And there are more reasons why top-down does not work:

• AI teams are by nature multi-disciplinary consisting of data scientists, Machine learning engineers, data engineers, and domain experts. All of which have different tasks to focus on but need to work together to integrate each other’s work and build a functional solution.
• AI systems built in the lab are often biased and lack real-world approval. Bias comes from a lack of diversity and top-down means a single person or only a few make decisions. Not a good idea if your system is going to impact thousands or even millions of diverse users. Amazon’s sexiest algorithm is an example of how it should not be done.
• Running AI projects requires solving multiple problems in various domains such as accessing data, preparing the data, fine-tune parameters, testing different data models, and communicating results in a story-driven and value-oriented way. No single person has all the knowledge to cover all tasks needed. 

The alternative? Bottom-up collaboration

The advantage of bottom-up planning is that the team members have a voice in the project planning and decisions are made collaboratively. This improves team communication and team building and also empowers the team members.

Identifying tasks first also leads to a more detailed project plan, with a potentially more accurate schedule, and a better understanding of the problem to be solved.

Benefits with regards to AI projects

Innovation

Bottom-up development sets the ground for innovation by combining diversity of thought with a safe environment where information exchange happens easily and openly. Especially in AI projects, bottom-up teams require people from different backgrounds and experience levels. This high degree of diversity not only prevents biased solutions but also enables breakthroughs via fast iteration cycles and constant perspective sharing, which connects to the next point.

Less risk of bias

More perspectives, higher gender diversity, and cross-cultural teams result in a more inclusive approach to preparing datasets and building solutions.

More and better data

Another potential benefit of bottom-up driven teams is to they find creative ways to access new data sources and augment existing ones. In AI the team with the best data wins.

Higher user adoption

Many AI solutions and products fail in the real world due to a lack of customer trust and no contextual applicability. A bottom-up approach takes the development process out of the lab and into the real world by embedding diverse perspectives and open dialogue where chances are higher to build inclusive and trustworthy solutions.

How it works in reality: a real-world case study

A company that wants to become AI-powered and build solutions for real-world problems has to overcome several challenges along the way.

In theory, bottom-up collaboration sounds all good but many companies struggle to get access to AI experts and data scientists that can help to translate a problem into a deployable AI solution or prototype.

In addition, many organizations have little or no data, to begin with. And even if data is plentiful, the question remains, how to leverage the raw data to solve problems or gain valuable insights. If an organization made the step to develop an AI system, the next wave of challenges is just around the corner.

In order to help organizations, we started Omdena — an innovation platform where organizations host AI projects that are being solved by global and collaborative teams of up to 50 engineers.