Partner and Managing Director Janet Tang was interviewed by Economic View at the World Economic Forum's 16th Annual Meeting of the New Champions. Read the original interview here (Chinese)
Economic View, June 26 (by Wei Wei, Zhou Yihang) – "Enterprise deployment of AI has entered a new stage. The rise of open-source models such as DeepSeek has shifted AI from 'innovation for innovation's sake' towards 'practical benefits and real-world application,'" Janet Tang, Partner and Managing Director at AlixPartners, told Economic View in an exclusive interview during the World Economic Forum's 16th Annual Meeting of the New Champions (Summer Davos Forum) on June 25, 2025.
Some global AI pilot projects in recent years have failed, Janet noted, primarily due to the unsustainable gap between effort and payoff. Companies quickly determined that the outputs failed to offset the substantial upfront costs of bringing the new technology into operation.
The turnaround has been dramatic, as Janet explains. "Today, DeepSeek has significantly reduced the cost of deploying AI. For commercial enterprises, the core issue has shifted from 'the innovative potential of AI' to 'the practical impact of AI implementation." She cites the decrease in investment costs as the primary driver for this shift, allowing businesses to balance their cost and benefit curves, making the commercial value of AI projects predictable and measurable.
Discussing the scale of enterprise investments in AI, Janet pointed out that budget allocation varies widely from firm to firm, making it difficult to generalize about overall trends. Businesses across industries must strategically allocate resources according to their unique operational needs and priorities, especially in areas like technology and R&D. Increasingly, enterprises are moving away from setting fixed percentages for AI investment, adopting a more rational and pragmatic approach instead.
"The primary and fundamental challenge companies face when applying AI is the data issue. AI heavily relies on high-quality data to function effectively. Companies with low levels of digitization or inadequate data quality will find it challenging to utilize AI effectively. Conversely, successful AI applications can drive enterprises to enhance their data and digitalization standards," Janet explained.
In her view, the key advantage of Chinese enterprises in the AI field lies in their extensive base of digitally native businesses. "Many Chinese internet companies were founded with a data-driven business model. This positions them advantageously in terms of digitization, data richness, and penetration."
However, Chinese firms face challenges in enterprise-level software and solutions. "Compared to mature SaaS ecosystems and enterprise software penetration in Western markets, Chinese businesses vary greatly in their levels of digitalization", she noted. This inconsistency hinders deep integration and the speed of AI deployment to some extent."
More companies are now making the strategic choice to "go global." Janet considers that there is a marked shift underway in international expansion strategies: from simply "exporting products" to deeper "localized operations."
"Enterprises must build comprehensive value chain capabilities in target markets, including supply chain restructuring, localized operations, and sales system reconfiguration," she explained. She emphasized that the advantage of low-cost Chinese manufacturing no longer guarantees international market entry. Overseas clients increasingly value suppliers' risk management capabilities, quality assurance collaboration, and their ability to offer value-added services beyond manufacturing, such as logistics, inventory management, and joint design.
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