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Artificial Intelligence Insights

The artificial intelligence landscape is ever-changing. These technological advancements are equally important to economic development as other major trends like globalization, social inclusion, and climate and energy transition. S&P Global aims to provide this information in three parts: AI Fundamentals, AI Applications, and AI Governance and Regulation

Special Reports

Dec 12, 2024

AI's shift from promise to practice

12 December 2024 AI's shift from promise to practice By Miriam Fernández, CFA, Sudeep Kesh, and Paul Whitfield This is a thought leadership report issued by S&P Global. This report does not constitute a rating action, neither was it discussed by a rating committee. Highlights AI's development remains nascent, as does its meaningful application at most entities and in most people's lives, but adoption is very much underway, and progress on new technologies, including multimodal AI, edge AI, causal AI, and agentic AI, promises to deepen AI's impact in unprecedented ways. Investment trends and human talent will be central to the course of that development, to increased applications of new technologies, and to the management of existing, potential, and yet-to-be-recognized risks and opportunities. Our library of research and podcasts offers extensive analysis by AI and industry experts and will continue to build over 2025 with early identification and assessment of important trends and forward-looking analysis of events as they unfold. History may yet record 2024 as the year that AI began to make the shift from promising technology to ubiquitous tool. In the public sphere, large language models (LLMs) and, more broadly, generative AI increasingly transitioned from curiosity to a functional aid (at least for a significant minority of the developed world), taking a role in everything from mundane report writing to the creation of personalized bedtime stories for kids. Across many sectors, previously speculative investment in the technology increasingly offered regular and tangible benefits as organizations deployed AI-powered initiatives (see AI In Pharmaceuticals Promises Innovation, Speed, And Savings, Oct. 1, 2024, and AI In Real Estate: What To Watch As Adoption Accelerates, May 28, 2024). There is no doubt that AI still has a long and uncertain road ahead. Yet we share a conviction with many other market participants that increased application and the increasing capabilities of AI will have profound, and accelerating, effects across all sectors of the economy, all organizations (both directly and often indirectly), on labor markets, on energy usage, and in society--for good and bad (see The Rise of AI-Powered Smart Cities, May 18, 2024; and Look Forward Journal Artificial Intelligence, Dec. 4, 2024). That conviction demands our ongoing attention. Over the past year, we have explored and regularly published analysis on the fundamentals of AI technology and its development (see Language Modeling: The Fundamentals, Jan. 23, 2024). And we have looked at its deployment, its impact, and its potential--including due to the paradigm shift in computational power that will arrive when AI is combined with quantum computing (see Artificial Intelligence and Quantum Computing: The Fundamentals, Sept. 10, 2024). Such analysis is necessarily a complex task. Partly, that is because the application of AI and the discovery of its boundaries remain uncertain, evolving, and will be dictated by investment trends and the development of human talent (see Investment And Talent Are The Keys To Unlocking AI's Potential, July 9, 2024). But it also reflects the reality that AI is not a single, monolithic technology, but myriad different processes, developments, and projects. Each of those has a combination of strengths and limitations that dictate their potential and are, in turn, dictated by the particularities of their underlying technologies, applications, the environments in which they operate, and their governance requirements (see Podcast: Artificial Intelligence Insights, Episode 2: Governance Considerations for AI, June 13, 2024). Furthermore, developments in AI are often non-linear, meaning that they often don't simply extend the uses of existing technologies. Rather, developments can lead to sudden breakthroughs and inspire the development of new technologies and applications targeting entirely new problems and multidisciplinary fields, such as synthetic biology (see Artificial Intelligence Powering Synthetic Biology: The Fundamentals, June 25, 2024). Furthermore, a combination of breakthroughs can lead to non-linear outcomes that could deliver exponential advancement in AI's capabilities. The indirect effects of AI Our coverage of AI necessarily extends beyond the direct application of the technology to encompass the wider impact of its adoption. Indirect effects of the growth in AI are already being felt across a host of sectors--notably those with exposure to the surge in demand for data center capacity, including real estate companies, the power and utilities sector, and midstream gas suppliers (see Data Centers: Rapid Growth Creates Opportunities And Issues, Oct. 30, 2024). AI's increasing applications will also entangle the systems in which it operates, with the potential for effects to reach far beyond the immediate users. That possibility is perhaps most evident in the financial sector, where the adoption of AI exacerbates the risk of operational disruption leading to systemic instability that could have wide-ranging implications (see Your Three Minutes In AI: Financial Systems Will Face New Systemic Risks, Oct. 4, 2024). Those risks could be mitigated through effective governance, regulation, and decentralization (see section on "edge AI," below). Yet the pace of development in AI and its expanding use exposes such efforts to the challenges of containing not only existing and potential risks but also to uncovering unrecognized risks (a variation on the "Rumsfeld matrix" of known knowns, known unknowns, and unknown unknowns). Predicting risk is core to our efforts to understand the evolution of AI's effects on organizational credit quality and will continue to drive our research (see Crypto and AI: Shaping The Future Of The Internet, Oct. 1, 2024). What we will be watching in 2025 Over 80% of organizations forecast their AI workflows will increase in the next two years, while about two-thirds expect pressure to upgrade IT infrastructure (see Generative AI: From Hype To Value, Nov. 25, 2024). The breadth and nuance of AI's development, its effects (and potential), are evident in the slate of subjects that we have tasked ourselves to explore over 2025. They include: Multimodal AI, which facilitates more human-like applications of generative AI models by enabling the integration and processing of diverse inputs such as images, audio, text, and video. The technology has gained traction in 2024 after OpenAI included the functionality in its models, Meta released a multimodal version of its LLaMA model, and NVIDIA launched an open-source multimodal foundation model (NVLM 1.0). We expect this technology will gradually gain relevance in industrial applications, particularly for companies that are already integrating language models into their businesses. Multimodal AI is notably being developed or is already in use in sectors including: Healthcare: where medical imaging can be combined with patient history and lab results to improve diagnosis and treatment, for example, by systems developed by Merative (formerly part of IBM Watson Health) and Bayer (see AI In Healthcare: A Path To Long-Term Immunity? June 25, 2024) Automotive: where inputs, including from cameras, GPS, and LiDAR, are being combined to improve autonomous driving, emergency response, and navigation systems at companies including Waymo and Li Auto. Finance: where multimodal AI is being applied to analyze customers' phone queries to support contact center employees' response and resolution activities. Retailing: where the combination of visual images, product reviews, product information, customer interaction data, and warehouse data is used to optimize marketing campaigns, inventory management, and minimize packaging and shipping costs. Amazon, for example, uses its "Just Walk Out" multimodal AI to enable checkout-less shopping at its physical stores. Edge AI, which moves AI computation to end users' machines. Though not a new concept, we expect that the increasing use of small language models (SLMs) by companies will support the scalability of edge AI. That shift offers numerous advantages, including, notably, a reduced risk of mass outages associated with failures at major infrastructure or technology providers--known as centralization risk. The combination of SLMs with crypto technologies should further mitigate centralization risk, paving the way for more secure and decentralized computing and data storage (see AI & DeFi: Can Crypto Innovations Offset Artificial Intelligence Concentration Risks, Dec. 4, 2025). And because SLMs at the edge require less computational power, they will also offer energy efficiency advantages compared to data and resource-hungry LLMs (see Can AI become net positive for net-zero? Nov. 14, 2024). However, meaningful application of scalable edge AI will have to overcome the sizable challenge of ensuring that nascent technologies make their way from the lab to the field and are deployed in sufficient numbers to be impactful. Causal AI, which can reason and make choices like humans do, uses causal inference to fundamentally understand why processes and decisions are made. This goes beyond the pattern recognition and correlation that underpins much of the current ability of AI (particularly discriminative AI and generative AI). We expect that capability will become increasingly important with increased AI adoption and the corresponding demand for data (both real and synthetic), as a means to identify true causality from spurious correlations. It should also enable AI models to deliver more reliable query responses, make more informed decisions, and potentially aid in the discovery of underlying causes--which could prove a powerful tool across sectors including healthcare and finance. Causal AI is generally regarded as one of the missing links to create artificial general intelligence (which matches or outperforms human cognitive abilities across any task). By nature, casual inferencing reduces complexity in decision-making, meaning it requires less data and power to deliver typically better performance. Active use cases are still relatively limited and tend to center on manufacturing, life sciences, decision sciences, and the study of human cognition and behavior. Agentic AI, which describes the development of increasingly autonomous "AI agents" that make decisions and execute tasks based on a set of provided instructions (see Podcast: Artificial Intelligence Insights, Episode 4: Agentic AI, Oct. 30, 2024). This advancement opens the door for AI to shift from meeting a problem by providing relevant data to formulating and enacting solutions. As Microsoft put it, when it launched its generalist agentic system Magentic-One: the difference is "between generative AI recommending dinner options… (and an) agentic assistant that can autonomously place your order and arrange delivery." Agentic AI promises to reduce the complexity inherent in many systems, enabling better human decision-making and a focus on higher-value activities like creativity, leadership, and communication and clarification of decisions. Cognitive AI, which refers to AI being able to mimic human cognitive functions such as reasoning, learning, and problem-solving--including by using cognitive computing structures known as neuro-symbolic architecture that simulate human patterns of learning, reasoning, and understanding. We expect cognitive AI will follow the widespread adoption of agentic AI to become the next step in the technology's evolution. Recent multimodal virtual chatbots, such as ChatGPT-4o and its latest model o1 (designed to think more deeply before responding), have some features that qualify as cognitive AI (i.e., the use of multimodal AI to provide reasoned responses and understand complex queries, and showcasing of adaptive learning in human interactions). Yet the models are not fully autonomous, meaning they require human inputs to achieve goals (unlike agentic AI), and do not yet integrate sensory information (from Internet-of-Things (IoT) devices, for example), telematics, or other sources of augmented real-time data needed for decision-making. Stepping into the unknown It is inevitable that other subjects will demand analysis over the course of 2025, not least because 12 months is a long time in AI. Our intention is to remain both proactive, through early identification and analysis of important (and high-potential) new trends and technological developments, and reactive, with forward-looking analysis of events as they unfold. As always, that analysis, including insights from our AI and sector specialists, will be available across S&P Global's platforms and collated at our dedicated site: Artificial Intelligence Insights. Contributors Editorial, Design & Publishing Cat VanVliet Associate Director, Data Visualization

Special Reports

Dec 04, 2024

AI and labor: Change is inevitable, but human capabilities remain essential

Look Forward — 4 December 2024 AI and labor: Change is inevitable, but human capabilities remain essential While AI will transform labor markets through enhanced productivity and efficiency, human capabilities will remain indispensable for tasks requiring emotional intelligence, creativity and complex decision-making. By Sudeep Kesh, Nathan Hunt, and Xu Han This article is intended to promote discourse regarding the interplay between AI and labor markets. The time frames referenced are meant to provide an illustrative scaffold, and certain concepts, including issues of distribution and governance, and nuances between capital, material, labor and total factor productivity, remain unaddressed here to maximize the reach of this discourse. Those further issues will be the subject of robust economic and policy analysis in additional S&P Global research. Highlights Over time, AI’s impact on labor will become increasingly evident across key areas such as efficiency and productivity, creation of new roles, transformative human-AI collaboration, autonomous systems, and labor redistribution and reskilling. Across short-term (one to two years), medium-term (four to six years) and long-term (seven to 10 years) time frames, we expect AI’s integration with labor to progress through distinct phases, each with unique challenges, ethical considerations and risks to balance against the technology’s benefits. Our research suggests that AI’s effects will be uneven across the employment landscape. Roles most likely to be affected will be those focused on information collection and processing, data analysis, and machine or process management. Look Forward Artificial Intelligence Explore More American folk hero John Henry is said to have been “a steel-driving man," a reference to his job hammering a steel bit into rock to make holes for explosives to excavate railway tunnels. A song memorializing John Henry tells of his race against a steam-powered drill, an avatar of the industrial age, which he beats, but at the cost of his life. The tale is one of determination and fortitude, but it is also a warning about the futility of resisting technological advancement. As generative AI expands, it may be tempting to view John Henry's tale as a parable for the future of labor, but this would be a mistake for two major reasons. First, our analysis shows that while large language models will prove effective at many tasks, they will likely have little effect on the market for manual labor. In this sense, the next John Henry is more likely to be a paralegal or a computer systems engineer than a construction worker. Second, industrial-age technological disruptions fall short of the upheaval that generative AI is likely to bring about. Industrial machines replaced workers’ roles within a defined task (like steel driving) but left the process intact. We expect that within seven to 10 years, the use of generative AI will upend and redefine many labor processes themselves. A structured analysis of AI's effects on labor Since the public launch of OpenAI’s ChatGPT in November 2022, conversations about AI and the future of work have polarized, with fear and exuberance in similar proportions. Both extremes reflect emotional responses rather than a practical understanding of how AI will transform the labor landscape. The reality is sure to be more nuanced. To frame our analysis of AI's effects on labor, we use three periods: the short term (one to three years), the medium term (four to six years) and the long term (seven to 10 years). Each phase will present challenges, ethical considerations and risks that must be balanced against the technology’s potential benefits. As we progress along this timeline, the effects of AI on labor and labor markets will become increasingly evident across a range of key elements. Efficiency and productivity Automation of repetitive tasks and improved decision-making across many areas should result in significant gains in labor productivity and efficiency, and in processes as labor is augmented or replaced by AI. Emerging roles New job categories such as AI trainers, explainability experts and AI ethicists will emerge, notably in quality and governance functions, and will themselves be augmented by AI. These roles' defining characteristic will be to do what AI cannot: AI excels at finding answers, while humans excel at asking questions. People who prove talented at asking the right types of questions — open-ended, probing and insightful — will be in high demand. Transformative collaboration Collaborative intelligence, a paradigm in which humans and AI work seamlessly together, will become increasingly mainstream, likely through the maturation of agent-based models (ABMs), which simulate the actions and interactions of autonomous agents. The impact of autonomous systems Improved ABMs will also facilitate increased use of autonomous systems, such as self-driving vehicles and AI-powered agricultural robots. The result should be a marked enhancement of some sectors’ efficiency and sustainability, notably through the synthesis of analytics and fieldwork. Labor redistribution and reskilling The redistribution of labor markets away from roles that are easily replicated by AI will require reskilling and upskilling, with a focus on developing emotional intelligence, critical thinking and interpersonal skills. AI integration will necessitate greater humanity in the workplace. Short term: Picking the low-hanging fruit In the next few years, AI applications will likely focus on enhancing labor efficiency and productivity in existing workflows. This focus will be dictated by employers’ initial limited understanding of AI's potential in the context of current processes and will reflect experimentation as enterprise decision-makers learn about AI’s core competencies, develop integration approaches and refine their strategies. Companies will likely begin leveraging AI to streamline operations, reduce costs and improve decision-making. Stakeholders will need to determine adequate levels of prudence in adoption, change-management and governance. Adoption without sufficient checks could lead to negative consequences, such as job displacement and privacy concerns, and even where care is taken, the ride is likely to be bumpy and include some retrenchment. Adoption and implementation of AI in various job families may add complexity to work, posing both opportunities and risks. A key challenge will be to balance the allocation of work between human strengths and AI capabilities in a way that enables the opportunities and abates the risks. Recent data suggests there is reason for optimism. S&P Global Market Intelligence 451 Research surveyed about 5,000 respondents regarding the impact of AI on jobs and on society at large. Between two waves of the survey in 2022 and 2024, respondent expectations regarding AI's impact on society notably shifted, with a significant increase in the expected degree of impact and a modest bias toward positive expectations. In that period, sentiment regarding AI’s impact on jobs remained stable. Notably, among those who expect AI to significantly impact their career, more than twice as many expect a positive impact on work versus a negative impact. Enhanced automation and productivity AI-driven automation will increase across capacities including manufacturing, logistics, customer service and data management. By combining information, systems and robotics to handle repetitive tasks, these developments should allow human workers to focus on more complex and creative activities or those with social aspects, such as management, coaching and inspiration. Application is likely to involve assessing the division of labor between tasks that require or benefit from human performance and those that are “machine ready.” For instance, AI-powered chatbots and virtual assistants will continue to evolve, in some cases providing more sophisticated support to human customer service agents, and in other cases providing direct customer support without human intervention. Meanwhile, optimization agents will assess operations to reduce human input and maximize AI's potential for productivity gains. While this concept may evoke sci-fi tropes that fuel fears of AI, this assessment is a crucial step to maximizing the effectiveness of a mutualism between AI and humans: parsing the tasks that humans are especially good at versus those at which AI excels and facilitating interoperability between the two. Diving deeper, we see that concerns about AI’s impact on jobs vary across industries, with some showing major concerns while others appear more optimistic. Measures that allay these concerns by abating negative impacts and supporting positive ones may present new opportunities in the work mix for existing positions and may even create new positions to help evolve the workforce toward a future that includes AI. AI capabilities can improve workforce tooling, especially in minimizing administrative burden, reducing overhead and providing objective performance feedback. In another survey, S&P Global Market Intelligence 451 Research asked approximately 450 respondents about their preferences for workforce tooling innovations. The overwhelming majority focused on efficiency gains and reducing administrative overhead, presumably to allow better focus on "value-add" tasks. Other tools, such as digital twins of physical objects and processes, received less priority among respondents, but these innovations are crucial for providing the digital infrastructure whereby AI can operate. Improved decision-making and knowledge Discriminative AI systems, which excel at classification, have proven adept at complex data analysis and assisting decision-making across industries such as automotive, manufacturing and logistics. We expect that the combination of discriminative AI and generative AI will also improve service economy activities by drawing on troves of service-oriented data collected over the past two decades. Financial institutions, for example, could use AI algorithms to expedite portfolio managers’ analysis of market trends, risks, correlation and other factors. In healthcare, AI could help diagnose diseases and recommend treatment plans based on vast amounts of medical data, potentially enhancing patient outcomes and reducing diagnostic errors. Medium term: Significant advancements Over the next three to five years, AI will significantly reshape labor markets, driven by increased investment in and knowledge of AI systems, and by competitive dynamics that should reduce AI's implementation costs. We expect significant advancements in machine learning, natural language processing and robotics, particularly due to improvements in agent-based systems (in which AI systems make decisions and take actions), inference (applying learning from patterns in data) and orchestration (connecting processes and systems). During this period, it will be crucial to address AI's ethical considerations and privacy concerns as regulatory scrutiny will also increase. Wider deployment of AI will also raise the risk of automating deficiencies, including bias, in downstream processes. Expansion of AI-augmented roles As AI technologies mature, the scope of roles augmented by AI will expand, especially in fields such as marketing and customer analytics. For example, improvements in AI's ability to meaningfully analyze vast amounts of data on consumer behavior and preferences will facilitate highly personalized marketing campaigns (akin to personalized treatment plans in healthcare). In education, AI-driven platforms will offer bespoke learning experiences, including educational content tailored to individual student needs and pacing, and corporate and client training that meets specific demands. Emergence of new job categories An increasing focus on governance, risk mitigation, bias mitigation and other AI-related issues will foster the emergence of new job categories. For example, AI trainers and explainability experts will be needed to ensure that AI systems are fit for purpose, transparent, understandable, and compliant with existing systems and governance frameworks. Skills to address and monitor AI ethics and quality will be crucial for responsible AI deployment, including the safeguarding of fairness and identification of biases. Long term: Transformative changes AI will become increasingly transformative over longer time frames due to the technology’s advancing maturity, humanity’s growing expertise in effective implementation and the regulatory landscape’s increasing clarity. Collaborative intelligence, a framework in which humans and AI work together, should notably increase. In creative industries, AI will assist humans by generating ideas, drafts and prototypes. Architects, for example, could use AI-driven design software to explore innovative building designs that incorporate complex environmental and structural factors, leveraging datasets that encompass architectural history, art, design, materials science and weather patterns. Autonomous systems AI will support autonomous systems that transform industries. Autonomous vehicles will revolutionize transportation and logistics, reducing the need for human drivers and increasing efficiency. In agriculture, AI-powered robots with computer vision and sensors to detect air pressure, moisture, color and light could oversee planting, crop monitoring and harvesting, increasing productivity and sustainability. Challenges will include cost-effectiveness, which must surpass that of human labor to justify adoption. Predictive maintenance Manufacturing and heavy industries will benefit from AI-enabled predictive maintenance. AI will analyze data from sensors embedded in machinery to predict when maintenance is required, preventing costly breakdowns and minimizing downtime. This capability requires AI’s data processing and pattern recognition abilities, coupled with digital telemetry systems to detect acoustic, visual and even olfactory signals. Autonomous data analysis In fields such as finance and research, AI-driven data analysis is already indispensable. AI systems can sift through vast amounts of data, identify trends and generate insights with speed and accuracy far beyond human capacity. This capability will be increasingly crucial for making informed decisions in dynamic environments and will enable a better understanding of the world around us, including risks such as climate change, the energy crisis and geopolitical dynamics. Shifting labor dynamics Advancements in the capabilities of AI systems and the dynamics of their integration with human labor will necessitate broader rethinking to understand the adaptations required of both AI and human work paradigms to improve cooperation. Humans, for instance, outperform machines in relational tasks such as comprehensive (verbal and nonverbal) communication and, at least to date, innovation. Thus, in a framework of human-AI mutualism, we expect the balance of such tasks to fall to humans, while many data analysis and computational tasks will fall to AI. This may require workforce education in interpersonal skills such as emotional intelligence, empathy and clarity of communication. Humans will also need better human-to-AI communication skills, such as prompt engineering, and a deeper understanding of AI operational requirements. Consider a typical customer service scenario: A customer is unsatisfied with the quality of goods received. In a world where AI and humans work together, required skills include understanding the customer’s problem (human), analyzing possible solutions (AI), relaying options with empathy to address the customer’s emotional and social needs (human), and executing a course of action to address functional needs (AI). Leadership and the workplace of choice Leadership in an AI-augmented world will evolve as workers look to their leaders for vision, systems thinking, and the ability to both analyze (extrapolate trends using context, qualitative judgments and data-driven insights) and synthesize (understand the interoperability of moving parts and connection to a greater whole). As leaders will manage humans and AI systems together, the importance of these abilities cannot be overstated. Analytical functions will become more crucial as AI adoption takes hold, requiring leaders to adapt to increasing customization and diversification of product offerings, as well as to the interplay of skilled workers and AI systems required to deliver those products. An understanding of synthesis will be critical as many traditional work functions and systems become intersecting, looping and chained processes in increasingly complex designs. Such complexity will likely change what humans demand of their workplaces. Traditional compensation schemes such as salary and variable pay will likely be augmented by purpose, culture and fit as an increasing proportion of tasks allocated to human workers will require distinctly human capabilities, such as emotional and social engagement. Organizations must recognize that their brand represents not only their product or service offerings or standard of quality to customers but also a workplace of choice for employees. Workplaces will need to earn their employees’ business, especially as employees’ nuanced personalities and skills become features in the workplace itself, as much of the labor characterized by homogeneity becomes the province of AI systems. Looking forward: From steam to silicon The idea of the modern-day John Henry will remain relevant as AI’s remit expands and matures. However, it is equally likely that many mundane, monotonous and undesirable elements of work could become increasingly automated, and that, as a result, humans — particularly those who learn to work in partnership with AI — may find increasing value and standing in the labor force by cultivating and expressing their most fundamentally human qualities. Look Forward: Artificial Intelligence AI and society: Implications for global equality and quality of life Next Article This article was authored by a cross-section of representatives from S&P Global and, in certain circumstances, external guest authors. The views expressed are those of the authors and do not necessarily reflect the views or positions of any entities they represent and are not necessarily reflected in the products and services those entities offer. This research is a publication of S&P Global and does not comment on current or future credit ratings or credit rating methodologies. Contributors S&P Global Ratings Miriam Fernández, CFA Director, AI Research Specialist S&P Global Market Intelligence Sheryl Kingstone Research Director S&P Global Market Intelligence Chris Marsh Research Director S&P Global Matt Tompkins Senior Editor S&P Global Cat VanVliet Associate Director, Data Visualization S&P Global Ratings Paul Whitfield Writer
Look Forward

Artificial Intelligence

In today’s rapidly evolving landscape, AI is a transformative force revolutionizing business, the economy and society. In the eighth edition of Look Forward, S&P Global offers a balanced look at AI complexity by highlighting the opportunities and risks in three parts: AI and labor, AI and energy, and AI and society.

Climate Center of Excellence

Advancing climate, environmental, and nature research

Climate Center of Excellence

The S&P Global Climate Center of Excellence sits within S&P Global Sustainable1’s Research and Methodology Team. The center is home to a group of world-class scientists and strategists dedicated to addressing the frontiers of long-term climate, environmental, and nature research and methodology development

Energy Transition

Explore the climate crisis as it intensifies calls for the energy sector to transition from fossil fuels like oil, gas, and coal to renewable energy sources.

News

Jan 03, 2025

Nayara focuses on refining, retail fuels, exploring clean fuel options

Refined Products, Chemicals, Agriculture, Energy Transition, Biofuel, Renewables January 03, 2025 Nayara focuses on refining, retail fuels, exploring clean fuel options Getting your Trinity Audio player ready... HIGHLIGHTS Nayara operates 20 mil mt/year Vadinar refinery Refiner setting up 450,000 mt/year polypropylene plant Aims to set up ethanol plants, produce hydrogen for captive usage Nayara Energy has embarked on a diversification drive aimed at expanding its retail fuel and petrochemicals footprint while making inroads into clean fuels amid efforts to strike a balance between traditional as well as new energy in its portfolio, company officials and analysts said. While its petrochemicals unit -- which forms a big part of the refinery expansion – is set to add to the country's supplies of key feedstocks, the company's recent move to partner with Gulf Oil Lubricants India Limited for its retail business and plans to set up an ethanol plant signals its intent to move beyond the traditional refining business and have a diversified presence. "The much awaited, phase one of its diversification project will propel the company to cement its position in the petrochemical sector in the demand-intensive western region, while phase two will be vital for expanding it to the polyethylene and downstream derivatives," said Abhishek Ranjan, South Asia oil research lead at S&P Global Commodity Insights. According to Commodity Insights, Indian refiners were proactively embracing petrochemical ventures as part of a broader strategy to adapt to changing market dynamics, enhance competitiveness, and position themselves for sustained growth in the future in the event energy transition and electric vehicles hit demand for transport fuels. Nayara operates the Vadinar refinery, India's second-largest single-site refinery, with a capacity of 20 million mt/year and delivers about 8% of India's refining output. The refinery has a complexity index of 11.8. It also operates over 6,300 retail fuel outlets. Under Phase 1 of its petrochemical expansion project, Nayara Energy is setting up a 450,000 mt/year polypropylene plant within its Vadinar Refinery in the western state of Gujarat. The plan includes a propylene recovery unit along with upgrades to the existing fluidized catalytic cracking unit. Focus on the core "For Nayara, petroleum refining and retail fuel distribution will continue to form key parts of its portfolio, but the company is open to explore new business opportunities given the energy landscape is changing. Therefore, we are seeing some developments around ethanol and hydrogen," said one senior refining source. Recently, Nayara Energy announced plans to set up two ethanol plants, each with a capacity of 200 kiloliters per day -- in the states of Andhra Pradesh and Madhya Pradesh. The company has already identified and purchased land in both states for the proposed plants. Company officials said the establishment of ethanol facilities would significantly enhance its ethanol supply reliability and play a crucial role in meeting the Indian government's 20% blending target by the end of fiscal year 2025-26 (April-March). The company was planning to eventually increase the number of plants to five. "Although Nayara is contemplating about expanding the refinery in its phase two of the project, it is expected that the primary focus will remain on the expansion of its domestic retail outlets and ethanol blending. This will also be driven by the government policy supporting the ethanol blending target, and further expansion as per the vision of the government," Ranjan added. In its latest retail initiative, Nayara Energy announced a strategic partnership with Gulf Oil Lubricants India Ltd. under which Gulf's entire automotive product range, including lubricants for two-wheelers, passenger cars, commercial vehicles, and agriculture-related vehicles, would be distributed through Nayara Energy's extensive retail fuel network. "This strategic alliance is part of a three-year contract leveraging Nayara Energy's reach to further strengthen Gulf Oil's brand presence and product availability to cater to the expanding automotive market, particularly along the country's rapidly developing highway infrastructure in India," the two companies said in a recent joint statement. In addition, NTPC Green Energy Limited, a wholly owned subsidiary of integrated power producer NTPC Ltd, and Nayara Energy have recently signed an agreement to jointly explore opportunities in the green hydrogen and green energy space. The agreement aims for collaboration to produce green hydrogen for Nayara Energy's captive usage, which would help the company reduce its carbon footprint. Sambit Mohanty Editor: Debiprasad Nayak
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