- 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.
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- From flashy demos to practical impact, AI finds its real enterprise purpose
Presented by OutSystems
After two years of flashy AI demos, rushed agent prototypes, and breathless predictions, enterprise technology leaders are striking a more pragmatic tone in 2026. In a recent webinar hosted by OutSystems, a panel of software executives and enterprise practitioners made the case that the most consequential AI work happening now is focused on the practical matters of governance, orchestration, and iteration, along with integrating agents into the systems they've spent decades building.
Enterprise leaders are increasingly focused on fundamentals. The priority is using new AI technologies
to accelerate productivity, improve delivery, and produce measurable business results.
Three elements shape this work:
The move from AI agent prototypes to agentic systems that deliver measurable ROI in production
The growing role of enterprise platforms in governing, orchestrating, and scaling AI agents safely
The rise of the generalist developer and enterprise architect as the most valuable technical profiles in an era of AI-generated code
Against this backdrop, the panel discussed governance frameworks, the economics of enterprise AI, and the limits of large language models without orchestration. The conversation ultimately turned to how leading organizations are building multi-agent systems grounded in existing enterprise data and workflows.
Agents in the real world
Enabling agents to work in production across the enterprise is best accomplished with a unified platform that handles development, iteration, and deployment. And that'swhere capabilities like the Agent Workbench in the OutSystems platform matter, said Rajkiran Vajreshwari, senior manager of app development at Thermo Fisher Scientific. It provides the infrastructure to learn, iterate, and govern agents at scale.
His team at Thermo Fisher has moved away from single-task AI assistants in customer service to building a coordinated team of specialized agents using the workbench. When a support case arrives, a triage assistant classifies the request and dynamically routes it to the right specialist agent, whether that’s an intent and priority agent, a product context agent, a troubleshooting agent, or a compliance agent.
"We don’t have to think about what will work and how. It’s all pre-built," he explained. "Each agent has a narrow role and clear guardrails. They stay accurate and auditable.”
Governing the risks of shadow AI
A new category of risk emerges when AI makes it possible for anyone in a company to generate production-level code without IT oversight. Basically, this is ungoverned shadow AI. These homegrown products are prone to hallucinations, data leakage, policy violations, model drift, and agents taking actions that were never formally approved.
To get ahead of the risk, leading organizations need to do three things, said Luis Blando, CPTO of OutSystems.
"Give users guardrails. They’re going to use AI whether you like it or not. Companies that seem to be getting ahead are using AI to govern AI across their full portfolio,” he explained. “That is the difference between shadow AI chaos and enterprise-grade scale.”
Eric Kavanagh, CEO of The Bloor Group, noted that governance requires a layered set of disciplines that includes securing data, monitoring models for drift, and making deliberate choices about where AI connects to existing business processes.
“Companies don’t have to be manually creating these controls," he added. "A lot of those guardrails and levers are baked in to platforms like OutSystems.”
Why the real orchestration challenge is models vs. platforms
Much of the early excitement around enterprise AI focused on selecting the right large language model. Now the harder challenge, and far more durable source of value, is orchestration. This includes routing tasks, coordinating workflows, governing execution, and integrating AI into existing enterprise systems.
Scott Finkle, VP of development at McConkey Auction Group, noted that LLMs, however impressive, are pieces of complex workflows, not final solutions. Organizations should be ready to hot-swap between Gemini, ChatGPT, Claude, and whatever emerges next without having to rebuild the agentic system around it.
A platform with orchestration capabilities makes that possible. It manages the lifecycle, provides visibility, and ensures processes execute reliably, even as AI handles the reasoning layer on top.
“The AI and the models change, the workflows can change, but the orchestration remains the same," Finkle said. "That’s how we’re going to extract value out of AI.”
The economics of enterprise AI investing
Security, compliance, governance, and platform-level AI capabilities will all command greater investment in 2026, particularly as AI moves into core workflows like finance and supply chain. Enterprises should favor incremental wins rather than expect big, immediate gains.
“We’re focusing on base hits," Finkle said. "The way it counts is by getting something into production and having it make an impact. Big investments in pilot projects that don’t make it into production don’t save any money. It’s not going to happen overnight, but over time I think we’ll see tremendous savings.”
There's still a split in how enterprises are approaching AI transformation. Some start from scratch and reimagine every process. Others, especially those with billions of dollars in existing infrastructure depreciating in-house, want AI to integrate with their systems. They want agentic systems to reuse data, APIs, and proven processes while speeding up delivery. The agent platform approach serves both camps, but particularly the latter. Organizations can deploy agents where they add clear value while preserving the integrity of established, deterministic workflows.
The rise of the enterprise architect and the generalist developer
As AI accelerates code generation, bottlenecks in software delivery are dissolving. In its place is a premium on systems thinking. This is the ability to understand the broader enterprise architecture, decompose complex business problems, and reason about how AI integrates with existing infrastructure. Kavanagh pointed to enterprise architects specifically as the professionals best positioned to capitalize on this moment.
“We’re entering a very interesting age of the generalist," he explained. "The better you know your enterprise architecture and your business architecture and how those things align, the better off you’re going to be. ”
“The result is faster delivery with fewer interruptions and fewer bugs," Kavanaugh said. "You can focus on the non-repetitive tasks. It’s a benefit to the developer, to the business, and to the whole IT organization.”
Catch the entire webinar here.
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- The Scaffolding Era Fades, Making Way for Smarter AI Workflows
The scaffolding layer that developers once needed to ship LLM applications — indexing layers, query engines, retrieval pipelines, carefully orchestrated agent loops — is collapsing. And according to Jerry Liu, co-founder and CEO of LlamaIndex, that's not a problem. It's the point.
“As a result, there's less of a need for frameworks to actually help users compose these deterministic workflows in a light and shallow manner,” Jerry Liu, co-founder and CEO of LlamaIndex, explains in a new VentureBeat Beyond the Pilot podcast.
Context is becoming the moat
Liu’s LlamaIndex is one of the foremost retrieval-augmented generation (RAG) frameworks connecting private, custom, and domain-specific data to LLMs. But even he acknowledges that these types of frameworks are becoming less relevant.
With every new release, models demonstrate incremental capabilities to reason over “massive amounts” of unstructured data, and they’re getting better at it than humans, he notes. They can be trusted to reason extensively, self-correct, and perform multi-step planning; Modern Context Protocol (MCP) and Claude Agent Skills plug-ins allow models to discover and use tools without requiring integrations for every one independently.
Agent patterns have consolidated toward what Liu calls a "managed agent diagram" — a harness layer combined with tools, MCP connectors, and skills plug-ins, rather than custom-built orchestration for every workflow.
Further, coding agents excel at writing code, meaning devs don’t need to rely on extensive libraries. In fact, about 95% of LlamaIndex code is generated by AI. “Engineers are not actually writing real code,” Liu said. “They're all typing in natural language.” This means the layers between programmers and non-programmers is collapsing, because “the new programming language is essentially English.”
Instead of manual coding or struggling to understand API and document integration, devs can just point Claude Code at it. “This type of stuff was either extremely inefficient or just would break the agent three years ago,” said Liu. “It's just way easier for people to build even relatively advanced retrieval with extremely simple primitives.”
So what’s the core differentiator when the stack collapses?
Context, Liu says. Agents need to be able to decipher file formats to extract the right information. Providing higher accuracy and cheaper parsing becomes key, and LlamaIndex is well-positioned here, he contends, because of its developments with agentic document processing via optical character recognition (OCR).
“We've really identified that there's a core set of data that has been locked up in all these file format containers,” he said. Ultimately, “whether you use OpenAI Codex or Claude Code doesn't really matter. The thing that they all need is context.”
Keeping stacks modular
There’s growing concern about builders like Anthropic locking in session data; in light of this, Liu emphasizes the importance of modularity and agnosticism. Builders shouldn’t bet on any one frontier model, or overbuild in a way that overcomplicates components of the stack.
Retrieval has evolved into “agent-plus-sandbox,” as he describes it, and enterprises must ensure that their code bases are tech debt free and adaptable to changing patterns. They also have to acknowledge that some parts of the stack will eventually need to be thrown away as a matter of course.
“Because with every new model release, there's always a different model that is kind of the winner,” Liu said. “You want to make sure you actually have some flexibility to take advantage of it.”
Listen to the podcast to hear more about:
LlamaIndex’s beginnings as a ‘toy project’ with initially only about 40% accuracy;
How SaaS companies can tap into complicated workflows that must be standardized and repeatable for average knowledge workers;
Why vertical AI companies are taking off and why ‘build versus buy’ is still a very valid question in the agent age.
You can also listen and subscribe to Beyond the Pilot on Spotify, Apple or wherever you get your podcasts.
