Operational inefficiencies today are often brought about by the rising costs of cloud computing, opening a new discussion regarding cloud cost optimisation and its importance in the business.
The concept of financial operations, or FinOps, which refer to a management practice promoting shared responsibility for an organisation's cloud computing infrastructure and costs, is currently being given a spotlight as companies transition to their adoption of artificial intelligence.
Challenges arise as technological advancements come to be, and financial leaders now find themselves dealing with hurdles in financial development as large-scale machine learning become one of the considerations in financial operations optimisation. It is crucial for companies to set up foundational principles, practices, and skills for effective cloud usage before diving into AI, and organisation leaders should consider these fundamentals when making decisions about their cloud infrastructure and AI integration.
It should be noted that some organisations mistakenly use cloud computing solely for cost control rather than strategic innovation. To avoid this, CFOs must work hand in hand with their companies' technological leaders to clearly define how AI adds value to the organisation as a whole. A robust cloud architecture and high-quality data foundation are crucial for AI.
Traditional lift and shift cloud migrations often yielded poor results, leading to a growing emphasis on cloud cost optimisation. The high cost of AI proves the need for effective model optimisation and governance. Skill gaps in data, analytics and other areas highlight the importance of building cloud expertise within organisations as cloud fluency is essential.
Skills development should involve not only technologists but also finance leaders and stakeholders to seamlessly integrate AI with human intelligence through ongoing education.
