#agentic AI

[procurement-insights]

Procurement Decentralization 20 Years Later

Are you in the software business or the procurement business? 

EDITOR’S NOTE: AI, Generative AI, and Agentic AI are not new. They are not an exciting breakthrough, nor are they a competitive edge. Like my teenage daughter’s recent discovery of the Beatles, these “tools” are as great today as they were 20 years ago. However, to succeed, we must finally learn how to use them properly, e.g., using an agent-based versus equation-based development and…

[jcmarchi]

Archana Joshi, Head – Strategy (BFS and EnterpriseAI), LTIMindtree – Interview Series

New Post has been published on https://thedigitalinsider.com/archana-joshi-head-strategy-bfs-and-enterpriseai-ltimindtree-interview-series/

Archana Joshi, Head – Strategy (BFS and EnterpriseAI), LTIMindtree – Interview Series

Archana Joshi brings over 24 years of experience in the IT services industry, with expertise in AI (including generative AI), Agile and DevOps methodologies, and green software initiatives. She currently leads growth strategies and market positioning for the Enterprise AI service line and the Banking and Financial Services Business Unit at LTIMindtree. Joshi has worked with Fortune 100 clients across various geographies and is a regular speaker at industry forums and events.

LTIMindtree is a global technology consulting and digital solutions company that works with enterprises across various industries to support business model evolution, innovation, and growth through digital technologies. Serving over 700 clients, LTIMindtree provides domain and technology expertise aimed at enhancing competitive differentiation, customer experiences, and business outcomes in an increasingly interconnected world.

Given your extensive experience in transforming IT services across various organizations, how has your personal leadership style evolved at LTIMindtree, particularly in driving the adoption of Generative AI?

With over two decades of experience in IT Services, I have dedicated my career to driving transformative technology solutions for customers, be it Agile/DevOps or generative AI (GenAI). At LTIMindtree, my focus is on empowering organizations to leverage GenAI for strategizing and executing their digital transformation journeys. I prioritize customer-centric strategies, working closely with clients to understand their unique challenges and deliver tailored AI solutions that drive business value. As the head of strategy, I need to collaborate with teams across various departments to promote GenAI adoption and stay informed about new developments to guide my decisions. GenAI processes vast amounts of data to provide actionable insights. This capability is particularly beneficial for a data-oriented leader like me, who values evidence-based strategies.

For example, every morning when I start my day with GenAI-based copilots to help me understand the top items that need my attention or provide insights to create reports that I can share with my team on adoption. In fact, I often say within the team that GenAI-based copilots have essentially become integral members of our team, much like trusted wingmen. They support us by providing valuable insights, automating tasks and keeping us aligned with our strategic goals.

How is Generative AI reshaping traditional IT service models, particularly in industries that have been slower to adopt digital transformation?

GenAI is revolutionizing traditional IT service models across all industries by significantly enhancing IT developer productivity. From co-pilots that generate code to synthetic data for testing and automating IT operations, every facet of IT is being transformed. Consequently, the focus of IT service models is shifting from cost-driven to efficiency- and impact-driven approaches. This means that the value of IT services is now measured by their ability to deliver tangible outcomes rather than just cost savings. This shift is also leading to new types of work in IT services, such as developing custom models, data engineering for AI needs and implementing responsible AI.

Just 18 months ago, these services were not the norm. Even in heavily regulated industries like healthcare and financial services, where legacy systems are prevalent, the value of GenAI in improving operational efficiency is increasingly recognized.

Our own research at LTIMindtree, titled “The State of Generative AI Adoption,” clearly highlights these trends. In healthcare, we’re seeing GenAI make a big impact by automating things like medical diagnostics, data analysis and administrative work. This is helping doctors and healthcare providers make quicker, more accurate decisions—though adoption remains cautious due to strict compliance and regulatory frameworks. In financial services, GenAI enhances risk management, fraud detection and customer service by automating manual tasks. However, the sector’s adoption is driven by concerns around risk, governance and sensitive data.

Can you share specific examples of how LTIMindtree has successfully integrated GenAI into traditional IT workflows to drive efficiency and innovation?

At LTIMindtree, we have a 3-pronged strategy towards AI. The philosophy of “AI in Everything, Everything for AI, AI for Everyone” underscores our commitment to integrating AI across all facets of our operations and services. This approach ensures that AI is not just an add-on but a core component of our solutions, driving innovation and efficiency.

Customers are looking at AI to improve efficiency across the board. From reducing hours spent on repetitive, time-consuming tasks to scaling operations and improving the reliability of business processes, AI is becoming a core part of their strategy. Our engineers are focused on integrating AI copilots into their workflows, covering everything from coding, testing, and deployment to software maintenance.

For example, in a transformational move for a Fortune 200 company, we’ve employed GenAI-based copilots to convert large stored procedures into Java, enabling their modernization journey. We recently worked with a large insurance company that wanted to automate its data extraction processes. They were facing scalability and accuracy issues with their manual approach. So, our team developed a companion bot, which now helps process multiple documents, extracting critical information like risk, eligibility, coverage and pricing details. This has significantly reduced the time it takes them to file product offers and manage various coverages.

With the rapid adoption of GenAI across various sectors, what are some of the ethical considerations enterprises should be mindful of, and how does LTIMindtree ensure responsible AI use?

The evolution of AI is promising but also brings many corporate challenges, especially around ethical considerations in how we implement it.

At LTIMindtree, we have an AI council comprising cross-functional experts from AI, security, legal, data privacy, and various industry verticals. This council has established AI assurance frameworks and collaborates with industry bodies on AI regulatory guidelines. Additionally, it works with teams implementing AI to validate their ethical risk postures.

To effectively implement GenAI, we have established a set of core ethical principles aligned with corporate values, addressing fairness, accountability, transparency and privacy. This requires executive sponsorship and support from legal and security teams. Next, technical interventions are incorporated into our internal processes that focus on high-quality, unbiased data, with measures to ensure data integrity and fairness. Fostering an ethical AI culture involves continuous training on AI capabilities and potential pitfalls, such as AI hallucinations. Finally, regular audits and updates of AI systems are done to address vulnerabilities and ensure the accuracy of AI outputs. This comprehensive approach ensures that GenAI is implemented responsibly and effectively, driving business value while maintaining ethical standards.

How does LTIMindtree’s AI platform address concerns around AI ethics, security, and sustainability?

As we continue to roll out new AI tools and platforms, we must ensure they meet our standards and regulations around the technology’s use. In addition to maintaining data quality to provide accurate and unbiased outputs, we are committed to meeting high standards for security and sustainability.

Our platform is built around the principles of responsible and mindful AI. In terms of sustainability, we are aware of the growing energy demand required to support AI models, from training to its continued operation. We have adopted a reduce, reuse and recycle approach to AI to address the carbon footprint and the importance of creating environmentally friendly and sustainable AI practices. Through this process, we focus on reducing the parameters by focusing on smaller, more specific large language models (LLMs) that can efficiently address the needs of enterprise applications while creating a smaller carbon footprint. Additionally, we repurpose data for various applications and use cases to avoid redundancies and reuse mechanisms and prompts that can be used for similar tasks to promote efficiency and sustainability. We are also looking at quantized models to reduce memory footprint, receive faster inference, reduce cost and build sustainable applications.

As I mentioned earlier, security is a key concern with the use of any AI tool or application. At LTIMindtree, we have not only prioritized data security and fair usage, but we have made it a cornerstone of our AI strategy. We have also incorporated 50+ best-in-class moderation APIs and responsible AI frameworks from third party providers like the Nvidia Nemo guardrails and the IBM Watson Governance models. Our platform efficiently manages data while factoring in privacy, security, ethical use and sustainability by leveraging sound governance measures and a well-built framework.

How is GenAI influencing Agile project management at LTIMindtree? What advantages does it bring to Agile teams, and are there any trade-offs?

Integrating GenAI into Agile practices is transforming how teams work. It boosts productivity, streamlines processes, and opens new avenues for innovation. As the software development landscape evolves, we are leveraging GenAI to automate those repetitive tasks that can bog teams down. This shift allows them to focus more on creative problem-solving and innovation—exactly where they should be.

When we start integrating GenAI into Agile frameworks, there are a few key points we would like to emphasize. First, it is important to understand the nature of AI tools and their potential impact on team collaboration. For instance, Agile teams need to be mindful of the limitations of these tools. They rely on pre-existing data rather than providing real-time insights, so it is essential to validate and refine their outputs.

Our AI native DevOps leverages cutting-edge technology like knowledge graphs, custom SLMs (small language models) along with software development lifecycle (SDLC) agents. This has the potential to achieve 35-50% efficiency in productivity across the Agile-DevOps cycle for an enterprise. It helps an Agile pod during user story creation, sprint planning, code generation to the CI/CD pipelines and subsequent incident management.

With AI transforming the IT industry, how is LTIMindtree addressing the need for new talent and skill sets? What initiatives have you led to ensure your teams are equipped for the AI-driven future?

The rise of innovative technologies in the IT industry has highlighted a gap between the skills our workforce currently has and what is needed to thrive in an AI-driven world. GenAI has the potential to completely reshape the daily roles of many employees, so preparing for new skills and roles is essential.

At LTIMindtree, we are taking the lead in this transformation by focusing on upskilling our employees to meet these emerging demands. We have our GARUDA initiative, specifically designed for training and onboarding teams in GenAI and enterprise AI. We recognize that effective training and educational resources are crucial, and we are committed to creating a culture of continuous learning.

Our training strategies include data-driven adaptations, real-time online learning, advanced reinforcement learning, transfer learning and feedback loops. This way, we ensure that our teams are not just keeping pace with change but are genuinely equipped to excel in their evolving roles. It is an exciting time, and we are all on this journey together.

In addition to this, we have tied up with seven academic institutions to equip future talent on AI skills. Here we are involved right from curriculum design to administering the curriculum, as well as equipping the professors via train-the-trainer approaches.

How do you see the role of human talent evolving in an increasingly AI-driven workplace, and what steps are you taking to prepare your workforce for this shift?

In the past, there were distinct roles for creative individuals and technology experts. However, there’s a noticeable shift towards adopting, mainstreaming and scaling innovative content creation techniques, blurring the lines between creativity and technology. This integration is impacting various industries, where the conventional separation between creative roles and technology jobs is gradually diminishing. While promising, this evolution comes with its challenges that indicates a substantial shift of focus on reskilling as an essential for capitalizing on AI’s benefits.

The big conversation now is how to make this GenAI change stick and scale. Here’s where change management becomes crucial. It requires a structured approach and a dedicated team to oversee the AI adoption process. People, not just technology, are at the heart of successful GenAI adoption. It can be a powerful tool for empowerment, even among those who initially perceive it as a threat. Forrester forecasts that by 2030, only 1.5% of jobs will be lost to GenAI, while 6.9% will be influenced by it. Therefore, leaders must prioritize transparency and motivate their workforce about the future of AI in the workplace.

AI is changing job roles across the IT sector, automating everyday tasks, and placing emphasis on strategic decision-making and complex problem-solving. At LTIMindtree, we believe this is a mindset shift and hence have established a dedicated central initiative GARUDA – that focuses on this change adoption. The GARUDA initiative is not just about role-based training and upskilling but also on creating AI ambassadors that can drive this adoption across various layers. We are also working with our HR function to look at impacts on various roles within the organization, along with their career paths and associated rewards and recognition. Today at LTIMindtree we have three levels of upskilling pathways – foundation, practitioner and expert. Over 50,000 of our associates have already completed the foundational skilling initiatives that include concepts of AI to the usage of copilots as well as responsible AI considerations.

What are some of the most innovative GenAI applications you’ve seen recently, and where do you see the technology headed in the next 3-5 years?

We are just scratching the surface of what GenAI can do, and I am thrilled about its potential across the IT industry and beyond. As more sectors jump on board, I find myself particularly excited about their applications to transform human lives.

At LTIMindtree, we have partnered with the UN Refugee Agency to enhance its crisis response capabilities using GenAI. This collaboration aims to accelerate on-the-ground crisis response, providing timely aid and support to refugees in need. The innovative use of technology helps bring hope and relief to vulnerable populations during their greatest times of need. For an American life insurance company, we developed a GenAI solution that translates spoken words in real-time, significantly improving the customer experience. By bridging communication gaps, this technology fosters better understanding and connection between people, bringing us closer together and ensuring that language barriers no longer hinder effective experiences.

Looking ahead, Agentic AI will enable autonomous task performance and decision-making. By 2027, industry-specific models will dominate, synthetic data use will rise, and energy-efficient implementations will grow. Multimodal models integrating text, image, audio and video inputs will enhance capabilities, driving significant economic impact and innovation. GenAI is poised to add up to $4.4 trillion to the global economy annually, revolutionizing industries and driving efficiency and sustainability, retail, healthcare and life sciences.

The reality is that every workplace will be touched by GenAI in some capacity, becoming a part of our everyday operations. As we continue this transition, I cannot wait to see how it evolves and what innovations will come next.

[esignature19]

Emerging Trends in AI in 2024

Artificial Intelligence (AI) is not just a buzzword anymore; it’s a driving force behind the digital transformation across industries. As we move into 2024, AI continues to evolve rapidly, introducing new possibilities and challenges. From enhancing business processes to reshaping entire sectors, AI's influence is expanding. Here, we explore the emerging AI trends in 2024 that are set to redefine how we live, work, and interact with technology.

Emerging trends in Artificial Intelligence (AI) in 2024

AI-Driven Creativity: Expanding the Horizons of Innovation One of the most exciting trends in AI for 2024 is its growing role in creative processes. AI is no longer limited to analyzing data or automating tasks; it is now actively contributing to creative fields. AI-driven creativity refers to the use of AI to generate new ideas, designs, and even art. This trend is particularly prominent in industries such as fashion, entertainment, and design, where AI algorithms are being used to create novel designs, suggest creative concepts, and even compose music. For example, AI can analyze vast amounts of data to identify emerging design trends, which can then be used to create new products that align with consumer preferences. In the entertainment industry, AI is being used to generate scripts, compose music, and even create digital art. This trend is pushing the boundaries of creativity, enabling human creators to collaborate with AI in unprecedented ways. As AI continues to develop its creative capabilities, we can expect to see more AI-generated content across various media, leading to a fusion of human and machine creativity that will redefine innovation.

AI-Powered Automation: Transforming Business Operations Automation has been a key application of AI for years, but in 2024, AI-powered automation is set to reach new levels of sophistication. AI is increasingly being used to automate complex business processes, from supply chain management to customer service. This trend is driven by advancements in machine learning and natural language processing, which enable AI systems to perform tasks that were previously thought to require human intelligence. One area where AI-powered automation is making a significant impact is in customer service. AI chatbots and virtual assistants are becoming more advanced, capable of understanding and responding to complex customer queries in real-time. This not only improves the customer experience but also reduces the need for human intervention, allowing businesses to operate more efficiently. In addition to customer service, AI-powered automation is also being used in manufacturing, logistics, and finance. For example, AI algorithms can optimize production schedules, predict maintenance needs, and even automate financial transactions. As businesses continue to adopt AI-powered automation, they can expect to see increased efficiency, reduced costs, and improved decision-making capabilities.

AI and Sustainability: Driving Environmental Innovation As the world grapples with the challenges of climate change, AI is emerging as a powerful tool for driving sustainability. In 2024, AI is being used to develop innovative solutions that reduce environmental impact and promote sustainability across various sectors. This trend is particularly evident in areas such as energy management, agriculture, and transportation. One of the most promising applications of AI in sustainability is in energy management. AI algorithms can analyze energy consumption patterns and optimize the use of renewable energy sources, such as solar and wind power. This not only reduces carbon emissions but also lowers energy costs for businesses and consumers. In agriculture, AI is being used to optimize farming practices, from precision irrigation to crop monitoring. By analyzing data from sensors and satellites, AI can help farmers make more informed decisions, leading to increased crop yields and reduced resource use. This trend is critical for addressing the global challenges of food security and environmental sustainability. Moreover, AI is playing a crucial role in the development of smart cities, where it is used to optimize transportation systems, reduce traffic congestion, and minimize pollution. As AI continues to drive sustainability, it will play a pivotal role in creating a more sustainable and resilient future.

AI Ethics and Responsible AI: Ensuring Trust and Transparency As AI becomes more integrated into our daily lives, concerns about its ethical implications are growing. In 2024, AI ethics and responsible AI development are emerging as critical areas of focus for businesses, governments, and researchers. Ensuring that AI is developed and used responsibly is essential for maintaining public trust and preventing unintended consequences. One of the key ethical concerns surrounding AI is bias in decision-making algorithms. AI systems are often trained on historical data, which may contain biases that can lead to unfair outcomes. For example, AI algorithms used in hiring or lending decisions may inadvertently discriminate against certain groups. To address this issue, researchers and companies are developing techniques to detect and mitigate bias in AI systems. Another important aspect of AI ethics is transparency. Users need to understand how AI systems make decisions, especially when those decisions have significant impacts on their lives. This has led to a push for explainable AI, where the decision-making process is clear and understandable to humans. Additionally, there is a growing emphasis on AI governance, where organizations are establishing frameworks and guidelines for responsible AI development. This includes ensuring that AI systems are used in ways that align with ethical principles, such as fairness, accountability, and transparency. As AI continues to evolve, addressing its ethical challenges will be critical to ensuring that it benefits society as a whole.

AI in Healthcare: Revolutionizing Patient Care The integration of AI in healthcare is not a new trend, but in 2024, it is set to revolutionize patient care in unprecedented ways. AI is being used to improve diagnostics, treatment planning, and patient outcomes, making healthcare more efficient and accessible. One of the most significant applications of AI in healthcare is in medical imaging. AI algorithms can analyze medical images, such as X-rays and MRIs, with incredible accuracy, often detecting abnormalities that might be missed by human doctors. This can lead to earlier diagnosis and treatment of diseases like cancer, ultimately saving lives. In addition to diagnostics, AI is also being used to develop personalized treatment plans. By analyzing a patient's genetic information, medical history, and lifestyle, AI can recommend treatments that are most likely to be effective for that individual. This personalized approach not only improves patient outcomes but also reduces the likelihood of adverse reactions to treatments. Moreover, AI is playing a crucial role in drug discovery. AI algorithms can analyze vast amounts of data to identify potential new drugs and predict how they will interact with the human body. This accelerates the drug development process, bringing new treatments to market faster. As AI continues to advance in healthcare, it will lead to better patient outcomes, more efficient healthcare systems, and ultimately, a healthier population. Conclusion The year 2024 is set to be a transformative one for AI, with emerging trends that will shape the future of technology, business, and society. From AI-driven creativity and automation to sustainability and ethics, these trends highlight the growing influence of AI in our lives. As we navigate this rapidly evolving landscape, it is essential to stay informed and prepared for the changes that lie ahead. By embracing these emerging AI trends, businesses and individuals can harness the power of AI to drive innovation, improve outcomes, and create a better future.

[innova7ions]

Meet the Future: Proactive AI Agents Changing Our World!

Agentic AI signifies a groundbreaking evolution in artificial intelligence, transitioning from reactive systems to proactive agents.

These advanced AI entities possess the ability to comprehend their surroundings, establish goals, and operate independently to fulfill those aims. In this video, we delve into how agentic AI is revolutionizing decision-making processes and taking actions autonomously without human oversight.

A prime example includes environmental monitoring systems that identify and respond to threats such as forest fires.

Discover the implications of this technology on our future!

Video Automatically Generated by Faceless.Video

For other details on other Generative AI Platforms - Visit our YouTube Channel - AI Innovations

or Visit our Website at INNOVA7IONS

#AgenticAI

#ArtificialIntelligence

[ai-innova7ions]

Video Automatically Generated by Faceless.Video:

Agentic AI refers to AI systems designed to operate as agents that can autonomously perform tasks, make decisions, and interact with their environment and other systems or agents. These AI agents are goal-oriented, capable of sensing their environment, processing information, and taking actions to achieve specific objectives. Unlike traditional AI, which may require explicit instructions for each task, agentic AI systems can act independently within predefined parameters to achieve their goals.

Key Features of Agentic AI:

Autonomy:Agentic AI systems operate independently, making decisions and taking actions without needing constant human supervision.Goal-Oriented Behavior:These AI agents are designed with specific goals or objectives, and they use their capabilities to work towards achieving these goals.Environmental Awareness:Agentic AI can perceive and interpret its environment using sensors, data feeds, or other inputs. It adapts its behavior based on changes in the environment.Decision-Making and Problem-Solving:These AI agents use algorithms to evaluate options, solve problems, and make decisions that align with their goals.Interactivity and Communication:Agentic AI can interact with other systems, agents, or humans, exchanging information and coordinating actions to achieve collective objectives.Learning and Adaptation:Some agentic AI systems can learn from their experiences, improving their performance and adapting to new challenges over time.Task Execution:These AI agents can execute tasks within their domain of expertise, whether it’s navigating a physical environment, processing data, or coordinating with other agents.

Benefits of Agentic AI:

Efficiency in Task Automation:Agentic AI can automate complex tasks, freeing up human resources for more strategic activities.Improved Decision-Making:By processing large amounts of data and considering multiple variables, agentic AI can make more informed decisions than humans might.Scalability:Agentic AI systems can be deployed at scale, managing large, complex operations across multiple domains simultaneously.Adaptability:These systems can adapt to new environments or changing conditions, ensuring that they remain effective even as circumstances evolve.Enhanced Collaboration:Agentic AI can work alongside humans and other AI systems, facilitating better teamwork and coordination, particularly in complex environments.Cost Savings:Automating routine or complex tasks with agentic AI can reduce labor costs and minimize errors, leading to significant cost savings.24/7 Operation:Like autonomous AI, agentic AI can operate continuously, providing services or monitoring systems around the clock.

Target Audience for Agentic AI:

Enterprise Operations:Large businesses use agentic AI to automate complex processes, manage supply chains, optimize logistics, and enhance customer service.Healthcare:Agentic AI is employed in personalized medicine, patient monitoring, and automated diagnostics, where it can operate independently to improve outcomes.Financial Services:Financial institutions leverage agentic AI for automated trading, risk assessment, fraud detection, and customer interaction.Robotics and Automation:In industries like manufacturing, agentic AI powers robots that can operate autonomously in dynamic environments, adapting to new tasks or challenges.Smart Cities and Infrastructure:Governments and urban planners use agentic AI to manage traffic, energy consumption, public safety, and other aspects of urban living.Agriculture:Agentic AI is applied in precision agriculture, where it manages crop monitoring, irrigation, pest control, and other tasks autonomously.Defense and Security:Defense organizations deploy agentic AI for autonomous surveillance, threat detection, and coordination of unmanned systems.Consumer Technology:In the consumer space, agentic AI powers smart assistants, autonomous home devices, and personalized user experiences.

Comparison with Autonomous AI:

Autonomy vs. Agency:While both autonomous and agentic AI operate independently, agentic AI is specifically designed to achieve defined goals within a particular environment, often interacting with other agents or systems to do so.Interaction:Agentic AI often involves more interaction, whether with humans, other AI agents, or systems, as it’s designed to work in a collaborative or multi-agent setting.

Agentic AI is particularly valuable in environments where collaboration, decision-making, and adaptive behavior are essential, offering significant benefits across various industries.

Credit: ChatGPT

[aitalksblog]

From Monolithic Models to AI Agents: A New Era of Generative AI

(Images made by author with Microsoft Copilot) Large Language Models (LLMs) have demonstrated impressive capabilities, yet their tendency to produce incorrect or misleading information, known as hallucinations, limits their reliability for tasks requiring high accuracy. To address this challenge, agentic AI has emerged as a promising solution. Recently, IBM Technology shared an insightful…

[enden-k]

you can tell the moment my brain paused LMFAO....

[gleaming-glasses]

Family antics

[fischiee]

omega when he’s implanted in tex: you should give into your rage and abandon those you might love to fulfill the urge to find revenge for all those who hurt you. kill your daughter in cold blood while she is incapacitated with agony at even the mention of your name

omega when he’s with the reds and blues: muahahaha 😈😈!!!!1!!! !!! im eevviilllll and im going to blow up the whole! WORLD! !1! 😈😈😈 !!!

[spaceistheplaceart]

the Knight of Chaos saving the Princess of Order...

Marina's daydreams about being rescued by Pearl are so cute, I had to draw something about it :)

Bonus Designs:

(Pls Reblog! and also pls use they/them for my agent 8 thx)

[eldritch-ambrosia]

Do you ever think about how for almost 1000 years there's this story about a King named Arthur with an old man wizard advisor named Merlin and then in 2008 (and onward) it got rewired to where we're writing fanfiction and making art and edits of them kissing?

If you told someone 20 years ago that you wanted to see King Arthur and Merlin kiss they would've thought you were crazy.

[procurement-insights]

How to avoid the wrong UiPath or Why is a company with $1.5 billion in revenue stock tanking?

Why is a company with $1.5 billion in revenue, year-over-year growth of 19%, 10,810 customers, and over 2,100 clients generating $100,000 or more in ARR stock crashing?

𝗜 𝗹𝗼𝘀𝘁 𝟴𝟰% 𝗼𝗻 𝗨𝗶𝗣𝗮𝘁𝗵, 𝗯𝘂𝘁 𝗵𝗲𝗿𝗲’𝘀 𝘄𝗵𝘆 𝗜 𝗰𝗮𝗻’𝘁 𝗹𝗼𝗼𝗸 𝗮𝘄𝗮𝘆…10 years ago, I started following UiPath’s journey & even wrote a book about it. Here’s what’s happening:✅ What UiPath did right:• Built an amazing community, especially with the free version• Consistently innovated – the best in the space• Showed better financial growth than others🚨 But here’s the painful truth nobody’s talking about…They…

[jcmarchi]

Dr. James Tudor, MD, VP of AI at XCath – Interview Series

New Post has been published on https://thedigitalinsider.com/dr-james-tudor-md-vp-of-ai-at-xcath-interview-series/

Dr. James Tudor, MD, VP of AI at XCath – Interview Series

Dr. James Tudor, MD, spearheads the integration of AI into XCath’s robotics systems. Driven by a passion for the convergence of technology and medicine, he enthusiastically balances his roles as a practicing radiologist, Assistant Professor of Radiology at Baylor College of Medicine, and AI researcher.

Founded in 2017, XCath is a startup focused on advancements in medical robotics, nanorobotics, and materials science. The company develops next-generation endovascular robotic systems and steerable guidewires aimed at treating cerebrovascular disorders and other serious medical conditions.

Dr. Tudor, what initially sparked your interest in the intersection of AI and medicine, particularly in the field of radiology?

In 2016, as I was beginning my radiology residency, DeepMind’s AlphaGo defeated world champion Go player Lee Sedol. AlphaGo’s ability to compress and abstract the vast complexities of Go, a game with more possible board positions than atoms in the observable universe, captured my imagination. Excited about AI’s potential to transform radiology and medicine as a whole, I dove headfirst into AI. During residency, I’d spend my evenings and weekends doing AI projects.

Can you tell us more about your journey from medical school to becoming the VP of AI at XCath? What motivated you to pursue AI integration within healthcare robotics?

My career path has taken some unexpected turns. After finishing my radiology residency, I wanted to dedicate more time to AI and its commercial applications. I joined a fitness robotics startup, founded by Eduardo Fonseca, who is now XCath’s CEO.  It was a formative experience, but I never anticipated it would lead down the path of treating acute stroke with endovascular telerobots.

Around a decade ago, a revolution occurred in acute stroke care. The standard of care used to be a medication called tPA that would break up the clot. In 2015, clinical trials demonstrated the superiority of directly removing the clot from the cerebral arteries by navigating tiny guidewires and catheters within the arterial vasculature, a procedure called mechanical thrombectomy. Despite the procedure being markedly effective for large vessel strokes, less than 40% of the US population has access to it. There are a limited number of stroke centers, generally limited to urban areas, that have specialists who can perform the procedure. Globally, the statistics are even more dismal: less than 3% of the world has access.

XCath’s mission is to increase access to mechanical thrombectomy with a hub-and-spoke model, where specialists can provide expert stroke care from a distance with endovascular telerobots deployed to regions without access.

Eduardo asked me how AI could augment the safety of the telerobotic system. I was so curious I spent a few weeks deep in research, having conversations with interventionalists and learning about the telerobot. The mission and potential humanitarian impact are so compelling I had to answer that call to arms.

How did your experiences as an academic radiologist shape your approach to integrating AI in medical devices?

Teaching radiology residents has sharpened my ability to explain complex ideas clearly, which is key when bridging the gap between AI technology and its real-world use in healthcare. It also keeps me grounded in the challenges clinicians face, which helps me design AI solutions that are clinically practical and user-friendly.

As the VP of AI at XCath, what are some of the key challenges you faced while integrating AI into XCath’s robotic systems? How did you overcome them?

Integrating AI into surgical robotics presents a U-shaped challenge. The greatest difficulties lie at the beginning—acquiring and managing data—and at the end—integrating it into an embedded software package. In comparison, the actual training of the AI models is relatively straightforward.

Acquiring medical data is challenging, but fortunately, we were able to establish excellent image-sharing partnerships. Implementing the models for clinical use requires orchestrating the efforts of various teams, including AI, Quality, Software, UI/UX, and Robotic engineers, all while constantly validating with the clinical team that the solution is useful and effective. With so many moving parts, success ultimately depends on having dedicated, high-performing teams that communicate frequently and effectively.

Could you elaborate on how AI enhances the capabilities of XCath’s endovascular robotic systems? What role does AI play in improving patient outcomes?

AI algorithms can serve as a constant teacher and assistant, decreasing the cognitive load and leveling up all providers to provide world-class care. AI can provide intraoperative and postoperative feedback, accelerating the training and adoption process of endovascular robotics. We aim to make the system so effective and accessible that other intravascular specialists such as interventional body radiologists and interventional cardiologists can be trained to provide acute stroke care with the robot.

Additionally, locally embedded algorithms can provide an extra level of safety from cyber-attacks and network failures as they anticipate the expected path of a procedure and can alert and pause the procedure in the case of the unexpected.

At the end of the day, we do not want to take control from the interventionalist, but augment their abilities so that every patient can be confident they are getting world-class care.

How does XCath’s AI-driven technology address the complexities of navigating the human vasculature during endovascular procedures?

XCath’s Endovascular Robotic System represents a major advancement in precision medicine, designed to navigate intricate human vasculature with sub-millimeter accuracy. Our system is designed to minimize procedural variability and enhances control over various endovascular devices through an intuitive control console.

Additionally, XCath’s ElectroSteer Deflectable Guidewire System, the world’s first electronically-controlled steerable smart guidewire, features a steerable tip engineered to navigate complex vascular anatomies and challenging vessel angulations.

AI will further enhance navigation capabilities with locally embedded computer vision and path planning models. These models play a crucial role in reducing the cognitive load on interventionalists during procedures by assisting with real-time image analysis and enhancements and providing safeguards through parallel autonomy.

XCath recently achieved a significant milestone with the world’s first telerobotic mechanical thrombectomy demonstration. Could you share your insights on the role AI played in this groundbreaking procedure?

We used an earlier version of the robot for that groundbreaking achievement, so AI did not play a role. However, it’s an incredible milestone that lays the foundation for future integration of AI into telerobotic procedures.

In this live demonstration, Dr. Vitor Pereira performed an MT procedure from Abu Dhabi on a simulated patient in South Korea, removing a blood clot in the brain in minutes. We were thrilled by the results of the telerobotic demonstration, which found low latency and reliable connection between the robotic controller located in Abu Dhabi and the robotic device in South Korea. We project regional robotic telestroke networks, but we went to an extreme to demonstrate the capabilities of the technology.

What do you believe is the future of telerobotic surgery in the treatment of acute neurovascular conditions, and how is XCath preparing to lead in this space?

Justifying the necessity of telerobotic surgery in many medical scenarios can be challenging, especially when a surgeon is readily available or patient transfer is feasible. However, in the context of stroke treatment, where every minute counts and neurons are rapidly lost, telerobotic interventions become crucial.

XCath is uniquely positioned to pioneer telerobotic surgery, initially focusing on stroke treatment. Our approach addresses the critical need for rapid intervention in areas with limited access to specialized care. Once we’ve successfully tackled this challenge, I believe it will pave the way for telerobotic solutions in other time-sensitive medical emergencies. Also, given the extreme precision of the robotic controls, there is potential for using the robot locally to perform technically difficult surgeries, such as aneurysm repairs.

Where do you see the future of AI in healthcare heading, particularly in relation to robotic systems and minimally invasive procedures?

AI has immense potential to revolutionize healthcare. The initial wave of AI applications has primarily focused on triage and efficiency improvements. We’ve seen significant advancements in radiology, particularly in flagging urgent cases or automating acquisition of measurements. I’m also excited about automated medical record documentation. A current challenge is that doctors often spend more time documenting in front of computers than interacting with patients. I anticipate the development of systems that can document patient interactions or surgeries in real-time, freeing up valuable physician time. In the realm of robotics, AI will play a crucial role in assisting and proctoring, thereby enhancing the consistency and quality of care.

In the foreseeable future, AI is going to augment, but not replace surgeons. The implementation of parallel autonomy in robotic systems will significantly improve both the safety and efficiency of procedures.

As someone deeply involved in AI research, what advancements in AI do you think will have the most significant impact on medical device development over the next decade?

In the last few years, we’ve witnessed a wave of supervised deep learning models receiving FDA approval and are just now starting to fulfill their promise of transforming healthcare. A wave of generative AI applications will likely dominate the next few years.  Agentic AI, by comparison, is in its infancy, but holds much greater promise.  As AI is rapidly evolving, it’s very likely we will see multi-agent systems that can diagnose and treat in real time. There will be additional regulatory hurdles for these agents whose actions are both opaque and probabilistic. However, global need will drive the demand for adoption. In Rwanda, the company Zipline is using flying drones to deliver vital medical supplies within minutes around the country. Similarly, in places that lack access to medical resources, the risk/benefit equation is very different and would likely prompt them to leapfrog the developed world in the deployment of multi-agentic AI medical devices.

Thank you for the great interview, readers who wish to learn more should visit XCath. 

[haylessa]

she said ENOUGH

[shadz420]

Tex is very disappointed in her husband:(

[demetera-kaziaik]

Dad Stone <3