Contrary to widespread fears about mass job displacement, artificial intelligence is driving net job creation across organizations globally, with 77% of companies reporting increased hiring compared to just 46% experiencing role reductions. However, the story is far more nuanced than a simple win for workers. A new report from Snowflake in collaboration with Omdia by Informa TechTarget surveyed 2,050 business and technology leaders across 10 countries, including Singapore, Australia, New Zealand, Canada, France, Germany, India, Japan, the United Kingdom, and the United States between August and September 2025 .

The data reveals a critical insight: AI adoption is reshaping the workforce rather than simply expanding or shrinking it. Among responding organizations, 42% say AI has created jobs, 11% say it has eliminated roles, and 35% report a mix of both. Of those experiencing both hiring and cuts, 69% say the overall effect of AI on the workforce has been positive, signaling that as adoption accelerates, AI is driving overall job growth rather than consolidation .

Which Industries and Roles Are Seeing the Most Job Growth?

The benefits of AI-driven job creation are not evenly distributed across all sectors and functions. Technical teams are experiencing the strongest gains, with job growth concentrated in specific areas. The functions seeing the greatest net employment gains include IT operations, cybersecurity, and software development, where organizations are adding roles to manage and optimize AI systems .

• IT Operations: 56% of organizations report job gains in this function, the highest among all departments surveyed
• Cybersecurity: 46% of organizations report job gains as AI systems require new security protocols and oversight
• Software Development: 38% of organizations report job gains as teams build and customize AI solutions

Interestingly, the same functions experiencing the strongest job growth are also seeing the most job losses. IT operations, for example, reported both the highest amount of job gains and the highest job losses at 40%, suggesting that AI is fundamentally restructuring these teams rather than uniformly expanding them. Customer service and support (37%) and data analytics (37%) are also experiencing significant job losses alongside gains .

Why Does AI Maturity Matter for Workforce Outcomes?

One of the most revealing findings is that organizational maturity in AI deployment directly correlates with positive workforce outcomes. The more embedded AI becomes within an organization, the more likely it is to see overall employment gains and productivity improvements. This suggests that early-stage AI experimentation may create short-term disruption, but sustained adoption leads to net job creation .

Organizations deploying AI across many use cases report significantly better workforce outcomes than those still in early stages. Among companies with multiple AI use cases, 75% report a net positive impact on jobs, compared to just 56% of those using AI in more limited, early-stage deployments. Similarly, 75% of organizations deploying AI across many use cases report positive impacts on employee productivity and operational efficiency, compared to lower percentages among early adopters .

"AI's impact won't be uniform. Some roles will dramatically amplify their influence and productivity, while others risk being left behind. The difference comes down to how effectively it's used: breaking down problems with first-principles thinking and guiding AI agents like high-performing teams," said Anahita Tafvizi, chief data analytics officer at Snowflake.

Anahita Tafvizi, Chief Data Analytics Officer at Snowflake

How to Maximize AI's Positive Workforce Impact

• Build a Trusted Data Foundation: Organizations should prioritize establishing robust data infrastructure before scaling AI, as this enables more effective deployment and better outcomes across departments
• Embed AI Into Core Operations: Rather than treating AI as a standalone experiment, integrate it into critical business processes to drive sustained job growth and productivity gains
• Strengthen Data Governance Policies: Implement clear governance frameworks to ensure AI is deployed responsibly and effectively, which correlates with better workforce outcomes and ROI
• Develop Skills Aligned With AI Needs: Invest in training programs that prepare employees for AI-augmented roles, particularly in technical functions where job restructuring is most pronounced

However, translating AI ambition into measurable outcomes remains a significant challenge, particularly in certain regions. While 39% of organizations globally report using generative AI for many use cases, the ability to identify specific use cases and measure return on investment (ROI) varies dramatically by geography .

Singapore, for instance, matches the global average in the volume of AI activity at 39%, but lags significantly in actual deployment penetration across departments and in quantified ROI. Only 33% of Singapore respondents report quantified ROI, compared to 49% globally. This gap correlates with more cautious investment strategies; Singaporean organizations estimate generative AI will account for just 15% of their tech budget over the next 12 months, the lowest globally compared to the 23% global average .

"The strongest ROI isn't coming from experimentation alone. It's coming from embedding AI into core operations while strengthening data readiness and governance policies. The future of work will be shaped by companies that pair AI ambition with trusted infrastructure and the right skills to turn it into lasting impact," said Anahita Tafvizi.

Anahita Tafvizi, Chief Data Analytics Officer at Snowflake

The regional disparities suggest that AI's job creation benefits are not automatic. Organizations must actively work to identify viable use cases, establish proper data infrastructure, and develop governance frameworks to realize positive workforce outcomes. Jenny Koh, Snowflake's country manager in Singapore, noted that the recurring challenge for local leaders is not a lack of vision, but the need for a trusted data foundation to power AI initiatives effectively .

The broader takeaway is clear: AI is reshaping the workforce in ways that favor organizations that approach adoption strategically. Rather than a simple story of job displacement, the data shows that AI adoption is creating new roles, eliminating others, and fundamentally restructuring how work gets done. The organizations winning in this transition are those that combine ambitious AI strategies with the infrastructure, governance, and skills development needed to turn that ambition into sustained business value and positive workforce outcomes.