Insights with Intel on Navigating Agentic AI Transformation
At TechArena, we’re continuously exploring the ways AI is shaping industries, and my recent Fireside Chat with Lynn Comp, VP and head of Intel’s AI Center of Excellence, brought to light some thought-provoking insights. One major takeaway: agentic AI is set to disrupt how enterprises manage their workflows, data and infrastructure.
During our chat, Lynn provided an in-depth look at how enterprises are navigating their AI journeys, moving beyond traditional machine learning (ML) to embrace more advanced approaches, such as generative AI and agentic computing. For Lynn, this shift begins with data — specifically, the ongoing challenges around data governance and readiness that continue to hinder enterprise AI adoption.
Lynn highlighted a key point that underpins all AI systems: it all starts with data. Whether it’s structured databases or the increasingly popular vector databases for generative AI, data management remains a significant challenge. For enterprises implementing agentic AI, the complexity ramps up even further, as multiple agents can sample data from
different sources, leading to potential misalignment. The need for observability in these systems is critical to avoid costly errors and ensure that the right data is being used by the right agents at the right time.
As enterprises explore agentic AI, Lynn also addressed the computational demands that come with it. The need for more powerful infrastructures that can handle the increased traffic between agents and the massive data flows of generative AI models is becoming evident. This is not just about scaling; it’s about optimizing performance while managing cost variability — another factor many enterprises are grappling with as they explore cloud-native solutions and increasingly complex workflows.
Lynn also delved into the commercialization challenges that lie ahead for agentic AI, particularly when it comes to interoperability and creating a thriving ecosystem. Drawing parallels to cloud-based services, she highlighted how successful agentic computing will require an open framework, akin to a software-as-a-service model, where agents can communicate seamlessly across different platforms. Google’s recent developments around their agent-to-agent platform point toward the necessity of such interoperability to avoid a lock-in scenario. By enabling agents to exchange data across ecosystems, enterprises can unlock the full potential of AI while maintaining flexibility.
Building out a marketplace for agents — where enterprises can purchase and implement AI tools based on specific needs — is another hurdle to overcome. Lynn emphasized that while the technology is promising, organizations will need to ensure that usage models are clear and predictable, especially in terms of cost. Enterprises are currently challenged by a lack of standardization in how agents are priced, with complexities such as pay-per-use and time-of-use models still in the works. Without clear pricing structures and observability tools, organizations will struggle to scale AI effectively while avoiding unforeseen financial burdens.
Looking ahead to the next 12 to 18 months, Lynn stressed the importance of laying a strong data foundation. For businesses, this means focusing on data pipelines and architectures that are flexible enough to support a variety of AI applications. Whether implementing machine learning or natural language processing, organizations that can build adaptable systems will be in a better position to leverage agentic AI for everything from logistics optimization to advanced predictive analytics.
What’s the TechArena take? In the fast-moving world of agentic computing, the next few years will be critical. As Lynn pointed out, the real breakthroughs will come when enterprises, hyperscalers and startups work together to build scalable, interoperable and user-friendly AI ecosystems that will transform industries and business processes alike. At TechArena, we can’t wait to see how these developments unfold — and to continue bringing you the latest insights on the intersection of AI and enterprise innovation.
Check out the full Fireside Chat. To keep up with Lynn and her insights on AI, connect with her on LinkedIn, where you can also check out her recent blog posts on agentic AI and scaling AI.
At TechArena, we’re continuously exploring the ways AI is shaping industries, and my recent Fireside Chat with Lynn Comp, VP and head of Intel’s AI Center of Excellence, brought to light some thought-provoking insights. One major takeaway: agentic AI is set to disrupt how enterprises manage their workflows, data and infrastructure.
During our chat, Lynn provided an in-depth look at how enterprises are navigating their AI journeys, moving beyond traditional machine learning (ML) to embrace more advanced approaches, such as generative AI and agentic computing. For Lynn, this shift begins with data — specifically, the ongoing challenges around data governance and readiness that continue to hinder enterprise AI adoption.
Lynn highlighted a key point that underpins all AI systems: it all starts with data. Whether it’s structured databases or the increasingly popular vector databases for generative AI, data management remains a significant challenge. For enterprises implementing agentic AI, the complexity ramps up even further, as multiple agents can sample data from
different sources, leading to potential misalignment. The need for observability in these systems is critical to avoid costly errors and ensure that the right data is being used by the right agents at the right time.
As enterprises explore agentic AI, Lynn also addressed the computational demands that come with it. The need for more powerful infrastructures that can handle the increased traffic between agents and the massive data flows of generative AI models is becoming evident. This is not just about scaling; it’s about optimizing performance while managing cost variability — another factor many enterprises are grappling with as they explore cloud-native solutions and increasingly complex workflows.
Lynn also delved into the commercialization challenges that lie ahead for agentic AI, particularly when it comes to interoperability and creating a thriving ecosystem. Drawing parallels to cloud-based services, she highlighted how successful agentic computing will require an open framework, akin to a software-as-a-service model, where agents can communicate seamlessly across different platforms. Google’s recent developments around their agent-to-agent platform point toward the necessity of such interoperability to avoid a lock-in scenario. By enabling agents to exchange data across ecosystems, enterprises can unlock the full potential of AI while maintaining flexibility.
Building out a marketplace for agents — where enterprises can purchase and implement AI tools based on specific needs — is another hurdle to overcome. Lynn emphasized that while the technology is promising, organizations will need to ensure that usage models are clear and predictable, especially in terms of cost. Enterprises are currently challenged by a lack of standardization in how agents are priced, with complexities such as pay-per-use and time-of-use models still in the works. Without clear pricing structures and observability tools, organizations will struggle to scale AI effectively while avoiding unforeseen financial burdens.
Looking ahead to the next 12 to 18 months, Lynn stressed the importance of laying a strong data foundation. For businesses, this means focusing on data pipelines and architectures that are flexible enough to support a variety of AI applications. Whether implementing machine learning or natural language processing, organizations that can build adaptable systems will be in a better position to leverage agentic AI for everything from logistics optimization to advanced predictive analytics.
What’s the TechArena take? In the fast-moving world of agentic computing, the next few years will be critical. As Lynn pointed out, the real breakthroughs will come when enterprises, hyperscalers and startups work together to build scalable, interoperable and user-friendly AI ecosystems that will transform industries and business processes alike. At TechArena, we can’t wait to see how these developments unfold — and to continue bringing you the latest insights on the intersection of AI and enterprise innovation.
Check out the full Fireside Chat. To keep up with Lynn and her insights on AI, connect with her on LinkedIn, where you can also check out her recent blog posts on agentic AI and scaling AI.
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