What are the key areas where artificial intelligence (AI) and machine learning (ML) is proving to be a game changer
There are three key areas where AI and ML are helping organisations transform their businesses. The first area is digitising the customer experience. Today, banks meet clients face-to-face to discuss opportunities and for portfolio management. All this is changing with digitally aligned processes. Now, we have many use cases of customer interface going completely digital. The second area of focus is developing smart products by creating digital extension of the machines and having AI support these products, thereby changing the value proposition completely. And the third area of focus and the one that is growing the fastest of all is robotic processes. This relates to automation of large-scale processes managed by employees which are not seen by customers directly. One such process is management of claims by the workforce in an insurance company. Organisations are looking to introduce AI platforms to take over these processes.
How excited are clients to adopt AI and ML solutions?
Some of the clients are very sophisticated as they recognise the potential of AI and ML solutions. Then there are those who have no idea about the transformative power of these technologies. As recently as three years ago, clients were just intrigued by social computing and the SMAC
(social, mobile, analytics and cloud). Companies were looking at the same set of vendors for standardised solutions. But all that is changing with players like Amazon and others discovering the power of AI etc. Such companies started connecting the dots early on and it’s incredible to see where they are now. A lot of organisations are in exploratory stages as they realise that their strategy and customer engagement needs to get smarter. The combination of optimism and fear that clients today have shows that for them it is a competitive necessity to adopt AI and digital technologies.
What does the adoption curve for ML technologies by organisations look like?
The uptake is slow but ML technology
is fast hitting an inflection point and we will soon see adoption picking up. The adoption of ML and AI depends on where the organisation is focused. If you are an IT organisation, you are likely to recognise digital as a competency and focus on getting smarter, develop ML solutions on a priority basis. So, it is this focus that is going to trigger the kind of technology
deployment, network architecture, data transfer and security protocols that an IT organisation will go for. Then, there would be very specific use cases. There are clients that are looking to redefine the product, customer experience and business process. And that is fairly heavy work. It requires confidence. Players like us need to show what organisations can possibly achieve by adopting AI and ML.
Also, the adoption is skewed in favour of big brands as a lot of them have the resource to invest in these technologies. Where the AI and ML market is right now, one needs to hire the right talent and partners as a lot of these solutions are bespoke, one cannot buy them off the shelf.
Do we have global examples of organisations embracing AI and ML?
One such company is JP Morgan. It has successfully implemented a couple of AI and ML-led solutions. The company has adopted a solution called COIN which manages its contract portfolio with commercial clients. It’s a laborious process. And a machine manages this task with speed and more accuracy.
On the job front, are AI and ML a threat?
In 20 years, probably every job will be touched by AI. The technology
is growing universally. WhatsApp and Facebook — everything is driven by AI. And what this means is that on the job front, there may be blood. Worldwide, about 12 per cent white-collar jobs will get eliminated by machines. On the other hand, three quarters of jobs are going to be protected by AI. It will actually make people’s jobs more secure and better. Once AI, ML, and virtual and augmented reality go mainstream, these technologies will prove to be a huge job creator.
If you are a worker, a company and a society that’s sitting and not doing anything you may get victimised. But if you are smart and make the right investments, AI and ML will create a lot of opportunities.
What is the suggested road map for organisations to adopt AI and ML?
I will suggest don’t plough the ocean. A grand plan to go digital and adopt AI at one stroke is going to fail. Start small. Pick two-three very focused areas and develop AI and ML practices. Within the company begin with processes that customers don’t see because there will be mistakes. Once you get confidence, leverage the team’s knowledge to design more AI and ML powered applications.
General Electric is a great example. Several years ago, the company started by putting Internet of Things sensors on their engines. Thereafter, they watched engines, collected data and built new business models from it and brought in a predictive platform. So, start something bite-size and then scale up.
How quick and demonstrable is the return on investment on AI and ML solutions?
It varies from case to case. But results most of the times are quick to show. A great example is the extraordinary performance of Los Angeles-based wealth management platform Betterment Asset Management, Inc. In the first five years of its operations, the company had assets worth $6 billion under management. The adoption of AI solutions saw the company’s asset portfolio grow to $10 billion in a short span of time. This is amazing growth and it is an incredible story that one is going to sell. But competitors need to remember, Betterment Asset Management is a really old company, they had the distribution channel and customer touch points which made it easier for them to perhaps inspire their existing customers to invest more as opposed to acquiring new customers, which is costlier. So, depending upon the stage that a company is in, its business model and if the market is ready, one can get returns on investment in six to 12 months.
When do you expect the AI market to mature?
This reminds me a great deal of 1993 where all the pieces of client servers and enterprise application services started coming together. Networking solutions came from Cisco and data storage solutions came from companies like EMC. Solutions such as SAP and CRM too showed up at the same time. And the server market just took off like a rocket. AI technology, too, is at a similar stage. With key projects in the pipeline and specific solutions coming from companies like us, the AI market too is set to take off soon.