People often ask me what it’s like to work as an “Insight Engineer” for a PE and advisory firm that is nearly 13,000 kilometers from where I live in Kenya. Here are three words that I would use to describe the experience: immersive, formative, and unscripted.
I joined Tercera through Tana, an organization that connects early-career African talent with global companies. That path into Tercera reflects a much larger shift taking place – young, ambitious, AI-native talent entering the global workforce at a high rate. According to Tana, by 2035, one in every two people entering the workforce will be African.
Africa has the youngest population in the world, and has become a technology talent hub for many kinds of companies. Tercera saw the potential of this region early on after embarking on a tour of Africa in 2024. In their blog, “Is the quest for global talent shifting to Africa?”, Tercera highlights the size of the talent pool, but also the chance to find people who are comfortable learning new technology, can adapt quickly, and understand how new tools can improve the way work is done.
For companies trying to adopt AI, that matters. The hardest part of AI adoption is often not finding a new tool. It is understanding where that tool fits within an existing business process, and helping people use it in a way that is practical, safe, and valuable.
The hardest part of AI adoption is often not finding a new tool. It is understanding where that tool fits within an existing business process, and helping people use it in a way that is practical, safe, and valuable.
Young professionals who have grown up around technology may be more willing to test new tools and question processes that have always been done manually. But that does not mean every young person is automatically ready to lead AI projects. They still need training, business context, mentorship, and the trust of the teams around them. The real value comes from pairing their comfort with technology with the experience of people who understand the company, its customers, and its industry.
Finding my place at the intersection of AI and business
My background in economics and statistics gave me a foundation in data analysis, research, and structured problem-solving. I was also drawn to the growing role AI was playing in business, and how it could help people understand information and make better decisions.
Tercera was a good place to explore that. The firm already had ideas about automation and was beginning to put them into practice, learning from their portfolio companies that had started to see results. That gave me real business processes to work on from the start.
My role as an Insight Engineer sits at the intersection of AI, data, research, and business analysis. A large part of my work involves supporting internal research and data systems: improving databases, sourcing information, checking data quality, and turning large amounts of research into insights that are easier to use. All done with the support of AI.
I don’t just work with one team, I support the entire business, building AI tools and agentic workflows for different members of the team and teaching them how to use and evolve them. Initiatives I work on support our thought leadership initiatives (with a special focus on our annual Tercera 30 report). They keep data and benchmarks accurate and up to date, expand and accelerate our research, and assist with presentations and recurring analysis.
The projects may look different, but they share one goal: reducing repetitive work while making information easier to find, understand, and use.
AI adoption starts with the process
One of the most important lessons I have learned is that success in an AI role is not only about writing good prompts.
Before trying to automate something, I first need to understand the process. What is the team trying to achieve? Who will use the output? Which information can be trusted? Where could errors create a problem? Which decisions still require human judgment?
The most useful AI workflow is not always the most impressive demonstration. It is the one that solves a real problem, fits into the team’s work, and produces an output people can trust.
This also means that AI adoption is partly a cultural challenge. A tool may work well, but it will create little value if the people it was built for do not understand or trust it. Supporting adoption means listening to the team, learning how they currently work, documenting the new process, testing the results, and improving the solution based on feedback.
It also means positioning AI as something that supports people rather than simply replacing the work they do.
Working with the limits
Even with all the promise AI carries, working with these tools each day is not always seamless.
There are times when productivity is slowed by practical constraints. I may hit usage limits in tools such as Claude, face restrictions with connectors and workflows, or run into problems when trying to make tools such as ChatGPT and Google Sheets work together smoothly.
These moments have taught me that working effectively with AI is also about planning. I have to decide which tasks matter most, break large projects into smaller parts, and keep an alternative approach ready when a tool does not behave as expected.
AI can make work faster, but getting that value still requires patience, structure, and human judgment.
The opportunity for next-generation talent
What excites me most about this role is that the work keeps changing. Each project gives me a better understanding of the business while also showing me new ways AI can improve a process.
It has also shown me why next-generation talent can play an important role in AI adoption. People who have grown up around technology bring a useful AI viewpoint into a company. They may look at an existing process and naturally ask whether part of it can be made faster, simpler, or more reliable.
But technology fluency alone is not enough. Its value grows when it is paired with experienced colleagues, strong business knowledge, clear standards, and a culture that gives people room to learn and experiment.
AI will continue to change how work is done. The companies that benefit the most won’t put all their energy into building agents to replace people, but those that invest in people who can connect those tools to real business needs.
My journey at Tercera has given me the opportunity to learn what that connection looks like in practice, and to help build it one person and process at a time.