The fact that data is the new oil is becoming more and more obvious for leaders in all types of businesses. Even though this analogy is not perfect for a number of reasons (eg, oil is a finite resource, data is effectively infinitely durable and reusable) the analogy can still be used. For companies to leverage on its data, there are a number of challenges that they need to overcome first. AI will play an important role in helping businesses to discover the oil and pump it out of the ground.
In this blog post we aim to give you some guidance on how to master these challenges and what approach you can take using AI.
Successful start-ups of today are data driven and use AI from day one
The first week of July we participated in the Serendipity Challenge event at Almedalen in Sweden. The Serendipity challenge is a scene for start-ups and fast growing tech companies where 400 Swedish companies are competing to be nominated as this year’s best Swedish start-up. 50 of these get nominated by a professional jury and attend the Serendipity Final at Almedalen. 7 out of the 50 get the opportunity to deliver an extra pitch for the jury before the winner is announced.
We had the opportunity to listen in on their pitches and we got truly impressed. Many of the companies have a huge potential to become the next Swedish big success following companies such as Klarna, Spotify or Skype.
It was obvious that the 7 top performers are taking a similar approach to creating their solutions and building up their businesses. All of them are focused on disrupting old processes for things such as recruitment of staff, waste management, buying and selling real estate, education material and so on.
They are data driven and use AI as a natural part of the solution from day one. This makes it possible for them to significantly enhance the value their solutions deliver and the experience they deliver to the end-user. It is also obvious that their strategy builds on using the data that is being generated for other types of purposes and when the company expands, and they get more and more data, they start monetizing that. These companies view the data as oil from day one and have a strategy for what to do with it. And to be able to do this, AI plays a crucial role.
Traditional businesses need to step up and start to get data driven by enhancing and modernizing their current solutions. When you do that, AI comes into the picture and can help you master some of the challenges of becoming truly data driven.
AI implementation challenges
A typical scenario is that an organisation has got data but they do not know how to leverage the value that it represents.
If you invest in AI solutions you will be able to both create higher customer value using the data and at the same time get more structured data that you can use for other purposes, just like the successful start-ups.
Driving that type of implementation is not easy and demands a strategic investment. This is the type of things that you need to master:
- Build an organization that will become capable to drive new innovation based on AI
- Find out how the solution or the process should be changed and enhanced using AI
- Find the AI solutions that you can use that suit your specific needs
- Manage the process for developing new solutions or business processes that leverages AI
The process for developing the actual solution can be divided into 3 main steps:
- Data collection
- AI modelling and training
Most organizations do not have experience from managing this kind of AI projects. Also, there is a risk that this AI implementation approach will take too long and not become successful. Therefore you should build strategic partnerships with vendors that have got the right skills and offer AI software for different purposes.
The AI vendor can help you discover the value in your data
The AI vendor can typically help you extract value out of data that you already have. Many times, the AI vendor can also help you start generate and structure new types of data that will be generated as a result of implementing AI in combination with IoT solutions.
The investment that is needed to get this up and running is however normally quite big. An alternative approach is to give the data to the AI vendor in exchange for a lower price for setting up the solution and start deliver higher customer value based on it. The problem with this approach is obviously that you then can not make money on the data for other purposes yourself.
A third approach could be to look for AI vendors that already have large datasets that suits your specific needs and can enhance your solutions. You can then potentially buy access to that as a service that you then add on to your own solutions.
Get the benefits faster and cheaper
The second and third approach will give you benefits like:
- A faster way to deliver higher customer value
- Less deep skills needed related to AI in your organization
- A more efficient way to add on and create new future solutions
This is for many companies a better strategy than not doing anything at all. They have the data but do not understand the value of it, or they can not get it out of the ground. Instead they risk getting disrupted by new and upcoming players, such as the start-ups on the Serendipity Challenge scene.
For more info about the value of data, read this great article by Bernad Marr at Forbes.