The Role of Experimentation in Artificial Intelligence

What Does Experimentation Mean?

Companies in today’s world can gain a lot of actionable insights by conducting simple business experiments. Experimentation here means augmenting the insights from the data you have and planning business processes on a test and learn a basis to see how they respond. The process of experimentation can reap multiple rewards for businesses, considering how they will find themselves in a better position to continue with a given strategy if it proves to be successful.

Who is Involved?

An experiment can have multiple representatives working together to achieve the desired results. In layman’s terms, we can suggest that the Research and Development (R&D) team would be at the forefront of a business’s experimentation. Since experiments require focus groups and different participants, the process might require getting the assistance of external participants to ensure that the results aren’t biased. Additionally, experiments could be assisted through the presence of analytics for substantial reasoning and proof.

How to Approach an Experiment

Approaching an experiment isn’t an easy task, which is why you should think of ways that can facilitate your desired results. Below are some of the rules that you should follow for your experiments at the beginning:

1. Think of Individuals and Make Short-term Plans

If you’re experimenting with a new offering for your customers, try a short trial and analyze the yield before you make long-term production and distribution plans. You need to know the kind of return on the manufacturing you are doing before you make long-term plans.

2. Keep It Simple

The simpler you keep your experiments, the more you can leverage them in the future. Don’t make experiments overly complicated for your team or yourself. People don’t want to work on experiments that are extremely time-intensive only for the idea to be scrapped later on.

3. Follow the Proof-of-concept Test

Get proof for whatever experiment you are running beforehand. For instance, if you, as a retailer, think that lowering the price for one good would decrease the demand for another, then test this concept and see how it goes. When you’ve tested the concept and seen it in action, then you can use that proof to run a future experiment based on your inference.

4. Shoot Big

Don’t let the scope of your current operations keep you from shooting outside of the box. For instance, if you want to revamp the prices you have for specific items, try thinking of new supplier chains altogether. Don’t just think of optimizing what you have now; try implementing completely new systems and see how they evolve.

5. Conduct Natural Experiments

Trygve Havelmo, who won the Nobel Prize in Economics in 1989, has specified two basic types of experiments around the world as, “those we should like to make” and “the stream of experiments that nature is steadily turning out from her enormous laboratory, and which we merely watch as passive observers.” 

Organizations need to realize the right time for a natural experiment and work accordingly to get the best out of the situation.

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Why Experimentation Is Key?

Experimentation is the key to developing and forming new insights that help you innovate fast. Two-thirds of the professionals that were interviewed as part of our survey mentioned that they provided plenty of room for natural and thorough experimentation. However, one in five organizations from the education, telecom, and public sector wasn’t open to the idea of testing regularly.

Additionally, 83 percent of all UK-based organizations were unsure or neutral regarding their experiments. Thirty-one percent of all professionals surveyed mentioned that their organizations had plenty of room in place for experimentation. Only 2 percent of all professionals surveyed said that there wasn’t room for experimentation within their organization.

Experimentation not only bridges the gap between analytics and the real world but also ensures that organizations can turn their expectations into a reality. 

Challenges in Implementing Data-Driven Solutions

Forty-four percent of all participants believed that the building up of knowledge about data science and big data was the biggest roadblock in analytics for them. However, second only to this was the challenge of making time available for conducting experiments. Around 40 percent of all participants believed that finding time for challenges was the second most significant challenge for them when it came to starting a data-driven culture.

Experimentation is, therefore, an essential part of the data culture and ensures that the organization can get the results it desires from the efforts being put into analytics.

With attention to experimentation and finding the right learning curve from them, organizations can conquer the world of data in its pure form.