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While artificial intelligence streamlines the execution of routine tasks in modern companies, the pressure for rapid deployment leads them to generate technical debt. Atlassian’s “State of Product 2026” report also shows that although AI saves time on less complex daily tasks, companies still face integration difficulties and data security concerns.
Key Insights:
- Only 27% of teams in modern product companies regularly set aside time to address technical debt.
- As many as 77% of professionals believe that artificial intelligence enables them to save time on routine tasks.
- The main barriers to AI adoption are a lack of integration with existing processes (46%) and a low level of trust in generated results (45%).
- 77% of product teams do not use artificial intelligence for managing priorities and schedules, even though planning is one of the most desired areas for AI development.
- Data security concerns effectively block access to AI tools for 43% of specialists in large companies (employing over 1,000 workers).
- The study was conducted among 700 executives from companies employing between 500 and 10,000 workers across the United States, Germany, and France.
What are the risks of ignoring technical debt in retail?
Modern retail and related industries rely on extensive e-commerce systems, loyalty applications, and advanced omnichannel infrastructure. The pressure to quickly roll out new sales features often causes companies to overlook code optimization.
According to the “State of Product 2026” report, most companies postpone this work and react only when such inaction blocks key operational processes. Only 27% dedicate their time to technical debt reduction.
In the fast-paced retail environment, this approach can backfire during peak shopping seasons, causing system outages, inventory synchronization delays, or transaction platform instability.
Reactive technical debt management forces teams into constant firefighting, taking away time that could otherwise be dedicated to innovation and strategic business growth.
How does artificial intelligence support product teams?
77% of respondents confirm that artificial intelligence has at least a moderate impact on productivity growth (with 38% describing it as high or very high). The most popular application of AI in companies is the automation of routine duties (77% of responses). Above all, creating product documentation – including PRD specifications and user stories – is highlighted by 60% of respondents.
Furthermore, nearly one-third use artificial intelligence algorithms for market research, data analysis, or processing customer feedback. Meanwhile, every second respondent (52%) turns to AI to gain a starting point or a ready template for executing more complex projects.
The introduction of AI-based tools has transformed the daily operations of many organizations. Data presented in the report indicates that most specialists use a maximum of 3 AI tools in their daily work. The most popular are ChatGPT, Microsoft Copilot, and Claude.
Why does artificial intelligence in retail face barriers and limitations?
Despite tangible benefits in saved time, implementing artificial intelligence in a business environment carries many challenges. As the “State of Product 2026” report shows, 46% of executives point to a lack of integration between AI tools and legacy systems, as well as difficulty aligning them with current operational processes.
Another significant barrier is limited trust – 45% of respondents do not fully trust the accuracy and reliability of results generated by AI algorithms. Information protection issues remain equally important. In large organizations with over 1,000 employees, 43% highlight data security concerns as a barrier restricting access to desired AI tools—by comparison, in smaller companies (under 50 employees), this percentage stands at just 20%.
Planning and prioritization using AI remain a challenge
Although automating simple tasks helps in daily work, product teams still face limitations in the most strategic areas. Atlassian’s report shows that 77% of enterprises do not use artificial intelligence to manage changing priorities and schedules, even though strategic planning ranks high on their list of needs. Technological limitations mean that writing consistent acceptance criteria or managing complex projects in retail still relies on human experience and proprietary analysis.
– In the fast-evolving retail industry, technical debt and artificial intelligence deployment are two pillars of digital transformation. Working on many projects for retail chains, we know that success depends not only on introducing modern solutions to the company, but above all on their deep integration with system architecture and business processes. Effectively building market advantage relies on systematically limiting technical debt and strategically utilizing artificial intelligence – says Dariusz Stolarczyk, Business Strategy and Development Advisor at Exorigo-Upos.
Summary
Managing technical debt and optimizing the use of artificial intelligence are key elements in the development of modern retail enterprises. Although AI brings tangible time savings on routine tasks, its full strategic potential depends on the quality of IT foundations. To build a lasting competitive advantage, organizations must systematically reduce technical debt and ensure smooth integration and data security.
