In the past few years, several trends accelerated adoption of AI for business applications. The abundance of data, cheap computing, advances in AI algorithms, and the advent of platforms that facilitate implementation of AI systems are making AI ever more accessible. Still, extracting business value from AI remains elusive for many companies. The solution lies in part in the ability to assemble a cross-functional team with skills appropriate for the task. Which skills are needed is largely determined by which business problems are worth solving using AI and what creates a competitive advantage or is strategic for the company.
Kitchen builders, chefs, cooks, and microwave builders
In her blog Why businesses fail at machine learning, Cassie Kozyrkov, Chief Decision Scientist at Google, points out two main types of machine learning: research and applied, and draws a compelling analogy with cooking. In this analogy, machine learning researchers who develop new algorithms are likened to engineers who build microwaves and other appliances. The applied machine learning specialists, on the other hand, are cooks who use appliances to produce tasty dishes.
Extending this line of thought, just like individual dishes don’t necessarily make a meal, predictions coming from machine learning systems are usually not the end goal. Someone needs to define how to use them in a product that solves a specific customer or business problem. For example, if the AI system predicts a user’s music preferences, there’s still work on how this information is optimally used in a music streaming app, and how to measure its impact on key performance indicators. Such a person is similar to a chef in a restaurant, who is capable of selecting and combining dishes together to serve a full dinner.
Another important aspect of AI systems is their day-to-day operation, and so people who develop and integrate machine learning platforms are those who build automated and scalable kitchens. Kitchen builders are making the cooks efficient so that the chefs are able to deliver more meals.
To summarize, there are four broad AI skillsets:
- Development of new and improvement of existing algorithms (microwave and other appliance builders).
- Application of existing algorithms to build machine learning models and produce predictions that are useful in solving a business problem (cooks, who prepare individual dishes).
- Defining how predictions are used to solve specific business problems (chefs who create a full meal and dining experience).
- Building platforms that facilitate and automate machine learning (kitchen builders).
It is important to keep these skillsets in mind when building a team that is tasked with developing AI-driven products. So which of them are necessary, which are optional, and which can be outsourced?
How to assemble a successful AI team
Considering a large number of readily available machine learning algorithms, one would normally not start with developing a new algorithm or modifying an existing one. Therefore, we usually would not need microwave builders to start (unless it’s a microwave design business). Unfortunately, these are the most common skills taught in machine learning and data science courses.
Cooking skills are definitely required, and sometimes cooks specializing in certain dishes (pastries, sauces) may be needed. For example, if solving the business problem involves text analysis, knowledge of NLP could come in handy.
Having a chef, whose skills cross into the product management area, is absolutely critical to making AI valuable for the business. It doesn’t have to be a separate role, and it can be synthesized from more than one person.
Finally, if there is a lot of cooking to be done, kitchen builders are also required in order for the whole process to scale. While one can certainly rent a ready-to-go kitchen, some of the kitchen builder skills are usually needed in-house to make it operational. The main reason is that while platforms make certain aspects of machine learning easier, integration with existing production systems and processes remains a challenge.
To assemble a successful AI team, one first needs to evaluate what business problems need solving, which skill sets are needed, and which of them are required in-house. In some cases, it is sufficient to rent a kitchen, hire a chef who can also cook various dishes, and provide adequate engineering support to make the chef effective. In others, the competitive advantage comes from the ability to cook simple meals at scale and serve many customers. Then kitchen builders become a key to success. In yet other cases, none of the existing algorithms can solve the business problem well, and one has to develop better microwaves and hire microwave builders.
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