It was 2020. The pandemic at its peak. A career shift. I stepped into the leadership role of an applied research team at a new company with a clear mission: to kick off a new era of technology-driven innovation.
This team had always played a pivotal role in the company’s journey. Over the years, it spearheaded experiments with emerging technologies that became true turning points. For instance, the team led the company’s transition to a cloud-native architecture, replacing legacy application servers with microservices—a move that laid the foundation for today’s platform, supporting dozens of products, more than 700 microservices, and over 1.2 million active monthly users.
It was also the team that explored and validated facial recognition and NLP-based chatbots—initiatives that earned international recognition. Long before ChatGPT made AI mainstream, we were already experimenting with language models. So when generative AI exploded, we were well-positioned to act fast.
But experimenting is very different from turning new tech into product. That’s where the real challenge lies. Especially in an environment where testing hypotheses was expensive and slow. That bottleneck was throttling innovation.
Working with innovation means embracing uncertainty—and accepting that most of what you try won’t work. Statistically, only a small fraction of ideas create real business value. If your success rate is 10%, and you want 10 validated ideas, you need to test 100. It’s basic math.
In 2020, our average cost per experiment was $186, and we were completing just 0.57 experiments per professional per year. Scaling innovation with those numbers simply wasn’t feasible. Hiring more people or increasing budget linearly wasn’t the answer.
We ran an internal assessment to understand what was getting in the way. Here’s what we found:
- Provisioned infrastructure that incurred cost even when idle;
- Limited AWS expertise, which slowed development and created bottlenecks;
- Projects executed individually, limiting collaboration and peer learning;
- Lack of a clear cadence in our workflows, leading to inconsistency and inefficiency.
Other wins soon followed. We built a no-code machine learning platform that allowed our clients to run models without writing code—featured on the AWS Blog:
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