Original article was published by Liesbeth Dingemans on Artificial Intelligence on Medium
Organising for 10x AI Innovation
Part one of the Prosus AI series on how to achieve AI-by-design product and business innovation with short-term impact
What are the most successful AI use cases you know in your business, product or field? And why do you define them as successful? They’ve likely made a significant impact on a business metric by taking a process or customer proposition and improving them. Take radically optimised search, much cheaper logistics or highly relevant question answering, for example. While these applications and their impact are impressive and important, they often do not fundamentally change or future-proof a business, product or company.
This may sound counter-intuitive, as AI has become synonymous with innovation, but in reality, the vast majority of implemented use cases are for incremental process optimisation. It makes sense to allocate a large share of your AI team to work on incremental applications, because risks are lower as both impact and feasibility can be approximated — but not your whole team.
Spending 100% of the data science resources in your organisation on solving problems of existing customers, products and processes, means that you run the risk of missing AI applications that can put you at the forefront of disruption and stay ahead of your competitors.
At Prosus, we launched an AI Innovation Lab and developed an approach to work on AI innovation in parallel to business-as-usual incremental AI. The goal of the Lab is to find ways to accelerate the path towards radical, yet practical applications of AI that solve real customer problems. It places us in the middle ground between solving current problems users are facing (incremental, user pull) and taking an R&D innovation approach (too long term, tech push). The journey toward this middle ground is not a linear one, and we have many lessons to share — which is why we are launching this blog series on AI innovation.
In our Prosus AI Innovation Lab, we accelerate the path towards radical, yet practical applications of AI within our businesses
In this blog series, we’re starting a conversation on what AI-driven innovation really means — and what you need to be successful, based on the lessons from building our own AI Innovation Lab. Even though an increasing number of how-to guides are written to help businesses uncover applications of AI, we found that most of them focus on what we at Prosus Group call incremental applications. Google, Spotify and others have written blogs and created guides to get teams started with AI and optimise workflow to reduce the average time to production. Our focus is on AI-by-design innovation and we will detail and share our five-step approach in our next blogs:
- Explore user needs and AI trends to understand big opportunities for innovation;
- Define which ‘mission’ is most important to solve first — where user needs could meet AI potential;
- Concept how to radically solve these missions, building on the unique strengths of AI and an in-depth understanding of your customers or business;
- Test to quickly de-risk concepts — both technically and in terms of desirability;
- Deliver impact at scale.
If you are serious about innovation, you need to be serious about creating the opportunity in your organisation for teams or individuals to spend time on forward-looking ideas. There are many ways to do so, but here we highlight the main choices we made when launching our AI Innovation Lab and synthesised them into four takeaways.