Research objectives and hypothesis/research questions:
The study’s main objective was to explore how cloud computing and AI jointly influence the speed and effectiveness of innovation processes in IT firms. Grounded in theories of strategic management, innovation capabilities, and technology adoption, the research tested two hypotheses: H1 – Cloud computing adoption significantly reduces time-to-market for new IT products by improving scalability, flexibility, and resource availability, H2 – AI integration significantly enhances innovation efficiency through automation, predictive analytics, and decision-support. These hypotheses reflected the assumption that cloud and AI act as dynamic capability enablers, supporting the facilitating and reconfiguring of innovation processes.

Research methods:
A mixed-method approach was used to capture both measurable impacts and contextual insights. The quantitative component involved a structured survey of 20 IT professionals in management, engineering, and innovation roles. Additionally, five semi-structured interviews with project managers and innovation leads explored implementation challenges and synergy effects. Survey data were analyzed using descriptive statistics, while interview data were thematically coded to identify patterns related to benefits, barriers, and strategic implications.

Main results:
Findings confirmed both hypotheses. Cloud computing reduced implementation time by 41-60%, especially during testing and integration, by eliminating hardware delays and enabling flexible resource use. AI improved innovation efficiency by 21-60%, particularly in data analysis, code generation, and quality assurance. Respondents noted that AI-driven automation and decision-support enhanced planning, risk assessment, and reduced rework. The combined use of cloud and AI was seen as highly synergistic, enabling rapid experimentation, agile development, and cost-effective scaling. However, challenges such as vendor lock-in, skills shortages, and data security concerns were also identified. Overall, the study shows that integrating cloud and AI enhances innovation capabilities and organizational agility in the IT sector.

Implications for theory and practice:
This study contributes to strategic innovation management literature by showing how cloud and AI jointly function as dynamic capability enablers. It extends existing frameworks by analyzing these technologies not as isolated tools but as synergistic drivers of innovation. For practitioners, the findings highlight the strategic value of integrating cloud and AI to accelerate development, enhance agility, and improve decision-making. Organizations are encouraged to adopt multi-cloud strategies and AI-as-a-Service (AIaaS) models, invest in workforce upskilling, and implement governance frameworks to manage data security, compliance, and ethical AI use, to effectively respond to opportunities and challenges of rapidly developing digital innovations market.