AI can process and analyze large datasets quickly, identifying patterns and trends that might be missed by human analysis.

This helps in making data-driven decisions and forecasting future trends based on historical data, helping managers make more informed and accurate decisions.

Al can quickly, efficiently, and accurately collect internal enterprise data, including cash flow data, project benefit situation, etc., and conduct comprehensive analysis.

Al can also analyze and evaluate the market for enterprise products, formulate strategic plans, and allocate resources, providing decision-makers with multiple implementation options.

Li Jiaxing

For a small biotech company aiming to expand, AI offers a way to boost efficiency without heavy HR costs. It accelerates drug discovery by quickly identifying viable targets, predicts future product demand, and helps management allocate resources wisely. For example, AI can screen compounds for drug-receptor interactions in days rather than months, allowing researchers to focus on the most promising leads. Predictive analytics also forecasts demand, ensuring resources are allocated effectively.

AI may further support financial planning by simulating investment scenarios and automates R&D tasks, freeing up the team to focus on high-value work. Additionally, AI aids in regulatory compliance by flagging potential issues early, avoiding costly penalties.

However, AI’s success depends on a long-term mindset. Unlike simple automation and RPA, AI is a strategic investment, not a shortcut. The company must commit to building an understanding of AI’s potential, recognizing it as a transformative tool that evolves with the company and market demands.

AI can also help companies think differently. It must enable them to think out of the box. Often, employees in the same company think the same way, with the same background, and they know the company's processes well. AI can bring a fresh, different perspective.