1. Why AI?
Today the industry is at the inflection point where it has made the exponential shift from basic analytics to leveraging the power of deep learning, machine learning and AI. Mckinsey collated and analysed Artificial Intelligence Use Cases across industries and functions. They were trying to study what incremental value can application of advanced AI deep neural network techniques provide to the business. The answer was that in more than 2/3rd of the use cases artificial intelligence can improve performance beyond that provided by traditional analytics techniques.
This has unleashed tremendous potential that can be mined. The incremental value that AI can provide in travel is 128%. Retail 87% and Banking 50%.
Almost 99% of all successful travel portals have AI based recommendation engines. This is very similar to Netflix or Amazon recommendations. These engines recommend to you based on your previous choices or history.
Cyber Bank Robberies or Fraud contributes to $1 Trillion in cybercrime losses. A rule-based method of fraud detection will typically take about 40+ days for a Bank to assess a fraud. That is where Machine learning and AI play a huge role. Fraud detection problems are typically constructed and analyzed as classification problems in machine learning. Based on supervised learning these models become intelligent enough two classify a claim as either an authentic & legitimate claim or a fraudulent one and are super-fast.
By learning from past experience, the algorithms become smart and can start identifying potential fraudulent transactions which have never occurred before. We must remember that hackers and fraudsters excel at find completely novel ways to beat the system every time. The fact that a Machine Learning algorithm can also beat the supervised learning by becoming smarter over time as it starts identifying hidden patterns, is a big win. Another very practical business application of AI is in claim assessments. Insurance companies like Lemonade are using AI and chatbot to give their customers a completely hassle free and personalized claim assessment service which can take as low as 3 min.
If you are filing a claim you can open their app to record a video of you talking about what happened, machine learning will convert speech to text and then match what you said with existing claims in the database to do an authenticity check. If all is green your claim will be approved within minutes, and money transferred to your account.
Like this across every industry you will find use cases for AI & Machine learning. No wonder in 2019, global private AI investment was over $70 billion, with startup investment $37 billion, M&A $34 billion, IPOs $5 billion, and minority stake $2 billion.
2. Why Robotics & Automation?
This study was conducted by E&Y in 2018 it concluded that functions like Finance, Administration Customer Service are very high on the automatability scale at around 80%. Human Resources is at 29% and Marketing and R&D at 24% and 22%.
Globally it is estimated by World Economic Forum that automation will displace 75 million jobs but generate 133 million new ones worldwide by 2022. A mammoth change in the skill sets.
3. What is the success rate for organizations adopting AI & Machine Learning?
According to Mckinsey’s Automation Survey, only 55 percent of institutions believe their automation program has been successful to date. And about 50% said that the program has been much harder to implement than they expected.
4. What does it take to run a successful AI & Robotics program in a company?
The biggest driver one should focus on is involving the employees and engaging them upfront. The entire success of your AI & Robotics program can be dependent on this.
33% of the employees fear that they will
lose their job to automation.
There is a lot of un-certainty introduced in the work environment as soon as AI is brought in. Workers feel scared for their employability as well as they are unsure of whether they will be able to adapt to the new digitized high-tech way of working.
As a company, you have to recognise that automation poses more challenges to the workforce because of the need to upgrade skills and thus you have to shift the culture to support continual adjustments to the way people do their work. And how do you this:
Provide employees proper training of what is Automation & AI. What they can expect, and what role they can play. The question that comes up here is that what level of training should be provided, and to whom. A good analogy to draw is the six-sigma program which has 5 levels, each designed to serve a specific business need:
For the Automation and AI program as well, a business should look at how they want to segment their employees across different levels and have a very targeted training objective for each. We recommend the following model:
Provide high level contextual understanding of AI Machine learning with emphasis on the key pitfalls to the success of the program & the role they need to play. They should be taken through a couple of case studies from industry similar to theirs. The importance of solutioning and Data Collection, labelling and mining should be highlighted
The Program Leads
She has transformation and reengineering background along with Project management and a solid understanding of AI & Robotics. She needs to be trained continually on the latest and most upcoming advances in AI machine learning and deep learning algorithms.
Project Managers will need to be trained on AI & Robotics concepts focusing on the solutioning. They need to have an in-depth understanding of the process and business they are supporting. They should be trained on the end to end process as well. Refresher trainings on continuous improvement and reengineering should also be organized.
Project Team Members
Project Team members will be employees from the function and the process. They are process SMEs. They need to be given an overview level training on AI & Robotics. Since they will be participating in data collection, solutioning, data analysis & testing they need to understand the development lifecycle and should be taken through both agile and waterfall methods. They should be grounded on basics of data analytics and statistics.
Process Team Members
These are people who are not directly participating in the project but are getting impacted by it. They should go through a basic level course on AI & Robotics which is more focused around the solution being built for them as a case study. Training on the change that’s about to come and how their roles will transform with what will get eliminated and what will get added should be provided. Wherever reskilling requirements have been identified the training should be conducted. How to work with Robots is also an important subject to be covered. Provide hands-on experience and live demos early in the process, clearly explaining constraints. This will involve them upfront and thus they will be more accepting of the change.
5. Transform a culture of Fear into a culture of Collaboration and Innovation
Treating employees as problem solvers can be culturally very transformational. Delegating authority over the bots to those employees (versus running the bots centrally) can also be a way to ensure continuous improvement and employee participation and instead of fearing a culture of collaboration and innovation will be created.
Amazon is a great example. It announced last year that it will spend $700 million to train about 100,000 workers in the US by 2025, in Robotics and AI.
At Amazon Fulfilment Centres It’s not humans vs. robots, it’s humans + robots. Amazon runs 175 fulfilment centres worldwide. In 26 of them, robots and people work together to pick, sort, transport, and stow packages. Carrying and transporting inventory across building for example is taken over by BOTs and understanding how best to unpack honey or maple syrup is something left to humans. Thus, establishing a good Human and Robot collaboration is the way to go!
Abraham Lincoln said “Give me six hours to chop down a tree and I will spend the first four sharpening the axe”. Good preparation means more than half the battle is won. Prepare well, invest big on your biggest assets – your employees and reap the fruits of Automation and AI.