- The enterprise edge shifts to governed data, not newer models.
Presented by Box
As frontier models converge, the advantage in enterprise AI is moving away from the model and toward the data it can safely access. For most enterprises, that advantage lives in unstructured data: the contracts, case files, product specifications, and internal knowledge.
For enterprise leaders, the question is no longer which model to use, but which platform governs the content those models are allowed to reason over.
"It's not what the model does anymore, it's the enterprise's own unstructured data – their content, how it's organized, how it's governed, and how it's made accessible to the AI." says Yash Bhavnani, head of AI at Box.
"The organizations that will lead in AI are the ones that built the governance infrastructure to make any model trustworthy, with the right permissions in place, the right content accessible, and a clear audit trail for every action taken," says Ben Kus, CTO of Box.
Enterprise AI must be grounded in secure systems of record
As the advantage in AI shifts from models to governed content, systems of record are becoming the foundation that makes enterprise AI trustworthy.
Employees use frontier models to summarize documents, draft reports, answer questions, but when those tools are disconnected from authoritative internal repositories, the results are difficult to trust, impossible to audit, and potentially dangerous. AI that cannot trace its outputs back to a governed source of record becomes a liability.
"It's not a theoretical concern," Bhavnani says. "For an insurance enterprise using AI to analyze client claims, low accuracy is simply not acceptable, and untraceable output can't be acted upon."
Systems of record provide authoritative, version-controlled content with embedded permissions and compliance controls already built in, and RAG pipelines retrieve data from live repositories at inference time, connecting responses directly to current, traceable sources.
Without integration into systems of record, employees build their own workarounds, content gets duplicated across tools that don't talk to each other, and shadow knowledge stores accumulate outside the visibility of IT and compliance teams.
"Customers tell us employees are uploading sensitive documents to personal accounts and running their own AI workflows, with no visibility from the enterprise into what is being shared or what is being generated," he says. "It's not just a security risk, it's an organizational one."
Permission-aware access is a requirement for agentic AI
As AI moves into agentic territory, executing multi-step tasks autonomously across documents, workflows, and enterprise systems, the risk profile changes entirely. Agents act faster than humans, often without the contextual judgment needed to decide what data they should access, making permissions-aware access essential.
"An AI platform without permissions-aware access is too dangerous to use," Kus says. "It's a precondition for safe enterprise AI deployment, and the more it appears to have been added after the fact rather than built into the foundation, the more it should concern the enterprise considering it."
In regulated industries, frameworks like HIPAA, FedRAMP High, and SOC 2 demand audit trails, policy enforcement, and demonstrable controls over who and what has accessed sensitive data.
"The audit trail should cover not only the source files but the AI session that used them, and accessed only with the same controls and the same encryption mechanism," Kus says. "We don't want customers to end up with a compliance breach because the agent was looking at sensitive data and the agent records got stored somewhere unexpected."
Content platforms are evolving into AI control planes
Enterprise content platforms are evolving from repositories into orchestration layers — an AI control plane that sits between models, agents, and enterprise data. Rather than just storing documents, the platform governs how content is accessed, routes it to the right reasoning engine, enforces permissions, and maintains a complete audit trail of every action.
"An AI-ready content platform needs to support human navigation and use in the way platforms always have, and it needs its own AI agents that understand the platform's data structures deeply enough to get the best out of them," Kus says. "It also needs to be open enough that any external agent can reach into it. An open agent ecosystem is the future of how these platforms will work."
When content, permissions, audit trails, and application access are all handled by the same platform, governance stays attached to the content itself. More than any capability of the models on top of it, a unified governance layer is what allows enterprise AI to scale safely.
Turning unstructured content into structured intelligence
Unstructured data has long been a sticking point for organizations, which had to build specialized models to handle every subtype of unstructured data.
"What's changed is that general-purpose large language models now bring enough intelligence to extract structured data from unstructured content without that level of bespoke investment," Kus says. "Box Extract applies this capability at scale, automatically pulling key information from contracts, forms, claims, and reports and applying it as structured metadata within Box. The content that previously had to be read by a person to yield its value can now be processed, structured, and made queryable across an entire repository."
And once that data is extracted and operational logic lives in the system, users can visualize, search, and act on that extracted information through custom dashboards and no-code tools.
Box Agents take this further by enabling multi-step reasoning and task execution grounded directly in enterprise content, with persistent sessions that support iterative knowledge work with simple, natural language direction. And because agent sessions in Box are persistent, the work is not lost between interactions.
The practical result is that end-to-end workflows that previously required human coordination across multiple systems can be orchestrated directly on systems of record.
"When those workflows are built on Box agents and automation operating directly on governed content, the handoffs become automated, the audit trail is built in, and the system of record remains the authoritative source throughout," Bhavani says. "Nothing falls through the cracks between systems, because there is only one system."
The enterprises seeing real returns are not the ones that simply plugged in a frontier model and waited for results. They are the ones that connected AI to their systems of record, governed what it can access, and built the operational layer that makes its outputs trustworthy enough to use at scale.
Platforms that bring together content management, security, automation, and AI integration in a single layer are emerging as the foundation for enterprise AI, because model capability alone is not enough. Without governance built into the platform, the gaps between systems become the point of failure.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
- From isolated AI tools to an intelligent enterprise
Presented by EdgeVerve
For most enterprises, AI adoption began with a straightforward ambition: automate work faster, cheaper, and at scale. Chatbots replaced basic service requests, machine‑learning models optimized forecasts, and analytics dashboards promised sharper insights. Yet many organizations are now discovering that deploying individual AI solutions does not automatically translate into enterprise‑level impact. Pilots proliferate, but value plateaus.
The next phase of AI maturity is no longer about deploying more models. It is about adapting AI continuously to changing business objectives, regulatory expectations, operating conditions, and customer contexts. This shift is particularly critical for complex, globally distributed organizations such as Global Business Services (GBS), where outcomes depend on orchestrating work across functions, regions, systems, and stakeholders.
From automation to adaptation
AI can no longer be treated as a standalone tool to accelerate discrete tasks. To remain competitive, enterprises must move from isolated, single‑purpose models toward systems that can sense context, coordinate actions, and evolve over time.
This is where adaptive AI ecosystems come into play. An adaptive AI ecosystem is a network of interoperable AI agents, models, data sources, and decision services that work together dynamically. These ecosystems integrate capabilities such as natural language processing, computer vision, predictive analytics, and autonomous decision‑making, while remaining grounded in human oversight and enterprise governance.
For GBS organizations, the relevance is clear. GBS operates at the intersection of scale, standardization, and variation, managing high‑volume processes across markets that differ in regulation, customer behavior, and operational constraints. Static automation struggles in such environments. Adaptive AI, by contrast, allows GBS teams to orchestrate end‑to‑end processes, intelligently route work, and continuously improve outcomes based on real‑time signals.
Why enterprise AI deployments stall
Despite strong intent, scaling AI remains a challenge. Research consistently shows that while many organizations invest in generative and agentic AI initiatives, far fewer succeed in operationalizing them across workflows and business units. The issue is rarely ambition; it is fragmentation.
SSON Research highlights several persistent barriers to generative AI adoption in GBS, including poor data quality, lack of specialized skills, data privacy concerns, unclear ROI, and budget constraints. Beneath these symptoms lies a common root cause: siloed environments. Data is fragmented, ownership is unclear, and AI initiatives are driven locally rather than through a shared enterprise strategy.
As a result, enterprises accumulate AI solutions that cannot easily work together. Models lack shared context, decisions are hard to explain, and governance becomes an afterthought rather than a design principle.
Adaptive AI ecosystems and platforms: Clarifying the relationship
An adaptive AI ecosystem describes the enterprise‑wide outcome for how AI capabilities collaborate across the organization. An adaptive AI platform is the foundation that makes this possible.
The platform provides common services and guardrails that allow AI agents and models to:
access harmonized, trusted data
orchestrate end‑to‑end processes
enable intelligent agent handoffs between systems and humans
interoperate with both agentic and legacy applications through out‑of‑the‑box connectors
operate within defined security, compliance, and ethical boundaries
Without this platform layer, adaptive ecosystems remain theoretical. With it, AI becomes composable, governable, and scalable.
What an adaptive AI platform must enable
To meet the demands of modern enterprises, and especially GBS organizations, an adaptive AI platform must deliver a set of core capabilities.
Real‑time data harmonization is foundational. Adaptive decisions require access to both structured and unstructured data across functions and regions. Platforms must provide a unified data foundation, with observability built in, so AI systems understand not just the data itself but its quality, lineage, and relevance. Edge‑to‑cloud architectures play a role here, ensuring insights are available where decisions occur whether at the point of interaction or within a centralized decision engine.
Adaptive process orchestration is equally critical. GBS organizations increasingly rely on AI platforms that can orchestrate workflows dynamically across business units and systems. This includes coordinating multiple AI agents, enabling seamless agent‑to‑agent and human‑in‑the‑loop handoffs, and adjusting process paths in response to real‑time conditions.
Cognitive automation with governance moves beyond rule‑based automation. AI systems must be able to make context‑aware decisions with minimal human intervention, while still providing explainability, confidence indicators, and ethical constraints. The goal is not to remove humans from the loop, but to elevate their role from manual execution to oversight and judgment.
Decision governance and observability tie these capabilities together. Enterprises must be able to trace how decisions are made, understand which models contributed, and audit outcomes across markets. As regulatory expectations around AI risk management, data protection, and accountability increase globally, embedding governance into the platform becomes essential rather than optional.
Establishing trust at scale
Trust is the foundation of scalable AI. Enterprises that lack confidence in their AI systems across data integrity, model behavior, and regulatory compliance will struggle to move beyond experimentation into sustained adoption.
Building this trust requires deliberate investment. Organizations must ensure explainable AI, so decision logic is transparent to business and risk stakeholders, alongside privacy‑ and security‑by‑design principles that protect sensitive data from the outset. Continuous bias detection, model reliability, performance management, and clearly defined responsible AI guardrails are critical to maintaining consistent and ethical outcomes.
Equally important is a clear Target Operating Model. This model defines ownership across the AI lifecycle, clarifies roles and escalation paths, and aligns accountability from frontline teams to executive leadership. In GBS environments where AI‑driven decisions often span functions, geographies, and regulatory regimes these trust mechanisms are not optional. They are essential.
The road ahead
Enterprises that continue to rely on fragmented AI deployments and siloed operating models will find it increasingly difficult to keep pace. The future belongs to organizations that adopt a platform‑based approach — one that enables them to move from incremental efficiency gains to transformational, enterprise‑wide impact.
Success will not be defined by a single model or use case. It will be defined by adaptive AI ecosystems built on strong agent architectures, interoperable connectors across agentic and legacy landscapes, and shared foundations for data, orchestration, and governance. For GBS organizations in particular, this approach provides a clear path to scale AI responsibly delivering agility, trust, and sustained value in an increasingly complex world. In an era where change is constant and scrutiny is rising; the real question is no longer whether enterprises use AI but whether they are truly adaptive to it.
N. Shashidar is SVP & Global Head, Product Management at EdgeVerve.
Sponsored articles are content produced by a company that is either paying for the post or has a business relationship with VentureBeat, and they’re always clearly marked. For more information, contact sales@venturebeat.com.
- Two paths emerge for taming AI agents in production.
The era of enterprises stitching together prompt chains and shadow agents is nearing its end as more options for orchestrating complex multi-agent systems emerge. As organizations move AI agents into production, the question remains: "how will we manage them?"
Google and Amazon Web Services offer fundamentally different answers, illustrating a split in the AI stack. Google’s approach is to run agentic management on the system layer, while AWS’s harness method sets up in the execution layer.
The debate on how to manage and control gained new energy this past month as competing companies released or updated their agent builder platforms—Anthropic with the new Claude Managed Agents and OpenAI with enhancements to the Agents SDK—giving developer teams options for managing agents.
AWS with new capabilities added to Bedrock AgentCore is optimizing for velocity—relying on harnesses to bring agents to product faster—while still offering identity and tool management.
Meanwhile, Google’s Gemini Enterprise adopts a governance-focused approach using a Kubernetes-style control plane. Each method offers a glimpse into how agents move from short-burst task helpers to longer-running entities within a workflow.
Upgrades and umbrellas
To understand where each company stands, here’s what’s actually new.
Google released a new version of Gemini Enterprise, bringing its enterprise AI agent offerings—Gemini Enterprise Platform and Gemini Enterprise Application—under one umbrella.
The company has rebranded Vertex AI as Gemini Enterprise Platform, though it insists that, aside from the name change and new features, it’s still fundamentally the same interface.
“We want to provide a platform and a front door for companies to have access to all the AI systems and tools that Google provides,” Maryam Gholami, senior director, product management for Gemini Enterprise, told VentureBeat in an interview. “The way you can think about it is that the Gemini Enterprise Application is built on top of the Gemini Enterprise Agent Platform, and the security and governance tools are all provided for free as part of Gemini Enterprise Application subscription.”
On the other hand, AWS added a new managed agent harness to Bedrock Agentcore. The company said in a press release shared with VentureBeat that the harness “replaces upfront build with a config-based starting point powered by Strands Agents, AWS’s open source agent framework.”
Users define what the agent does, the model it uses and the tools it calls, and AgentCore does the work to stitch all of that together to run the agent.
Agents are now becoming systems
The shift toward stateful, long-running autonomous agents has forced a rethink of how AI systems behave. As agents move from short-lived tasks to long-running workflows, a new class of failure is emerging: state drift.
As agents continue operating, they accumulate state—memory, too, responses and evolving context. Over time, that state becomes outdated. Data sources change, or tools can return conflicting responses. But the agent becomes more vulnerable to inconsistencies and becomes less truthful.
Agent reliability becomes a systems problem, and managing that drift may need more than faster execution; it may require visibility and control.
It’s this failure point that platforms like Gemini Enterprise and AgentCore try to prevent.
Though this shift is already happening, Gholami admitted that customers will dictate how they want to run and control any long-running agent.
“We are going to learn a lot from customers where they would be using long-running agents, where they just assign a task to these autonomous agents to just go ahead and do,” Gholami said. “Of course, there are tricks and balances to get right and the agent may come back and ask for more input.”
The new AI stack
What’s becoming increasingly clear is that the AI stack is separating into distinct layers, solving different problems.
AWS and, to a certain extent, Anthropic and OpenAI, optimize for faster deployment. Claude Managed Agents abstracts much of the backend work for standing up an agent, while the Agents SDK now includes support for sandboxes and a ready-made harness. These approaches aim to lower the barrier to getting agents up and running.
Google offers a centralized control panel to manage identity, enforce policies and monitor long-running behaviors.
Enterprises likely need both.
As some practitioners see it, their businesses have to have a serious conversation on how much risk they are willing to take.
“The main takeaway for enterprise technology leaders considering these technologies at the moment may be formulated this way: while the agent harness vs. runtime question is often perceived as build vs. buy, this is primarily a matter of risk management. If you can afford to run your agents through a third-party runtime because they do not affect your revenue streams, that is okay. On the contrary, in the context of more critical processes, the latter option will be the only one to consider from a business perspective,” Rafael Sarim Oezdemir, head of growth at EZContacts, told VentureBeat in an email.
Iterating quickly lets teams experiment and discover what agents can do, while centralized control adds a layer of trust. What enterprises need is to ensure they are not locked into systems designed purely for a single way of executing agents.
