Inspire AI Summit 2020 Top Takeaways: Episode 3

After a week off, we were really excited to return to the Inspired AI Summit 2020 yesterday. As usual, we’ve collated our top takeaways from stand-out sessions below. We hope these insights prove useful to those considering implementing AI solutions into their businesses and for those trying to deepen their understanding of emerging technologies.

This week’s episode surrounded ‘AI in Action - How to make AI Work for Your Business’. Our takeaways focus on two sessions in particular: ‘Building AI frameworks that are safe and scalable in the future’ and ‘Augmentation or Automation: What is the future for human machine relationships’.

Safe and scalable AI frameworks:

Mark Caine (Project Lead, Artificial Intelligence and Machine Learning, World Economic Forum) and Kay Firth-Butterfield (Head of AI & Machine Learning, World Economic Forum) delivered an insightful session on the importance of creating AI frameworks that are both safe and scalable.

Barrister and former UK Judge, Kay Firth-Butterfield emphasised the importance of using technology to achieve beneficial outcomes. Central to this aim, she highlighted the necessity of ensuring that new generations benefit from AI, but doing so in a way that is safe and ethical. Against this background, organizations must be aware of the ethical principles that govern AI and shape its safe deployment. As emphasised in previous episodes, the operationalisation of these principles is of utmost importance. Being aware of their existence is one thing but putting them into practice demonstrates commitment to their underlying purpose.

In a similar vein, the speakers stressed the fact that attention must be drawn to the asymmetry of AI; between companies that use AI versus the customers that do not, and between governments that have the means to deploy AI versus citizens that do not. Ultimately, the imbalance of power between those that have access to AI and those who don’t must be remedied and addressed before conversations about scalability can even begin.

Assistance, Augmentation or Automation:

Benji Coetzee, Digital Director at VodafoneZiggo, outlined a fantastic case study for the implementation of Artificial Intelligence which highlighted some of the best practices for any company seeking to create AI.

Coetzee detailed how a clear overall goal of improving customer satisfaction with their services allowed for the effective use of data. Breaking down the silos of data from within the company helped ensure that all employees could leverage it to achieve the best possible customer experience. This was a great example of why a good data governance policy is so important. Ensuring that your team understands what data is necessary will help you to meet your businesses goals.

Coetzee also highlighted an ingenious way her organization is using AI: deploying the technology to gage issues customers are facing online. By appreciating or anticipating these problems, the company was able to take a proactive approach towards customer services. Not only does this reduce strain on IT services but it offers a fantastic competitive advantage in being able to best meet your customers’ needs.

Reframing Business Challenges as Prediction Problems

Brainpools’ very own Head of Business Development, Clayton Black also led a session in the Brainpool virtual booth with the daring challenge of trying to simplify AI for Business Leaders.

For many business leaders, one of the big challenges is understanding how to approach adopting AI; the scale, complexity and hype all make it out to be some insurmountable task. In Brainpools’ experience, when it comes to making decisions about Artificial Intelligence, the first step isn’t trying to wrap one's head around all its complexity and capability. It’s understanding how this capability aligns with business objectives and can create business and customer value.

In our session titled “Simplifying Getting Started with AI for Business Leaders’, Clayton took a step back from some of the hype and excitement of different AI applications to focus on some of the key principles business leaders should understand about AI and provide our recommendations on how to best approach AI to create real and relevant business value.

A vital bit of insight from the session was not to get distracted or preoccupied with the technology landscape but to instead focus on reframing business challenges as prediction problems. Fundamentally, AI is about using data to generate predictions that informs how machines and people will act and behave in the future. Predictions are the basis for all decision making. That means, if we reframe business challenges as prediction problems, we can begin to understand how we might use AI to address those business challenges - and the types of data and technology we might need to achieve it.

Once business leaders determine the predictions they need to achieve their business objectives and desired outcomes, focus can be extended to the data and technology that enables them. But the first steps are understanding the types of predictions that lead to making better decisions. The great news? You don’t need to know a thing about AI. Just a deep understanding of your business.

Brainpool Thoughts:

What is clear from these sessions is that careful considerations need to be made before innovating. Without consideration of issues such as the goals of your AI solutions, the ethics and safety of AI deployment and ensuring a robust data governance policy is in place, companies will expose themselves to an enormous amount of risk. This is why Brainpool ensures that it undertakes the due diligence required to properly ascertain all requirements and considerations that need to be made before launching into AI development. Not only will this mitigate the risks you face over the journey to deployment, but allows for effective, scalable long-term solutions.

Next Time:

Next time, we’re really looking forward to Episode IV: World Summit AI - Getting the future right. In particular, we’ll be keeping an eye out for ‘The role of human error in AI - preventing algorithmic bias’ by Google’s Timnit Gebru,‘The Age of With - Humans working with AI’ by Deloitte’s Evert Haashijk and many more.

Written by Anjali Kapila and Dominic Richmond