Across the SaaS landscape, a quiet evolution is underway. Companies that once sprinkled AI features into their products are now rebuilding their foundations around it. The ‘AI-native’ shift is no longer just about adding intelligent assistants; it is about rethinking what software can autonomously decide, create and optimise.
By 2026, SaaS will no longer be solely about service delivery. It will be about intelligence delivery. The world’s biggest software platforms are now positioning themselves to lead this new phase, one that blends automation, prediction and personalisation at the core layer.
Here are some of the seven SaaS platforms potentially going all-in on AI-native transformation next year.
1. Salesforce
Salesforce’s Einstein platform has evolved into a larger strategy that connects customer data to generative and predictive models across the CRM stack. The aim is to move beyond suggestions to prescriptive actions that can automate outreach, routing and next-best actions inside workflows.
“We’re already seeing 30% productivity improvements, but it’s not just about code generation,” said Muralidhar Krishnaprasad, president and CTO of Salesforce, earlier in an exclusive interview with AIM, adding that the company is also using AI for test case generation.
Salesforce is also set to acquire Informatica for $8 billion, integrating the company’s AI-powered enterprise cloud data management capabilities, including data integration, governance, cataloguing and MDM. This will strengthen Salesforce’s trusted data foundation and power scalable, safe, and responsible AI across modern enterprises.
2. Atlassian
Atlassian Intelligence, which incorporates AI features, is now integrated into Jira, Confluence and other cloud products, offering drafting, summarisation and contextual prompts that reduce friction in everyday workflows. The firm has made these features generally available and lists AI improvements on its cloud roadmap.
In India, Atlassian's R&D efforts are driving a new era of innovation, with a focus on developing AI-powered solutions. These include AI-driven service agents, advanced incident management tools and consumption-based billing platforms, fundamentally changing enterprise SaaS operations in an AI-first environment.
Moreover, Atlassian is currently testing Atlassian Studio, a low-code/no-code platform for agent development, designed to enable even users without engineering expertise to create AI agents.
3. HubSpot
HubSpot began rolling out Content Assistant and ChatSpot to integrate AI into marketing and sales workflows. More recently, the company promoted Breeze, an assistant that utilises CRM data and external signals to prepare meeting briefs, generate content and surface strategic insights.
HubSpot’s approach is orchestration, tying content generation, CRM context and analytics together so small and mid-market teams can personalise at scale without stitching multiple point tools. That integration is what could make AI genuinely operational for growth teams relying on HubSpot.
4. Notion
Notion has layered AI features into documents, meeting notes and databases. Summarisation, autofill, and flowchart generation are all offered as native capabilities aimed at turning the workspace into an active knowledge assistant.
The practical result is improved discoverability and reduced cognitive load; teams can extract action items, surface past decisions and spin up project plans from existing notes, making knowledge a live, usable asset rather than static text.
Users are now running their entire workflow with the help of the Notion AI agent. Confluent, at its core, helps Notion scale the AI features.
5. ServiceNow
ServiceNow is setting its Now Assist and agentic AI as part of a platform strategy to embed generative intelligence across IT, HR and customer workflows. The company has strengthened its partnerships with NVIDIA and Hugging Face, contributing to the development of open models.
It is now positioning itself as an AI company by building its own open models, including Apriel 2.0 co-developed with NVIDIA, and embedding multimodal, reasoning-driven intelligence directly into enterprise workflows. Its platform now powers autonomous agents, smarter infrastructure and AI-driven operations across industries, from retail to public services.
With its NVIDIA partnership, Apriel 2.0 launch and strong financials, ServiceNow is signalling what the next phase of enterprise AI looks like: smaller models, bigger results and no waiting around.
At this rate, the only question left is when ServiceNow reaches the $1 billion AI milestone. It might happen faster than anyone expects.
6. Zoho
Zoho has been integrating its in-house LLM, Zia, across its products. Some of the new capabilities follow the launch of Zia Hubs and Zia LLM, Zoho’s proprietary large language model designed for B2B use.
Together, these additions extend Zoho’s AI strategy by making agentic capabilities directly accessible across its ecosystem of over 55 business applications, according to the company.
Because Zoho runs a tightly integrated product portfolio, Zia can act across apps (CRM, finance, workplace) with shared context, a practical operational advantage for customers who want AI to operate cross-product rather than as an isolated feature.
7. Canva
Canva is going AI-native by embedding intelligence across every step of the design journey, with over 13 billion AI feature uses. Its strategy blends in-house models, partnerships with leaders like OpenAI and RunwayML and a growing developer ecosystem, enhancing creativity while keeping human-led design at the centre.
Canva’s Magic Studio, including Magic Design, Magic Media and Brand Kit automation, and Creative OS have turned generative design into a core product capability, enabling rapid generation of templates, videos and brand assets from text prompts. That suite has been expanded through various upgrades to improve the quality of image and video generation.
Canva’s momentum is visible in product milestones. Magic Studio is positioned both for individuals and enterprise teams to scale creative work, shifting labour from manual design to prompt-driven composition and iteration.
The Road Ahead
The next generation of SaaS will likely blur the line between tool and teammate. AI-native platforms won’t just support work, they will participate in it. As models are embedded deeper into product cores, the measure of success will shift from user activity to outcome autonomy.
For software builders, this marks the beginning of a new discipline where they will create systems that can reason, adapt, and self-improve. SaaS, as it seems, is quietly writing its next chapter in the language of intelligence.
Note: The list is in no particular order of ranking and is randomly based on some of the significant AI-focused developments.
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