From AI features to AI workers: The 2026 enterprise shift
Despite talk of an ‘AI bubble’ happening on the sidelines, enterprises are heading into 2026 with more investment earmarked for AI. According to Boston Consulting Group’s annual AI Radar, corporations expect to double their AI spending this year, from 0.8% to about 1.7% of revenues.
Who stands to benefit? It wouldn’t be wise to write off the chances of startups commanding a significant portion of this enterprise spending. Autonomous workflows are moving from concept to reality. In practice, this means enterprises moving away from “AI features” towards AI workers handling entire processes. AI-native startups, launched and scaling at pace in a post-LLM world, can gain an edge over incumbents offering legacy solutions that can’t keep up.
Tipping autonomous workflows for take-off
There’s no doubt that agentic AI is where the enterprise AI puck is moving next. We need to be clear about what that means. Far from AI agents as glorified LLM chatbots with more autonomy, agentic AI can handle entire business-critical processes, from KYC (know-your-customer) to FP&A (Financial Planning & Analysis) and compliance remediation, through autonomous workflows.
Under the hood of these workflows are the layers of an emerging agentic AI software stack: self-correcting workflows (allowing AI to fix its own mistakes and maintain autonomous processes), secure memory (a sophisticated mechanism enabling AI agents to improve workflows using retained context) and multi-agent collaboration (the framework for AI worker collaboration that underpins sophisticated autonomous workflows).
Intuitively, enterprise software incumbents should be leading the way in the pivot towards autonomous workflows. Many, however, are still stuck in the mindset of shipping “AI features”. Low churn breeds slow innovation, and incumbents struggle to retrofit legacy solutions from a pre-LLM world for the agentic future.
AI-native startups, on the other hand, are building these systems from scratch. They’re embedding automation into their products from day one. Agentic thinking shapes their company structure, culture, and pace of execution. They are therefore primed to deliver autonomous workflows first.
The startups behind the momentum
In 2026, we’ll see even more AI-native startups launch products centred around autonomous workflows. There are some recurring themes when we look at the backgrounds of the European founders behind these companies.
Firstly, it’s increasingly common to see industry-expert entrepreneurs behind AI-native startups, pioneering solutions to the problems they encountered first-hand in their careers. Be it legacy ERP software or cumbersome data practices, these founders often have a very specific, often vertical-specific, process in mind that agentic AI can make far more high-performing, and they are building towards this vision.
Additionally, there’s a growing cohort of savvy entrepreneurs launching new AI companies designed to disrupt a specific revenue stream or service they have identified as ripe for disruption through agentic AI.
We’re also seeing more founders raise capital in sectors that venture capital used to avoid. Startups in sectors such as defence, energy and procurement are benefitting as more investors come to terms with traditional sticking points in these sectors, such as long sales cycles and regulatory constraints, and look beyond the typical areas of startup disruption where competition is fierce. By automating compliance, defence procurement, energy management and construction workflows, AI-native companies in these sectors are benefitting from this shift in VC behaviour.
Whatever the founder’s approach, these startups are finding common ways to challenge crucial moats that incumbent players have long used to maintain their dominant market positions. The first is customer onboarding. AI-native startups are automating onboarding processes to slash timescales around data migration, training and configuration from months to days. Long onboarding cycles and subsequent vendor lock-in are becoming less reliable moats for incumbents.
The second moat is technological advantage. This is a crucial development that speaks to a broader trend shaping enterprise AI and what it means to be a successful enterprise technology company in 2026 and beyond.
User experience as the new key differentiator
To put it simply, AI is making software far easier to replicate than ever before. Founders no longer need deep technical knowledge or extensive support to create basic applications with a prompt. Likewise, enterprise employees can now reimagine their business processes through LLM prompting. No familiarity with software development is required.
This presents a huge risk for incumbent enterprise platforms and a major opportunity for AI-native startups. Incumbents can no longer get away with offering clunky, unintuitive software without experiencing customer churn, on the basis that it can’t be easily replicated. This means there’s a new battleground defining enterprise software excellence: user experience.
In response, AI-native startups can offer strong UX by building user communities around their solutions that act as feedback loops for continuous product improvement. Founders engage users early and involve customers more directly in the design and iteration stages than ever before. This encourages loyalty and retention, and attracts more users and investors. Being AI-native, startups already have a more straightforward route to building the intuitive and adaptable workflows that enterprise users value.
Coming ahead
For Europe’s early-stage founders, 2026 presents an opportunity to build companies that are first movers in introducing autonomous workflows across a range of industry verticals and common business processes. The startups that lean into their inherent strengths over incumbent competition and prioritise user experience can steal a march and ride the next wave of software disruption.
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