{"@attributes":{"version":"2.0"},"channel":{"title":"Chaoming's Blog","link":"https:\/\/chaoming.li\/","description":"Recent content on Chaoming's Blog","generator":"Hugo -- gohugo.io","language":"en-us","lastBuildDate":"Mon, 05 Jan 2026 00:00:00 +0000","item":[{"title":"AI in SaaS in 2026: A Founder's Perspective","link":"https:\/\/chaoming.li\/blog\/ai-in-saas-in-2026-a-founders-perspective\/","pubDate":"Mon, 05 Jan 2026 00:00:00 +0000","guid":"https:\/\/chaoming.li\/blog\/ai-in-saas-in-2026-a-founders-perspective\/","description":"\"Featured

2025 was a breakout year for AI in my workflow. I started using AI for coding assistance in 2024, but by 2025 it was no longer just generating snippets. It began shaping architecture decisions, accelerating execution, and helping move real projects forward.<\/p>\n

That shift naturally leads to a broader question: what does AI mean for SaaS products in 2026?<\/strong><\/p>\n

Below are my perspectives as a founder building and using SaaS every day.<\/p>\n

From answers to actions\n<\/h2>

AI in SaaS started the same way AI in coding did. You ask a question and get an answer. Early copilots focused on explanations, suggestions, and surface-level assistance.<\/p>\n

Coding tools did not stop there. They evolved into systems that draft code, wire components together, generate tests, open pull requests, and iterate alongside developers. SaaS is following the same trajectory.<\/p>\n

In 2026, the most valuable AI copilots will not simply explain how to use a product. They will take action on behalf of the user, guided by intent and constrained by clear controls.<\/p>\n

Consider a digital marketer who wants to compare mobile versus desktop performance for a Boxing Day campaign. Today, this usually means navigating multiple reports, applying filters, selecting segments, and exporting charts. Instead, the user should be able to say:<\/p>\n

\n

\u201cCompare mobile vs desktop performance for Boxing Day. Explain what changed and why.\u201d<\/p>\n<\/blockquote>\n

The agent builds the report, applies the correct filters, pulls the relevant data, and summarizes the outcome.<\/p>\n

This direction is already visible in the market. Gartner predicts that 40 percent of enterprise applications will include task-specific AI agents by 2026, up from single-digit adoption today. Leading developer tools show that moving from suggestion to execution dramatically increases daily usage and stickiness. The shift is not about novelty. It is about reducing time-to-outcome.<\/p>\n

Trust and approval become the real bottleneck\n<\/h2>

As AI agents gain the ability to act, trust becomes the central product challenge. Users will want to understand what the agent is doing, why it is doing it, what data it used, and which actions require approval.<\/p>\n

From a product standpoint, fully opaque agents will struggle to gain adoption in serious B2B software. This is not only a UX concern. Governance and regulation are already reinforcing this expectation.<\/p>\n

The NIST AI Risk Management Framework emphasizes transparency, explainability, and human oversight as core principles for trustworthy AI. Regulatory efforts such as the EU AI Act further highlight the importance of human oversight for systems that can materially affect outcomes.<\/p>\n

In practice, the winning pattern will look less like a generic chat window and more like an embedded copilot. One that operates inside the product UI, shows intermediate steps, previews changes, and asks for confirmation when the stakes are high. Trust will be earned through visibility.<\/p>\n

Personalization is how agents become genuinely useful\n<\/h2>

An agent that treats every user the same will feel shallow very quickly. In B2B SaaS especially, usefulness depends on context.<\/p>\n

That context includes company goals and KPIs, the user\u2019s role and seniority, permissions and risk tolerance, and preferred level of detail. A CFO and a marketing analyst can look at the same dataset and expect entirely different assistance. One wants summarized impact. The other wants drill-downs and anomalies.<\/p>\n

Agents only begin to feel intelligent when they adapt to the person, not just the data. This is less about model capability and more about thoughtful product design.<\/p>\n

Why AI agents in SaaS become non-optional\n<\/h2>

The strongest case for agents is not hype. It is productivity.<\/p>\n

When AI is embedded directly into workflows, the gains are measurable. GitHub has reported that developers using Copilot completed certain tasks roughly 55 percent faster in controlled experiments. In a real customer support environment, a well-cited field study found approximately 14 percent higher productivity, with even larger gains among less experienced workers. Research in consulting and knowledge work shows similar patterns, while also highlighting that poor integration can reduce quality.<\/p>\n

Translated into SaaS terms, this has several practical implications.<\/p>\n