In a time where artificial intelligence is rapidly reshaping the enterprise landscape, few leaders have a vantage point as deep as Itay Haber, CEO of Datanoetic. With decades of experience driving digital transformation at the global enterprise level, Haber has seen first-hand how the traditional SaaS model is straining under the weight of modern business demands. In this Q&A, he shares how Datanoetic is pioneering the shift to truly AI-native enterprise software to move beyond static workflows and siloed systems toward a future of intelligent orchestration, adaptive decision-making, and unified data.
1. Itay, you’ve spent decades leading digital transformations at the enterprise level. What major shift are we seeing in how enterprises think about software — and how does Datanoetic reflect that shift?
We’re at a point where the dominant software delivery model is being upended. For years, since the advent of “The Cloud” and “Software as a Service”, enterprises have relied on packaged software built around static workflows that are standardized, inflexible, and often siloed. But today, every business function is being reimagined through the lens of AI. We’re no longer just talking about digitizing a process or about making it more easily accessible from more devices; we’re talking about rethinking it altogether.
Datanoetic is built from the ground up for this new world. Instead of retrofitting AI into legacy systems, we’re creating an AI-native platform that understands context, connects fragmented data, and adapts as the business evolves. We’re moving beyond process automation to intelligent orchestration where software helps to make real decisions.
2. The term “AI-native” is gaining traction. What does it mean in the context of enterprise infrastructure, and how is Datanoetic moving beyond the traditional SaaS paradigm to enable true AI-native operations?
“AI-native” means that AI is built into the foundation of the solution the customer interfaces with as opposed to non-AI-Native, where AI is layered on top as a feature. In a nutshell: in AI native solutions the AI-enabled capabilities are the core part of the solution, whereas in non AI native solutions, the AI capabilities are just an added feature. Traditional SaaS platforms operate in a deterministic fashion – i.e. with predefined rules and static logic, whereas an AI-native system is built to operate in a probabilistic fashion taking into account not just the ability, but the expectation to learn & adapt. In the context of enterprises, while a traditional SaaS platform will tend to only use data that is part of its own database, whereas an AI-native solutions would have been built with the underlying premise that the data which it would need to process would come from a variety of databases, often from a range of traditional SaaS solutions
At Datanoetic, the use of AI is embedded into everything we do. From unifying data – where rather than aiming to replace customer’s various systems, our architecture is built to work with them – through to delivering insights and providing context-aware suggestions.
3. Why is now the critical moment for enterprises to rethink how software and systems are integrated? What forces — technological or business — are making this transformation urgent?
As Deming said: “Survival is optional. No one has to change”. Companies can ignore the change enabled by AI and not rethink how software and systems are integrated. However, by choosing that path, they take a big risk of being overrun by others, who do.
The main force that’s driving the change is technological advancement – the same force that drove the industrial revolution, and the prevalence of the internet and of smart phones. While AI is “just” the latest technological advancement, it is likely to have at least as material an impact as the ones I just mentioned and it is definitely advancing at a faster pace.
While the AI technology advances at breakneck speed, data continues to grow exponentially – both in volume and complexity – and business, as well geopolitical, environments are changing at a very fast pace. All these factors combined, make transformation highly advisable – at least for organizations that want to stay relevant and viable.
4. Many enterprises have adopted AI in isolated use cases, but Datanoetic seems to be going further. How does your platform operationalize AI as a foundational layer across the enterprise — not just a feature or an add-on?
First, it’s worth noting that there’s nothing wrong with initiating the implementation of AI in specific use cases, before implementing it more widely. Also, given that in the history of work, there has never been any single solution that addressed all the needs of any given company, I wouldn’t expect any AI solution to tackle all the various tasks that need to be performed.
At Datanoetic, we use AI to unify data across multiple systems to build a Value Stream Network Map (VSNM) knowledge graph, enriched with semantic/ontology layer that includes data about locations, people, systems, machines and sensors. The AI “brain” then uses that knowledge graph to perceive what’s happening and either take or recommend actions based on it.
Think of it as a digital brain that understands how the business works. Whether it’s optimizing workflows, resolving inefficiencies, or enhancing decision-making, AI is now orchestrating both isolated tasks and wider business flows.
5. Legacy systems and fragmented data continue to plague large organizations. How is Datanoetic solving the “last-mile” integration challenge that traditional platforms haven’t addressed effectively?
There are two key challenges preventing legacy platforms from solving the “last mile” challenge. One challenge is that the data these platforms hold tend to be specific and siloed. The other challenge is they tend to rely on expert users to make use of the data – e.g. an SQL expert to work with a given database, or a business analyst to work with a business intelligence tool.
A solution like Datanoetic’s, helps tackle both these challenges. On the data front, it builds a knowledge graph that’s based on data from multiple sources. On the user front, not only does it use normal language to interface with the user – thus negating the need for expert training – it can also provide proactive suggestions, rather than just passively wait for a user to make a request.
6. Can you walk us through how Datanoetic changes the way enterprise decisions are made — and why automating context-aware workflows is a game-changer?
Traditionally, enterprise decisions follow rigid flows, predefined triggers, static conditions. But business doesn’t operate in a vacuum. Context matters.
Datanoetic uses AI to interpret real-world signals, connect the dots between systems, and trigger actions based on the broader picture. For example, if a supplier delay is going to affect customer delivery times, the platform doesn’t just flag it, it can suggest alternate fulfillment paths, account prioritization, and customer service alerts. That’s the power of context-aware automation.
7. As enterprises face growing pressure to be agile, efficient, and data-driven, what role does AI-powered process automation play in unlocking new business value?
For decades, if not centuries, technology has been used to make things quicker or more efficient. AI-powered automation is similar in that it provides the opportunity to do the same. However, it is different, in that previous new technologies only allowed for increased agility and efficiency gains in instances where their application could have been clearly predefined. In contrast, the application of AI is not constrained to such deterministic scenarios. It can also be applied where the only thing you know is what your goal is, even when the required steps to get there are not 100% clear.
As such, it finds inefficiencies you didn’t know existed, predicts problems before they happen, and proposes better paths forward.
From automating invoice collections to optimizing product development workflows, we’ve seen how AI can act as both analyst and operator. And as these systems mature, they’ll take on more proactive roles — suggesting improvements, not just executing instruction.
8. Datanoetic is emerging as a platform that can unify siloed systems without requiring a rip-and-replace approach. Why is this modular, interoperable architecture vital to enterprise adoption?
Enterprises can’t afford to start over. They’ve invested years, even decades, in their systems. We don’t ask them to replace those systems; we empower them to extend and evolve.
Our architecture is modular by design. That means you can start small , unify a few processes, test automation in a specific area — and scale from there. It’s a pragmatic approach that respects enterprise complexity while unlocking future flexibility.
9. You’ve been vocal about the limitations of SaaS in today’s enterprise ecosystem. What comes after SaaS, and how is Datanoetic paving the way for that future?
SaaS gave us accessibility and standardization. But it also boxed us in. One-size-fits-all apps, rigid workflows, and isolated data models are no longer enough. Gartner calls it “The Fourth Software Paradigm”.
We agree and believe the future is composable, intelligent, and adaptive. Post-SaaS platforms like Datanoetic are agentic, acting on your behalf, learning from your context, and responding dynamically to change. It’s a shift from software as a tool to software as a thinking partner.
10. Beyond technology, what cultural or organizational shifts must occur inside enterprises for AI integration to succeed — and how does Datanoetic help drive that change?
AI demands a mindset shift. Leaders must move from controlling every decision to trusting intelligent systems. Teams need to get comfortable working alongside AI and interpreting its recommendations, refining its outputs.
Datanoetic helps build that trust by being transparent. Our platform explains its logic, shows its work, and invites collaboration. We’re not trying to replace human judgment but augmenting it with intelligence and speed.
11. Looking ahead, what industries or sectors are most primed to benefit from the kind of AI-native transformation Datanoetic is delivering?
Any sector with complexity, scale, and data fragmentation is ripe. That includes supply chain-heavy industries like logistics and retail, regulated industries like healthcare and finance, and fast-moving sectors like tech and e-commerce.
In practice, the need is universal. AI-native transformation can drive massive impact where decisions are being made with partial data and outdated workflows.
12. Finally, what’s your long-term vision? How do you see Datanoetic reshaping the enterprise technology stack over the next 5–10 years?
We envision a world where software is no longer a set of siloed applications, but a unified intelligence layer that spans the enterprise. Datanoetic will be a connective tissue in bridging data, guiding decisions, and continuously learning.
Over the next decade, we’ll move from automation to orchestration from smart tools to truly adaptive systems. Our goal is to become the autonomous digital brain for process optimisation and automation
