The Rise of the Agentic Workforce: Data and AI Platform Enterprises can Trust

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We’re moving past copilots and dashboards. AI is no longer just answering questions or surfacing insights, it’s beginning to take action. In early pilots, agents are already cutting workloads by as much as 90% when they’re given bounded workflows. 

Microsoft’s 2025 Work Trend Index calls this the dawn of the Frontier Firm, organizations that redesign work around human-plus-AI teams. The research shows that 81% of leaders expect AI agents to be integrated into their strategy within the next 12-18 months, and that 24% of companies already deploy AI at scale.

The message is clear: the agentic future isn’t on the horizon. It’s here. And companies that hesitate will fall behind.  

But this isn’t about piling on more AI tools. It’s about building what we call a context-aware enterprise, an organization whose systems understand not just the data, but the circumstances around it: who is asking, why, under what policy, and with what downstream impact. That context requires an agentic substrate, the foundational layer where agents can act with governance, transparency, and trust.

This is the agentic workforce. It’s the next chapter in enterprise AI adoption. Humans and intelligent agents working side by side,reasoning and acting with confidence, powered by trusted data. 

Defining the Agentic Workforce

So what do we mean by an agentic workforce?

At its simplest, it’s humans and AI agents working together across workflows to analyze, decide, and act faster. Traditional copilots and analytics dashboards can make suggestions, but they stop short of execution. An agentic workforce embeds intelligence directly into the processes. Agents can pull the right data, run the analysis, trigger a workflow, and carry out an action, all with human oversight where it matters most. 

This isn’t theory. It’s already producing measurable results. Accenture finds that generative AI can free up more than 12% of working hours and improve output quality by nearly 9%. Microsoft’s research points to the same trend: enterprises are hitting the limits of “assistive AI.” To break through, they need agents that move from recommendation to action. And to trust those agents, they need a rock-solid foundation of governed, high-quality data.

We’re seeing agents succeed today in focused use cases such as ticket triage, reconciliations, data quality fixes and code refactoring. But when the work spans functions, policies, or ownership lines, autonomy breaks down. Without contracts, guardrails, and governance, progress stalls. 

The numbers tell the story. More than half of GenAI projects fail. By 2026, 60% of AI projects will be abandoned  because the data isn’t ready. And for agentic AI specifically, more than 40% of projects are expected to be canceled by the end of 2027 due to cost, unclear value, or risk. The lesson is clear: without trusted data and governance, agentic AI becomes a liability, not an advantage. 

That’s why context matters. The agentic workforce only thrives inside a context aware enterprise. One where agents can reason with nuance, apply the right policy, and show why a decision was made. Without context, autonomy is brittle. With it, humans and AI can work side by side with confidence. 

Why Enterprises are Stuck in Pilot Mode. 

For all the excitement around generative AI, most enterprises are held back by the very systems they depend on. Data is fragmented across business units and borders. Legacy platforms demand centralization before data can be used, a process that is both costly and slow, and often risky. And every time data is forced into motion, compliance risks multiply, especially in industries where residency and sovereignty are non-negotiable.

Lack of transparency makes it worse. Too many AI deployments rely on black-box retrieval methods, producing answers without a clear view into how they were reached. For executives, that’s a non-starter. How can you trust an agent’s judgement if you can’t trace it back to governed, high-quality data?

On top of the technical barriers, organizational blockers pile up: 

  • Capacity gaps: 53% of leaders say productivity must increase, yet 80% of employees report lacking the time and energy to keep up. 
  • Data fragmentation: The same customer looks different in billing, support, and product telemetry. Centralizing it is costly, slow, and risk-laden. 
  • Skills and roles: Few enterprises have “agent owners”, runtime engineers, or auditors to professionalize “AgentOps”. 
  • Trust and accountability: Legal and risk teams stall deployments when they can’t prove what data was accessed, under which policy, and why a decision was made. 

It’s no wonder most organizations are stuck in proofs of concept. Without a foundation of governance and transparency, AI remains an experiment, not an operational force woven into daily decision-making. 

Yet for those that do scale, the payoff is real. Accenture reports that frontrunner companies anticipate productivity gains of ~13% after scaling AI initiatives. The gap between leaders and laggards will only widen.

What’s missing is an agentic substrate: a secure foundation where agents can access governed data, coordinate across workflows, and act within policy. Without it, organizations remain stuck in pilot mode. With it, they can finally scale agentic work with confidence.

Starburst’s Role in Powering the Agentic Workforce

Solving these challenges takes more than small fixes. It requires a platform that treats governed data as the foundation of every intelligent action. That’s exactly what Starburst was built to do.

Agents shouldn’t be warehousing copies of your data. They should request governed slices from a federated substrate and return results with full lineage. Starburst provides the “control plane for skepticism”, the trust layer that makes agentic work both possible and reliable.

  • Built-in lineage delivers visibility across data products and queries, enabling teams to trace and govern how data is used
  • Least-privilege access: Column- and row-level controls travel with data across systems, clouds, and borders. 
  • Federated access: Data stays where it is, reducing movement risk and preserving sovereignty in regulated industries.
  • Open architecture: Model-neutral, format-open, and cloud-agnostic, so you can change models and tools without replumbing data. 

Equally important is interoperability. AI  doesn’t operate in isolation, and neither should your data platform. Starburst connects seamlessly with multi-agent frameworks such as LangChain or CrewAI, enabling agents to collaborate across workflows. It also unifies access to vector stores, whether on Iceberg, PostgreSQL with PGVector, Elastic, or beyond, allowing retrieval-augmented generation and search tasks to run without lock-in.

Our vision extends to headless AI agents that embed intelligence directly into applications and workflows, plus new visualization tools that give teams a clear view of agent behavior and data lineage. Together, these form a trust layer that allows enterprises to deploy AI at scale without sacrificing transparency or governance.

In our  latest announcement we unveiled new capabilities that make this possible, giving organizations the control, transparency, and scalability needed to turn AI into an operational advantage.

Compliance, Trust, and Global Scale

For global enterprises, compliance isn’t something you tack on at the end. It’s the starting point for any serious AI strategy. Regulations such as GDPR and Schrems II make one thing clear: data sovereignty has to be built into the architecture from the start.

This is exactly where Starburst comes in. By enabling federated access to distributed data, we let sensitive information stay in place while still making it available for analysis and action. Metadata-driven policies enforce governance at every step, creating a consistent layer of control no matter where the data resides. And with lineage and query profiling, teams can see not just what answer the agent produced, but how it got there. 

The result is AI that’s not only powerful, but compliant by design. For enterprises  in finance, telecom, manufacturing, and public services, that distinction is critical. It’s what allows them to move beyond pilots and deploy the agentic workforce at scale, without compromising on security, privacy, or regulatory mandates.

The Bigger Market Shift

We’re in the middle of a fundamental shift in how organizations think about intelligence. For years, analytics lived in dashboards; static reports that offered a rearview mirror on performance. Then came copilots, embedding recommendations into workflows but stopping short of execution.

The agentic workforce is the next stage. It’s a shift from analysis to action, from suggestion to autonomy. Enterprises don’t just want AI that points to an answer; they need agents that can carry a decision forward, informed by both real-time context and grounded in trusted data. 

This isn’t just about deploying agents. It’s about redesigning the enterprise itself into a context aware organization, where every decision is explainable and every action traceable. The agentic substrate is the fabric that makes this possible, transforming fragmented systems into coordinated intelligence.

This evolution is not optional. Competitive pressure is rising.  Companies that remain tied to legacy architectures will be slowed by the drag of manual decision-making. Those that embrace governed, agentic workflows will respond faster, operate more efficiently, and unlock entirely new business models.

Starburst provides the data foundation for this shift; an AI-ready lakehouse where governance, sovereignty, and performance aren’t afterthoughts, but core design principles. That’s what allows enterprises to move with confidence into a future where human and machine intelligence act as one.

Customer Momentum and Early Signals

The agentic workforce is already taking shape inside forward-looking enterprises. Companies such as Annalect and Asurion are beginning to operationalize AI agents across complex workflows, using governed data as the foundation. Their progress shows how organizations can move from experimentation to scaled deployment.

What unites these early adopters is not just their ambition, but a commitment to trust. In industries where compliance, sovereignty, and data quality can’t be compromised, the ability to bring models directly to governed data, without costly movement or black-box retrieval, creates a decisive advantage. 

These companies are proving that agentic workflows can be both powerful and safe when built on a foundation of governed, federated data. And the rewards compound. Accenture finds that the top 25% of companies are already growing productivity more than 8% annually across industries and geographies. Those who scale agentic workflows will only extend that lead.

Microsoft’s research backs this up at global scale. Frontier Firms report 55% greater capacity to take on work, and 90% of employees say they’re doing meaningful work, compared to just 77% globally. And their leaders feel the difference: 71% of them say their companies are thriving, compared to just 39% overall. 

These signals matter. They show that enterprises are ready to move past proofs of concept and into production, if they have the right foundation. The organizations leading this charge are proving what the agentic workforce can deliver: faster decisions, greater resilience, and a competitive edge built not by raw AI power alone, but on the integrity of the data beneath it.

Building the Agentic Future

The shift toward an agentic workforce isn’t a distant vision. It’s happening now.  Enterprises are moving past dashboards and copilots and embracing agents that can act in concert with human oversight. But the success of this new model hinges on one factor: trust in the data behind every decision.

Starburst delivers that trust. By unifying governed data products with open access to AI ecosystems, and by ensuring sovereignty and compliance across borders, the platform makes it possible to operationalize AI at scale. The result isn’t just more insight. It’s more action, delivered with the transparency and control that regulated industries demand.

The lesson from early adopters is clear. Companies that embrace governed agentic workflows today are building more capacity, greater resilience, and stronger competitive positions than their peers. The winners of the next decade won’t be those who chase “agentic everything”. They’ll be the ones who know exactly where agents add value, prove it with data, and redesign work around that reality. 

The future of enterprise productivity will be written by humans and AI working together, each amplifying the other’s strengths. With the agentic workforce, that future has already begun.

Read our press release to see how Starburst helps organizations turn governed data into intelligent action.