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3 Questions To Ask Before Becoming AI-Native3 min read

September 8, 2021 2 min read

3 Questions To Ask Before Becoming AI-Native3 min read

Reading Time: 2 minutes

At Blox.ai, we are constantly in conversations with decision-makers in various organizations. These leaders are across tech, product & business and talk to us about deciding on their A.I. roadmaps. Whenever we speak to them, we give them a simple, and easy-to-use framework to help them make these decisions, and embark on their A.I. journey. This is a framework that puts their organization in the driver’s seat so they can make the best decision for their business.

Today, we wanted to share that framework with you. Here are 3 questions every business needs to ask themselves before becoming AI-Native:

Question 1 – What are the mission-critical problems that you can fix with A.I.?

Every organization has problems that are “mission-critical” or fundamental to the very working of the business. These are the problems that business leaders should look to solve with A.I., versus nice-to-have experiments that may or may not have an impact on the organization. When A.I. is applied to solve mission-critical problems, whether it’s more efficient onboarding or superior user experiences, the ROI on the investment becomes instant. When the organization sees immediate value from A.I. through greater revenues, faster processes or better tools for its teams, it becomes possible to enable large-scale cultural changes with A.I. adoption. So, it’s vital that organizations pick mission-critical problems to solve if they are to create winning mindsets with A.I.

Question 2 – How do you plan to invest in data science/ML teams?

There are three ways to approach this question: Build, buy or take the hybrid route.

Build: Many organizations today are keen to take the build route by hiring large data science teams and are investing in building these teams in-house. This is certainly the way to go for organizations that are lush with funds to experiment. It is worth noting, however, that it takes a minimum of 2 years to see outcomes from this investment.

Buy: By choosing to partner with existing A.I. companies, organizations gain an advantage by getting their engineers to work on quick wins with ready-to-use APIs. Partnering with A.I. companies enables organizations to drive a culture of data-driven decision-making and focus on immediate value and ROI. Additionally, there is less pressure with respect to hiring in the ML talent market.

Build-Buy-Hybrid: Organizations irrespective of size can go for a hybrid approach that combines the best of the build & buy models with a laser focus on ROI.

Ultimately, it is the value that organizations wish to create with A.I. and the ROI that they expect which will determine the right decision for each business.

Question 3: What does your organization wish to achieve with A.I.?

Finally, it is important for organizations to remember the “why” of their investment in A.I. Some organizations are simply curious about A.I. and invest in A.I. to learn and experiment versus driving metrics that affect the business. Being honest about the intention behind investing in A.I. will help your organization determine the way to go.

Ultimately, the journey towards building a truly AI-native business is about making investment decisions that focus on value & identifying the best avenues through which they can see ROI at, at scale, and at speed.