As computing and storage went cheaper, Big Data came up. The words being used as buzz shifted to the machine and deep machine learning when early adopters had a single source of truth. Now, learning from this single source of data took the main-stage.
This wave created a lot of new jobs and the profile of Data Analyst was born with most of them working for US companies who were investing in this. It graduated to Big Data Analyst and then Data Scientist and Machine Learning Engineer and the profiles evolving and becoming more and more specific in nature.
Now, with these Data Products talking back to reality and creating more data, Artificial Intelligence and Machine Learning are happening. The ‘Corporate Innovation’ space has gained momentum across the globe and more so in India, with a significant increase in the number of corporates figuring out different channels to tap into in order to drive a strong innovation agenda.
The Indian Scenario
India has lagged and it is only now when the likes of Jio are building their teams in Palo Alto that other businesses are starting to adopt. Another impetus has been the cloud companies building up their stacks and promoting Data Lakes and Deep Analytics stacks as a value-added service in India.
But Businesses care for a solution. The reality is that AI solutions feed on the Data. It is like a pattern based engine which can create new rules for businesses. These rules can be really counter-intuitive but at the same time deeply causal for critical business indicators.
For example, we witnessed that when we created a machine learning driven engine for one of the leading retailers in India, the sales of its stores in tier 2 towns was being caused by primarily the presence of temples/mosques in the store’s catchment.
So, the maturity of Data collection, storage, governance, computing and consumption is what enterprises have to build first. This is not an extension of traditional information technology initiatives but a separate hub within the firm. Deep Awareness of why a particular knowledge can be recreated deeply through data is access to build this Data culture.
When creating use cases for pilots, business leaders carefully choose pilot contexts with outcomes which are realistic and practical. This has to be confirmed based on the data exploration. For example, Machine learning models require much less data than deep learning models so the context in which they are designed and developed is different although both of them can be used to solve a particular problem.
Enterprises in India who are adopting AI need to equip their leaders with the access of data-driven thinking. Unlike IT, AI would demand serious involvement and directional thinking from leadership as it has the potential to fundamentally disrupt their respective business models.
Enterprises Would Want to
1) Start training their leaders and enable them to incorporate and own up AI/ML in the respective business functions
2) Centralise their data and increase their data collection
3) Start running pilots to create products that talk to reality for immediate impact
4) Build their in-house/outsourced AI/ML team.
Businesses and government should invest in creating the right skill. This can be done by creating open innovation programs and enabling skill building platforms and AI residencies. The serious impetus on research in AI is to the hour of need.
Exemplary steps by both China and US in these fields are to be learnt from. Canada and France are really aggressive with their AI program and have plans up to 2030 on why and how they would leverage AI. It’s time when the makers, instructors, learners and contributors come together to build a powerful Indian ecosystem of AI where its ubiquitous application is unhindered. AI is no more academic, it is being practised in the industry and is potent enough to disrupt businesses. Enterprises would want to be AI ready!
We have seen a trend with large enterprises now looking to Startup Accelerators which can help them imbibe this disruptive culture by being the bridge between outside innovation and the organization. The overall interest in entrepreneurship has spurred the growth of accelerator programs to service a startup culture and create future-ready startups. Tech startups and solution providers in the realm of AI are in talks with some of the big enterprises to run a well-defined object-oriented program for the corporate. The Accelerators market the program well, review and select startups for each cohort, provide mentors and manage the program on the corporate’s behalf with the goal to make shortlisted start-ups enterprise-grade, and integrate them into the corporate business.
– Nandan Mishra
Founder & CEO, Algo8