Once more, it was established what massive data volumes coupled with enormous computing power can do for humans to transform lives. But the quality of data is not to be lost sight of – our good ol’ blighter, GIGO, is still around and never misses an opportunity to mess up things. If you are contemplating on Digital at Scale then the last 8 months’ rapid acceleration is what you should be looking at. What has been achieved in a matter of months would have normally taken many years.  

The lines that have separated industries are now blurring. For example, when we look at healthcare, our attention is instantly drawn to clinical diagnostics and we don’t always think of retail & SCM to be critical to this particular value chain. Ask around and anguished people will tell you how difficult it is to cope if there’s a shortage of Paracetamol tablets. AI/ML-based models can ensure these chains aren’t broken and there’s information sharing between the two industries to averta supply-side voodoo. 

Not only is data ubiquitous, but it’s also machine-readable (instantly) and we shouldn’t let it grow cold. Insights collectible can lead us to a predictive/prescriptive state if we show a high sense of purpose and act on it. Are we prepared to harness this data and put it into context to derive a continuous stream of value? The age-old construct of being at the right place and at the right time can be extended to include data as well. 

The agriculture sector is a powerful reminder of what can be achieved. Weather conditions, for hundreds of years, have held farmers to ransom, often destroying lives beyond repair. Due to the deteriorating environment, predicting the weather based on “experience” is increasingly becoming unreliable. Combining data from a multitude of sources (historical, sensor-led, etc.) aberrations can be predicted well in advance to help the farmers in crop selection, water usage, nutrients filling, and the use of pesticides.

Education is the other big block. AI-based models are being used to bring trainers up to speed and help them stay relevant. The Cloud-based models have agility as a part of their DNA – you choose, use, and pay as required. There’s no compulsion of being burdened by high capex cost. 

The messaging is loud and clear – adapt, accelerate, scale & succeed with data & AI. 

The convergence of technologies has had an impact on business models, industry dynamics, policies, and talent. Here, a banker’s outlook may be a little different. They aren’t necessarily chuffed about what magic technology can wield. Jaws drop only if it’s purposeful. There are three areas where AI has shown great results – financial inclusion, customer experience enhancement, and of course, greatly improved productivity. Typically, banks use CIBIL scores to determine credit-worthiness but this may not be relevant for the under-privileged sections. Data collected from multiple sources and put through an AI engine can throw up valuable behavioral traits that are extremely useful towards giving out small loans.Data-driven insights help in targeting the right customer segments which lead to a reduction in customer acquisition cost and increased revenue. 

The automotive sector has its uniqueness too. Building a new model takes 3 – 4 years so the wrong choices will have a long-term impact. Cars are mass-produced and yet, there’s a high degree of personalization that goes beyond the basics. There can be no short-cuts to personal preferences. Sustainable mobility is a major change agent and companies are spending tremendous amounts of money to design vehicles accordingly. It’s also driven by a powerful ecosystem that fosters data sharing. 

In fashion retail, trends change every 4 – 6 weeks so being there at the right time is crucial. A lot of data-crunching can help to address this problem. It’s always a tricky situation to know when a particular style may have gone out of fashion or if the designer is ahead of time. And of course, merchandising involves global sourcing that is catered through complex supply chain operations. 

At any given point, the expectation from the healthcare system is best-in-class treatment at affordable price points. Using traditional methods and legacy systems, there’s no way that a billion-plus people can be served in this manner. AI is being used to improve clinical efficiency and capacity building. How do we use data to ensure that resources are optimally used? Non-communicable diseases in India lead to very high mortality. This can be brought under control if patients’ health-related data are used effectively to alert & avert impending health hazards. 

Finally, the most important aspect - legislation. Policymaking has to be done in a way so that it does not impinge on innovation. We have to foster an environment that encourages PoCs to be scaled and towards this, a principle-based approach is warranted. AI is not a silver bullet so organizations have to be prepared for the long haul. Things will not change overnight and an environment needs to be created that sustains innovation and celebrates small success to keep it going. 

All these sectors are prominent users of data & AI-enabled models that improve the quality of services, degree of personalization, optimize cost, and improve productivity. Also, data sitting in silos is a waste of valuable resources. In highly regulated industries, it may not be possible to share data quite freely and that’s why we need to put standards in place so that permissions and rights can be given easily. And, we need to do this quickly – data cannot be allowed to get cold. Then it becomes useless.     

Image by Michael Schwarzenberger from Pixabay