CWIN17 london ai ml and automation for digital operations - vanessa kho

Editor's Notes

• #3: Why now? AI has been around for many years….
• #4: Why now is more important than ever to focus on customer experience? We create 2.5 quintillion bytes of data everyday, consumers expect more these days from the data we shared, whether is through social media, our fit bit devices, engagement with customers has experience a paradigm shift in the past 2-3 years and will only continue to change E.g. We expect our Bank / Credit card companies to give us offers/discounts based on our shopping pattern. We expect to complain about train journey using our twitter account We expect to buy insurance through facebook messenger on our mobile device. With the explosion of data and more demanding customers (millennials), traditional analytics is not enough for companies to stay ahead of the games and be competitive. This is where Cognitive computing to help transform how companies should engage with customers. With the advantage of ML and NLP, we no longer need to analyse data in the traditional way, data analytics or big data software will soon be replaced by Cognitive technologies.
• #5: 2930% Increase in sales leads experienced by Harley-Davidson using an AI tool in three months Cosabella, a luxury lingerie retailer, has moved to an AI-managed marketing platform. This smart platform automates digital advertising and marketing efforts, such as targeting a high-value audience and driving paid search ROI. In a three month pilot, the platform produced a 336% return on ad spend (ROAS) and a 155% increase in revenue (Q4, 2016). Before they shifted to the AI platform, social media accounted for 5 to 10% of Cosabella’s paid ad revenue. Since the adoption of the platform, social media consistently accounts for 30%.
• #6: Where are the benefits of AI? This is a research done by Capgemini Digital Transformation Institute, we surveyed about 993 companies,
• #7: A lot of interesting startup in the market plus the technologies giants like Google, IBM and Microsoft, some of these startup are focusing on industry specific use cases. The key to remember here is : with the convergence of technologies, a lot of companies claims to have AI and the truth is, there are thousands of ML models in the market, to give you an example, I recently attended a robotics software company who has developed ML capabilities, their success rates is 5000 examples to achieve a success rates of 60%, versus IBM Wastson – you need about 300-400 examples to deliver a success rate of 86%. Picking the right vendor with the balance of cost, functions and effectiveness is where Capgemini has been working with our customers on.
• #8: Use Case walkthrough: Low hanging fruit – automate and read customer email, classify them and route them directly to relevant parties, or even perform straight through processing Call centre optimization – you will be surprised a lot of call centre volume are handling requests such as balance inquiry and change of address. We are working with Retail banks to reduce the call volume by Chatbot and Voice Bot. Voice bot add an element of human touch unlike traditional IVR. More sophisticated use cases will include Claims assessment by AI and Robots, Compliance and Wealth Advisor. We are working with Insurer to look at how AI can help assess Injury Claim by recognizing pattern, augmenting decisions by case workers At JP Morgan, lawyers spent thousands of hours studying financial deals. Now, an AI system is doing the challenging job of interpreting commercial loan agreements, taking on a task that has swallowed 360,000 hours of work by lawyers and loan officers. The AI system reviews documents in seconds and is less prone to error. The system has cut down on loan servicing mistakes, many of which originated from human error in interpreting12,000 new wholesale contracts per year3. A similar experience is highlighted Royal Bank of Canada: AI offers the ability to scale our capacity 10,000-fold of human analysis and scale back as and when needed.” Mastercard intends using AI to improve the overall accuracy of real-time approvals of genuine transactions while reducing the number of false declines. Mastercard estimates that the value of false declines is over 13 times greater than the total amount lost to actual card fraud and that a third of customers stop shopping at retailers after being falsely declined. By using AI, Mastercard hopes to reduce the overall number of false declines, and thus help their retailer partners.
• #16: Esta imagem nao funciona; Espada em cor, nunca branco? Usar a forma de duas cores como os exemplos aqui em anexo.
• #18: Should I start introducing these technologies or directly jump to specific use case
• #19: There is separate session on each topic and positon papers to solve these problems.
• #29: Are we representing feature here….. C reverse C …..
• #30: Let machine to build its own feature set
• #36: Building business case with proper benefit quantification is another challenge. One can build the business case Automation focus OR AI focus Important thing is benefit realization is directly proportional to rate of learning therefore it is essential to define intermediate business outcomes with assumptions tracking. As you have seen in this point of view : This requires different mind-set of empirist, rationalist and connectionist. If we combine maths+stats , CS, Business Knowledge then opportunities are infinite.