Artificial Intelligence: How to Move Away from the Hype | 7wData

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Innovation is perhaps the buzzword of the past decade. But is there another term you’ve heard buzzing around quite a bit?

Artificial intelligence (AI). As one of the hottest topics on the agenda of the world’s top companies, AI is considered a vital part of their business success. However, only 31 percent of companies feel capable of applying AI, according to Deloitte’s “2019 Global Human Capital Trends” report. The reality is that it takes more than a brilliant team of scientists to set up successful AI projects. So how can your company speed up its digital transformation?

While there’s no easy recipe, there are steps that companies can take to develop a stronger AI innovation process.

To lay the groundwork for successful AI projects, set up a data-analytics platform in your company through which scientists can explore data, build prototypes and experiment with AI models and the tooling needed to scale them. Many companies have their proprietary data organised in a data lake. In itself, this isn’t enough. Data has to be sorted, organised and labeled. This often happens through the data-management team. For scientists to use it, the data has to interpreted and sometimes even “cracked” to make it valuable. This is normally done by data engineers. This process, known as data wrangling, can take up a sizeable percentage of the time you will spend on a project (sometimes most of it!). Needless to say, to use the data, you will need to adhere to applicable laws and regulations, and you will have to take into account access rights, privacy laws and so on.

A common misunderstanding is that data has to be of perfect quality. It sure helps if it is, but lower-quality data shouldn’t be used as an excuse to not provide it to an AI team. Data scientists are capable of filling the “gaps” with their advanced analytical techniques.

Step two: Set up a diverse team with a broad skill set.

AI is not about data science alone. It takes a village to build an AI model. Designers, data engineers, developers and product managers have to collaborate closely and stay connected to the business to create a solution that can be of real service to customers or employees. This helps ensure that AI prototypes can be scaled into production and don’t just lead to interesting insights and then land in the “graveyard of models”. This means that you need tooling that scores high in the user-experience department so that people want to use it.

An often overlooked aspect of building a strong AI team is diversity. This is as important in a team as its broad range of hard skills. It’s how you avoid running one of the biggest risks in building AI models: bias. For example, take a model that focuses on projecting the success of entrepreneurs in a certain country. If you look at the Netherlands, for instance, where approximately 37 percent of entrepreneurs are women, and correlate successful startups with gender, you’ll find out that men are significantly more successful in business than women—an unfair conclusion because the model doesn’t take into account the gender imbalance in the population of entrepreneurs. The more diverse your team is, the more diverse the questions they’ll ask will be—and the more perspectives from which they’ll look at models will be, leading to better, more accurate outcomes.

Step three: Is your line of defense strong enough?

There are many other risks that can have unwanted consequences on models in AI innovation. The use and management of data has to adhere to applicable laws and regulations. Critical to achieving this is having a strong support system involving departments such as Compliance, Non-financial Risk (NFR), Legal, Human Resources (HR), as well as Quality Assurance, Research and Ethics teams:

The role of NFR teams is to challenge and advise innovation teams on the non-financial risks of an AI project.