The Road to AI is Paved With Potential

By Muhammad Asif Abbasi

You can, as the saying goes, lead a horse to water, but you cannot make it drink. The same saying also seems to be true of analytics and particularly of platforms: You can provide a platform, but how can you make sure that it will be used, providing both value and a return on investment?

There is no question that acquiring the platform is only the beginning of the story. Developing its use until it becomes part of how we do business is a whole new challenge in its own right.

The purpose of platforms

To answer the question of developing use, I think you have to look at why customers turn to platforms. In my experience, there are two main types of approach. The first is the customers who have a definite use case in mind. They buy, build or develop a platform with a view to solving that problem. In many ways, that works really well: There is a problem, and here is a purpose-built solution.

These customers tend to get high short-term value from the platform as they use it to address the particular problem. The platform will have high levels of use, but chiefly among the team that first identified the problem.

In the longer term, however, the value may not be so high. The initiating team may continue to use it, but others may not be able to jump on the bandwagon, especially if the purpose-built platform turns out not to be so good for other problems.

People, platforms, potential: AI road

It is important to be looking into the longer term, and being confident that the platform has wider applicability for the organisation.

Other organisations are therefore taking a different approach. They see that a platform may be helpful to solve a number of problems, buy an off-the-shelf model, and then try to encourage users to come and play. They show people how to explore data in the hope of finding a few insights during the process.

This is feasible with the advent of Hadoop and other storage options, and the rise of application programming interfaces, or APIs. It is also easier with visualisation tools, as this opens up data exploration to more users, and particularly business users rather than pure data scientists.

Platforms in practice

There are also questions about scalability: making sure that the platform has options for multiple teams to use it, with small incremental costs charged to each new team.