In stark contrast to the boom among tech giants, businesses operating outside the technology sector are confronting a protracted and challenging journey to achieve a return on investment (ROI) from their AI initiatives. The latest reports indicate that integrating AI into traditional operational processes demands more restructuring time than initially projected.
Key Developments
While major technology corporations consistently report revenue growth from selling AI infrastructure and services, traditional industries such as manufacturing, retail, and financial services find themselves in a vastly different position. The adoption of new-generation AI tools, particularly Generative AI, in these enterprises largely remains at the small-scale experimental stage. The transition from trials to full-system implementation faces numerous obstacles, significantly delaying the capital recovery timeline compared to initial investor expectations.
Context & Causes
The primary cause of this delay stems from disparities in existing digital infrastructure. Tech companies are built on cloud-native platforms and clean data, which facilitates easy integration and optimization of AI models. Conversely, traditional businesses contend with fragmented, legacy, and inconsistent data systems. Data cleansing and technical infrastructure preparation consume a substantial portion of budgets before any AI algorithm can truly operate effectively.
Technical & Technological Analysis
Technically, deploying Large Language Models (LLMs) or Machine Learning systems demands high computational power and specialized personnel for fine-tuning. Non-tech enterprises often lack readily available high-caliber data engineers, compelling them to outsource or leverage intermediary Software-as-a-Service (SaaS) solutions. This implicitly increases recurring operational costs and further complicates the enterprise's current information system architecture.
Expert Opinions & Insights
Many financial analysts contend that the market is overestimating the speed of AI profitability outside the tech sector. The scarcity of direct financial performance metrics is causing many boards of directors to exercise greater caution in approving subsequent AI budget allocations. Nevertheless, experts also emphasize that this represents a natural development cycle for any disruptive technology, akin to the internet or cloud computing waves of the past.
Impact & Future
For the Vietnamese market, where most businesses are small to medium-sized and undergoing digital transformation, the lessons regarding AI ROI pathways are invaluable. Instead of impulsively investing in expensive AI technologies, Vietnamese enterprises should focus on optimizing internal data structures and applying AI to specific, immediately measurable tasks. Patience and a pragmatic investment strategy will determine long-term success.