Here’s some 2026 Industry Predictions by Justin Borgman, CEO and Cofounder, Starburst.
The Rise of Human-and-Machine-Centered Data Ecosystems – “We’re moving toward a world where data platforms won’t primarily serve people anymore; they’ll serve machines. The new consumers of data are AI agents, which will increasingly drive decisions, generate insights, and automate processes at speeds humans can’t match. These AI agents will require direct, governed, real-time access to all enterprise data to reason, generate, and act effectively. As AI agents become the primary consumers, enterprises must decide whether their data governance models empower or constrain them. This shift fundamentally changes everything about how we build and operate data infrastructure, from architecture and pipelines to governance and security, demanding a new approach that prioritizes machine-first accessibility without sacrificing trust or compliance.”
Hybrid AI Becomes the New Default – “The ‘cloud-everything’ era is coming to an end. Data gravity, sovereignty laws, and inference cost control are drivers for on-premises and model-to-data architectures. Enterprises are realizing that critical AI workloads need to remain close to their data, whether on-premises or in hybrid environments, to meet stringent requirements for performance, compliance, and data sovereignty. As a result, DevOps and data teams will increasingly build intelligent, governed ‘AI factories’ inside the enterprise, integrating AI pipelines directly with existing systems rather than relying solely on public cloud services. This approach ensures organizations can scale AI responsibly while maintaining control over sensitive information and operational efficiency.”
The Real Battle Moves Above the Data Format – “The last decade was about standardizing how we store data; the next is about standardizing how we trust it. With open table formats like Iceberg now widely adopted as the standard, the next competitive frontier isn’t the format itself. It’s the management of metadata, governance, and secure access. AI explainability depends on how well metadata is managed. Enterprise success will hinge on how effectively DevOps and data teams curate data catalogs, enforce policies, and provide federated access across diverse environments. Without unified metadata and policy, enterprises risk an AI compliance crisis. It’s no longer just about where the data lives; it’s about how intelligently it can be accessed, trusted, and leveraged to drive actionable outcomes.”
DevOps for Machines, Not Just Humans – “DevOps is evolving beyond its traditional focus on deploying applications. DevOps for machines means governing the real-time interaction between AI agents and enterprise data, with the same rigor once reserved for production apps. Modern teams will now treat data and AI pipelines as mission-critical workloads, ensuring that AI agents have real-time, governed access to enterprise data while maintaining reliability, security, and observability at scale. DevOps for machines is about managing the data-to-action lifecycle, not model training pipelines. Humans remain responsible for defining access, policy, and safety nets. For example, tomorrow’s DevOps teams will monitor not only application uptime, but also AI decision health to ensure agents operate within defined parameters. This evolution requires a new mindset: one where DevOps teams are responsible for orchestrating an ecosystem in which machines, not just humans, can operate safely, efficiently, and autonomously.”
Like this:
Like Loading...
Related
This entry was posted on November 25, 2025 at 4:20 pm and is filed under Commentary with tags Starburst. You can follow any responses to this entry through the RSS 2.0 feed.
You can leave a response, or trackback from your own site.
2026 Technology Predictions from Starburst
Here’s some 2026 Industry Predictions by Justin Borgman, CEO and Cofounder, Starburst.
The Rise of Human-and-Machine-Centered Data Ecosystems – “We’re moving toward a world where data platforms won’t primarily serve people anymore; they’ll serve machines. The new consumers of data are AI agents, which will increasingly drive decisions, generate insights, and automate processes at speeds humans can’t match. These AI agents will require direct, governed, real-time access to all enterprise data to reason, generate, and act effectively. As AI agents become the primary consumers, enterprises must decide whether their data governance models empower or constrain them. This shift fundamentally changes everything about how we build and operate data infrastructure, from architecture and pipelines to governance and security, demanding a new approach that prioritizes machine-first accessibility without sacrificing trust or compliance.”
Hybrid AI Becomes the New Default – “The ‘cloud-everything’ era is coming to an end. Data gravity, sovereignty laws, and inference cost control are drivers for on-premises and model-to-data architectures. Enterprises are realizing that critical AI workloads need to remain close to their data, whether on-premises or in hybrid environments, to meet stringent requirements for performance, compliance, and data sovereignty. As a result, DevOps and data teams will increasingly build intelligent, governed ‘AI factories’ inside the enterprise, integrating AI pipelines directly with existing systems rather than relying solely on public cloud services. This approach ensures organizations can scale AI responsibly while maintaining control over sensitive information and operational efficiency.”
The Real Battle Moves Above the Data Format – “The last decade was about standardizing how we store data; the next is about standardizing how we trust it. With open table formats like Iceberg now widely adopted as the standard, the next competitive frontier isn’t the format itself. It’s the management of metadata, governance, and secure access. AI explainability depends on how well metadata is managed. Enterprise success will hinge on how effectively DevOps and data teams curate data catalogs, enforce policies, and provide federated access across diverse environments. Without unified metadata and policy, enterprises risk an AI compliance crisis. It’s no longer just about where the data lives; it’s about how intelligently it can be accessed, trusted, and leveraged to drive actionable outcomes.”
DevOps for Machines, Not Just Humans – “DevOps is evolving beyond its traditional focus on deploying applications. DevOps for machines means governing the real-time interaction between AI agents and enterprise data, with the same rigor once reserved for production apps. Modern teams will now treat data and AI pipelines as mission-critical workloads, ensuring that AI agents have real-time, governed access to enterprise data while maintaining reliability, security, and observability at scale. DevOps for machines is about managing the data-to-action lifecycle, not model training pipelines. Humans remain responsible for defining access, policy, and safety nets. For example, tomorrow’s DevOps teams will monitor not only application uptime, but also AI decision health to ensure agents operate within defined parameters. This evolution requires a new mindset: one where DevOps teams are responsible for orchestrating an ecosystem in which machines, not just humans, can operate safely, efficiently, and autonomously.”
Share this:
Like this:
Related
This entry was posted on November 25, 2025 at 4:20 pm and is filed under Commentary with tags Starburst. You can follow any responses to this entry through the RSS 2.0 feed. You can leave a response, or trackback from your own site.