By Anil Bhasin
The current landscape of generative AI in India is marked by significant growth and enthusiasm, with technology poised to unleash transformative potential across industries.
A research with MIT Technology Review surveying 600 global CIOs, CTOs and CDOs found that India is one of the fastest adopters of gen AI, with more than three in five (68%) of the interviewed tech leaders in India stated that their companies are moving fast/very fast in AI adoption. A recent report by EY also states that gen AI has the potential to add a cumulative US$1.2-1.5 trillion to India’s GDP over the next seven years.
Gen AI’s potential for innovation has captivated the attention of Indian tech leaders who are increasingly investing in research, development, and implementation of these solutions. In the same MIT Technology Review report, all CIOs from India interviewed expect AI budgets to increase in 2024, with some expecting their AI spending to double.
Addressing hurdles in gen AI implementation
Despite the opportunities for gen AI to deliver growth, there are also obstacles that companies need to overcome in order to reap the benefits of AI. These include the availability of talent and AI skills, data and AI governance, as well as security.
- Investing in AI talent and skillsets
The availability of talent and having the right skillsets is one of the biggest challenges for tech leaders around the world. When asked how their company’s data strategy needs to improve, almost four in 10 respondents (39%) say investing in talent and upskilling the workforce. An even larger share (72%) say it will be “very important” to encourage innovation that will help attract and retain talent.
- AI governance is key for Indian companies to succeed
Other pain points faced by enterprises include data and AI governance as well as security. Quality data, robust data management, and consistent governance are essential to implementing AI effectively in organizations. However, many Indian enterprises have siloed and disparate data systems which result in inconsistent data and duplication of data sets, which increases data security risks.
- Embracing the emergence of data intelligence platforms
A unified data and AI architecture like the lakehouse which can query all data sources, both structured (like excel) and unstructured (such as emails, images, and videos), within an organization is essential. It allows a streamlined workflow including all business intelligence (BI) and AI use cases and drastically simplifies governance. Building upon the lakehouse architecture, there is also a new category called ‘Data Intelligence Platforms’ where AI models are used to deeply understand and interpret the semantics of enterprise data, and users can use natural language to query data-sets, allowing even non-technical users to derive value from enterprise data.
By leveraging data intelligence platforms, Indian businesses are not only able to overcome challenges posed by data quality, compliance and governance, they are also able to create the next generation of data and AI applications with quality, speed and agility.
Crystalizing gen AI use cases for Indian companies
Gen AI holds immense potential for transforming key functions within Indian organizations. These include more personalized customer interactions, automating repetitive tasks and enhancing product development and marketing strategies. The adoption of gen AI tools across various departments in Indian organizations is gaining momentum.
Today, companies are particularly focused on exploring gen AI solutions for functions in HR, sales, and customer service, as well as within departments relying on robust predictive analytics. While departments such as legal, finance, and compliance, may adopt generative AI tools at a slower pace, they are expected to experience substantial benefits.
Weighing costs and considerations for rolling out gen AI in India
When considering the deployment of gen AI in India, leaders face a pivotal decision – whether to opt for ready-made solutions or invest in the development of customized models. Indian enterprises must optimize their model selection process, selecting options that align with both business objectives and technical requirements. Leaders must decide whether to buy and/or build, and to choose between large or small models. And all these depend on the specific use cases that organisations have in mind.
For example, an online retailer that wants to build chatbot for their e-commerce site can be employing a small model and training it with lots of internal data so that it can understand the specific needs of the customers and answering it with relevant product information. This can be way more effective (in terms of results and cost) than using a large model trained on general public data.
Conclusion
As a technologically-forward country, India is poised to take advantage of gen AI by positioning its workforce and businesses at the forefront of this curve.
By embracing gen AI’s potential for disruptive innovation, India can be a leader in an AI-driven era that promises to reshape industries and drive economic growth.
The author is VP and country manager, Databricks India
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