The Rise and Reality of Software-as-Service Startups: Replace or embrace incumbents like ServiceNow, Salesforce, Oracle?

In recent years, we've witnessed a surge of software-as-service (SaS) startups boldly claiming to be the faster, cheaper, and better alternatives to industry giants like ServiceNow , Salesforce , and 甲骨文 . These ambitious newcomers are eager to have their own " Salesforce - Siebel Systems moment," referring to how Salesforce disrupted Siebel Systems in the early 2000s. However, the landscape of enterprise software has evolved significantly since then, and these startups may soon face a harsh reality check.

The Entrenched Giants and AI Integration

ServiceNow, Salesforce, and Oracle have not merely rested on their laurels. They've strategically designed their products to become indispensable systems of record for enterprises, while also integrating advanced AI capabilities. These industry leaders are leveraging multimodal AI agents to enhance their offerings, providing more intelligent and context-aware solutions. For instance, Salesforce with Agentforce has incorporated AI-driven workflow automation that can handle complex, multi-step processes across various departments, significantly improving efficiency and user experience.

Once a company integrates these AI-enhanced solutions into their core operations, the idea of migrating away becomes increasingly daunting. The risk associated with data migration in today's data-driven world is simply too high for most organizations to consider, regardless of the potential benefits offered by newcomers.

The AI Promise and Its Limitations

Many startups are leveraging artificial intelligence, particularly multimodal AI agents, as their unique selling proposition. While AI undoubtedly offers exciting possibilities, it's not a silver bullet for displacing established players. The value of enterprise software often lies in its ability to handle complex, organization-specific processes and vast amounts of historical data – areas where the giants have a significant head start.

Multimodal AI agents, which can process and integrate multiple forms of data such as text, images, and audio, are becoming increasingly important in enterprise software. These agents can enhance decision-making, automate complex workflows, and improve operational efficiency across various industries. However, implementing such advanced AI systems requires substantial resources and expertise, which established players are better positioned to provide. As an instance, ServiceNow has unveiled thousands of pre-configured AI agents designed for use cases across customer service, IT, HR and other core enterprise functions, which can be accessed via their Now platform.

The Lock-In Effect and AI-Enhanced Ecosystems

The "lock-in" effect is a powerful force in enterprise software, now further reinforced by AI-driven integrations. It's not just about the technical challenges of migrating data; it's also about the organizational inertia, the training invested in current AI-enhanced systems, and the ecosystem of integrations and customizations built around these platforms. Breaking free from this ecosystem is a monumental task that few enterprises are willing to undertake without compelling reasons.

Moreover, industry giants are developing AI agentic workflows that leverage intelligent agents to automate tasks requiring decision-making, problem-solving, and adaptability. These workflows can significantly improve efficiency and reduce human error, making the existing systems even more valuable to enterprises. For example, Oracle announced new AI agents for Oracle Cloud ERP that help customers work more efficiently by fully automating end-to-end business processes.

Alternative Strategies for Startups

Instead of attempting to replace these giants outright, startups might consider alternative strategies that leverage AI and multimodal data:

1. Target underserved markets: Focus on markets where the established players are prohibitively expensive or where implementation consultants are scarce. Startups can develop specialized AI agents and workflow automation solutions tailored to these niche markets.

2. Build on top of giants: Rather than trying to replace established platforms, startups could develop AI-powered solutions that enhance or extend the capabilities of existing systems. This approach allows them to leverage the distribution channels and customer base of the giants while still providing innovative value through advanced AI technologies.

3. Strategic partnerships: By partnering with established players, startups can position themselves as value-added solutions rather than direct competitors. This strategy can turn potential adversaries into powerful allies and force multipliers for growth, especially in areas like AI-driven workflow automation and multimodal data processing.

The Power of AI-Enhanced Ecosystems

The enterprise software landscape has evolved into a complex ecosystem where interoperability, integration, and AI capabilities are key. Startups that recognize this reality and position themselves as complementary rather than competitive solutions may find more success. By enhancing rather than replacing existing systems with advanced AI agents and multimodal data processing capabilities, they can tap into established customer bases and benefit from the trust and stability associated with industry leaders.

Conclusion

While the allure of disrupting industry giants is strong, the reality of enterprise software adoption presents significant challenges for startups, especially in the age of AI and multimodal data processing. The path to success may not lie in direct competition but in finding innovative ways to coexist and add value within the existing ecosystem. By focusing on niche markets, building AI-enhanced solutions on top of established platforms, or forming strategic partnerships, startups can carve out their own space in the enterprise software world without the need for a head-on collision with entrenched players.

As the enterprise software landscape continues to evolve with AI agents and multimodal data processing at its core, the most successful startups will likely be those that can navigate the complex relationships between established systems and emerging technologies. The future may not be about replacing the giants, but about creating a more diverse, interconnected, and innovative ecosystem that leverages AI to benefit all stakeholders in the enterprise software space.

References and disclaimer

[1] Multimodal AI Agents: Reimaging Human-Computer Interaction https://www.akira.ai/blog/ai-agents-with-multimodal-models

[2] AI Workflow Automation: What is it and How Does It Work? https://www.moveworks.com/us/en/resources/blog/what-is-ai-workflow-automation-impacts-business-processes

[3] Top 7 Platforms to Build Multimodal AI Agents in 2025 - Creole Studios https://www.creolestudios.com/top-platforms-to-build-multimodal-ai-agents/

[4] AI Agents Explained: The Future of Task Automation and Productivity https://www.ciklum.com/resources/blog/ai-agents-explained

[5] AI Agentic Workflows: A Guide to Automate Businesses - Intuz https://www.intuz.com/blog/ai-agent-workflows-across-industries

[6] The Rise of Multimodal AI Agents: Redefining Intelligent Systems https://www.xenonstack.com/blog/multimodal-ai-agents

[7] Automation, AI Workflows, or AI Agents? Key Considerations https://www.virtasant.com/ai-today/automation-ai-workflows-or-ai-agents-key-considerations

[8] What Are AI Agentic Workflows & How to Implement Them https://www.multimodal.dev/post/ai-agentic-workflows

[9] Google: AI agents, multimodal AI, enterprise search will dominate in ... https://venturebeat.com/ai/google-ai-agents-multimodal-ai-enterprise-search-will-dominate-in-2025/

[10] Bringing AI Agents to Enterprises with Google Agentspace https://cloud.google.com/blog/products/ai-machine-learning/bringing-ai-agents-to-enterprises-with-google-agentspace?e=48754805

[11] 7 Types of AI Agents to Automate Your Workflows in 2025 https://www.digitalocean.com/resources/articles/types-of-ai-agents

[12] Multimodal AI: Revolutionizing Data Integration - SmythOS https://smythos.com/ai-integrations/tool-usage/multimodal-ai/

[13] Multimodal | Automation Platform for Complex Workflows https://www.multimodal.dev

[14] AI Agents - Workflow Tool : r/PromptEngineering - Reddit https://www.reddit.com/r/PromptEngineering/comments/1fnesq6/ai_agents_workflow_tool/

[15] 13 Types of AI Agents (with Examples) - Multimodal.dev https://www.multimodal.dev/post/13-types-of-ai-agents

[16] AI Agents - Qdrant https://qdrant.tech/ai-agents/

[17] Understanding AI Agents & Agentic Workflows | Dataiku https://www.dataiku.com/stories/detail/ai-agents/

[18] Build Multimodal AI Agents with Dataloop and MongoDB to ... https://dataloop.ai/blog/build-multimodal-ai-agents-with-dataloop-and-mongodb-to-empower-smarter-customer-service/

[19] Customize and automate data pipelines with AI agents - Encord https://encord.com/ai-workflows-agents/

[20] How are you leveraging Ai agents to automation and marketing and ... https://www.reddit.com/r/AI_Agents/comments/1hcj719/how_are_you_leveraging_ai_agents_to_automation/

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