The Art of Scaling Technology through Product Innovation
Manav Khurana has 24 years of total experience in product management, marketing, and leadership roles across various technology companies. He leads product strategy, vision, and management at New Relic, focusing on empowering engineers with data-driven insights. As SVP, Product GM, he implemented the company's consumption business model. With expertise in creating new technology categories and product-led growth, he previously held leadership roles at Twilio, Aruba (HPE), and Motorola. Manav holds a Bachelor’s in Electrical and Computer Engineering from the University of Rochester and an MBA from Santa Clara University. His passion lies in driving innovation through data-driven decision-making.
In a recent interaction with M R Yuvatha, Senior Correspondent at siliconindia, Manav Khurana shared his insights on ‘The Art of Scaling Technology through Product Innovation’.
In today’s rapidly evolving digital landscape, scaling technology requires more than just groundbreaking ideas it demands strategic execution, continuous experimentation, and adaptive innovation. As industries embrace AI-driven solutions, agile methodologies, and emerging technologies, businesses must navigate the complexities of scaling while maintaining efficiency and market relevance. The art of scaling technology lies in fostering a culture of innovation, leveraging data-driven insights, and ensuring seamless integration of advancements. By understanding key enablers like ownership, behavior, and process, enterprises can transform innovative concepts into scalable, sustainable solutions that drive long-term success.
AI-Driven Development and Intelligent Observability
AI is actually adding a new layer of abstraction, making software development much easier than ever before. Decades ago, engineers had to write hundreds of lines of code for a short set of assembly instructions. With the introduction of languages such as C and C++, much of the hardware was abstracted, making development simpler. More recently, contemporary programming languages have further raised this abstraction. Coding concepts are more accessible to students and AI tools like ChatGPT and IDE are making software development easier than ever, making it accessible to all.
This change is creating a new generation of software developers, who can create platform capabilities using AI. Before AI-powered software development was a reality, managing incidents was a huge challenge. A survey of 1,700 customers showed that, on average, organizations have 242 incidents annually, amounting to about 77 hours of downtime. Downtime translates to poor customer experiences, revenue loss and reputation damage.
Observability is key to countering these challenges. Today, AI is speeding up software development, and increasing the complexity of managing software at scale. Observability too has evolved to optimize software performance management. Intelligent observability, powered by AI Agents, takes over responsibilities that were previously mundane and repetitive, improving efficiency, all while ensuring humans are still in control of what’s automated. With this, organizations have been able to better manage the increasing demands of today's software ecosystems.
Previously, to write even a short set of assembly instructions involved vast amounts of coding, frequently dozens of pages. With the introduction of languages such as C and C++, though, much of the hardware detail was abstracted, making development simpler.
Scaling Innovation through AI and Observability
One of the key elements of scaling innovation is acknowledging that experimentation is a precursor to success, a notion that is common to all industries. Experimentation in software development is easy, and with AI, it becomes even easier, leading to speedy innovation. Scaling it requires the right tools, strong monitoring, and thorough observability to ensure it truly serves customers. Without these safeguards, companies stand to guide users into dead ends and operational hurdles. Instrumentation software, telemetry collection, drawing useful insights, and responding to them, are essential but underrated parts of innovation.
Most companies concern themselves with the speed with which they can write and iterate upon new concepts but not with running software at scale. “This is where observability comes into play”, says Manav, “And companies like New Relic offer solutions that enable enterprises to monitor, optimize, and scale their software correctly. Monitoring AI Models for Performance and Compliance with generative AI is bound to create more code. Functionality ultimately needs proper monitoring and observability. Intelligent observability like New Relic’s platform, uses the power of AI to better manage software at scale, ensure that it's performant, and have AI agents do some of the work that was previously all had to be done manually. There is still a human in the loop in the automation, but it is just possible for a lot more people to manage software at scale”, he added.
Additionally, businesses cannot rely on one AI model. With the launch of DeepSeek, access to AI is democratized and businesses are adopting a combination of models including cloud-based, open source deployed on private cloud, and specialized models built internally. Managing it requires governance policies that monitor which models are being applied to which customer experiences. Observing AI models will be essential to calculate the cost of each model, test performance, and determine possible bias or other flaws within various use cases.
Understanding the need for intelligent observability, New Relic launched AI monitoring was launched last year to help businesses identify what cloud models are being utilized. Today, support is provided for a maximum of 20 various models, allowing businesses to construct an inventory along with monitoring the performance and the cost of every model.
Optimizing Cloud Costs through Real-Time Observability
Organizations utilizing consumption-based formats for cloud and observability software have to provide real-time visibility and cost management so as not to exceed budgets while keeping the service efficient. Offering end-to-end visibility into consumption trends, some companies like New Relic enable IT and engineering teams to identify which accounts, apps, or users are causing over consumption. With automated alerting, groups can identify sudden spikes in usage and react to deviations from anticipated patterns. Further, the platform's new predictive features make it possible for businesses to predict cloud consumption going forward, helping them improve governance and optimize resource allocation while avoiding surprise cost increases. Full-stack observability provides businesses and developers with the ability to improve software performance monitoring while reducing costs with an as-you-go pricing model.
Platforms like New Relic are providing engineering and IT teams with complete visibility into application transactions, detecting errors, latencies, and resource bottlenecks. Real-time alerts notify teams of issues instantly, enabling quick action to resolve performance problems. This proactive approach enhances software efficiency, minimizes downtime, and improves the overall customer experience.
Looking Ahead
The convergence of AI, agile practices, and adaptive production will redefine product scalability and competitiveness for businesses in the next few years. Just as how the internet revolution changed packaged software to a cloud-delivered model in a decade and a half, AI will spearhead an even quicker change. Every software experience will be equipped with an AI-fueled alternative, enhancing applications to become more personalized, and powerful than ever.
As this shift accelerates, it is essential for enterprises to monitor both traditional and AI-powered software environments side by side to ensure optimal performance. Every company is now considering how AI will shape the future, with generative AI already demonstrating significant improvements in productivity. However, the rise of agentic workflows where AI agents collaborate to handle repetitive tasks efficiently will further revolutionize operations. Even though complete automation is not envisioned, human intervention will be important to keep the digital experience smooth.
In a recent interaction with M R Yuvatha, Senior Correspondent at siliconindia, Manav Khurana shared his insights on ‘The Art of Scaling Technology through Product Innovation’.
In today’s rapidly evolving digital landscape, scaling technology requires more than just groundbreaking ideas it demands strategic execution, continuous experimentation, and adaptive innovation. As industries embrace AI-driven solutions, agile methodologies, and emerging technologies, businesses must navigate the complexities of scaling while maintaining efficiency and market relevance. The art of scaling technology lies in fostering a culture of innovation, leveraging data-driven insights, and ensuring seamless integration of advancements. By understanding key enablers like ownership, behavior, and process, enterprises can transform innovative concepts into scalable, sustainable solutions that drive long-term success.
AI-Driven Development and Intelligent Observability
AI is actually adding a new layer of abstraction, making software development much easier than ever before. Decades ago, engineers had to write hundreds of lines of code for a short set of assembly instructions. With the introduction of languages such as C and C++, much of the hardware was abstracted, making development simpler. More recently, contemporary programming languages have further raised this abstraction. Coding concepts are more accessible to students and AI tools like ChatGPT and IDE are making software development easier than ever, making it accessible to all.
This change is creating a new generation of software developers, who can create platform capabilities using AI. Before AI-powered software development was a reality, managing incidents was a huge challenge. A survey of 1,700 customers showed that, on average, organizations have 242 incidents annually, amounting to about 77 hours of downtime. Downtime translates to poor customer experiences, revenue loss and reputation damage.
Observability is key to countering these challenges. Today, AI is speeding up software development, and increasing the complexity of managing software at scale. Observability too has evolved to optimize software performance management. Intelligent observability, powered by AI Agents, takes over responsibilities that were previously mundane and repetitive, improving efficiency, all while ensuring humans are still in control of what’s automated. With this, organizations have been able to better manage the increasing demands of today's software ecosystems.
Previously, to write even a short set of assembly instructions involved vast amounts of coding, frequently dozens of pages. With the introduction of languages such as C and C++, though, much of the hardware detail was abstracted, making development simpler.
Scaling Innovation through AI and Observability
One of the key elements of scaling innovation is acknowledging that experimentation is a precursor to success, a notion that is common to all industries. Experimentation in software development is easy, and with AI, it becomes even easier, leading to speedy innovation. Scaling it requires the right tools, strong monitoring, and thorough observability to ensure it truly serves customers. Without these safeguards, companies stand to guide users into dead ends and operational hurdles. Instrumentation software, telemetry collection, drawing useful insights, and responding to them, are essential but underrated parts of innovation.
Most companies concern themselves with the speed with which they can write and iterate upon new concepts but not with running software at scale. “This is where observability comes into play”, says Manav, “And companies like New Relic offer solutions that enable enterprises to monitor, optimize, and scale their software correctly. Monitoring AI Models for Performance and Compliance with generative AI is bound to create more code. Functionality ultimately needs proper monitoring and observability. Intelligent observability like New Relic’s platform, uses the power of AI to better manage software at scale, ensure that it's performant, and have AI agents do some of the work that was previously all had to be done manually. There is still a human in the loop in the automation, but it is just possible for a lot more people to manage software at scale”, he added.
Additionally, businesses cannot rely on one AI model. With the launch of DeepSeek, access to AI is democratized and businesses are adopting a combination of models including cloud-based, open source deployed on private cloud, and specialized models built internally. Managing it requires governance policies that monitor which models are being applied to which customer experiences. Observing AI models will be essential to calculate the cost of each model, test performance, and determine possible bias or other flaws within various use cases.
Understanding the need for intelligent observability, New Relic launched AI monitoring was launched last year to help businesses identify what cloud models are being utilized. Today, support is provided for a maximum of 20 various models, allowing businesses to construct an inventory along with monitoring the performance and the cost of every model.
Optimizing Cloud Costs through Real-Time Observability
Organizations utilizing consumption-based formats for cloud and observability software have to provide real-time visibility and cost management so as not to exceed budgets while keeping the service efficient. Offering end-to-end visibility into consumption trends, some companies like New Relic enable IT and engineering teams to identify which accounts, apps, or users are causing over consumption. With automated alerting, groups can identify sudden spikes in usage and react to deviations from anticipated patterns. Further, the platform's new predictive features make it possible for businesses to predict cloud consumption going forward, helping them improve governance and optimize resource allocation while avoiding surprise cost increases. Full-stack observability provides businesses and developers with the ability to improve software performance monitoring while reducing costs with an as-you-go pricing model.
Platforms like New Relic are providing engineering and IT teams with complete visibility into application transactions, detecting errors, latencies, and resource bottlenecks. Real-time alerts notify teams of issues instantly, enabling quick action to resolve performance problems. This proactive approach enhances software efficiency, minimizes downtime, and improves the overall customer experience.
Looking Ahead
The convergence of AI, agile practices, and adaptive production will redefine product scalability and competitiveness for businesses in the next few years. Just as how the internet revolution changed packaged software to a cloud-delivered model in a decade and a half, AI will spearhead an even quicker change. Every software experience will be equipped with an AI-fueled alternative, enhancing applications to become more personalized, and powerful than ever.
As this shift accelerates, it is essential for enterprises to monitor both traditional and AI-powered software environments side by side to ensure optimal performance. Every company is now considering how AI will shape the future, with generative AI already demonstrating significant improvements in productivity. However, the rise of agentic workflows where AI agents collaborate to handle repetitive tasks efficiently will further revolutionize operations. Even though complete automation is not envisioned, human intervention will be important to keep the digital experience smooth.
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