Responsible for making decisions on AI investment but unsure of how to evaluate its scalability?
Struggling to develop an AI roadmap that will have a company-wide impact?
Looking for a method to create a scalable & defensible AI system?
Planning on implementing AI into your business but don't know where to start?
With the boom of AI in recent years, organisations cross-industry have found it easy to develop AI pilots that deliver technically. But there's still one glaring problem that business leaders face when it comes to AI implementation:
"Our pilots work...but how can we make them scalable and reusable on a company-wide level?"
Very often, the shortage of resources would be addressed as the hurdle. But after many years of working closely with executives to develop their AI strategies, we've seen that this is not the case. The real issue is that businesses fail to create a way to collect proprietary data for AI reinforcement.
This interactive workshop will provide you with a proven framework (the "Data Harvesting Loop") that addresses this issue.
The Data Harvesting Loop is a (Nexus-born) framework that guides leaders to making AI solutions scalable and defensible for their businesses by focusing on identifying the right data to be harvested. The core principle behind Data Harvesting Loop is based on the notion that an AI solution can only scale if it is continuously reinforced by the unique data a business owns.
On completion of this workshop, you’ll walk away with:
- A deep understanding of why your AI roadmap should be business-driven and not tech-driven
- A robust, proven framework to critically assess which AI business cases can scale and be reused
- A guide to developing an effective "Data Loop" for your business in order to best leverage the data you already have (Spoiler alert! We guarantee you'll feel confident enough to speak data with your CTO and tech teams.)
- Ideas for AI business cases that you can start immediately within your organisation
— Andrew Ng, The State of AI, EmTech Conference 2017
- The Data Loop in AI: What is it and why it can make or break an AI project
- Human in the Loop: The role of humans in the data loop
- Data Harvesting Loop: Nexus-born framework to evaluate AI application ideas
- Apply the Data Harvesting Loop method to your chosen organisation
1-hour private consultation:
Want to know more about the workshop?
What our participants say
"The workshop was really effective in giving me an overview to approaching AI technologies and AI project management. It gave me a great starting point to explore potential solutions and find out which AI technologies can be beneficial for our business."
"Hajime and Danny made the complicated simple, in an interesting and memorable way. There were opportunities for networking in breakout sessions, where ideas and experiences could be shared with individuals from your industry. All in all a very interactive professional and educational experience."
KYC/AML Training Manager
Instructors and Advisors
Hajime Hotta, PhD-
Hajime Hotta is a serial entrepreneur who has had several successful ventures in Japan. He invented and led the core product at Cirius Technologies, which was acquired by Yahoo Japan. He has a PhD in Artificial Intelligence and a strong academic background in AI and AI-web application.
Danny is a serial entrepreneur and an early-stage investor. He currently serves as an Entrepreneurship Expert at the Said Business School and is an advisor and judge to several technology start-ups and accelerators including Microsoft Accelerator, Startupbootcamp IoT, and LBS Launchpad.
Terence Tse, PhD-
Terence Tse is an advisor who has worked with several corporates and the EU, UN and World Economic Forum. With over 100 published articles and two other books to his name, Terence is a sought-after global speaker.
Mark Esposito, PhD-
Mark is a socio-economic strategist researching megatrends, business model innovations and competitiveness. He has advised and consulted cities, governments and UN agencies at the interface between business, technology and government.