Are Investors About To Back A New Era Of Lean Startups Built Around AI And Smaller Teams?

New Mercer research spoke to about 12,000 executives and found that they expect some serious organisational design changes with 98% of these executives expecting them within the next couple of years. Also, 99% expect AI to lead to at least some headcount reduction within two years.

Executives in Mercer research report 63% see redesigning work with AI and automation as main driver of return on people investment. Investors also prefer organisations with digital-first cultures, and that includes being AI-driven which is interesting because only 32% of the execs believe their workforce can actually combine human and machine capability well.

Talent constraints are apparently also an issue, with 54% of executives naming talent scarcity as the main factor influencing people plans. HR leaders report 59% struggle to hire digital skills for 2026 which is a call for leaner operating models built around machine support.

Do Investors Support Smaller Automated Startup Models And Job Losses?

Dr Melonie Boone, executive adviser at Boone Management Group, says investors are shifting attention towards small automated startup models. She says, “AI compresses the cost of execution, but it multiplies the cost of misalignment. When you give an aligned team AI, you get velocity; when you give a misaligned team AI, you just create institutional drag at a faster pace.”

She adds, “Strategy rarely fails on paper, and it won’t fail because of your tech stack. It fails where decision clarity breaks down.”

Boone says smaller automated teams raise demands on leadership alignment as AI execution speeds outcomes from weak decisions. She says operational discipline becomes central as team size falls and automation expands. Investors targeting low burn models face execution drag when decision clarity is weak.

Mercer research also shows 57% of executives expect some other massive changes over the next 10 years. Only 8% see HR as an embedded strategic function, while 56% expect HR and IT to merge. Organisations with embedded HR report 76% resilience compared TO 62% overall.

Investor interest continues towards lean startup models using AI systems to raise output from smaller teams. Mercer data shows executives aligning organisational design with automation and efficiency goals.

Dr Boone speaks of execution risk when leadership alignment weaken. AI use increases output speed, yet decision clarity determines results for compact teams relying on automated systems. This model attracts investor attention in early stage companies…

More experts share their thoughts on whether this next era of layoffs will inspire this kind of model, here’s what they say:

Our Experts:

• Pankaj Khurana, Founder, Firki
• Iuliia Mineeva, Sibedge
• Shanka Jayasinha, Principal, S&J LLC
• Matias Rodsevich, CEO, PRLab

Pankaj Khurana, Founder, Firki

“I think investors are absolutely moving toward a new generation of lean AI-native startups, but not simply because “AI replaces people.” What’s really changing is the operating model.

“In my experience over the past year and a half, I have worked with small teams who were able to complete tasks through automation in ways that previously required several levels of operational layering (the sourcing side, internal workflows, analytics, documents and even the technical recruiting operations).

“For example, when implementing an automated candidate screening workflow for a recruiting team I worked with, the automation allowed the team to reduce the candidate screening time/effort from approximately 22 hours per week down to 4 hours per week and to review nearly three times as many candidates than before without increasing headcount.

“Operating with this kind of operational leverage is going to change the way that investors think about the early stage scalability of a company. In the past, an early stage startup typically needed to quickly scale their operations and team in order to keep up with the speed of execution. However, many of the applications of AI will create a long period of operational efficiency for a company with a relatively small team and less overhead.

“At the same time, I believe that the market is overestimating the capabilities of full automation. There are still many areas of difficulty for many systems regarding their ability to contextually interpret and distinguish between high-quality decisions and what I would call “signal integrity” (inability to separate truly relevant information from optimised/AI noise). This type of situation is currently being seen in hiring; AI generated resumes are continuing to be screened by AI driven systems which are creating negative recursive noise versus better decision making.

“The startups that will win are not necessarily the ones with the fewest employees. They’ll be the companies that combine AI-driven operational efficiency with strong human oversight, domain expertise, and contextual decision-making.”
 

Iuliia Mineeva, Sibedge

“From what I see in early-stage startups, AI is gradually becoming part of everyday operations, not just an additional productivity tool.

“In our startup, like in many lean tech companies, we’ve always had to be careful with budgets and team scaling. Over the last year especially, we started actively replacing some routine operational workload not with more hires, but with AI subscriptions and tools that help existing team members work faster.

“In many cases, paying for a strong AI tool for a specialist is simply more efficient than expanding the team. A relatively small product or engineering team can now handle tasks that previously required significantly more people and coordination.

“We actively use AI in product research, preparing documentation, analytics, prototyping, content preparation, testing, internal communication, and some strategic product work. The biggest advantage for us is not only lower operational costs, but also speed.

“For startups, speed is often more important than perfect processes. The faster a team can test an idea, launch an MVP, gather feedback, and adjust direction, the higher the chances of survival and growth. AI noticeably accelerates this cycle.

“I also think this is already influencing investor expectations. Investors are paying more attention to teams that can achieve strong delivery results with smaller structures and lower burn rates. Startups today can stay lean much longer while still building competitive products.

“At the same time, I don’t think this trend is simply about replacing people with AI. In practice, the strongest results still come from experienced specialists who know how to use these tools effectively. AI works best as an amplifier for strong teams, helping them move faster and focus more energy on high-value decisions instead of routine operations.”

Shanka Jayasinha, Principal, S&J LLC

“I would say that investors are already investing in lean companies, especially in the AI space. However, it is more the how and the why that change. I see 3 main categories of lean companies that attract investors:

“First, there are the lean companies that are super scalable and have VC backing, such as Unconventional AI, Cursor, Cognition AI, Sierra, Decagon, Glean, Harvey, Onto AI, among others, which are already in unicorn territory. Whether they survive to different breakthroughs in AI remains to be seen.

“Then you have the super lean companies, solo founders, or teams of 2-3 that have been acquired by one of the larger AI companies, such as Openclaw by OpenAI. They mainly regroup all the acquihires. Usually, they do not have many investors on the cap table as the founder had a strategic network or became ‘popular overnight’.

“Last but not least, you have the small teams that are completely bootstrapped because they can afford to be and prefer peace of mind over returning capital to investors, even though they have been proposed many term sheets. They might be the ones with the most automated processes as they care about their quality of life overall.”

Matias Rodsevich, CEO, PRLab

“The next wave of great tech companies will be small by design, not by constraint.

“As I see it, AI is not just reducing headcount. It is changing what headcount is even for. AWe work exclusively with tech startups and scaleups, so we talk to founders constantly. The ones raising right now are not apologising for being small. They are using it as a pitch. Investors have caught on. Revenue per employee has become a real signal. The lean model is no longer a workaround. It has become the actual strategy.”