AI at the Edge: How Small Teams Are Using AI to Deliver More With Less

When considering how to best deploy AI across their organisations, it is often the case that corporate leaders will look to launch a major project that results in significant changes. But many of the most impactful gains from AI tools can be found in the smaller, more practical uses of AI that free up crucial staff time, instead of aiming to transform an entire business overnight.

Focusing on how AI can pay real dividends is essential when considering how it can be used in both start-ups and major enterprises. Small teams and start-ups are ideally placed to make significant gains from AI, especially when it comes to enhancing productivity and capacity. Thanks to years of research on the actual outputs of generative AI tools, it’s increasingly clear that, while far from perfect, they offer genuine benefits across countless industries.

According to a 2024 Stack Overflow Developer Survey, more than 8 out of 10 respondents agree that increasing productivity is the most important benefit from using AI tools as part of their development workflow. A majority of respondents also agree that AI has sped up learning and unlocked greater efficiency.

In smaller teams, where backlogs are a common reality, being able to speed up development and achieve concrete efficiency gains is the reality of AI. Perhaps most importantly, the benefits come from widely available and easy-to-use AI solutions, not extraordinarily expensive personalised programs that take months and months to build.

By targeting each AI tool at a specific bottleneck, whether that be using an LLM to draft responses or helping write code, it’s far easier to measure improvement and determine if the theoretical benefits line up with the real-world benefits.

From Productivity to Fulfilment

A survey from GitHub also found that developers reported not only higher levels of productivity when using GitHub Copilot, but they also found work more fulfilling and less frustrating.

“In the survey, we saw that developers reported they complete tasks faster when using GitHub Copilot, especially repetitive ones. That was an expected finding (GitHub Copilot writes faster than a human, after all), but >90% agreement was still a pleasant surprise. Developers overwhelmingly perceive that GitHub Copilot is helping them complete tasks faster,” explains GitHub in a research post.

There’s no question that there are limits to what small teams can do with AI, compared to well-sourced multinational organisations. According to Lareina Yee, senior partner and McKinsey Global Institute director, many businesses remain in the early production phases of AI use, and it is yet to be seen what will happen to jobs at risk of being impacted by AI. But skilled workers and those individuals with IT skills may actually be focused on during hiring rounds.

“Still, even in these early days of adoption, we are seeing changes in the skills demanded for a range of jobs,” explains Yee. “Some of these jobs will become more critical as AI adoption increases. AI success, for example, requires data readiness and MLOps. We see larger companies in particular hiring for those skills; they are twice as likely to hire roles that integrate, model, and industrialise data.”