Over the last century computers have sped up exponentially, according to Moore’s law, whilst their production costs have halved every 18 months. That pace is increasing even more with quantum computers that are already running at 50 qubits. By comparison, a 100 qubit computer could theoretically be more powerful than all the supercomputers in the world combined. That’s a lot of computing power.
Data, data everywhere…
Meanwhile, the amount of data produced in the world is advancing at a staggering pace. Today we are generating 16.3 zetabytes of data per year and that number is set to grow to 163 zetabytes by 2025, according to IDC. That’s a 10-fold increase in just seven years.
For this reason, Gartner bills AI as the most disruptive technology to emerge over the next decade, because it has the power to process vast pools of data and turn them into critical insights that enhance lives. Although AI has been around since the 1960s, progress has been slow – mainly due to the lack of data and poor computer power. Both are now available in abundance.
Enter the Data Scientist
One thing that is in short supply is the Data Scientist. This skillset did not exist 10 years ago and is an example of new roles being created by this exciting and fast-paced industry. As technology and data grow exponentially, firms need to have a robust data strategy to convert complex data patterns into meaningful insights. However, anyone that has worked in a bank will know just how disparate and disjointed the data can be. Hidden amongst all of these complex systems and silos is trapped value waiting to be unlocked through an effective strategy and technology deployment.
Transitioning from silo architecture
The challenge now is for incumbent banks to transition their legacy silo infrastructures into responsive data-first pools built around the needs of the customer. Customer data should flow freely, consistently and uninhibited in order to flex at the speed of customer expectations. Adding AI is less complex than devising a robust data architecture integrating multiple data sources and exposing them through an efficient API access layer. AI can be plugged in and deployed quickly whilst the data that feeds it requires much attention to detail in many different areas:
- Data acquisition and sources
- Quality and relevance
- Support and maintenance
- Performance and security
These are not trivial issues to solve, which explains why banks have been slow to change in the past, but now large IT budgets are being allocated to overhaul this problem.
Sensors and IoT
Just as AI can be plugged into a robust data platform, so too can sensors and IoT devices. With this comes important location-sensitive insights into real-time sources of data that can be used creatively to build new customer journeys. Increasingly, sensors and IoT, together with behavioral analytics, will drive the new generation of customer experiences. Banks need to plan for this strategy now and have a future proof architecture to deal with the different types of machine learning, AI, and array of Virtual Reality / Augmented Reality technologies that are fast approaching mainstream.
Culture, mindset and integration
Implementing change with this degree of depth is more than a technology problem in isolation. It redesigns processes within the firm, repositions flows of data, creates new roles and disappears others. Big data and AI projects affect the whole organization across all functions and hence require strong executive leadership and change management function. As the maturity of projects grows, there is the overhanging worry amongst many employees that their jobs are at risk – so why should they buy into projects that make them redundant? In fact, history has proven time and again that new technologies create more jobs than they take away so the key thing to focus on is a continuous learning mindset. Staying curious, asking the right questions and challenging the status quo will keep talent pools growing and relevant.
With so many disruptive forces shaping the future, it can be a daunting task to keep ahead whilst making the correct ‘no-regret decisions’. Investments in technology may seem risky and expensive but are rapidly becoming table stakes. Data is here to stay, and knowing how to build a robust platform that can incorporate multiple AI learning models, real-time engines and smooth transactions based on fast moving customer experiences will ensure banks stay ahead of the competition.