Artificial Intelligence is now becoming a core part of enterprise technology. It is helping companies automate work, use data better, and make faster decisions. This change is clearly visible in logistics and supply chains, where businesses have always struggled with complexity and lack of visibility.
I have been closely following how enterprise software is evolving. We are moving away from rigid systems to more flexible platforms that can adapt to business needs. AI is playing a big role in this shift.
To understand this better, I spoke with Shobhit Singh, Co-founder and CPTO of Hexalog. The company is working on using AI to simplify cross-border supply chains. In this interaction, he explains how AI is changing enterprise adoption, why data is becoming more important than ever, and what challenges companies still face during digital transformation.
In the conversation below, we discuss how AI workflows are evolving, the role of data in modern applications, and why mindset is still one of the biggest challenges in digital transformation.
How is AI and automation reshaping enterprise technology adoption across industries today?
In the pre-AI era, SaaS products were rigid ecosystems primarily curated for large enterprises. Smaller players were often neglected because they couldn’t afford the heavy customization required to fit their unique workflows. Today, we are seeing a massive behavioral change. AI workflows and Digital Twins have reversed the adoption curve: instead of the user adapting to the software, the AI workflow adapts itself to the user’s established processes. This level of fluidity was previously impossible, democratizing high-end enterprise tech for companies of all sizes.
What are some of the most impactful AI-driven solutions that Hexalog is currently building or deploying for clients?
Hexalog is currently focused on two primary architectural layers:
The Semantic Layer: This is our customer-facing breakthrough. By reworking traditional pain points through Direct AI workflows, we provide clients with unprecedented visibility into their operations that simply wasn’t accessible before.
The Orchestration Layer: This handles the internal complexities, ensuring that while the customer sees a simplified, intuitive interface, the backend workings remain seamless, automated, and error-free.
How do you see data engineering and data infrastructure evolving with the rapid rise of AI-powered applications?
We have moved into an era where Data as Code is no longer an optional “best practice”—it is a mandate. AI is only as powerful as the architecture beneath it; without a sophisticated evolution in data infrastructure, AI hits a functional ceiling. Infrastructure must now be built to support the constant, circular flow of data that fuels iterative AI learning.
In your view, what are the biggest technology gaps that companies face while implementing digital transformation initiatives?
The gap is less about the “tools” and more about the Mindset. Digital transformation often stumbles because of a friction between tech-native systems and non-tech personnel. This is compounded by a significant lack of AI talent capable of bridging that gap. Successful transformation requires a system that syncs intuitively with both technical and non-technical stakeholders.
How does Hexalog ensure that its technology solutions remain scalable and future-ready for enterprises?
Our “Right to Win” in this industry is predicated on Flexibility. Because every supply chain is unique, a rigid platform is a failing platform. We ensure scalability through:
Static Workflow Automation: Moving repetitive tasks to our platform to free up human capital.
Adaptive Architectures: We build with the understanding that if we aren’t flexible enough to adapt to a client’s specific logistics DNA, we lose our competitive edge.
With the rise of Generative AI, how are businesses rethinking their product development and customer experience strategies?
Businesses are moving away from “one-size-fits-all” products. With GenAI, the product development cycle now includes real-time feedback loops where the product itself can suggest iterations. In CX, the focus has shifted from “solving tickets” to “anticipating needs” through the semantic layers mentioned earlier, providing a transparent, frictionless journey.
What role does advanced analytics and machine learning play in helping enterprises make faster and more accurate decisions?
Analytics have shifted from being a “rear-view mirror” to a “windshield.” By utilizing ML to process vast datasets, enterprises can identify bottlenecks in real-time. This reduces the friction in decision-making, allowing leaders to move from gut-feeling choices to data-backed actions in a fraction of the time.
How can companies ensure ethical and responsible AI adoption while scaling their technology capabilities?
Responsibility is maintained by building Guardrails directly into the code. It’s not enough to have an ethical policy on paper; you must have system-level checks that monitor AI outputs for bias, inaccuracies, or security breaches. These automated guardrails ensure that as the technology scales, the safety measures scale with it.
What trends are you observing in AI-led automation across sectors such as finance, logistics, and healthcare?
We are seeing a significant reduction in industry friction if I talk about logistics and a massive build-up of Trust. While “Autonomous Supply Chains” were once dismissed as a mere trend, they are becoming a reality—but with a twist. Due to the high-stakes nature of logistics and healthcare, the trend is moving toward AI as a Co-pilot. AI handles the heavy lifting and data processing, while humans provide the final strategic oversight.
Wrap Up
This interaction makes one thing clear. AI is not just improving existing systems. It is also changing how they are built. Companies now need flexible and scalable solutions that can grow with their needs.
From what I have seen, the biggest challenge is still the mindset. Many companies have access to the right tools but struggle to use them properly.
Hexalog’s approach shows where things are heading. Focus on flexibility, automation, and better use of data. Companies that understand this shift early will be in a better position as AI continues to evolve.
