Agentic AI Comparison:
Make AI vs RPA AI

Make AI: 8

Make AI provides agentic automation capabilities where users can build AI agents that orchestrate processes across 3,000+ apps, combining adaptive AI decision-making with deterministic automation on a single visual canvas. These agents can gather information, prompt AI models, and direct outputs with minimal ongoing human intervention, especially in use cases like marketing operations, content research, and content publishing. Because the platform is designed for autonomous workflows that react to triggers and data across multiple tools, and because it supports AI-driven branching and decision logic, it offers a high degree of autonomy in real-world business scenarios. However, autonomy is typically constrained by the scope of app integrations and by business users’ configurations, and it is less focused on deep process mining or enterprise-wide orchestration than traditional RPA+AI platforms, which justifies a score of 8 rather than 9–10.

RPA AI: 9

RPA AI platforms like UiPath and IBM AI for RPA combine deterministic software robots with AI agents and machine learning models, enabling both fully unattended and attended automations for complex, end-to-end enterprise processes. UiPath explicitly positions its platform as one where RPA robots handle predictable, rules-based tasks, while agents and AI components manage ambiguous work (unstructured documents, exceptions, and context-dependent decisions), all orchestrated centrally. AI Center and similar components allow deployment, management, and continuous improvement of ML models that are embedded directly into workflows via drag-and-drop, enabling bots to operate autonomously on unstructured data and to adapt over time. Enterprise-grade orchestration, long-running workflow support, and integration with case management and human-in-the-loop steps extend autonomy across entire business processes, including cross-departmental and cross-system flows. Given this deep focus on enterprise-scale, end-to-end autonomy and AI-enhanced decision-making, RPA AI merits a 9 on autonomy, slightly higher than Make AI’s more app-centric scope.

Make AI: 9

Make AI emphasizes a visual, drag-and-drop, no-code/low-code environment where users build workflows and AI agents directly in a canvas connecting thousands of pre-built apps. Its marketing stresses that individuals can design, build, and automate in just a few clicks, and that automation and AI are built in a visual-first interface accommodating non-technical users. Use cases like marketing automation and content creation demonstrate that domain experts can configure end-to-end AI-enhanced flows without writing code, including triggering AI models and handling outputs. This design substantially reduces barriers for business users relative to traditional enterprise automation platforms, justifying a very high ease-of-use score. It does, however, still require users to understand workflow logic and data flows, so a score of 9 reflects that there is some learning curve but it is minimized compared to heavier RPA suites.

RPA AI: 7

RPA AI platforms such as UiPath and IBM AI for RPA have made deliberate efforts to improve usability, offering drag-and-drop interfaces in tools like UiPath Studio and pre-built AI models and templates in AI Center so that users can integrate AI with automations without being data scientists. UiPath highlights drag-and-drop insertion of ML models into workflows and availability of pre-built models to simplify AI integration. However, these platforms still target complex enterprise environments and often require more technical expertise (e.g., understanding selectors, orchestrations, infrastructure, governance) than typical no-code SaaS automation tools. Training academies and certifications from RPA vendors underscore the skill depth involved. As a result, usability is strong relative to other enterprise tools but more demanding than Make AI’s visual SaaS approach, yielding a balanced score of 7.

Make AI: 8

Make AI is highly flexible for SaaS and API-based automation: it connects over 3,000 pre-built apps and supports workflows that span marketing, HR, sales, IT, finance, operations, and more. Users can integrate multiple AI models, combine them with deterministic automation steps, and orchestrate complex multi-app scenarios, including AI-driven content generation, lead processing, and analytics-driven triggers. The platform allows building agents, standard workflows, and event-driven processes, and can be configured via prompt-building, drag-and-drop, or code where needed. This breadth of integrations and configuration modes provides strong flexibility for cloud-native and SaaS-centric organizations. However, compared with enterprise RPA, Make AI’s flexibility is more constrained around UI automation over legacy desktop systems, mainframes, or highly regulated on-prem environments that RPA traditionally excels at, which is why it scores 8 instead of 9–10.

RPA AI: 9

RPA AI platforms are designed for broad enterprise flexibility: they automate via both UI and API, integrate with legacy applications, web apps, desktop apps, and modern cloud services, and combine RPA with AI models for documents, vision, and language. UiPath’s Business Automation Platform emphasizes that it runs deterministic RPA and AI agents on one platform, letting teams choose the right tool for each work type and orchestrating both via a unified control plane. AI Center and similar components support bringing your own ML models, using vendor-provided models, or third-party models, and provide MLOps capabilities for deployment and lifecycle management, extending flexibility across data science and IT operations teams. These platforms also often integrate with broader ecosystems (e.g., IBM’s AI and cloud services), enabling hybrid and multi-cloud deployments and supporting sophisticated governance and compliance requirements. This combination of environment coverage (legacy to cloud), AI model flexibility, and enterprise orchestration justifies a high flexibility score of 9.

Make AI: 8

Make AI is delivered as a SaaS AI automation platform with tiered pricing and a focus on enabling organizations to design, build, and automate workflows in just a few clicks. Its positioning targets a broad user base—from individuals and SMBs to enterprise teams—suggesting relatively accessible pricing and lower total cost of ownership compared to heavy enterprise RPA deployments, especially when factoring in infrastructure and specialized staffing needs. The visual no-code environment reduces development and maintenance costs since business users can build and adjust workflows without extensive engineering resources. However, as automations scale and as AI usage increases, SaaS subscription and usage-based pricing may increase, and some advanced enterprise features could require higher tiers, so it is not at the absolute lowest cost across all scenarios, warranting a strong but not perfect cost score of 8.

RPA AI: 6

RPA AI platforms like UiPath and IBM AI for RPA provide substantial value at scale but typically come with higher initial and ongoing costs relative to lightweight SaaS automation tools. Licensing for enterprise RPA, AI components (such as AI Center), orchestration servers, and governance tools is usually structured for medium to large enterprises and can be significant, particularly when deploying hundreds or thousands of bots. These solutions also often require investments in infrastructure (on-prem or cloud), specialized implementation teams, and ongoing maintenance and model management, all of which increase total cost of ownership. While the platforms can deliver strong ROI by automating high-volume processes, the entry cost and complexity are usually higher than Make AI’s SaaS-based approach, so a cost score of 6 reflects good value at scale but relatively higher financial and organizational investment.

Make AI: 7

Make AI is described as a leading AI automation platform, trusted by over 400,000 organizations across 200+ countries, reflecting significant adoption and brand recognition in the no-code automation and AI workflow space. It is especially popular among digitally native businesses, marketing and operations teams, and SMBs that need rapid automation of SaaS apps and AI tools. However, in the broader automation market—particularly when compared with long-established enterprise RPA vendors—Make AI is still relatively younger and more concentrated in certain segments and use cases. This yields a solid popularity score of 7, acknowledging strong traction but not the same depth of enterprise penetration as leading RPA vendors.

RPA AI: 9

RPA AI, as represented by vendors such as UiPath and IBM, is part of the widely adopted enterprise RPA market, which has become a standard approach for automating back-office and operational processes in large organizations worldwide. UiPath in particular is frequently cited as a market leader in RPA and business automation, with extensive global enterprise deployments, a large partner ecosystem, and a substantial training community through its academy and certifications. IBM’s AI for RPA is integrated into its broader AI and automation portfolio, benefiting from IBM’s long-standing enterprise presence. These factors point to very high popularity and market penetration in the enterprise segment, which justifies a popularity score of 9.