As enterprises accelerate AI adoption, a major obstacle remains: much of the data AI needs is still trapped inside legacy systems, proprietary software, and applications that lack modern interfaces. Traditional integration tools have helped connect enterprise systems, but they often cannot access the full scope of information or capture the business context needed to make that data useful.

For Ran Cherniavsky, CEO and Co-Founder of Unfold, the solution is to rethink how enterprises access their data. Unfold works through the interfaces businesses already use, extracting information and mapping the underlying logic without requiring system replacements or disruptive migrations.

In this Q&A, Cherniavsky discusses why data accessibility is becoming a defining challenge for enterprise AI, how Unfold unlocks information from otherwise closed systems, and what becomes possible when AI agents can finally understand the full picture of an organization’s operations.

The enterprise data problem

1. Why do so many enterprises still struggle to access critical data trapped inside closed systems, despite years of investment in integration technology?

Because integration was built for systems, not for AI. It connected system A to system B, one field at a time, through whatever the vendor chose to expose. AI needs access to everything a human being can see in a system, and an understanding of what it means. And it needs it fast, without a project for every field. That’s a different problem.

2. What are the biggest risks and business costs of leaving data locked inside systems that lack APIs or other standard interfaces?

Companies that can’t put AI on their core data will fall behind the ones that can, and that gap opens fast.

The costs show up long before that. Companies pay specialized consultants, year after year, to map a system’s fields by hand. Analysts pull capped reports manually and stitch them together in Excel. AI pilots stall because the model only sees the slice the API exposes, and gets the answers wrong. In effect, you’re renting access to your own data through someone else’s UI.

And there’s a risk you can’t see: if nobody can read what’s inside a system, nobody can be confident it’s secure either. You’re left hoping.

3. Between security, IT modernization, and AI adoption, where does inaccessible enterprise data create the biggest problem today, and why?

AI adoption, and it’s not close. That’s where the pressure from the board is, and that’s where locked data costs you right now. You can’t just open Claude and ask, “A dealership owner can’t ask Claude how old the warranty money owed to him is, because that data is stuck behind reports?” Not because you can’t write the question in plain English, but because the data isn’t accessible.

Security and modernization feel the same problem, but differently. Security is a symptom: you can’t secure what you can’t see. Modernization is the long way around. According to Gartner, 45% of core systems still run on mainframes or on-prem client/server, and 34% of organizations now leave core applications as they are because modernizing them is too risky. AI can’t wait for that to change.

Unfold’s technology and differentiation

4. How does Unfold access systems that have no APIs, exports, or open interfaces?

The same way a person does. Employees don’t work with a system only through its API. They use the UI, reports, exports, files and more. Unfold works through every interface the application exposes, with or without an API.

Our team spent years doing deep software research, integrating with systems regardless of what they exposed. That used to take months per system, by hand. With AI, Unfold becomes the subject-matter expert on a system in days, maps where every piece of data lives and how it moves, and then builds a deterministic read-only collector that gets it out through the system’s own permissions.

That works on mainframes like AS/400, on-prem ERPs, and modern SaaS that only exposes part of its data. If a system has a UI or a command line, we can work with it. Onboarding takes up to 14 days, for any system.

5. How does Unfold understand the context behind a system’s data, rather than simply extracting the raw data?

Raw data without meaning is noise. In Oracle’s ERP JD Edwards, tables carry numbers instead of names. A status field holds a code nobody remembers defining. Two systems call the same customer by different names.

Unfold learns each system the way a trained employee would: what each object means, how objects relate, which business rules and exceptions apply, and how people actually use it. Because we see the screen and what sits underneath it, we can tie what a user sees in the UI to where it lives in the data.

That knowledge used to live with SMEs, implementation partners and vendors, and much of it walked out the door. We rebuild it as a catalog of every data object, with its business meaning and its full journey through the system. AI gets the data and the context together, and stops guessing.

6. How do you ensure that data from different enterprise systems is normalized, governed, and ready for AI applications?

Once each system is understood, we connect them. The same customer is KUNNR in SAP and an Account in Salesforce. The same site has a different ID in each ERP. Unfold maps all of it into one canonical enterprise schema, so data from every system arrives normalized and consistent.

Governance is built in from the start. The data stays with the customer, in their own source systems. Lineage comes with it, because we know where every field came from and how it was transformed.

And the collectors are deterministic: the same question returns the same data every time. That’s what you need before you let an agent act on it.

7. How does Unfold complement existing platforms like Splunk, Snowflake, and Databricks, and what makes its approach different from traditional integration tools?

First, we don’t replace data platforms; we feed them. Splunk, Snowflake and Databricks are where data gets searched, analyzed and put to work by AI, and they’re only as good as what reaches them. Today, that’s whatever a source system’s API or export allows. And you don’t have to move the data at all: Unfold can work with it where it lives, in the source systems, or deliver it straight to agents through our connector to Claude, Gemini and others.

Unfold sits upstream. We get the data and the logic out of the systems that never fed those platforms, with the meaning attached, and deliver it wherever the customer needs it: their lake, their warehouse or their agents. At a global retailer, point-of-sale data from an AS/400 mainframe is heading into a Gemini Enterprise fraud-detection agent. At a manufacturer, plant control system data is heading into a CMDB.

Traditional integration starts from a catalog of prebuilt connectors and moves what each vendor exposes, one project at a time. Unfold starts from the system itself: every interface, the full data model and the business context, in days rather than months.

8. What are the most compelling real-world use cases for Unfold, and how do customers measure the value of unlocking previously inaccessible data?

The best use cases start the same way: someone sees data in a screen and can’t get it into the workflow they need. A few examples:

A global retailer needs point-of-sale data from AS/400 systems in more than ten countries feeding its fraud detection, which now runs on Gemini Enterprise. One country’s regulator told them to stop selling until that control was in place.

A top-10 US insurer pays contractors with specialized expertise to map claims-system fields to the database by hand. Then another team of engineers comes in to build the ETL. All of that takes weeks. We do it automatically, in days.

A multi-store auto dealership group had its parts, service and warranty data locked behind reports in its dealership management system.

Now the owner asks, in plain English, “How old is the warranty money owed to us?”

Customers measure value in three ways: time (days instead of months), cost (consultants, professional services and engineering they no longer need), and outcomes that weren’t possible before, like an answer across systems, or a business that keeps selling.

9. How does Unfold help enterprises modernize legacy infrastructure without disrupting critical operations or replacing existing systems?

By not touching them. Unfold doesn’t require code changes or a migration. It works with the system as it is, through the interfaces that are already there, and read only, by design.

That matters because modernizing core systems is risky, and everyone knows it. Gartner finds that 34% of organizations now leave core applications as they are for exactly that reason, up from 16% in 2023, and that 40% of the modernizations needed for autonomous business will be late by 2030.

So instead of waiting years for a replacement, you get the data and the business logic out today and put AI to work on top of it. And when you modernize, you already have a full map of what the old system holds and what it means, which is a big part of the migration work.

10. What new capabilities become possible when AI agents can access and understand data from previously closed enterprise systems?

AI adoption stops crawling and starts running. Today, enterprises work for AI: teams spend months on integrations just to feed it data. Once every system is open and understood, that flips. We work with AI, and AI does the integrations and runs the workflows itself.

Agents stop guessing and start working on the real business. Most agents today reach email, documents and a few SaaS APIs. The core of the business, the orders, claims, inventory, warranty and plant operations, lives elsewhere.

Open that up, and a dealership owner asks one question across parts, service and warranty. A fraud model sees point-of-sale data from every country. A finance team gets on-time-in-full numbers across two ERPs that use different IDs for the same site.

Then it compounds. Agents follow a process end to end, like order-to-cash, across every system it touches, instead of stopping at the edge of one. That’s when AI moves from answering questions to running the business.

The future of enterprise AI

11. Will data accessibility become one of the biggest barriers to enterprise AI adoption, and how can organizations overcome it?

It already is. Gartner says that through 2030, the biggest compounding challenge for agentic AI is data access and control. Models keep getting smarter, but more intelligence alone won’t move the enterprise. Models need the enterprise’s proprietary data, and the context of how each system is used.

How to overcome it: start from the business outcome, not from a data project. Pick one AI initiative that’s blocked by a system you can’t get data out of, solve it, and expand system by system on the same layer. Stop treating access as a string of one-off integrations, and don’t wait for modernization to finish.

And treat the knowledge inside your systems as an asset. The people who understood those systems are retiring or gone. Capturing that context now is part of being AI-ready.

12. How do you see the enterprise integration market evolving over the next three to five years, and what role do you expect Unfold to play?

History tends to follow the same path. First we invent something. Then we find a commercial way to use it. Only years later does it actually change how we live. The first modern car came in 1886, but it took decades to transform society.

That time went into building the infrastructure around it: roads, gas stations, bridges. AI is at that same point today. We have probably the most powerful engine we’ve ever built, and the models keep getting better. But the transformation inside enterprises hasn’t happened yet, and the reason isn’t intelligence. The roads aren’t there.

Integration is a big part of those roads, and it was built for a different kind of traffic. It connected systems to systems with connectors and pipes, moving one field at a time. Over the next three to five years, the traffic changes. AI is now the one that needs the data, and AI needs all of it, along with what it means.

So I think the market splits. Moving data becomes a commodity, like a basic road. The value moves to the layer that understands the enterprise: full access to every system, the business context behind it, and one schema across all of them.

Gartner already treats AI context platforms as a separate market, growing from $28B in 2026 to $78B by 2030. It also notes that these platforms are limited by how much they can actually get out of systems of record. That’s exactly the gap we fill.

The difference this time is that we don’t have to wait 40 years. With AI, enterprises can build the roads much faster than they built them for the car, but only if their data can actually reach the engine. Our goal is to lead this category and become the knowledge foundation for enterprise AI, so that every AI use case in the enterprise runs on Unfold, from the mainframe to SaaS.

The founder’s vision

13. What inspired you to build Unfold, and what is your long-term vision for the company?

Our team spent years doing deep software research, getting data out of systems that were never designed to give it up. Then AI arrived, and suddenly every company had the same problem we’d been solving by hand, at a scale nobody could staff. Twenty years ago, your data sat on your own servers.

Today it sits behind vendors, interfaces, and knowledge that’s retiring. We built Unfold so AI can understand any enterprise system the way a long-time employee does. The long-term vision is simple: every AI use case in the enterprise runs on Unfold, from the mainframe to SaaS.