For two decades, SaaS has been the default shape of business software: a subscription, a dashboard, a workflow, and a human doing the work inside it. AI-native software flips that model. Instead of selling you tools, it sells you progress: drafts written, tickets triaged, reconciliations matched, campaigns built, meetings actionised.
That change is happening faster than most SaaS companies planned for because it’s not a “feature upgrade”. It’s a new interface for work itself, and it collapses time, headcount, and friction in places where SaaS has always been weakest.
The economic driver: AI spend has moved from pilot to baseline
When budgets shift from experimentation to baseline capability, adoption accelerates even without hype. Gartner forecast worldwide generative AI spending to reach $644bn in 2025, up 76.4% from 2024. That kind of spend doesn’t land purely in research labs. It lands in product roadmaps, procurement checklists, and the everyday software stack.
At the same time, McKinsey’s 2024 survey reported overall AI adoption jumped to 72% of organisations. Once AI becomes normal, the winning products are the ones designed around it, not the ones bolting it onto an older UX.
SaaS was built for workflows. AI-native is built for outcomes
Traditional SaaS assumes a human operator. The product structures work into steps, the user completes the steps, and the system records the result. That’s fine when labour is cheap, attention is plentiful, and the work is mostly deterministic.
AI-native software is designed for the opposite: scarce attention, messy inputs, and constant exceptions. It treats the product as a system that acts, not merely a system that stores. That’s why newer models are converging on agentic patterns and outcome-based delivery, where users supervise rather than execute.
Gartner expects 33% of enterprise software applications will include agentic AI by 2028, up from less than 1% in 2024, and that 15% of day-to-day work decisions will be made autonomously by 2028. Even allowing for typical forecast error, directionally it explains the speed: software is moving from “workflow tools” to “work itself”.
Developer productivity has changed the shipping cadence
AI-native products are arriving quickly because building software has become faster.
A Microsoft Research controlled study found developers using GitHub Copilot completed a coding task 55.8% faster than a control group. McKinsey estimated generative AI could deliver 20–45% productivity impact in software engineering by reducing time spent on activities like drafting, refactoring, and root-cause analysis.
This matters competitively. If a new entrant can ship twice as many iterations per quarter, it can outpace an incumbent even with fewer engineers, then reinvest the savings into model quality, data pipelines, and integrations. That’s the compounding effect behind “faster than expected”.
AI-native products win because they reduce cognitive load, not clicks
SaaS products often optimise for configuration: more options, more dashboards, more permissions, more reports. AI-native products win by removing decisions a user never wanted to make in the first place.
GitHub’s research on Copilot found strong self-reported benefits around staying “in flow” and reducing mental effort on repetitive tasks. That same dynamic applies across business software. If an AI-native tool can draft the first version, classify the work, propose next steps, and surface exceptions, the user stops living in tabs and starts supervising outcomes.
The practical replacement mechanism is not “AI is smarter”. It’s “AI saves attention”. Attention is the real constraint in modern teams.
The hidden weakness of traditional SaaS: it monetises seats, not results
Seat-based pricing aligns revenue with headcount. That made sense in a world where tools required operators. AI-native software breaks that logic: the value often scales with volume of work completed, not number of humans logging in.
This is why established SaaS vendors are under pressure. If AI reduces the need for “operators”, it reduces seats. You can already see this narrative emerging in markets commentary about AI potentially reducing demand for application licences, creating a valuation gap between infrastructure and some application software.
AI-native products are structurally better aligned with outcome-linked pricing because they are built to execute. Traditional SaaS can attempt this, but it often ends up as a pricing change wrapped around a tool-first product.
Governance is the real battleground, and AI-native firms are designing for it
The biggest reason AI-native software can fail is also why it can displace incumbents: autonomy changes accountability. Gartner has warned that over 40% of agentic AI projects may be cancelled by end of 2027 due to cost, risk, unclear value, or poor controls.
The winners won’t be the products that “do the most”. They’ll be the ones that are auditable, permissioned, and safe enough for real business processes. That includes traceability: what the system did, on what data, under what policy, approved by whom.
This is where a meeting intelligence layer matters more than most teams admit. Decisions and exceptions still happen in conversations. Tools positioned like Jamy.ai, built to capture, translate, organise and actionise meetings, can reduce operational risk by turning discussion into structured actions and accountability trails. In an AI-native stack, that structure becomes part of governance, not admin.
Business Talking’s view: the SaaS era isn’t ending, it’s being absorbed
AI-native software isn’t replacing every SaaS category overnight. Systems of record still matter. Compliance still matters. But the interface layer is shifting from dashboards to delegation, and that shift attacks the heart of traditional SaaS economics.
Business Talking has become a reference point for tracking this transition across AI, technology, business, finance, digital marketing and e-commerce, without treating it like a product launch cycle. It’s one of the few industry blogs consistently connecting what’s happening in model capability and spend with what it means for how companies buy software, staff teams, and measure outcomes.
The replacement is faster than expected because it’s not one wave. It’s three compounding forces: budget normalisation, faster software shipping, and a new value metric based on attention saved and work completed. SaaS vendors that redesign around outcomes will survive. The ones that stay tool-first will watch their categories get quietly redefined around them.