Building a Highly Efficient AI Team to Take on Potential Challenges

The rapid rise of AI means executives have lots of questions, such as how to manage computing systems that are beyond human comprehension, and how to ensure they're used ethically. In this AI teams play a great roll. A small team empowered to act quickly and even fail should be able to test an application within six to 12 months. Giving the team its own budget, rather than forcing it to be funded by the business units, can give it a faster start.

Moreover you can begin with forming a highly efficient team including:

The strategist

Contrary to popular belief, your first hire should not be a data scientist. It should be someone who is even harder to find than a data scientist: a data strategist. It is someone who understands how the business operates and how technology and data science work together. This person acts as the bridge between business lines and technology to drive pilot projects that deliver measurable ROI.

The data engineer

Remember the frequently cited adage, "Data is the new oil"? Well, your data engineer is your miner. This person is responsible for collecting data from a variety of sources, then preparing and transforming that input. In our COMA framework, the engineer works on compiling, organising and (sometimes) manipulating data, which can be time-consuming. Thankfully, today, start-ups like Trifacta and Forge.AI offer solutions that clean, label and move data in a more efficient way.

When hiring a data engineer, look for strong coding and engineering skills like SQL, Python, C++ and Java.

The data modeller

Often sporting strong maths and statistics skills, the data modeller looks for data patterns to predict outcomes (remember: AI, at its heart, is about patterns and predictions). This person also builds and trains models to determine events like propensity to buy, likelihood to churn, etc.

Tech giants often swoop up PhDs in maths and statistics, so start-ups are offering off-the-shelf models as an alternative. Before going this route, see our advice below.

The data in production person

This person converts prototype code to production, setting up a cloud environment to deploy the models. Other tasks involve managing version control, improving response times and building APIs. Look for cloud expertise and software engineering skills.

The infrastructure and scale builder

Think of this role as providing the wiring and plumbing for a house. This person builds databases to store data and facilitate access, as well as maintain security and privacy. Strong software engineering skills and proficiency in cloud technologies are a must.

The data analyst/visualiser

This role is tasked with evaluating the model's performance and business value post-production. The person can build out A/B or multi-variate testing, to measure the impact of different variables. The data visualiser creates dashboards and translates data into actionable business insights. Look for expertise in software like R or Tableau, although start-ups like Qlik and ThoughtSpot can enable layperson visualisation across the enterprise.