agents using low-code, no-code, and pro-code tools, supported by pre-built templates for faster setup. 
• AI Workflow Builder offers a library of pre-built actions, making it easy to construct and customize workflows.

Pros of Aisera: 

• Strong capabilities for IT and HR service desk operations 
• An extensive library of pre-built workflows 
• Deep integration ecosystem with major ITSM, HRIS, CRM, and collaboration tools

Cons of Aisera:

• Not suitable for broad use cases like customer support or process automation
• Longer onboarding timelines

Overall verdict:

If you’re looking for an agentic AI platform to modernize and automate service desk operations, Aisera is a strong option. Its pre-built workflows and domain-specific agents make it well-suited for high-volume IT and HR service environments.

7. NiCE Cognigy: Ideal for enhancing contact-center operations 

NiCE Cognigy is a contact-center-focused platform that has evolved its offering to support enterprise AI for customer experience. It enables organizations to build AI agents that can manage a wide range of customer inquiries, collaborate with human agents, and operate effectively across voice, messaging, social channels, and established contact-center platforms. 

For teams running multichannel customer service, Cognigy provides an intuitive interface and a strong set of action capabilities that make it straightforward to design and deploy customer-facing agents. 

However, Cognigy’s emphasis on simplicity means that certain advanced configuration options may be less extensive than those of some other no-code and pro-code platforms. Its flow-based architecture, which evolved from traditional conversational design patterns, can also feel less flexible when teams are building very specialized or highly dynamic agent behaviors.

More broadly, now that Cognigy is part of NiCE, the roadmap may be uncertain, and some organizations may want to watch how the product settles over time. Gartner also, in its 2025 Gartner® Magic Quadrant™ report, notes that Cognigy’s strategy tends to prioritize near-term commercial execution over a longer-range product vision, which is worth keeping in mind for teams planning multi-year agentic CX programs.

Key features of NiCE Cognigy:

• Multilingual AI agents supporting more than 100 languages, allowing organizations to deliver consistent customer experiences across global markets. 
• Visual conversation flow builder with a node-based interface, making it easier for teams to design and refine conversational journeys without heavy coding. 
• Role-based access and governance controls that help enterprises manage permissions, workflows, and compliance requirements. 
• Flexible deployment options, including on-premises or private cloud, to meet varying security and infrastructure needs.
• Prebuilt omnichannel connectors for platforms such as Slack, WhatsApp, and web chat.

Pros of Cognigy: 

• Suitable for CX operations
• Robust enterprise compliance 
• Broad omnichannel support 
• Accessible visual builder 
• Extensive integration ecosystem

Cons of Cognigy:

• Flow-based architecture can feel rigid for highly dynamic use cases 
• Limited to no support for employee productivity and process intelligence use cases
• Higher learning curve for very deep customizations 
• Roadmap may be uncertain post-acquisition
• Limited long-range product vision

Overall verdict:

NiCE Cognigy is a solid choice for organizations aiming to enhance their contact-center operations with agentic AI, offering omnichannel capabilities. While some advanced configurations may take time to master, NiCE Cognigy provides a capable and mature platform for teams looking to modernize customer interactions with AI-driven workflows.

How to choose an enterprise AI platform

Choosing the right enterprise AI platform is critical, especially as organizations shift from building isolated pilots to running AI at scale across departments. Below are the core capabilities enterprises should prioritize when evaluating any enterprise AI platform.

1. Breadth of supported use cases 

A strong enterprise AI platform should support more than one function (for example, not just CX or just IT). Look for a platform that can extend across departments without forcing you into separate tools or architectures. 

Why it matters: Most enterprises don’t want a new AI stack for every team.

2. Deep enterprise integrations 

The platform must connect seamlessly with core business systems and provide deep integrations with systems such as CRM, ERP, ITSM, HRIS, data warehouses, communication tools, and legacy applications. This includes pre-built connectors as well as open APIs for custom integrations. 

Why it matters: AI is only as strong as the systems it can act on. 

3. Governance, safety & observability 

Enterprise-grade security and governance are non-negotiable. Look for role-based access controls (RBAC), audit logs, policy enforcement, decision tracing, and monitoring dashboards that provide transparency into how agents behave. 

Why it matters: Governance makes AI safe, predictable, and compliant, especially when deploying at scale across regulated industries. 

4. Human-in-the-loop controls 

Even advanced automation needs clear escalation paths. Platforms should allow humans to review, approve, or intervene in decisions when needed, with seamless handoffs that maintain context. 

Why it matters: Enterprises require a balance between autonomy and oversight, especially for high-risk or exception-heavy processes. 

5. Flexible AI model support 

The platform should support multiple LLMs and reasoning engines, and ideally allow enterprises to bring their own models. This enables cost optimization, model modularity, and future-proofing. 

Why it matters: Relying on a single model limits adaptability and increases long-term vendor lock-in. 

6. Orchestration across workflows

Beyond answering questions, the platform should support orchestration across tools and processes, such as routing, approvals, actions, and multi-step workflows that span systems.

Why it matters: Real business value comes from completing work, not just generating responses.

7. Enterprise security & compliance 

Look for encryption, data-isolation controls, compliance certifications (GDPR, HIPAA, SOC2, etc.), and secure handling of sensitive data across systems and integrations. 

Why it matters: Agents often interact with sensitive records, such as customer data, financial details, and internal documents, which must be protected at all times. 

8. Scalable architecture 

The platform should support running dozens, or even hundreds, of agents in parallel without performance drops. This includes autoscaling, multi-region deployment, and consistent throughput under heavy load. 

Why it matters: Today’s pilot becomes tomorrow’s global rollout. Scalability is how enterprises avoid re-platforming in two years.

Conclusion: The right enterprise AI platform for you

Choosing an enterprise AI platform comes down to how far you plan to take AI across the business over the next 24–36 months. Before you pick a platform, get clear on a few fundamentals: 

• Current and future use cases: Are you solving one narrow problem today, or planning to expand into multiple workflows and business functions? 
• Scale and reliability: Can the platform handle growing volumes, new regions, and more complex journeys without breaking or slowing down? 
• Flexibility across models, channels, and deployment: Do you need to bring your own models, support new channels quickly, or run in specific cloud or hybrid environments? 
• Governance, security, and guardrails: Especially in enterprise settings, can you trace decisions, enforce policies, and manage risk confidently? 
• Commercial fit: Does the pricing model align with your rollout pace and long-term operating costs? 
• Validation and maturity: What proof points exist, customer outcomes, analyst coverage, or large-scale deployments that reduce adoption risk?

Each platform in this list is a top-tier platform in its respective domain. For instance, for knowledge discovery, Glean is well-suited, while Sierra, Decagon, and Cognigy align more with customer service automation, and Moveworks and Aisera are best-suited for internal service environments. If your goal is to solve a specific function, then the specialized platforms in this guide can deliver strong results within their respective domains.

But if your organization needs a true enterprise AI foundation, that can support multiple AI solutions across CX, EX, and operations, integrate deeply with core systems, and provide the governance required for safe large-scale deployment, Kore.ai stands out as the most comprehensive option. It offers:

• Broadest sets of agentic use cases across customer experience, employee experience, and operational automation
• Multi-agent orchestration that coordinates tasks across teams, systems, and environments
• Model, cloud, and data agnostic platform
• Plug-and-play enterprise integrations built for complex environments 
• Mature governance and security frameworks 
• Flexible pricing (Request-based, session-based, per-seat, or pay-as-you-go pricing structures)
• Proven scalability, trusted by 400+ Fortune 2000 enterprises
• Recognized as a leader by third-party analysts

If you want a platform that can evolve with your organization, supporting new use cases, new departments, and expanding AI maturity, Kore.ai offers the most future-ready foundation.

Ready to see how Kore.ai can support enterprise AI scale? Schedule a custom demo. 

Not ready yet? Explore our resources section to learn more about enterprise AI platforms and best practices. 

FAQs

Q1. What is an enterprise AI platform? 

An enterprise AI platform provides the infrastructure to build, deploy, secure, and scale AI across the business. It typically combines integrations, orchestration, governance, observability, and tooling to operationalize AI solutions across CX, EX, and core operations. 

Q2. How do I choose the right enteprise AI platform for my organization? 

Start by identifying whether your needs are function-specific (IT, HR, CX, search) or enterprise-wide AI adoption. Then evaluate governance, integration depth, multi-agent orchestration, deployment flexibility, long-term scalability, and whether costs stay predictable as usage grows. 

Q3. When should a company choose Kore.ai?

Kore.ai is ideal for enterprises that need need a broad enterprise AI foundation to operationalize AI across CX, EX, and business processes, multi-agent orchestration, enterprise-grade AI governance, 300+ deep integrations, 250+ pre-built agents, flexible and transparent pricing models, and proven scalability across global enterprises. 

Q4. When should a company choose Aisera? 

Aisera is ideal if your primary focus is on IT and HR service desk automation. Its pre-built workflows and domain-trained models offer strong value for high-volume internal service environments. 

Q5. When should a company choose Glean?

Glean excels in knowledge discovery. If your challenge is information fragmentation across email, chat, documents, and apps, Glean provides permission-aware search and intelligent retrieval, rather than broad operational automation.

Q6. When should a company choose Sierra?

Sierra is best for customer service operations. It specializes in transactional CX workflows like refunds, billing adjustments, and account management, but does not yet cover wide enterprise automation. 

Q7. When should a company choose Moveworks?

Moveworks is well-suited for organizations prioritizing employee support. Its AI assistant helps resolve IT and HR issues quickly through deep integrations with internal systems. 

Q8.  When should a company choose Decagon? 

Decagon is a strong fit for teams wanting flexible, natural-language-driven customer service automation. It’s particularly useful for omnichannel CX environments where speed and consistency are critical. 

Q9. When should a company choose Cognigy? 

Cognigy is ideal for enhancing contact-center operations. It offers powerful omnichannel capabilities and a visual builder suited for CX teams that want to modernize customer interactions.

Q10. What is the future of enterprise AI platforms? 

Enterprise AI platforms are converging toward end-to-end “AI operating layers” that combine search, copilots, automation, and agentic execution with governance built in. Expect more multi-agent collaboration, deeper system-level integrations, stronger observability and risk controls, and more modular support for different models and deployment environments.

‍

(Legal disclaimer: The content in this guide is intended solely for general information and does not constitute professional, legal, financial, or procurement advice. All assessments are based on publicly available materials and customer-visible product information. Any mention of competitor limitations is for comparative context, not disparagement.

As vendor products evolve rapidly, details may become outdated. Kore.ai makes no representations or warranties regarding the completeness or accuracy of competitor information, and no party should rely on this article as the sole basis for a purchasing decision.)