Since early 2010, digital transformation has become a buzzword synonymous with adopting cloud, mobile or analytics technologies without considering how people and processes collaborate, operate and evolve. As a result, IT solutions tended to be technology-centric, sometimes addressing business problems by adapting them to what technology offers.
Artificial intelligence (AI), however, has the potential to fundamentally shift this approach. The early signs of this can be seen in the usage of generative AI (genAI) in the customer service industry, where AI can efficiently resolve most customer queries. Though still in the initial stages of maturity, the ability of AI to understand context and human intent gives a glimpse of the silent evolution towards human-centric transformation. The evolution could help organizations finally bridge the gap between technology adoption and true operational change.
The technology-first trap
For businesses pursuing enhanced customer experience, improving operational efficiency or driving innovations, digital transformation is often undertaken as a technology modernization initiative. Though logical on the surface, this approach has created what is known as the “technology-first trap” — the mistake of prioritizing technology adoption over the genuine business problem. In this scenario, businesses adapt their processes and people to fit what technology can deliver, rather than designing solutions around the business needs.
The result? Often, a sophisticated system with less-than-expected adoption processes that feel forced and unnatural and transformation initiatives that deliver technical upgrades rather than meaningful transformation. While technology can theoretically complete the transformation, adoption, efficiency, productivity and desired business value remain questionable.
From technology-first to human-centric transformation
AI promises to disrupt this dynamic. Trained on vast amounts of data, AI is uniquely positioned to understand individual preferences, behaviors and decision-making patterns. Unlike traditional systems that require humans to learn specific languages and/or logic, AI can understand human language and intent and respond effectively.
This capability points to a fundamental pivot to what could be called “human-centric digital transformation,” where systems understand needs and intent, adapt to the context and respond based on the available data to complete processes — much like how people naturally think and work, rather than forcing people to adapt to technological constraints.
Early examples of this can be seen in the usage of genAI in customer service systems to address most of the common issues. Its ability to reason based on past patterns and efficiently deal with structured or unstructured data amplifies human capabilities, leading to higher productivity and a richer customer experience.
Further patterns are emerging where AI can drive automation, data-driven decision-making, assist in software development and even execute end-to-end workflows. Bringing it all together, AI’s ability to understand context, personalized preferences and decision patterns and interact in natural language eliminates the traditional barrier between people and technology. This enables AI to bridge the gap between technology adoption and operational transformation ever since the beginning of digital transformation.
Easing the adoption with demonstrable value
GenAI refers to the branch of artificial intelligence focused on reasoning and generating new content — text, images, code or audio based on existing information and patterns. The technology exploded in 2024 when large language models (LLMs) like ChatGPT became widely available. They have experienced rapid adoption owing to the lower entry barrier, demonstrable value, level playing field — for both technical or non-technical people — and ease of using these technologies.
Justifying business value is always a big challenge before starting any transformation. A recent Gartner survey found that GenAI is more common than other technologies in demonstrating business value. It’s not just its ability to understand like a human, but its ability to generate high-quality, relevant output that is driving the massive adoption. Today, GenAI is a big boost for organizations looking to drive digital transformation by lowering the technology bar, enhancing customer experience, improving productivity and accelerating the innovation cycle.
Democratizing innovation and software development
Software development plays a crucial role in digital transformation by facilitating the systems to create, operate, process and communicate with each other. Introduced in February 2025 by Andrej Karpathy, vibe coding is a way of “giving in to vibes” to write code. Here, you articulate what you want through what is called prompts, analyze the output and iterate over and over until you get the results you want without looking at the underlying code. With genAI significantly lowering the adoption bar, vibe coding is fundamentally changing who can build software and how organizations can innovate.
Throughout its history, one of the goals of software development has always been to bring it closer to a human-understandable form. From Fortran and C, which enabled human-readable forms of writing the code, to the more recent low-code/no-code development platforms that democratized development through visual interfaces, to AI-assisted development that automated coding suggestions, a significant effort has been made to reduce barriers to code.
In contrast to AI-assisted development, with vibe coding, you let AI take the lead to generate all the code for your application. Even in case of errors, the recommended approach is to let the tool analyze errors and provide the fix. If it fails to do so, that’s when human intervention may be required. Though still in the early stages, where it shows significant promise in building content-based sites, internal tools and small-scale apps, tangible gains are visible with as much as 30% of Microsoft code being written by AI, to use just one prominent example in well-known software.
Along with other AI promises, vibe coding centers the software development around human conversations on needs and intents for the application. This approach democratizes application development and aligns with the original promise of digital transformation: empowering people to focus on creativity and innovation rather than worrying about development and implementation.
Autonomous workflow: End-to-end business process execution
Agentic AI refers to the next step toward intelligent automation (IA), where AI functions as an autonomous agent (digital worker) capable of reasoning, adapting, learning and making decisions on complex tasks to execute end-to-end flow. Whereas in vibe-coding, AI can autonomously generate, enhance and bug-fix the code, with agentic AI, systems exhibit greater autonomy in orchestrating an end-to-end workflow. Based on the design patterns like reflection, tool use, planning and multi-agent collaboration to generate responses, agentic AI can continuously learn from feedback and self-improve over time.
These systems are highly adaptive to changing patterns and need minimal human intervention. They can also seamlessly connect with other downstream systems, learn from diverse datasets and pursue given objectives. This is a big boost to organizations’ transformation initiatives, where they can effectively handle real-time data and derive insight from it. Thanks to AI’s ability to continuously learn, these systems can now analyze contextual data, generate insights and automate decision-making processes.
In manufacturing, for example, agentic AI can autonomously maintain the efficient functioning of production lines. Here, it can monitor and detect the production line underperformance, diagnose the issue, order parts/services, schedule maintenance, adjust production schedules and update ERP orders -all in an end-to-end autonomous workflow to minimize downtime and keep operating efficiently.
Achieving human-centric transformation objectives
The success of any transformation can easily be told by how well it’s adopted. Organizations must invest huge amounts of resources and efforts to not just complete the transformation but also drive adoption. Being human-centered, AI can readily deliver on a few of these objectives:
- Reduced training needs and greater user adoption. When the solutions, workflows and processes are adhering to natural languages, exhaustive training programs and efforts may not be required. This will improve overall user adoption as technology or solutions that are intuitive are easier to learn and adopt.
- Enhanced innovation with greater strategic focus. Easier adoption and lower technology barriers can bring more people to drive the transformation by contributing to bringing desired digital innovation beyond technical boundaries.
- Comprehensive process improvements. Rather than forcing business processes to conform to software limitations, organizations can design workflows that truly optimize human productivity and satisfaction, leading to comprehensive process improvement and higher productivity.
The maturity challenge
We may be over the initial hype and the potential is enormous, but it’s important to acknowledge that AI still has some way to go to reach maturity. Along with general AI challenges like biases, accountability and governance, there are a few other maturity challenges specific to their usage in transformation:
- Security risks. Without human supervision, it is difficult to confirm with certainty if there are any missing encryption, data breaches, code vulnerabilities or unauthorized access. For example, Agentic AI accessing sensitive data without proper governance or even exposing sensitive data to unauthorized participants.
- Overreliance and skill erosion. The ability to get the job done in natural language has a great promise, but solely relying on AI to do so may slowly erode the foundational technical skills. Thought vibe coding recommends AI to take the lead, critical troubleshooting skills and the ability to understand generated code are foundations for ensuring code quality and future maintenance. Furthermore, overreliance can erode even human accountability.
- Transparency and explainability. Complex AI models often function as a black box, making it difficult to interpret the decisions or results. On the contrary, making them more explainable may expose them to misuse, where hackers can know and interpret how the internal mechanism works.
The path forward
The real opportunity lies in AI’s potential to bridge the gap between technology adoption and genuine operational transformation. With its ability to understand human language and intent naturally, the barriers between people and digital tools begin to dissolve. Though this evolution will not happen overnight, the rapid pace of development in AI shows a significant shift underway. However, the key is to remember that true digital transformation has always been about people and processes, not just technology.
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