Venkat Sharma Gaddala
Venkat is a Senior Application Engineer at Google with expertise in enterprise data, AI/ML, and Large Language Models. His experience spans various Google products, including Recommendations AI, Looker , BigQuery , Gemini Models, and Vertex AI , Embeddings .
Venkat is passionate about developing innovative AI solutions that provide exceptional user experiences and address real-world challenges. He has a proven track record of delivering impactful business outcomes by focusing on collaborative partnerships and win-win solutions.
Authored Publications
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The accelerating pace of innovation is
fundamentally reshaping product development,
creating a complex environment that demands rapid
decision-making and efficient information
management. To remain competitive, organizations
must integrate Generative AI (GenAI) tools into
their Product Lifecycle Management (PLM)
processes. This integration is crucial because
traditional PLM systems, often built on decades-old
architectures, struggle to manage modern product
complexity, vast data volumes, and interconnected
supply chains.1 Limitations such as data silos,
inflexible change management, and inadequate
collaboration capabilities hinder the agility required
today.3 GenAI offers transformative potential by
automating complex tasks, enhancing data analysis,
and facilitating more dynamic design and
collaboration within the PLM ecosystem.5 This
integration is not merely an upgrade but an
essential evolution to overcome the inherent
architectural and process constraints of legacy
systems, which impede the speed and data fluidity
necessary in the current market.
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As the demand for data and digital services continues to escalate, data centers are evolving
into key players in the global energy consumption landscape. The necessity for sustainability
and energy efficiency in these facilities has led to the integration of Artificial Intelligence
(AI) technologies. This paper explores emerging AI trends that are shaping sustainable data
centers, focusing on optimization, predictive analytics, and machine learning applications,
along with their implications for operational efficiency and environmental impact. The rapid
growth of artificial intelligence (AI) has significantly impacted data center operations,
driving the need for sustainable practices. Emerging trends such as AI-driven energy
optimization, renewable energy integration, and advanced cooling technologies are
reshaping the industry. These innovations aim to reduce energy consumption, minimize
carbon footprints, and enhance operational efficiency. By leveraging AI, data centers can
predict maintenance needs, optimize energy usage, and adapt to real-time demands. This
paper explores the intersection of AI and sustainability, highlighting how these
advancements contribute to a more eco-friendly and efficient future for data centers.
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Streamlining Order Fulfillment using SAP and PEGA powered by AI/ML
International Journal of Management, IT & Engineering, 15 (2024), pp. 1-4
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In this fast changing digital environment, organizations continue to
grapple with the challenge of improving order fulfillment efficiency
while striving to keep accuracy and customer satisfaction at par. In this
research, the provision of artificial intelligence (AI) and machine
learning (ML) capabilities to the SAP and PEGA systems is analyzed
in detail, developing their use to radically transform the traditional
order fulfillment operations. A mixed method research approach is
employed using performance metrics and organizational impacts
assessments in a large scale enterprise environment over a 24 month
period of implementation.
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Leveraging Generative AI for Efficient Product Briefing Document Generation in Supply Chain
Shaswat Kumar
Gary Borella
Youjin Zhu
Krish Mohan
International Journal of Management, IT & Engineering, 12 (2024), pp. 87-95
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Generative AI enables an efficient product briefing document generation approach in the supply chain based on its leveraging to streamline the information flow and decision making. Product briefing documents are typically produced using a highly labor intensive process of manual data extraction, synthesis, and formatting. Because generative AI can process large amounts of data and generate coherent and structured outputs, generative AI can substantially improve the efficiency of this process. Using machine learning models, AI can use its ability to automate the development of briefing documents that integrate key data points — product specifications, supplier information, market trends, and logistical issues. This also saves time, effort and keeps everything accurate and consistent throughout. The use of AI in supply chain helps businesses create real time updates which facilitate quicker response to market changes, inventory fluctuation and supplier dynamics. In addition, all possible stakeholders can utilize AI generated document with their custom derived according to their needs for better communication and alignment between departments. With an increasingly complex supply chain, the usage of Generative AI helps solving the problem of responsible high volume data, which in turn creates operational efficiency and strategic decisions. This represents a big step toward digital transformation in the field of supply chain management.
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Unleashing the Power of Generative AI and RAG Agents in Supply Chain Management: A Futuristic Perspective
Iconic Research And Engineering Journals, 6 (2023), pp. 1411-1417
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Supply chain management (SCM) plays a critical role in today's complex business environment, and the advancements in artificial intelligence (AI) have the potential to revolutionize SCM practices. This research article explores the untapped potential of generative AI and RAG (Retrieval-Augmented Generation) agents in SCM, presenting a futuristic perspective on their application. The research begins with an overview of SCM and the growing significance of AI in transforming supply chain operations. It then introduces the concepts of generative AI and RAG agents, highlighting their unique capabilities and potential benefits in SCM. A comprehensive literature review examines existing research on AI in SCM and explores the applications of generative AI and RAG agents in other domains. The review identifies research gaps and opportunities for the utilization of generative AI and RAG agents specifically in SCM. The methodology section outlines the research approach, including data collection methods and the implementation details of generative AI and RAG agents. Evaluation metrics are explained to assess the effectiveness and performance of these technologies in SCM. The article presents practical applications of generative AI and RAG agents in SCM, focusing on their roles in demand forecasting, inventory management, supply chain operations, and real-time decision-making. Case studies and experimental results are provided to demonstrate their potential impact on SCM efficiency and customer satisfaction. The results and analysis section presents the findings of the experiments conducted, analyzing both quantitative and qualitative aspects. A comparison with existing approaches in SCM further highlights the unique advantages of generative AI and RAG agents.The discussion section interprets the results, discusses the implications for SCM, and addresses the limitations and challenges associated with the adoption of generative AI and RAG agents in SCM. It also identifies future research directions and opportunities for further exploration. In conclusion, this research article sheds light on the transformative power of generative AI and RAG agents in SCM. It contributes to the field by providing a futuristic perspective on their application, offering recommendations for practitioners and policymakers. The article concludes by emphasizing the promising future of generative AI and RAG agents in shaping the SCM landscape.
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The Future of Enterprise Data is Conversational
Iconic Research And Engineering Journals, 7 (2023), pp. 536-547
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Conversational interfaces have steadily emerged with the help of new technologies such as artificial intelligence or natural language processing, and these new ways have started radically changing how companies interact with their enterprise data. This paper explores the change enterprise data management has taken from traditional approaches to conversational platforms and examines the key technologies underlying this change. This paper explores how they transform decision-making, allowing organizations to engage with data in new ways. The goal of the paper presented is to explain this. Evidence from actual cases and quantitative data analysis shows how CUIs make data access easier, reduce reliance on abstract queries, and encourage more active data engagement. These are depicted graphically to show how organizations benefit from applying these technologies. The paper suggests that enterprises must refrain from pursuing conversational data initiatives to compete in the emerging market environment as the requirement for timelier and more accurate analytics increases. It investigates the future possibility of such platforms where the author postulates that businesses will harness AI conversations to accrue more valuable insights for enhanced organizational decision-making. Organizations can easily adopt such strategies to help improve productivity, user-tuned experiences, and overall competitive position in modern, complex digital environments. The paper offers suggestions on conversational platforms that firms planning to adopt as part of their enterprise data management systems should consider.
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Prompt Engineering in Supply Chain Enterprise Data
Iconic Research And Engineering Journals, 6 (2022), pp. 213-224
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Deep generation models, including GPT-4, can generate more efficient knowledge extraction from the text than conventional models. They are semantically flexible and powerful, but such flexibility hampers generating accurate, task-specific results that should be needed for a particular task; this is why precise, prompt engineering is necessary. This work assesses different prompt engineering strategies for proficient knowledge pulling using GPT-4 and the relation extraction dataset (RED-FM). , A new framework based on Wikidata ontology has been proposed to address the evaluation issue. The outcomes shown here indicate that the LLMs can extract an immense variety of facts from text. Using at least one example related to the prompt boosts performance by two to three times; performance with highly related examples is much better than that of random or conventional examples. Yet if more than one example is given, this results in a lesser effect the more examples are used. Surprisingly, reasoning-based prompting methods fail to beat non-reasoning strategies, which means that KE does not necessarily correlate with reasoning tasks in LLMs. On the other hand, retrieval-augmented prompts' results are very good, and combined with the other methods, they help improve the information retrieval process. Knowledge extraction is, therefore, not a problem when it comes to LLMs, but framing the process as a reasoning-based endeavor may not be effective. Well-designed prompts, particularly those with examples, enable the LLM potential in knowledge acquisition.
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