As enterprises explore the promise of Generative AI, many find the transition from idea to reality harder than expected. What begins as a creative demo often struggles in production. To understand what makes some initiatives succeed while others fade, we spoke with Ksenia Shishkanova, a senior solutions architect and one of the leading voices in AI system design. She works with AI-Enabled Organizations and global enterprises, helping them move from experiments to measurable results.
Q: Ksenia, what brought you to the intersection of data architecture and Generative AI?
I began my career in the trenches of data engineering, building systems that helped companies trust their data. Back then, AI was still a research curiosity, yet even early on, it was clear that no model could thrive without strong architecture beneath it. I loved the challenge of turning messy, inconsistent information into something reliable and fast. When large language models appeared, they felt like the moment the data world and intelligence finally met. I wanted to see how far that connection could go.
Over time, I moved from writing pipelines to shaping strategies for organizations that wanted to use AI in practice. Many of them operated at massive data scale and automation depth. Working with them taught me how to make GenAI part of real production systems, not just a short-lived experiment. Today, I help teams design architectures that keep creativity safe, scalable, and grounded in data they can rely on with confidence.
I also learned that strong architecture alone is not enough. Teams need decision frameworks, evaluation discipline, and the ability to align technical design with tangible business outcomes. Without this alignment, even the most elegant platform will produce prototypes that never convert into value.
Q: From your experience, what determines whether an organization can move quickly and successfully with GenAI?
Teams that progress quickly usually share a similar working style. They rely on short experimentation cycles, validate ideas early, and invest in automation wherever possible. This approach helps them explore opportunities rapidly, but it also exposes gaps when the supporting structure is not ready.
In many companies, the excitement around new models grows faster than the processes required to keep systems reliable. Evaluation, governance, and cost oversight often appear only after a prototype has already gained real users. I have seen promising concepts stall simply because no one was responsible for monitoring quality, managing expenses, or ensuring compliance with internal standards.
Organizations that scale GenAI successfully combine flexible exploration with a clear sense of discipline. They assign ownership before usage expands, introduce basic evaluation practices early, and view AI as a capability that requires continuous attention rather than a one-time experiment. This mindset helps them develop ideas into systems that can operate steadily in production.
What also differentiates successful teams is the ability to separate architectural complexity from business complexity. They take the time to understand which workflows genuinely need intelligence, which simply need automation, and which require redesign altogether. This prevents over-engineering and keeps GenAI positioned where it creates the most leverage.
Q: Many companies start strong with GenAI, yet their prototypes rarely reach production. Why does this happen?
Even the most innovative teams face the same challenge. Building a prototype is fast; turning it into a reliable product is slow. Most failures come from underestimating the complexity of real-world deployment. Hidden costs, compliance requirements, and unclear ownership appear late in the process. I have seen teams ignore inference costs or skip evaluation design, only to discover that each query costs dollars when scaled to thousands of users.
There are several recurring reasons why pilots collapse:
- Strategic ambiguity. Teams treat GenAI as an innovation exercise rather than a workflow or product initiative. Without concrete goals, the project drifts.
- Underestimated complexity. Teams often treat GenAI integration as a plug-and-play task, overlooking the engineering and governance effort required for production.
- Hidden costs. Inference expenses, latency optimization, and data storage can quickly make a product unsustainable if not planned from the start.
- Lack of clear ownership. Without someone responsible for compliance, observability, or cost control, even strong prototypes lose direction.
- Missing domain expertise. Data engineers can build pipelines, but they cannot define what "correct" means in specialized fields such as finance, law, or logistics.
- Weak evaluation discipline. Projects rarely include a solid gold dataset or measurable success metrics, so quality becomes subjective and inconsistent.
- A further challenge arises when business goals and engineering optimization diverge. Systems may become faster or more accurate from a technical perspective while simultaneously becoming more expensive or less aligned with real-world decision criteria.
Successful initiatives take the opposite approach. They involve subject-matter experts early, treat cost as a design constraint, and build evaluation and observability into every layer. Once ownership and measurement become part of the process, prototypes finally start evolving into real products.
Q: Once a company moves past the prototype stage, what does a sustainable GenAI architecture look like in practice?
When a prototype proves its potential, the next step is to lay the foundation for a system that can operate reliably at scale. Sustainable GenAI architecture grows out of clear business goals and a realistic understanding of constraints such as cost, data quality, and compliance.
A strong foundation begins with viewing GenAI as both a technical and a business initiative. Teams define what the system must achieve and set explicit limits on cost and performance. Inference pricing is considered early, and usage scenarios are simulated before choosing models. This prevents the solution from becoming too expensive once adoption increases.
Another essential element is collaboration between engineers and subject-matter experts. Engineers design the infrastructure, but only domain specialists can define correct outputs and shape the evaluation dataset. Without this partnership, the system cannot maintain consistent quality, especially in regulated or knowledge-heavy domains.
Sustainable architecture also depends on observability. Logging, tracing, and evaluation must be present from the start, so teams can understand how the system behaves, reproduce failures, and explain outcomes to business stakeholders. Without reliable observability, even a well-designed prototype becomes difficult to support.
Finally, successful teams expand capabilities gradually. They start with a narrow scenario, measure impact, and only then extend the system to additional workflows. This disciplined approach helps control risk and ensures that each new step delivers measurable value.
A sustainable architecture, therefore, rests on clear objectives, cost awareness, strong evaluation, involvement of domain experts, and careful, incremental scaling.
Q: After building the right architecture, how can companies keep their GenAI systems accurate as they scale?
Accuracy and reliability at scale depend on disciplined evaluation rather than one-time testing. Companies that succeed treat evaluation as an ongoing process. They begin by defining what the system must achieve for the business, and then translate these expectations into measurable criteria. These criteria become the foundation for every refinement and release.
A key element of sustainable quality is the evaluation dataset. It must be created together with subject-matter experts, because only they can determine what correct outputs look like in financial, legal, or operational contexts. Even large volumes of raw data cannot replace this contextual judgment. As the system evolves, the dataset is updated with new real-world cases so that improvements reflect actual usage rather than synthetic assumptions.
Observability also plays a central role. Teams need visibility into how the system behaves, where it fails, and under which conditions quality drops. Logs, traces, and interaction histories give engineers the ability to reproduce issues and understand their root causes. Without this foundation, even small performance regressions remain hidden until they create business impact.
Finally, accuracy must be assessed not only from a technical perspective but also through the lens of business value. Traditional metrics such as precision or recall are useful, yet they do not indicate whether the system reduces workload, shortens resolution time, or improves customer satisfaction. Companies that measure both technical performance and business outcomes gain a clear view of whether the system is truly improving operations or simply adding complexity.
In practice, maintaining accuracy at scale requires three things: clear success criteria, continuous evaluation with domain expertise, and transparent observability. When these elements are in place, organizations can refine their models confidently, respond to new requirements, and ensure that the system continues to deliver reliable results as adoption grows.
Q: Ksenia, could you outline what careful technical planning looks like when preparing a GenAI system for broader adoption?
Before expanding a GenAI system, organizations need to confirm that the foundation is economically and operationally sound.
The first step is to understand the expected business impact and set a clear budget. Many projects become too expensive once adoption grows, especially when inference costs rise faster than anticipated. Running cost simulations and defining an acceptable cost per query in advance helps prevent the system from becoming unsustainable at scale.
Another important preparation is agreeing on the minimum functionality the product must deliver. This protects teams from uncontrolled feature growth and ensures that every capability added to the system is tied to measurable value. Without this discipline, products grow complex long before they prove their effectiveness.
A reliable evaluation dataset must also be in place. It should be developed with subject-matter experts, since only they can provide the real questions and correct outputs that reflect operational requirements. This dataset becomes the basis for validating the system during each iteration and reduces the risk of critical mistakes.
Finally, scaling should be gradual. Instead of attempting to automate an entire workflow at once, successful organizations start with a narrow scenario, validate its performance, and then expand step by step. Each expansion is guided by clear KPIs, budget limits, and structured feedback, which helps maintain both quality and economic viability as the system evolves.
Q: And what practical advice would you give organizations that want to build GenAI systems with long-term impact?
The most important place to start is with focus and clarity. Instead of trying to automate everything at once, organizations should choose a few problems that genuinely slow down work or create unnecessary effort. Clear success criteria, defined ownership, and early involvement of domain experts ensure that even the first iteration reflects real business needs rather than abstract expectations.
Success also depends on measuring outcomes correctly. Technical metrics such as accuracy or recall matter, but they reveal only part of the picture. What ultimately counts is whether the system reduces workload, accelerates resolution time, or improves customer satisfaction. A model with modest accuracy that delivers tangible savings can be far more valuable than a highly precise system that is too costly to operate. This is why organizations need a dual perspective: technical dashboards on one side and business KPIs such as CSAT, NPS, task success rate, and escalation rate on the other.
Flexibility is another essential element. Teams should avoid becoming dependent on a single model or provider and instead let evaluation guide their choices. As usage expands, the system must evolve: new data informs refinements, workflows adapt, and architectural decisions adjust as real-world patterns emerge. Maintaining this flexibility ensures that GenAI remains aligned with the organization as it grows.
Organizational readiness is equally important. Even the strongest architecture cannot succeed if teams are unprepared for how GenAI changes their work. Employees need structured training to understand limitations, leaders must set realistic expectations, and stakeholders should align early on evaluation datasets and success metrics. Treating GenAI as an ongoing capability rather than a one-off project allows improvements to compound over time.
Organizations should also invest in knowledge rather than relying on a single model to solve everything. Many initiatives remain experimental, but the understanding gained from studying workflows, identifying real use cases, and building high-quality datasets becomes a durable asset. Models evolve, architectures mature, and costs decline, but a well-developed knowledge foundation endures. Companies that cultivate it are the ones that successfully transform early experiments into GenAI systems with lasting, enterprise-level impact.
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