How can you quickly assess the business impact of AI implementation? It’s one of the toughest questions CEOs, CIOs, and CTOs face today. Low-code technology offers an answer – enabling organizations to design and test prototype applications equipped with AI assistants and agents, all within a single, integrated IT environment, in just a few days.
Artificial intelligence has become one of the top investment priorities for many large organizations.
According to Deloitte’s State of AI 2024 report, 94% of business leaders believe that AI will be critical to their success within the next three years. Yet, at least 30% of AI initiatives never move beyond the pilot stage. Why is that?
According to Gartner, over 40% of agent-based AI projects are expected to be canceled by the end of 2027 due to rising costs, unclear business value, or insufficient risk controls. This uncertainty around AI returns has become a major barrier for many CIOs and CTOs when making strategic decisions. At the same time, McKinsey reports that business leaders increasingly expect measurable benefits from AI implementation. Rather than spreading efforts across numerous initiatives, they are now focusing on areas with the greatest potential for return. Gartner analysts also predict that by 2028, at least 15% of daily business decisions will be made autonomously by agentic AI, compared to virtually none in 2024. The opportunities for optimization are enormous – which makes it crucial to validate ideas quickly and thoughtfully.
Michał Janisz, Head of Business at Productive24, will soon discuss how to overcome this barrier and assess the profitability of AI implementations in practice at AI Summit Poland 2025. In his presentation, he will demonstrate how low-code technology enables rapid prototyping of applications with AI assistants and agents, testing them within a consistent IT environment, and making investment decisions based on real results rather than projections.
In traditional AI implementation projects, the first tangible results often take months to appear, requiring significant involvement from development teams. Every stage – from requirements analysis to development, integration, and testing – demands both time and financial resources. Often, by the time an organization sees the solution in action, much of the budget has already been spent. As a result, IT managers and boards must make decisions based on vendors’ promises rather than actual evidence of effectiveness, increasing the risk that expectations will fall short and causing AI projects to be seen as costly experiments.
An alternative is an iterative approach based on rapid prototyping. Rather than “diving in headfirst” with a full-scale, high-risk implementation, organizations can build a prototype application with AI assistants and agents in just a few days and evaluate whether AI can truly support their business processes. This method provides decision-makers with a practical tool for making safer, more informed business decisions.
Productive24 is a modern low-code platform with a built-in engine for creating and managing AI assistants and agents. It enables the rapid development of secure, scalable, and multi-platform IT systems – all without heavy reliance on development teams. Thanks to low-code technology, business analysts can independently design and build advanced applications where artificial intelligence operates natively, streamlining processes and accelerating innovation.
Productive24 applications organize data and standardize processes, providing a foundation for automation. Their built-in AI assistants and agents take this automation further – they analyze data and support decision-making in ways traditional IT systems cannot. It is the combination of applications, processes, and data that makes the AI implemented on the platform a true business tool – delivering repeatable results, supporting critical business processes, and reducing implementation costs.
In mature organizations, AI implementation is evaluated across three key dimensions: business impact, operational efficiency, and organizational readiness.
AI implementations should be evaluated based on the value they generate. Key performance indicators such as ROI (Return on Investment), TCO (Total Cost of Ownership), and time-to-value should be defined as early as the pilot stage. It is also worth assessing whether:
The traditional implementation model, where results take months to materialize, is no longer suited to today’s fast-moving markets. Increasingly, organizations are adopting the Proof of Concept (PoC) approach – rapid, controlled testing of solutions within a real technological environment. The Productive24 platform makes it possible to create such PoCs instantly, with AI assistants and agents natively integrated into the existing IT architecture. For CIOs and CTOs, this provides the ability to test project assumptions in practice – without the risk of costly, “blind” implementations.
Gartner predicts that over 30% of AI projects will be abandoned due to low end-user acceptance. For this reason, evaluating effectiveness should also consider user engagement and actual tool usage. Productive24 enables organizations to rapidly and securely build AI-powered IT solutions within a single, integrated company environment. This approach allows teams to assess the practical usefulness and impact of AI on work efficiency – even before deciding whether to scale the project.
In the context of AI investment decisions, the 3xF validation model can be applied – Fast, Feasible, and Fact-Based.
During his presentation (Track I, 11:50 a.m.), Michał Janisz will showcase selected examples of AI implementations in applications built on the Productive24 platform—ranging from HR assistants supporting recruitment processes, through automation within compliance systems, to management dashboards where AI recommends decisions based on operational data.
AI Summit 2025 also features Extentum AI, a comprehensive and intuitive GenAI platform designed for large organizations. It enables business teams to implement artificial intelligence securely and at scale, accelerating their transformation into truly AI-driven enterprises. During the event, Marcel Piekarski (Product Manager & Team Leader, Extentum AI) will deliver a talk titled “Why Do First GenAI Implementations Fail? An Approach That Avoids Mistakes and Delivers Value” (Track II, 3:35 p.m.). He will discuss how to move from GenAI experimentation to scalable, organization-wide implementations, addressing the most common pitfalls and how to overcome them.
Participants will see how the right technology can simplify solution development, streamline integration, and ensure scalability – the key to making GenAI truly effective. Both presentations share a common message: AI must be tested and validated in practice; otherwise, it risks remaining nothing more than an empty promise.
AI is no longer the domain of futuristic visions – it is becoming a practical, operational tool. The key to success lies not in the technology’s potential alone, but in an organization’s ability to validate it quickly and reliably. Productive24 demonstrates that the profitability of AI implementations can be assessed like any other investment – based on data, measurable indicators, and real results, rather than promises or assumptions. At AI Summit 2025, we will see that practical AI implementation is less about faith in technology and more about disciplined decision-making methodology.
We strongly encourage you to join the event and explore how AI can deliver real business value.
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