2026 DataOps Predictions - Part 1

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026.

THE RISE OF DATAOPS

In 2026, DataOps will become one of the most strategic functions in every enterprise. It will move from a behind-the-scenes enabler to the operational heart of AI success. DataOps teams will ensure that data entering AI systems is clean, governed, cost-optimized, and compliant, functioning as the "nutrition label" for enterprise intelligence. I expect advanced automation will play a role here as well, detecting duplication, drift, and lineage breaks, while AI itself assists in cleansing and transforming data in real time. As the volume and velocity of data continue to explode, organizations will depend on DataOps to deliver trust, speed, and efficiency. Simply put, DataOps will be the make-or-break discipline for AI and analytics maturity in 2026. 
Arturo Oliver
Senior Director, Market Strategy & Analyst Relations, ScienceLogic

AI-POWERED DATAOPS

AI-powered  DataOps  becomes table stakes: In 2026, data observability, lineage and governance will themselves be AI-augmented. Small and specialized models will continuously monitor pipelines for drift, anomalies, quality degradation and policy violations. Natural-language interfaces will allow business users to query data estates conversationally, while generative agents propose transformations, joins and mappings. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

AI-DRIVEN AUTOMATION

By 2026, DataOps will become deeply intertwined with AI-driven automation. Manual orchestration and brittle pipelines will give way to autonomous data agents that monitor lineage, repair models, and optimize cost-performance in real time. The boundary between DataOps and MLOps will blur as inference becomes a native part of every data workflow. Teams will manage mixed workloads, i.e., structured, unstructured, and semantic through unified, declarative interfaces. The next frontier of DataOps isn't faster ETL, it's intelligent, self-healing data systems that learn from usage and continuously adapt to deliver trustworthy, production-ready data pipelines.
Yoni Michael
Cofounder and CTO, Typedef

AI READINESS

DataOps  becomes the "front door" to enterprise AI:  In 2026, AI readiness will be defined by the quality and agility of  DataOps, not by model sophistication alone. Enterprises will treat  DataOps  as the core discipline that ensures data is discoverable, trusted, governed and ready for consumption by both traditional analytics and generative AI. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

REAL-TIME DATAOPS

Real-time  DataOps  becomes critical for operational AI: 
Edge and operational technology workloads move to cloud, streaming and event-driven data will become as important as batch pipelines.  In  2026,  DataOps  will be deeply embedded in OT environments,  supporting digital twins, predictive maintenance, autonomous  stores  and real-time risk models,  with stringent requirements on latency,  reliability  and explainability.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

HUMAN-AND-MACHINE-CENTERED DATA ECOSYSTEMS

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.
Justin Borgman
CEO and Cofounder, Starburst

DATA INFRASTRUCTURE COSTS

Data infrastructure costs, including cloud data warehouses, lakehouses, and analytics platforms, will rise by 30-50% as companies continue to invest in AI in 2026, unless they fundamentally change their approach to data platform management. The exponential growth in data volumes, the proliferation of generative AI across all aspects of the business, and the lack of visibility into what's actually driving data consumption will all contribute to this increase. Because data platforms bill based on consumption, costs can be highly variable and difficult to predict, especially for organizations that don't have insights and controls over their company's usage. Data observability will be key for managing and mitigating these cost increases. Understanding which workloads, users, and teams are driving costs and identifying inefficiencies in queries and pipelines is the only way organizations can reduce inefficiencies and ensure maximum ROI. Frankly, 30% to 50% of their data infrastructure spend is likely waste — but the most innovative companies think about data observability from a cost-optimization vs. cost-cutting perspective, uncovering opportunities to reinvest in AI/ML innovation without increasing their overall data platform budget.
Kunal Agarwal
Co-Founder and CEO, Unravel Data

DATAOPS ROLE: CONTEXT ENGINEERING

Data Engineering Will Evolve Into "Context Engineering" as AI Agents Become Primary Data Consumers: A fundamental shift is happening in how we think about data engineering. For decades, data engineers prepared data for human consumption — analysts, data scientists, and business users. In 2026, AI agents will emerge as primary data consumers, and this changes everything. "Context engineering" isn't just a rebrand — it's a recognition that agents have different requirements than humans: they need fresh, streaming context delivered in milliseconds, not batch updates delivered overnight. The best data infrastructure companies will embrace this evolution, using their deep expertise in streaming, storage, and processing to solve genuinely new problems around agent-facing analytics and real-time context delivery. While the underlying principles of good data engineering remain constant, the application layer is transforming.
Sijie Guo
CEO, StreamNative

DATAOPS ROLE: STRATEGIST

2026 Will Lay the Foundation for Data Engineers to Hand Work Over to AI Agents: The coming year will be the inflection point where data engineers transition from builders to strategists, preparing to hand off key tasks to AI agents. In 2026, AI won't just be a tool for data engineers — it will be a co-pilot, laying the groundwork for a new era of autonomous data pipelines. While 2025 was about preparing data for AI, this year is about a fundamental shift: data engineers are moving beyond writing SQL to becoming strategic architects who supervise and validate AI-generated code. As data volume and pipeline complexity continue to outpace team growth, the only way forward is to embrace intelligent automation. This will pave the way for a third phase, where autonomous AI agents seamlessly manage and orchestrate pipelines, freeing data engineers to focus on high-value business outcomes and innovation. 2026 will be a critical year for data engineers, as they lay the foundation for this agentic AI and unlock significant productivity gains.
Chris Child
VP of Product, Data Engineering, Snowflake

DATAOPS ROLE: DATA CHOREOGRAPHER

In 2026, DataOps will shift from managing data pipelines to orchestrating intelligent data ecosystems where AI agents autonomously handle data quality, validation, and routing decisions. The traditional DataOps engineer role will evolve into a "data choreographer" who sets policies and handles exceptions while AI manages the routine operations that currently consume 80% of their time. Organizations that embrace this human-AI collaboration model in their DataOps practices will see 10x improvements in data processing speed and accuracy, fundamentally changing how enterprises think about data operations, from reactive maintenance to proactive optimization.
Deepak Singh
Chief Innovation Officer, Adeptia

DATAOPS ROLE: DATA CURATOR

Data teams will evolve from builders to curators: In 2026, the composition of data teams will change. With AI handling much of the pipeline and analytics work, the emphasis will shift towards skills in governance, curation, and communication. The manual plumbing of data, including integrating, cleaning, and stitching across systems, will increasingly be automated by AI. This will free data teams to focus on higher-order concerns. Data scientists and engineers will increasingly partner with data product managers, stewards, and digital librarians who ensure that systems act on the right context with the right guardrails. The center of gravity for data work will move up the stack — from infrastructure to insight, from data to knowledge.

Recruiting will follow suit. CIOs will seek out information scientists, behavioral researchers, and policy experts who can bridge the gap between human reasoning and machine execution. The old jobs of moving data are giving way to new roles focused on meaning, context, and oversight. The new frontier of data work will be less about moving bits and more about maintaining meaning to keep AI grounded in the nuances that make decisions explainable, compliant, and aligned with business purpose. 
Juan Sequeda
Principal Researcher, ServiceNow

DATAOPS ROLE: BUSINESS DECISION-MAKING PARTNER

Data Engineers Will Increasingly Become Business Decision-Making Partners to Enterprise Leaders in 2026: It's well known that AI models are only as good as the data they are trained on, and because of this, data has become every businesses' most valuable asset. Enterprises need real-time access to high-quality data to be successful, and they're increasingly leaning on their data engineers to deliver that. This shift has elevated the role of the data engineer to a strategic position, where we'll see more and more business-defining conversations now including data engineers' perspectives. Likewise, it will become increasingly important for data engineers to understand the business context behind the problems they're solving — including the broader business impact and the needs of the customer. The organizations that will win are those that recognize data engineers as essential business partners, integrating their expertise into strategic conversations to ensure data drives success.
Chris Child
VP of Product, Data Engineering, Snowflake

Go to: 2026 DataOps Predictions - Part 2

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Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

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Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

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Your Observability Stack Has a Telemetry Pipeline Problem

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AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

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2026 DataOps Predictions - Part 1

APMdigest's Predictions Series continues with 2026 DataOps Predictions — industry experts offer predictions on how DataOps and related technologies will evolve and impact business in 2026.

THE RISE OF DATAOPS

In 2026, DataOps will become one of the most strategic functions in every enterprise. It will move from a behind-the-scenes enabler to the operational heart of AI success. DataOps teams will ensure that data entering AI systems is clean, governed, cost-optimized, and compliant, functioning as the "nutrition label" for enterprise intelligence. I expect advanced automation will play a role here as well, detecting duplication, drift, and lineage breaks, while AI itself assists in cleansing and transforming data in real time. As the volume and velocity of data continue to explode, organizations will depend on DataOps to deliver trust, speed, and efficiency. Simply put, DataOps will be the make-or-break discipline for AI and analytics maturity in 2026. 
Arturo Oliver
Senior Director, Market Strategy & Analyst Relations, ScienceLogic

AI-POWERED DATAOPS

AI-powered  DataOps  becomes table stakes: In 2026, data observability, lineage and governance will themselves be AI-augmented. Small and specialized models will continuously monitor pipelines for drift, anomalies, quality degradation and policy violations. Natural-language interfaces will allow business users to query data estates conversationally, while generative agents propose transformations, joins and mappings. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

AI-DRIVEN AUTOMATION

By 2026, DataOps will become deeply intertwined with AI-driven automation. Manual orchestration and brittle pipelines will give way to autonomous data agents that monitor lineage, repair models, and optimize cost-performance in real time. The boundary between DataOps and MLOps will blur as inference becomes a native part of every data workflow. Teams will manage mixed workloads, i.e., structured, unstructured, and semantic through unified, declarative interfaces. The next frontier of DataOps isn't faster ETL, it's intelligent, self-healing data systems that learn from usage and continuously adapt to deliver trustworthy, production-ready data pipelines.
Yoni Michael
Cofounder and CTO, Typedef

AI READINESS

DataOps  becomes the "front door" to enterprise AI:  In 2026, AI readiness will be defined by the quality and agility of  DataOps, not by model sophistication alone. Enterprises will treat  DataOps  as the core discipline that ensures data is discoverable, trusted, governed and ready for consumption by both traditional analytics and generative AI. 
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

REAL-TIME DATAOPS

Real-time  DataOps  becomes critical for operational AI: 
Edge and operational technology workloads move to cloud, streaming and event-driven data will become as important as batch pipelines.  In  2026,  DataOps  will be deeply embedded in OT environments,  supporting digital twins, predictive maintenance, autonomous  stores  and real-time risk models,  with stringent requirements on latency,  reliability  and explainability.
Sunil Senan
Global Head of Data, Analytics and AI, Infosys

HUMAN-AND-MACHINE-CENTERED DATA ECOSYSTEMS

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.
Justin Borgman
CEO and Cofounder, Starburst

DATA INFRASTRUCTURE COSTS

Data infrastructure costs, including cloud data warehouses, lakehouses, and analytics platforms, will rise by 30-50% as companies continue to invest in AI in 2026, unless they fundamentally change their approach to data platform management. The exponential growth in data volumes, the proliferation of generative AI across all aspects of the business, and the lack of visibility into what's actually driving data consumption will all contribute to this increase. Because data platforms bill based on consumption, costs can be highly variable and difficult to predict, especially for organizations that don't have insights and controls over their company's usage. Data observability will be key for managing and mitigating these cost increases. Understanding which workloads, users, and teams are driving costs and identifying inefficiencies in queries and pipelines is the only way organizations can reduce inefficiencies and ensure maximum ROI. Frankly, 30% to 50% of their data infrastructure spend is likely waste — but the most innovative companies think about data observability from a cost-optimization vs. cost-cutting perspective, uncovering opportunities to reinvest in AI/ML innovation without increasing their overall data platform budget.
Kunal Agarwal
Co-Founder and CEO, Unravel Data

DATAOPS ROLE: CONTEXT ENGINEERING

Data Engineering Will Evolve Into "Context Engineering" as AI Agents Become Primary Data Consumers: A fundamental shift is happening in how we think about data engineering. For decades, data engineers prepared data for human consumption — analysts, data scientists, and business users. In 2026, AI agents will emerge as primary data consumers, and this changes everything. "Context engineering" isn't just a rebrand — it's a recognition that agents have different requirements than humans: they need fresh, streaming context delivered in milliseconds, not batch updates delivered overnight. The best data infrastructure companies will embrace this evolution, using their deep expertise in streaming, storage, and processing to solve genuinely new problems around agent-facing analytics and real-time context delivery. While the underlying principles of good data engineering remain constant, the application layer is transforming.
Sijie Guo
CEO, StreamNative

DATAOPS ROLE: STRATEGIST

2026 Will Lay the Foundation for Data Engineers to Hand Work Over to AI Agents: The coming year will be the inflection point where data engineers transition from builders to strategists, preparing to hand off key tasks to AI agents. In 2026, AI won't just be a tool for data engineers — it will be a co-pilot, laying the groundwork for a new era of autonomous data pipelines. While 2025 was about preparing data for AI, this year is about a fundamental shift: data engineers are moving beyond writing SQL to becoming strategic architects who supervise and validate AI-generated code. As data volume and pipeline complexity continue to outpace team growth, the only way forward is to embrace intelligent automation. This will pave the way for a third phase, where autonomous AI agents seamlessly manage and orchestrate pipelines, freeing data engineers to focus on high-value business outcomes and innovation. 2026 will be a critical year for data engineers, as they lay the foundation for this agentic AI and unlock significant productivity gains.
Chris Child
VP of Product, Data Engineering, Snowflake

DATAOPS ROLE: DATA CHOREOGRAPHER

In 2026, DataOps will shift from managing data pipelines to orchestrating intelligent data ecosystems where AI agents autonomously handle data quality, validation, and routing decisions. The traditional DataOps engineer role will evolve into a "data choreographer" who sets policies and handles exceptions while AI manages the routine operations that currently consume 80% of their time. Organizations that embrace this human-AI collaboration model in their DataOps practices will see 10x improvements in data processing speed and accuracy, fundamentally changing how enterprises think about data operations, from reactive maintenance to proactive optimization.
Deepak Singh
Chief Innovation Officer, Adeptia

DATAOPS ROLE: DATA CURATOR

Data teams will evolve from builders to curators: In 2026, the composition of data teams will change. With AI handling much of the pipeline and analytics work, the emphasis will shift towards skills in governance, curation, and communication. The manual plumbing of data, including integrating, cleaning, and stitching across systems, will increasingly be automated by AI. This will free data teams to focus on higher-order concerns. Data scientists and engineers will increasingly partner with data product managers, stewards, and digital librarians who ensure that systems act on the right context with the right guardrails. The center of gravity for data work will move up the stack — from infrastructure to insight, from data to knowledge.

Recruiting will follow suit. CIOs will seek out information scientists, behavioral researchers, and policy experts who can bridge the gap between human reasoning and machine execution. The old jobs of moving data are giving way to new roles focused on meaning, context, and oversight. The new frontier of data work will be less about moving bits and more about maintaining meaning to keep AI grounded in the nuances that make decisions explainable, compliant, and aligned with business purpose. 
Juan Sequeda
Principal Researcher, ServiceNow

DATAOPS ROLE: BUSINESS DECISION-MAKING PARTNER

Data Engineers Will Increasingly Become Business Decision-Making Partners to Enterprise Leaders in 2026: It's well known that AI models are only as good as the data they are trained on, and because of this, data has become every businesses' most valuable asset. Enterprises need real-time access to high-quality data to be successful, and they're increasingly leaning on their data engineers to deliver that. This shift has elevated the role of the data engineer to a strategic position, where we'll see more and more business-defining conversations now including data engineers' perspectives. Likewise, it will become increasingly important for data engineers to understand the business context behind the problems they're solving — including the broader business impact and the needs of the customer. The organizations that will win are those that recognize data engineers as essential business partners, integrating their expertise into strategic conversations to ensure data drives success.
Chris Child
VP of Product, Data Engineering, Snowflake

Go to: 2026 DataOps Predictions - Part 2

Related Links

Hot Topics

The Latest

Enterprise Edge AI Reaches Inflection Point

Edge AI is strategically embedded in core IT and infrastructure spending across industries, according to the 2026 Edge AI Survey from ZEDEDA. The research shows that 83% of C-suite and IT executive respondents say edge AI is important to their core business strategy ...

AI Is Hitting Operational Limits

As AI adoption accelerates, operational complexity — not model intelligence — is becoming the primary barrier to reliable AI at scale, according to the State of AI Engineering 2026 from Datadog ... The report highlights a compounding complexity challenge as AI systems scale ... Around 5% of AI model requests fail in production, with nearly 60% of those failures caused by capacity limits ...

Alert Fatigue Is No Longer a Morale Problem, It's a Reliability Risk and a System Failure

For years, production operations teams have treated alert fatigue as a quality-of-life problem: something that makes on-call rotations miserable but isn't considered a direct contributor to outages. That framing doesn't capture how these systems fail, and we now have data to show why. More importantly, it's now clear alert fatigue is a symptom of a deeper issue: production systems have outgrown the current operational approaches ...

Most Enterprises Think They Can Switch AI Vendors in a Month ... Most Who've Tried Couldn't

I was on a customer call last fall when an enterprise architect said something I haven't been able to shake. Her team had just spent four months trying to swap one AI vendor for another. The original plan said three weeks. "We didn't switch vendors," she told me. "We rebuilt half our integrations and discovered what we'd actually been depending on." Most enterprise leaders don't expect that to be the experience ...

Your Observability Stack Has a Telemetry Pipeline Problem

Ask any senior SRE or platform engineer what keeps them up at night, and the answer probably isn't the monitoring tool — it's the data feeding it. The proliferation of APM, observability, and AIOps platforms has created a telemetry sprawl problem that most teams manage reactively rather than architect proactively. Metrics are going to one platform. Traces routed somewhere else. Logs duplicated across multiple backends because nobody wants to be caught without them when something breaks. Every redundant stream costs money ...

Operator to Orchestrator: 80% of IT Pros See Shift in Role as AI Permeates Workflows

80% of respondents agree that the IT role is shifting from operators to orchestrators, according to the 2026 IT Trends Report: The Human Side of Autonomous IT from SolarWinds ...

Gartner: 40% of Organizations Deploying AI Will Use AI Observability to Monitor Model Performance by 2028

40% of organizations deploying AI will implement dedicated AI observability tools by 2028 to monitor model performance, bias and outputs, according to Gartner ...

Almost Half of AI-Generated Code Fails in Production

Until AI-powered engineering tools have live visibility of how code behaves at runtime, they cannot be trusted to autonomously ensure reliable systems, according to the State of AI-Powered Engineering Report 2026 report from Lightrun. The report reveals that a major volume of manual work is required when AI-generated code is deployed: 43% of AI-generated code requires manual debugging in production, even after passing QA or staging tests. Furthermore, an average of three manual redeploy cycles are required to verify a single AI-suggested code fix in production ...

AI Requires Design, Not Edicts

Many organizations describe AI as strategic, but they do not manage it strategically. When AI plans are disconnected from strategy, detached from organizational learning, and protected from serious assumptions testing, the problem is no longer technical immaturity; it is a failure of management discipline ... Executives too often tell organizations to "use AI" before they define what AI is supposed to change. The problem deepens in organizations where strategy isn't well articulated in the first place ...

How Intelligent Orchestration Enables Enterprises to Move from AI POC to AI Production

Across the enterprise technology landscape, a quiet crisis is playing out. Organizations have run hundreds, sometimes thousands, of generative AI pilots. Leadership has celebrated the proof of concept (POCs) ... Industry experience points to a sobering reality: only 5-10% of AI POCs that progress to the pilot stage successfully reach scaled production. The remaining 90% fail because the enterprise environment around them was never ready to absorb them, not the AI models ...