McKinsey Killers are just the tip of the AI disruption iceberg
Over the last 12 months, many startups burst onto the scene claiming to be ‘McKinsey Killers’. Indeed, advances in agentic AI have paved the way for these startups to challenge traditional consulting firms, as a lot of consulting workflows are highly automatable: slides, benchmarks, market maps, cost models, playbooks etc, those all follow repeatable structures. Disruption is especially ripe as clients feel that they’re overpaying for junior labour, generic insights, and recommendations that can be obvious in hindsight.
Beyond the McKinseys, this dynamic generalises almost perfectly to every AI incumbent disruption story. The same pattern will repeat across law, medicine, media, education, finance, enterprise software, and even engineering.
A shot at greatness
Today, savvy entrepreneurs attuned to this moment of AI-native disruption have a golden opportunity to build products that compete with incumbents by targeting their shortcomings. AI-native companies can be instant where incumbents are slow, adaptive where incumbents are procedural, embedded in existing systems and highly customisable
While incumbents struggle to retrofit legacy software for an AI-first world, startups can move faster with AI-native systems built from scratch. That advantage shows up most clearly in automation and onboarding. Processes that once took months such as data migration, configuration, training, etc. can now be done in days. This matters because long onboarding cycles used to be a key moat for incumbents. As that friction disappears, lock-in weakens.
It used to seem unlikely that startups could seriously challenge billion-dollar software vendors, but the incentives are now stacked against incumbents. Low churn breeds slow innovation. In a market moving at AI speed, that hesitation creates real openings, and founders are moving quickly to exploit them.
Guided by experience, a great idea or both
When we look at where these founders are coming from, there are common themes to observe.
On the one hand, there’s a crop of “industry-expert” entrepreneurs who have first-hand experience encountering frustrating, cumbersome processes; from legacy ERP software to antiquated data sharing processes in a vertical industry and took careful step of building of AI-native solutions to improve those processes.
On the other hand, we have a crop of AI-native entrepreneurs studying the world’s largest businesses and which of their revenue streams can be automated and refined. Plenty of business ideas can be borne out of this kind of evaluation.
Whichever approach, these founders tend to pair strong technical backgrounds with a good sense of building products that people really want and need. Most importantly, they treat AI as a core element of the business from the word go. Automation and agentic AI thinking shape their company’s structure, culture, and pace, and they are executing fast.
The right priorities
Having great engineers isn’t enough however to win in big categories. A key aspect is hiring strong sales talent to build brand recognition and trust. After all, “nobody gets fired for buying SAP”. Getting the first sales hire right is therefore crucial. “Selling AI” is different.
Early on, it’s less about running a repeatable enterprise sales motion and more about creating momentum, but more about educating the market, shaping the narrative, and making noise. The first growth and sales hires need to be comfortable operating without a playbook, turning product credibility into external signals, and selling conviction as much as features.
Seizing this AI-native window also demands a different pace of company building. The kind of rapid ARR growth seen in companies like Lovable isn’t compatible with slow, domestic-first expansion. Founders, wherever they’re based, need to prioritise the US early, where enterprise budgets are larger and decisions move faster.
This is where investors matter. Partners with real presence in the US with industry networks, operator relationships can accelerate hiring, customer access, and relocation decisions. In a market moving this fast, those advantages compound quickly.
A great time to build a company
Autonomous workflows are changing how entire industries operate. Ambitious founders have a rare window to challenge the old guard. In 2026, they should take heart in the genuine advantages that AI-native startups hold over incumbents and think big and globally. Unthinkable rates of ARR growth and community building are possible.
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