{"id":62188,"date":"2025-10-29T15:59:20","date_gmt":"2025-10-29T13:59:20","guid":{"rendered":"https:\/\/itcluster.lviv.ua\/?p=62188"},"modified":"2025-10-29T16:18:27","modified_gmt":"2025-10-29T14:18:27","slug":"inside-ukraines-saas-ecosystem-real-insights-on-ai-adoption","status":"publish","type":"post","link":"https:\/\/itcluster.lviv.ua\/en\/inside-ukraines-saas-ecosystem-real-insights-on-ai-adoption\/","title":{"rendered":"Inside Ukraine\u2019s SaaS ecosystem: real insights on AI adoption"},"content":{"rendered":"\n

Artificial intelligence is becoming a defining layer of modern software, reshaping how companies build, deliver, and scale digital products. Reputable global surveys<\/a> show that a majority of enterprises are already experimenting with or deploying AI solutions to streamline operations, personalize user experiences, and unlock new sources of revenue.<\/em><\/p>\n\n\n\n

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Ukraine\u2019s SaaS ecosystem<\/a>, known for strong engineering talent and product-driven startups, is also moving decisively in this direction. From productivity tools and pricing platforms to financial automation and creative AI, local companies are weaving intelligent capabilities into their offerings to compete on the global stage. Their experience highlights both the opportunities and the real-world challenges of adopting AI in fast-growing product environments.<\/p>\n\n\n\n

To better understand the balance between promise and reality, CIGen<\/a> spoke with Ukrainian SaaS leaders about the AI scenarios they are piloting and the challenges they encounter in scaling them.<\/p>\n\n\n\n

Dmytro Kudrenko, the Founder and CEO of <\/strong>Stripo email template builder<\/strong>,<\/a> describes their AI adoption path with anticipation of further advancements in the domain:<\/p>\n\n\n\n

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\u201cAt Stripo, we began with simple AI use cases, such as text generation within email templates, quick wins that immediately saved time for marketers. However, real transformation began when we moved beyond content and started rethinking the entire user experience. We are now building a multi-agent AI architecture where agents can work together toward business goals: from refining a CTA button in a single email to orchestrating a full promotional campaign for a webinar, including structure, strategy, and HTML adaptation.<\/em><\/p>\n\n\n\n

I\u2019m convinced that AI won\u2019t just accelerate existing workflows, it will replace the traditional product UX layer. Drag-and-drop editors will soon become niche tools for special cases, while natural goal-based collaboration with AI agents becomes the primary way users build things. The main challenge is not the models, but product architecture, companies must redesign their systems so that AI becomes a first-class user alongside humans. <\/em><\/p>\n\n\n\n

My advice to SaaS teams: review how your microservices communicate, because from now on, you\u2019re building not only for developers and customers, but also for AI.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

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Top technical minds from other successful UA-rooted SaaS companies, like Competera<\/em><\/strong>, Sitechecker <\/em><\/strong>and FuelFinance<\/em><\/strong>, deep-dive into their journey of mastering AI for their business needs and cases by providing insights on their use cases, challanges and the future outlook of AI in their respective domains.

To begin, we asked the founders and product leaders how they currently apply AI in their day-to-day operations, from automating processes and enhancing user experiences to driving new levels of decision intelligence. Their answers illustrate how Ukrainian SaaS innovators are embedding AI at every layer of their product ecosystem:<\/p>\n\n\n\n

What are the most impactful ways your company is currently applying AI – whether in product features, operations, or customer experience?<\/strong><\/h2>\n\n\n\n

Dmytro Chernyak<\/strong>, Product Manager, Competera<\/a>:<\/p>\n\n\n\n

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\u201cCompetera was integrating AI into the core of our product long before the hype around GenAI began. Since 2018, our flagship AI-native Pricing Platform, built on Contextual AI, has helped retailers automate pricing optimization across their entire assortment, making them more resilient, profitable, and efficient. <\/em><\/p>\n\n\n\n

Our deep-learning models consider a wide range of factors that influence customer purchasing behavior, from product attributes and promotions to weather patterns and local competition, evaluating millions of price combinations in minutes to identify effective pricing strategies. <\/em><\/p>\n\n\n\n

In 2024, we extended our AI functionality by integrating agentic AI, built on RAG architecture, across our product ecosystem to provide customers with autonomous, precise decision-making and real-time responses, powering automated customer support, analytical insights, and explanations behind AI-generated price recommendations.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

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Simon Bezhenov<\/strong>, Product Marketer, Sitechecker<\/a>:<\/p>\n\n\n\n

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\u201cSo, for sure, we first started with the general use of AI: things like content generation and making routine tasks easier. However, in terms of the product, we\u2019ve only recently begun using AI for automation. We\u2019re currently combining it with the N8n platform. <\/em><\/p>\n\n\n\n

Essentially, we capture new leads and detect users who churn, and then we follow up with them via highly personalized AI-generated emails. The messages are customized based on the data they provided when registering with us, so each one feels unique and relevant.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

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Oleksandr Riabukha<\/strong>, Head of R&D, FuelFinance<\/a>:<\/p>\n\n\n\n

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\u201cOur goal is to make AI the core system of our product. One of the most impactful implementations so far is the automatic generation of financial reports for clients. Previously, our managers spent a lot of time preparing reports manually and sharing them via Slack.<\/em><\/p>\n\n\n\n

Currently, a text-based analytical summary is automatically generated from client account data, then it\u2019s formatted into a clear, easy-to-read visual report for the client. This automated feature delivers several key benefits: significantly reduces the team\u2019s manual workload, enables faster decision-making, and helps clients better identify financial risks and opportunities.<\/em><\/p>\n\n\n\n

We\u2019re also developing an AI module for real-time financial analysis and \u201cwhat-if\u201d scenarios, allowing clients to instantly see how changes in revenue or expenses could affect their business.<\/em><\/p>\n\n\n\n

Overall, our AI system accelerates access to insights and makes the decision-making process much more efficient.\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

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Every company\u2019s AI journey begins with deciding where to start. We asked how these teams identified the first areas worth automating or augmenting with AI, and what criteria guided their prioritization.<\/p>\n\n\n\n

How did you identify and prioritize the first AI initiatives in your organization?<\/strong><\/h2>\n\n\n\n

Dmytro Chernyak<\/strong>, Competera:<\/p>\n\n\n\n

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\u201cWe focus on AI initiatives that automate repetitive tasks, accelerate decision-making, and improve overall team productivity, freeing up our time for more strategic, complex work. <\/em><\/p>\n\n\n\n

We use AI agents integrated with LLMs to automate internal knowledge management and employ GenAI-based tools for rapid prototyping, code refinement, multi-language video tutorials, technical documentation, and customer research. <\/em><\/p>\n\n\n\n

Our guiding principle is simple: if AI can shorten the cycle from ideation to shipping features, we invest.\u201d<\/em><\/strong><\/p>\n<\/blockquote>\n\n\n\n

Simon Bezhenov<\/strong>, Sitechecker:<\/p>\n\n\n\n

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\u201cAs for AI initiatives, we do have a certain level of prioritization, but it\u2019s not our top priority. Often, some solutions simply don\u2019t require AI at all. Of course, AI is great for generalization, search, and building insights from data, but it needs to be thoroughly tested before we roll it out.\u00a0<\/em><\/p>\n\n\n\n

So right now, <\/em>our main focus is to implement AI only when it\u2019s been properly validated and ready to deliver real value to customers.<\/em><\/strong>\u201d<\/em><\/p>\n<\/blockquote>\n\n\n\n

Oleksandr Riabukha<\/strong>, FuelFinance:<\/p>\n\n\n\n

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\u201cWe started with a deep analysis of all project processes: what our financial managers do, which requests they receive most often, and what tasks take the most time.<\/em>
Based on this, we identified which of those tasks could be delegated to an AI agent.<\/em><\/p>\n\n\n\n

Our prioritization criteria are simple:<\/em><\/p>\n\n\n\n