[LISTEN] Building the AI combustible engine and other insights
Pearls of wisdom from Episodes 121-125 of the AI & Intelligent Automation Network podcast
The Pearls of wisdom from Episodes 121-125 of the AI & Intelligent Automation Network podcast include separating signal from noise, receiving deliberate consistent customer data, the global impact of digital transformation, €1B in value from analytics and building the AI combustible engine.
Seperate the sigal from the noise
Ep. 121: Vivek Wadwa, CMU
"What's bothersome to me is that they are hyping beyond all existence. We have people who are talking about things that don't exist and starting fear. Others who are speculating- it's all sort of being lumped together. Not to say that we don't have amazing tools and we can do incredible things with machine learning- it's just that we're confusing everything at the same time."
Vivek notes that there are incredible things that can be done with machine learning but that hype and distorted rhetoric are making it more difficult for corporate enterprise practitioners. As we move from the 'moment of setting out on the journey' to realizing the vision of AI and Intelligent Automation take pains to seperate the signal from the noise.
Deliberate consistent customer data
Ep. 122: Anshuman Das, Warner Bros.
"If you are planning to get a more robust solution where you know that you're going to get this format of data from your customers on a regular basis, you probably have to figure out some other commercial tools which serves the purpose. For us- we wanted to get something which was much more scalable and can be done in a very short span of time and less expensive."
Here Anshuman is distinguishing the combination of speed and scale from deliberate consistent customer data. If you want rapid speed and scale- go with something like AWS. But in his opinion, alternative tools are needed to dive deeper into your customer data. We'll come back to this distinction in coming months- watch this space.
Building the AI combustible engine
Ep. 123: Edda Blumenstein, Leeds University Business School
"Data can not tell you everything. You also need to see things through observation. There are things that that you could not see in [machine readable data]. Data is complex. There is a lot of data from different sources- customer data, industry data and competitive data. Are you positioning yourself as a leader and are you going to change the ecosystem? Are you going to change the industry? Or are you going to survive? Or are you going to be a leader within your niche segment? Are you responding to the changes that are happening and transforming? Are you proactively changing the industry with completely different business model?"
Data is the new oil but you still need to build a combustible engine to take advantage. Yes- you must be aware of your data in a brand new way for this brave new world. But the data is simply the backstory. You need to ensure that your data informs your current and future enteprise. Don't stop at 'current data,' continue to 'next data' and how that next data informs your 'next' enterprise.
The Global Impact of Digital Transformation
Ep. 124: Transformation Impact
"AI can assist with sustainable agriculture, poverty, global warming, and healthcare. What we want to do is really to make sense of all of this data available so that it help us to address some of the key challenges that we face.”
Digital transformation at the enterprise level provides the opportunity for a more profitable business which provides a more perfect customer experience. When each digital transformation is combined, that collaborative innovative reality provides the opportunity for a more perfect human experience.
€1B in value from analytics
Ep. 125: Görkem Köseoğlu, ING
"Being very explicit about it and also prioritizing your activities based on being open is fundamental. We need to in a way prove our significant value. In 2019 we generated €100 million plus in analytics and our ambition is to reach to one billion plus euro level for the bottom line of the bank."
Gorkem's problem seemingly isn't that he's providing the value- it's proving that he's providing the value. Once he achieves is goal, he'll be providing 10% of the revenue the company currently generates. That should get folks' attention.