AI Forces Enterprises to Rethink Transformation as Technology Creation Spreads Beyond IT — DataArt - October 6, 2026
An Executive Q&A Session with Yuri Gubin, Chief Technology Officer at DataArt
The number of people who can build applications, automate processes and redesign work has dramatically expanded with the introduction of generative artificial intelligence and rise of agentic systems, creating a new challenge for enterprises trying to capture the benefits of distributed innovation without sacrificing governance, security or operational discipline. The shift is also changing the role of IT as technology organizations increasingly establish the architectures, platforms and controls that enable employees throughout the business to develop capabilities themselves.
For CIOs and other senior executives, the implications extend beyond technology deployment. AI is making transformation a more continuous management discipline that requires organizations to reconsider leadership practices, organizational structures, workforce skills and culture as technological capabilities evolve.
BizTechReports recently spoke with Yuri Gubin, chief technology officer at DataArt, about how organizations can approach transformation in an increasingly agentic economy. The conversation explored the democratization of technology creation, the changing responsibilities of IT, the emergence of cross-functional AI governance, the relationship between top-down leadership and bottom-up innovation, and the organizational capabilities enterprises will need to translate AI experimentation into business outcomes.
Here is what he had to say:
Q: Companies have been pursuing digital transformation for decades. What changes as AI and agentic systems become part of that transformation agenda?
A: The pace of change keeps increasing. Transformation can no longer be something an organization does once and then revisits many years later. It needs to be continuously reassessed in much the same way companies continually revisit strategy.
Organizations need to keep asking whether they are ready for what is changing, where new threats and opportunities are emerging, what new technologies can do and how those capabilities might apply to the business. Transformation therefore becomes an ongoing process of identifying signals, evaluating opportunities and making adjustments. Companies need the ability to pivot because the environment does not stop changing.
Q: Does AI fundamentally change the relationship among people, processes and technology within the enterprise?
A: Yes. AI should be viewed as more than a technology or a specialized skill. It has organizational implications.
Traditionally, companies had an IT department responsible for developing solutions and managing most technology-related activities. With AI, agents and increasingly accessible development capabilities, almost everyone can create something. Employees can develop automations, skills or solutions even when they are not traditional technologists.
That means AI adoption should also be viewed as organizational transformation. Companies have to determine whether they have the resources and structures necessary to support continuous innovation, whether the organization is flexible enough to respond to new opportunities and whether existing processes are creating roadblocks.
Q: If employees can increasingly create software and automations themselves, does democratizing development risk reducing the quality of the technology deployed across the enterprise?
A: The important expertise is shifting. Institutional knowledge and professional experience are still extremely important, but the differentiator is increasingly less about who can write code.
When many people can create applications or automations, organizations need an environment in which that activity can happen safely. That environment needs governance. It needs standards. It needs an architecture that allows people to innovate while remaining within appropriate enterprise boundaries.
The expertise therefore moves toward designing and managing that environment. Experienced technology professionals who previously concentrated on solution development and architecture increasingly need to think about how to provide a service that enables other employees to perform activities that once existed primarily within the IT department.
Q: So how does the role of the IT organization change?
A: IT needs to think more broadly about the platform it provides to the organization. The platform is not simply a piece of technology. It includes technical and nontechnical components that determine how people can use AI, create solutions and apply those capabilities to their everyday work.
The challenge becomes creating an environment that makes innovation accessible while keeping it manageable. Technology professionals still provide critical expertise, but increasingly they are designing the conditions under which other people can safely create.
Q: Traditional companies are generally organized vertically around functions and business units. Does AI require more horizontal organizational structures?
A: We are already seeing that through AI centers of excellence, centers of competency, cross-functional committees and AI enablement teams. Organizations use different names, but the principle is similar.
AI affects virtually every part of the organization, and people throughout the company are being asked to make progress with it. That creates a need for a horizontal structure that can guide those efforts.
There should be a governing group that shares what works and what does not, provides advice, helps resolve bottlenecks and supports decision-making. It should not consist only of technical people. Organizations also need champions and AI ambassadors representing different departments.
That structure allows separate initiatives to learn from and reinforce one another instead of operating independently.
Q: Transformation has traditionally been driven from the top. What do CEOs, CIOs and other senior executives need to do differently in an AI-driven environment?
A: Top-down leadership remains important, but it should involve more than establishing goals and demanding results. Leaders need to demonstrate what is possible.
If an organization is serious about AI transformation, senior leadership needs enough vocabulary and understanding of AI to guide the organization. Executives need to understand what AI can realistically accomplish, how targets should be established, how the effectiveness of initiatives should be measured and where resources should be allocated.
They also need to make decisions about new roles, organizational structures and learning processes. Leadership by example becomes important because employees need to see that senior executives understand the transformation they are asking the organization to undertake.
Q: At the same time, many of the best opportunities for applying AI may be identified by employees doing the work. How should organizations encourage bottom-up innovation?
A: Leaders need to understand what people are actually doing every day and where the bottlenecks and opportunities exist. Employees working directly with business processes can identify use cases that may not be visible from the top of the organization.Another important issue is reducing information asymmetry. People in one department often do not know what another part of the company is doing. When instructions move from senior leadership through multiple organizational layers, different groups can interpret them differently.
Organizations therefore need deliberate cross-pollination. Employees who develop useful AI skills or solutions should have forums where they can demonstrate what they have done. Successful ideas can then be shared and scaled.
Bottom-up innovation is valuable, but it benefits from guidance. Otherwise, different groups may repeatedly solve the same problems or make the same mistakes, creating unnecessary waste.
Q: AI capabilities are also changing rapidly. How can executives understand whether the organization's capacity is keeping pace?
A: Companies need a baseline and a broader framework for observability.
Observability should include more than telemetry from production technology systems. Leaders should also understand which business processes are struggling, where AI is being adopted, what skills exist across the workforce and how many employees have completed relevant training.
Collecting those data points gives management greater visibility into organizational capacity. It also helps leaders establish targets, evaluate progress and adjust those targets as AI capabilities and workforce skills change.
Q: Major transformation ultimately changes how a company operates and sometimes how it creates value. How important is culture to accomplishing that kind of change?
A: Culture is one of the core elements. Organizations need clearly articulated values and principles that employees can understand without requiring multiple rounds of explanation.
Those principles also need to be considered from two perspectives. Leaders need to understand what the organization believes about itself, but they also need to understand how customers perceive the company and what customers expect it to deliver.
Culture can be extremely empowering during transformation. It can also create resistance and slow an organization down. Leadership, learning, targets and organizational practices all contribute to that culture.
Companies that actively work on culture are better positioned to navigate difficult periods of transformation and deliver against their objectives.
Q: Many companies have spent the past several years building AI proofs of concept. How should executives determine whether those experiments are actually contributing to transformation?
A: Organizations need to begin with the outcome rather than the proof of concept itself.
When a client asks DataArt to develop a proof of concept, one of the first things we ask is why the organization wants to do it and what it expects to achieve. Sometimes that discussion shows that the organization needs to spend more time defining the business objective before developing anything.
A proof of concept can be technically successful and still lead nowhere. The code can work, the project can be completed on schedule and the organization can still receive very little meaningful business impact.
It can therefore be more valuable to spend additional time determining why the project should exist, what expectations should be established and what value or targets it should produce before moving into development.
Q: How does DataArt apply these principles when helping clients navigate AI-driven transformation?
A: We try to approach the relationship as a technology and engineering partner rather than concentrating exclusively on an individual project. That includes bringing research, R&D, accelerators and lessons from what we are seeing across industries into discussions with clients.
We also use DataArt as what I call “client zero.” We are going through many of the same AI adoption and organizational changes ourselves. When we discuss AI-fication, governance structures or centers of excellence with clients, we can share what we have learned from applying those approaches internally.
The objective is to combine that experience with architecture, engineering and solution expertise so clients can accelerate initiatives while developing their own ability to transform.
Q: What is the broader lesson for executives trying to rethink transformation for the agentic economy?
A: Organizations need to recognize that transformation is continuous and that AI affects much more than technology. It changes what employees can do, how IT supports the business, how knowledge moves across departments and what leaders need to understand.
The organizations that make progress will need mechanisms for learning continuously, sharing what works, governing distributed innovation and adjusting their targets as capabilities change. Technology is an important part of that equation, but leadership, organizational structure and culture determine whether those capabilities can ultimately be translated into meaningful business outcomes.
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