For quicker and more effective medicine delivery, it is essential to efficiently collect and utilize data throughout the pharmaceutical process, from research to commercialization. However, a lot of businesses face challenges due to fragmented legacy systems and a lack of human analysis. These data-driven challenges in drug development can be addressed by next-generation agentic AI in Life Sciences.
Deepak Mittal, CEO and Founder of NextGen Invent, and Partha Anbil, Advisor at NextGen Invent, conducted an insightful interview on the transformative impact of Generative AI in healthcare for 2024. Partha is a long-time Life Sciences consulting industry veteran, currently working as SVP & Practice Leader for Life Sciences.
The days of manually mining, organizing, and managing vast amounts of heterogeneous data or conducting tests to verify theories are long gone for data scientists. By 2026, the big data in manufacturing is expected to grow to $9.11 billion. Manufacturers can predict business outcomes, improve product quality, and lower equipment downtime…
IoT in manufacturing is now a key factor that is changing the ever-changing Industry 4.0 landscape. The way industries use data, interact, and function has changed as a result of IoT. In fact, according to a survey of executives in the manufacturing industry, 86% of them thought that smart factory projects would propel the industry during the next five years.
Consider a scenario in which data is more than just numbers and figures. Rather, it is a dynamic force that molds the fundamental essence of business activities. That is the exact contribution that generative AI in manufacturing makes. Disruptions in the supply chain, however, have seriously affected the revenue of certain businesses.
Many times, people ask whether there is a distinction between data governance vs data management. Obviously, the response is yes. Organizational structures, data owners, rules, procedures, business language, and metrics for the entire lifecycle of data are all defined by data governance. Data management is the technical execution of data governance.
Ever contemplated ways to increase the bottom line of your business? Cognitive analytics is the solution. With the use of artificial intelligence and machine learning, it analyzes enormous volumes of data and produces insights that can spur growth for businesses. A study found that applying cognitive solutions reduced customer service expenses by 50%.
On June 4th, 2024, Deepak Mittal, CEO and Founder of NextGen Invent, and Duane Kotsen, Partner at Marlborough Street Partners and Advisor of NextGen Invent, partnered to delve into the critical topic of supply chain resilience. In this comprehensive interview, Duane shared why robust supply chain strategies are indispensable year-round.
One of the most revolutionary things the world is seeing right now is the sheer amount of data we are gathering. Approximately 2.5 quintillion bytes of data are created every day, according to research. The success of a company depends on all this data, as well as how companies preserve it.
Businesses are gathering enormous amounts of data in today’s ever-changing business environment. They have access to a multitude of data, ranging from website traffic and social media marketing to consumer contact and sales information. The amount of data in the globe is predicted to reach 175 zettabytes by 2025.
One of the most essential abilities for business success nowadays is the ability to derive significant insights from large amounts of data through data-driven initiatives. Fundamentally, data mining is more than just a trendy term in IT. It’s a game-changing technology that reveals hidden connections, patterns, and trends in the data repositories of your company.
Recent times have seen tremendous advancements in technology. One major advantage of technology in healthcare is that it can improve people’s quality of life. Additionally, it effectively enhances patient health while lessening the workload of hospital staff. Automation, digitization, and technological enablement are the three main factors in the healthcare industry.
In a recent interview, NextGen Invent took a significant step in reimagining the supply chain industry through modern technologies. Our digital marketing manager, Tarun Mittal, had the opportunity to sit down with our CEO and Founder, Deepak Mittal, to delve into the transformative potential of Artificial Intelligence, Generative AI, and automation in supply chain and logistics.
Numerous developments in healthcare technology have digitally redesigned clinical and operational procedures in addition to expanding the range of available medical treatments. Healthcare organizations must optimize their internal information flow to provide effective care given improved technology and affordable medical solutions.
There will soon be a severe physician shortage in the United States. There is projected to be a 46,900–121,900 physician shortage by 2032. According to estimates, there are roughly 15 psychiatrists and 30 psychologists for every 100,000 persons. You might wonder what these concerning figures have to do with telehealth apps.
When trying to make an appointment for a medical visit, have you ever found that you have to wait weeks for the next open time slot? Everyone has experienced that. Mobile apps, however, have completely changed how we make appointments since the invention of technology, simplifying and expediting the procedure.
Technology plays a more crucial role than ever in the quickly changing healthcare industry. Over 93% of US physicians concur that the use of technology in healthcare has enhanced patients’ accessibility to medical treatment. According to 77% of respondents, technology has improved patients’ and doctors’ time efficiency.
Implementing EDI in healthcare transactions simplifies various healthcare procedures like invoicing, enrollment, and referrals, leading to significant time and cost savings. Electronic Data Interchange reduces manual errors by up to 30%, enhancing patient care and operational efficiency.
Automation, artificial intelligence, machine learning, and other digital technologies have advanced significantly, which is impacting how we approach work and how various businesses operate. The supply chain is no different, with technology and digital trends continuously reshaping organizations that rely on inventories.
Logistics and supply chain management systems are vital enterprise solutions offering numerous use cases like cost savings, workflow optimization, and enhanced communication. A report estimates that the supply chain management industry will grow at a CAGR of 10.7% from its 2020 value of US$18,699.45 million to its projected $52,632.37 million by 2030.
When it comes to the supply chain, AI technologies such as machine learning, deep learning, automation, and predictive analytics hold tremendous promise for transforming entire industries and organizations. However, achieving successful implementation of AI in supply chain poses significant challenges.
In the modern ecosystem, efficient supply chains are critical for business success. Surprisingly, only 27% of companies utilize technology to enhance their supply chain operations. The inquiries from businesses are as follows: How to optimize labor costs? How to determine the best ways to distribute and warehouse inventory?
Warehouse and distribution tasks need to be optimized, monitored, and managed by any large, complex logistics or supply chain company. These responsibilities include inventory management, order processing, and dock scheduling. All these processes are made more difficult by manual methods like stock counts and spreadsheets.
AI in logistics plays a significant role by assisting executives in the consumer products industry in making decisions more quickly, precisely, and efficiently. This is made possible by its capacity to collect enormous, diverse data sets and then instantly evaluate the information to deliver insights.
With LLMs and deep learning techniques, Generative AI automation can alter customer engagement by understanding complex inquiries and producing natural-sounding conversational responses. It can also use customer data to provide tailored ideas and solutions, personalized responses and recommendations.
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