The Generative AI (GenAI) wave is upon us. Every enterprise is buzzing about its potential, and investments are skyrocketing. But amidst the excitement, a crucial question remains: What truly separates successful GenAI projects from the rest?

In a recent visit to India, Jihad Dannawi, Regional VP – APAC, DataStax (a leading real-time vector database company) sheds light on the hidden complexities of AI projects. This exclusive interview with CIO Dimension delves into:

• The power of the right database in unlocking the true potential of Large Language Models (LLMs)
• Why developers are the backbone of GenAI and how to empower them
• India’s unique advantage in leading the GenAI revolution

Edited excerpts: 

DataStax positions Astra DB as the ‘go-to’ database for GenAI applications. Can you explain why Astra DB stands out as the best choice for GenAI applications compared to your competition?

For starters, we are a 14-year-old company that has built the most widely used enterprise-grade database. It has been delivering high scale, high performance, and high availability — even before Generative AI came in. 

We are at a stage where a lot of organizations are scaling GenAI from PoC to production. At PoC level, organizations are not looking at scale or performance. But when they move to production, they suddenly start to have performance issues. They need to be able to pull in massive amounts of data to increase the relevancy and accuracy of the models, which means that the database must be bigger.

But volume often comes in the way of performance at the production stage. When organizations move beyond entry level scenarios to more mature use cases, they need to have a database that can handle their speed, scale, and performance requirements      – all at a reasonable TCO. A lot of databases in the market struggle to balance these aspects. 

An industry benchmark clearly demonstrates that DataStax’s Astra DB Serverless (the cloud-native database service built on Apache Cassandra) significantly outperforms the competition in throughput, latency, relevance, and TCO.  

Another key offering from DataStax is RAGStack, which claims to reduce the complexity associated with Gen AI for developers? Could you elaborate? 

When it comes to generative AI, one of the key issues that the developer community faces – especially when they start building the big stuff for production – is that they don’t know where to start. Because there are so many components and tools available in the market, it’s hard to decide what to use –  especially when dealing with vector databases.   

That’s why DataStax built a layer for the developers called RAGStack. This helps them build production-grade retrieval augmented generation (RAG) Gen AI applications in a simpler and faster way – without the developers having to learn everything from      scratch. It’s fully open source, connected and improves developer productivity significantly. It drastically simplifies the process of implementing RAG – the process of providing context from outside data sources to deliver more accurate LLM query responses. 

RAGStack is part of our vision is to enable and empower developers to build GenAI applications in production more efficiently. We are helping developers and companies to quickly innovate.  Developers do not come with AI skills; they don’t know how to build LLMs. We firmly believe that the GenAI journey of an organization starts from the developer, and not the AI team. With RAGstack, they can start right away, without waiting to acquire new skills. 

Another major concern that organizations must deal with is the AI hallucination. How is DataStax working to ensure fewer hallucinations? 

Hallucination, or in other words the “context” for an LLM, is the tricky bit. Now technology allows you to launch GenAI very quickly, but GenAI is not always accurate. This is why retrieval augmented generation (RAG) is so important for enterprises building GenAI applications. They must be able to augment an LLM with relevant, up-to-date data so it can provide relevant, factual responses.

More data means better context. And this will also bring in the big competitive differentiator into the models and the eventual outcomes for businesses. This further emphasizes the point that many databases in the market that claim to be vector do not have the capacity to perform high volume data. 

Can you share some examples of how Astra DB has helped customers achieve significant business outcomes?

Through Astra, we have started to discover a lot of customers with smaller needs, who can move very quickly through the GenAI journey. When customers begin to deploy impactful GenAI in their business processes, we believe that we stand to gain.  

A great case in point is Skypoint, the Portland-based Gen AI healthcare company. They leverage Astra DB’s vector search to enhance their services, offering users real-time, contextual, and personalized AI agent solutions. 

Another key customer for us is India’s leading edtech player, Physics Wallah. This unicorn’s app has over 15 million downloads as of January 2024. Its personalized AI bot is built on Astra DB and assists students with their academic and support queries in a personalized manner. The database’s stability helped the company handle 50x traffic increase with zero downtime during a live demo with 1 million students.  

A lot of organizations are still in the experimentation stage. Our study shows that only 4% of GenAI projects have moved to production, so we are in the absolute beginning of the wave, a lot of the     projects in PoC will move in     to production this year.      

India’s GenAI market is predicted to reach a massive $17B by 2030 and India is reportedly ahead of the US in AI adoption by businesses. What will be the catalysts for this growth and what are DataStax’s plans to capitalize on this opportunity?

AI will impact the top line and bottom line of every business out there. India has a strong community of developers and GenAI starts with developers. So, India has the skills and the mass to innovate and drive more use cases. They      also have the right mindset – the ecosystem here loves to build and that’s a very positive mindset to have when it comes to AI. Because AI is not something you buy and turn on, it’s something you build. And with the right mindset, India will be faster to innovate. The GenAI wave is poised to trigger a paradigm shift, comparable to the impact of mobile and web technologies. India has the skills, capacity and the mindset to really benefit.  

From a DataStax point of view, India is a big market and we are definitely investing here. We are expanding our team to meet to the growing needs.  

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UiPath, the enterprise automation software company, announced the top five trends in Artificial Intelligence (AI) and automation that will transform the Indian workforce in 2024. 2023 was the year of AI hype, and 2024 will be the year when we realize the full potential of AI. 

Arun Balasubramanian, Vice President and Managing Director, India and South Asia, UiPath said, “In the realm of digital transformation, the synergy between automation and AI is unlocking unprecedented value for enterprises worldwide. This isn’t merely about scaling up automation; it’s about unleashing the full potential of these technologies to revolutionize how we work. As we navigate the evolving landscape, it’s clear that the future lies in the seamless integration of AI and automation.”

“The future belongs to those who harness the power of AI and automation to navigate complexity, allowing a dynamic collaboration between human ingenuity and robotic efficiency. Navigating through the AI landscape has become more efficient through evolving technologies such as process mining, task mining, and co-pilots. As we pioneer safe AI, we’re shaping a future where enterprises thrive through innovation, efficiency, and the harmonious collaboration of people and machines,” Balasubramanian added.

LLMs, Co-pilots to transform work

According to the UiPath Automation Generation Survey 2023, 91% of Baby Boomer respondents think that automation would help them do their jobs better, while 87% of Generation Z, 86% of Millennial, and 84% of Generation X (84%) respondents feel similarly.

These sentiments would echo through 2024, as enterprise software providers are already integrating Large Language Models (LLMs). One notable application is copilots—virtual desktop assistants that comprehend individuals’ work contexts and tasks, communicating in plain language. Like the best human assistants, these copilots can rapidly learn to auto-complete various activities and proactively enhance workflow efficiency. UiPath, for example, incorporates Autopilot into tools for various users, streamlining tasks like data entry and email management.  With such significant developments, LLMs and Gen AI solutions are all set to reinvent how we work with machines and usher in a new boom in global productivity.

Safe AI becomes a focus of action and innovation

The C-suite will prioritize countering AI risks, emphasizing safe AI to improve the risk/benefit equation. Expect a significant acceleration in AI governance adoption, with enterprise software companies integrating robust AI controls. Technology providers will enhance human-in-the-loop capabilities for improved human review. In 2024, organizations are shifting from aspirational to implementation phases, driven by innovative approaches. The Indian government already emphasizes innovation and is formulating policy frameworks like the National Artificial Intelligence Strategy for responsible AI adoption, highlighting a commitment to transparency and accountability.

The C-suite seizes AI’s value with the help of automation

Today, executives are exhilarated by the potential of AI to drive growth, and innovation, and revolutionize various aspects of work, enhancing productivity in unprecedented ways. In 2024, their primary focus will shift towards translating the potential of AI into tangible returns. Per a Nasscom report, AI adoption is poised to add $500 billion to India’s GDP by 2025. Automation is proving to be a swift enabler of various AI benefits. As companies in 2024 zero in on realizing their AI ambitions, they will increasingly turn to enterprise automation to facilitate the achievement of their objectives.

Process and task mining for real-time visibility 

Until recently, enterprise managers grappled with poor visibility into end-to-end work processes, hindering problem diagnosis and issue resolution. Process intelligence, powered by AI in advanced process, task, and communications mining, has transformed this landscape. Now, organizations can continually monitor and evaluate processes comprehensively, gaining insights and identifying optimal solutions for efficient workflows. Leading innovators are even creating “digital twins” of their operations, providing dynamic real-time visibility, and modeling potential workflow improvements before implementation. This innovation is anticipated to drive increased adoption in 2024 and beyond.

Redefining work by building new relationships with machines

The rise of large language models (LLMs) is expected to significantly impact the future of work, with the potential to automate up to 30% of current tasks by 2030. As LLMs continue to advance, they will likely be applied in various professions, and the workforce will need to adapt to new roles and responsibilities. Key areas of focus for thought leaders, policymakers, and education professionals include learning from professions further along the adoption path, addressing the labor implications of a virtual workforce, and redefining the best skillsets for the future. By 2024, these efforts will begin to reshape the workforce, requiring workers to develop new skills and adapt to new roles, while also addressing the needs and views of employees, employers, unions, governments, and the public at large.

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Snowflake and NVIDIA announced that they are partnering to provide businesses with a platform to create customized generative AI applications using their own proprietary data in the Snowflake Data Cloud. The announcement came today at Snowflake Summit 2023.

With NVIDIA NeMo platform for developing large language models (LLMs) and NVIDIA GPU-accelerated computing, Snowflake customers can use data in their Snowflake accounts to make custom LLMs for advanced generative AI services, including chatbots, search and summarization. Snowflake plans to host and run NeMo in the Data Cloud. 

“Snowflake’s partnership with NVIDIA will bring high performance machine learning and artificial intelligence to our vast volumes of proprietary and structured enterprise data, a new frontier to bringing unprecedented insights, predictions and prescriptions to the global world of business,” said Frank Slootman, chairman and CEO, Snowflake.

“Data is essential to creating generative AI applications that understand the complex operations and unique voice of every company,” said Jensen Huang, Founder and CEO, NVIDIA. “Together, NVIDIA and Snowflake will create an AI factory that helps enterprises turn their own valuable data into custom generative AI models to power groundbreaking new applications — right from the cloud platform that they use to run their businesses.”

The partnership will enable customers to use their proprietary data — which can range from hundreds of terabytes to petabytes of raw and curated business information — to create and fine-tune custom LLMs that power business-specific applications and services. 

The companies maintain that the integration of AI technology from Snowflake and NVIDIA will enable users to quickly build generative AI apps for a number of business use cases. For example, a healthcare insurance model could answer complex questions about what procedures are covered under various plans. A financial services model could share details about specific lending opportunities available to retail and business customers based on a variety of circumstances. 

Another advantage, according to Snowflake, is the ability to create these apps where the governed data already resides – a benefit that reduces cost and latency while maintaining the security of the data. 

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Databricks, the Data and AI company, announced it has entered into a definitive agreement to acquire MosaicML, a generative AI company co-founded by Naveen Rao. 

The transaction is valued at approximately $1.3 billion, inclusive of retention packages.

MosaicML is known for its open-source and commercially usable MPT large language models (LLMs). With over 3.3 million downloads of MPT-7B (since its release in May 2023) and the recent release of MPT-30B, MosaicML has helped organizations to quickly build and train their own AI models using their data in a cost-effective way. Customers such as AI2 (Allen Institute for AI), Generally Intelligent, Hippocratic AI, Replit and Scatter Labs leverage MosaicML for a wide variety of generative AI use cases. 

Naveen co-founded MosaicML in 2021 after he quit Intel. He previously led Nervana Systems which was acquired by Intel way back in 2016 for $350 million. Intel subsequently announced in 2020 that it had decided to end development work on the Nervana neural network processors (NNP).

“We started MosaicML to solve the hard engineering and research problems necessary to make large scale neural network training and inference more accessible to everyone. With the recent generative AI wave, this mission has taken center stage. We fundamentally believe in a better world where everyone is empowered to train their own models, imbued with their own data, wisdom, and creativity, rather than have this capability centralized in a few generic models,” wrote Naveen and Hanlin Tang, Co-founder & CTO, in a blog post. 

“…. Together with Databricks, we will tip the scales in the favor of many — and we’ll do it as kindred spirits: researchers turned entrepreneurs sharing a similar mission. We look forward to continuing this journey together with the AI community,” said Naveen. 

The entire MosaicML team is expected to join Databricks after the transaction closes. The Databricks Lakehouse Platform, combined with MosaicML’s technology, promises “2x-7x faster training compared to standard approaches” and bring down the cost of training and using LLMs from millions to just a few thousands of dollars. 

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