There's no denying that Generative Artificial Intelligence (GenAI) has become a mainstream tech phenomenon. Its capabilities to play a central role in the Fifth Industrial Revolution and dispel the notion of mere hype are garnering acclaim across the globe. And India is leading this trend from the frontline.
Artificial intelligence adoption in India is higher than the global average. According to an IBM report, around 57% of Indian companies admit to actively using AI, which is higher than markets such as Australia (24%), the US (25%) and the UK (26%). is also significantly high. Startups in India are also actively adopting generative AI technologies, with him investing over $475 million in these ventures between 2021 and 2023.
Tech giants are leveraging generative AI to launch new competitive solutions. For example, Google is experimenting with new AI-powered search methods, including visual search with Lens, search across multiple modalities, and multi-search using images and text. Apple is developing technology for new AI systems in iOS and other apps. Analysts expect GenAI tools to be used more widely by consumers and businesses across a variety of applications, from text and image processing to audio and video, 3D modeling, code generation, and more.
Since its explosion in popularity in 2022, many organizations are rushing to ride the GenAI wave, with or without a plan. A year later, 79% of respondents in the Generative AI Benchmark report were found to have invested in GenAI tools or projects. However, since the impact of AI is inevitable, it is imperative to assess some fundamental aspects of how AI can significantly improve the future readiness of Indian organizations.
While every organization will be different, one of the fundamental formulas for driving AI outcomes is organizing data effectively. Most organizations creating or in the process of creating an AI strategy plan to leverage public or open source models to some extent, and many plan to enrich those models with their own data. doing. GenAI gives everyone access to faster and improved insights from their data. However, it is important to remember that the quality of the insights gained is directly dependent on the quality of the data used. Organizations need to build a capable, modern data and technology infrastructure to prepare for long-term generative AI adoption. This includes developing access to high-quality, well-coordinated data, supporting scalable data architectures, and implementing governance and security measures.
Leading companies understand that these tools need to be supported by a reliable data foundation. A healthy data foundation drives insights and advanced use cases that harness the power of generative and traditional AI. Data governance frameworks are essential for using data responsibly and effectively, especially in the context of large-scale language models. Companies can streamline this process using catalog and lineage solutions to automatically identify and document relationships between datasets. Additionally, AI systems have the benefit of accessing broader intelligence by aggregating data from disparate sources and consolidating it into a centralized warehouse or data lake. This integration helps AI models discover valuable insights, identify patterns, and make informed predictions.
Additionally, data conversion and consumables insights are also important. After gaining insights through data transformation, presenting those insights accurately, in real-time, and in an accessible format becomes essential for effective collaboration and decision-making.
Despite the increased market focus on generative AI, traditional AI still provides continued value in areas such as predictive analytics. For example, AI is delivering measurable value in space exploration. The Chandrayaan-3 mission's Landing Hazard Detection and Avoidance Camera (LHDAC) harnessed the power of AI to meticulously map the lunar surface and identify potential hazards that could jeopardize the landing. The true power of AI was revealed when it dynamically learned and adapted during Chandrayaan-3's delicate soft landing. Another potential applicability of AI is climate goals. C40 is a global network of nearly 100 mayors from the world's major cities united to fight the climate crisis, and is applying machine learning to climate datasets to help cities like Accra, Ghana improve the health of their residents. We are enabling concrete steps to be taken to reduce emissions while improving emissions. .
Traditional AI remains a key value driver for organizations, and generative AI can help by extending the power of AI beyond data scientists and engineers and opening up AI capabilities to more people. Generative AI can expand your ability to unlock deeper insights and find new and creative ways to solve problems faster. More organizations are now considering adopting a hybrid approach that combines generative and traditional AI to extend its impact across the organization.
While some may be nervous about the wave of AI, it embraces both traditional and generative forms and shows us the best path forward. Generative AI gives great power to end users, and it is clear that AI is becoming a fundamental requirement for enterprises. With more and more organizations in India incorporating generative AI into their AI roadmaps, it is important to recognize that generative AI is an inevitable trend. Generative AI represents the future, and the possibilities are endless.
This article was written by James Fisher, Chief Strategy Officer at Qlik.
