"We received quotes: Company A proposed a 10-person team for 30 million yen, while Company B proposed 3 people for 10 million yen. While the lower cost is appreciated, we wonder if 3 people can really handle it, and that anxiety is keeping us from deciding." We received this consultation from a business owner at a wholesale company considering a core system overhaul. For a long time, the sentiment that more people equals larger scale equals peace of mind was taken for granted when commissioning development. However, that very premise is quietly beginning to crumble.
In July 2026, research firm Gartner released a prediction: by 2029, 60% of organizations will fully deploy small development teams. As of 2026, this proportion stands at 15%, meaning it will quadruple in just a few years. Driving this is AI. By having AI take over testing and routine portions of code, engineers can concentrate on more complex judgment calls, enabling compact, high-caliber teams to handle larger projects than before. In this article, we examine what this shift means for SMB procurement and how clients should evaluate development partners from the client's perspective.
The assumption that headcount equals peace of mind is collapsing
What Gartner refers to as "tiny teams" currently consists typically of 4 to 5 people, with some organizations already operating with 2 to 3. The crucial point here is that these are not cut-corner, understaffed teams. Premised on AI as an amplifier, this team model delivers greater speed and agility than conventional setups with fewer people.
This shift strikes directly at client decision criteria. Until now, the sheer number of personnel and person-months listed in a proposal served as a direct signal of organizational strength and reliability. However, once it becomes reality that a 3-person team mastering AI delivers faster, higher-quality outcomes than a 10-person team unable to leverage AI, the headcount yardstick loses its meaning. In fact, a person-month estimate predicated on large teams carries the distinct possibility of being an overpriced quote that fails to factor in efficiency gains from AI. We also discussed how the boundary between in-house development and outsourcing shifts in our article on in-house vs. outsourcing in the AI-built era.
Three questions to evaluate small teams
What, then, should the client evaluate? We organize what to check in place of headcount into three questions.
| Point to verify | Positive sign | Warning sign |
|---|---|---|
| Where AI is being applied | Can explain use cases concretely | Stops at "We leverage AI" |
| How quality is assured | Possesses review and testing mechanisms | Emphasizes only speed |
| Whether handover is feasible | Maintains documentation and maintenance structures | Dependent on specific individuals with vague explanations |
First is where and how AI is applied across the development phases. Beyond mere statements like "We leverage AI," observe whether they can speak concretely about automated test generation, drafting code, or other specifics. Second is how quality is protected behind the speed. The fact that AI can write code quickly also means flawed code proliferates quickly. Having human review and testing mechanisms makes all the difference. Third is handover, which easily becomes personalized and siloed due to the small team size. A system understood only by the person who built it becomes a corporate risk the moment that individual departs.
What to look at regarding the aftermath before jumping at low costs
Small teams often submit lower quotes. As in the opening consultation, this is attractive yet simultaneously a source of anxiety. Here, what clients must evaluate calmly is not the cheapness of upfront costs, but post-launch operational costs and whether long-term stewardship can be entrusted to them.
Systems built quickly and inexpensively using AI still require ongoing operation and maintenance. In fact, code written quickly by AI easily becomes invisible technical debt that no one can explain later on. This point has been highlighted in articles on AI-generated code debt, and we have repeatedly emphasized that maintenance and requirements definition matter more than upfront costs in development outsourcing across our procurement guide to avoiding failure in software development outsourcing and article on business system maintenance costs. Small teams are not inherently risky. The real dividing line behind the low price is whether they possess mechanisms for quality and handovers, even with a small team.
The warning Gartner sounded at the same time
There is another noteworthy caveat to this forecast. Gartner warns that organizations reducing junior roles because of AI will deplete their talent pipelines by 2028. Small teams succeed when AI amplifies humans rather than replaces them. If organizations lose opportunities to train junior talent, the talent pool capable of sustaining small teams will run dry in a few years.
This holds strong implications for clients as well. Choosing a small team purely based on immediate low cost may mean shouldering the risk of maintenance halting later because nobody is left to run it. Good partners design their organizations to nurture talent and retain institutional knowledge while driving efficiency through AI. During procurement, rather than settling for "We can build it with three people," you will want to probe deeper into what happens if those three people leave.
Case study: A company that compared 10-person and 3-person quotes using metrics beyond headcount
Here is a concrete example. The wholesale company mentioned earlier (name withheld) was hesitating between two quotes: a 10-person team and a 3-person team. Upon consultation, we suggested setting headcount and price aside temporarily to compare both companies across the three questions.
What we did was straightforward. We asked both companies to explain concretely where and how they use AI in development, how they assure quality, and how handovers occur if assignees depart. As it turned out, 10-person Company A was vague about AI adoption, retaining legacy processes despite the large team. Meanwhile, 3-person Company B demonstrated automated testing and review workflows in detail and committed to providing maintenance and handover documentation as standard. Ultimately, this company selected Company B. The deciding factor was not low cost. It was because when removing headcount as a metric and comparing AI usage, quality, and handovers, the smaller team proved more reliable as an institutional framework. Letting go of the habit of seeking reassurance in high headcounts led to a sound choice.
First, prepare evaluation criteria other than headcount before requesting quotes
The trend toward smaller development teams is already showing up in actual estimates without waiting for Gartner's 2029 forecast. What SMBs need to prepare for is not becoming AI experts, but updating their procurement yardsticks. If you are getting started, prepare criteria other than headcount—AI utilization, quality assurance, and handover frameworks—as internal evaluation items before collecting quotes. With these in place, anxiety in the face of small-team proposals turns into grounded decision-making.
Whether you are wondering whether to trust a three-person team quote, want to compare multiple vendor proposals using criteria beyond headcount, or want to clarify evaluation metrics for AI-era development partners, please feel free to contact the GleamHub development, AI, and automation consultation desk. From evaluating proposals to designing procurement focused on requirements definition and maintenance, we will work alongside you tailored to your project.
