AI reshapes corporate innovation; boosts both exploitative and exploratory performance

The study shows that firms applying AI report higher levels of exploitative innovation. These activities focus on improving ongoing operations, refining existing goods, and upgrading production systems. Such innovation helps firms survive intense competition by improving efficiency and reducing waste. AI strengthens this by giving companies sharper search tools and faster access to relevant information, allowing them to identify flaws or chances for improvement more quickly.

A new study has found that the rising use of artificial intelligence inside traditional industries is reshaping how companies innovate, helping them strengthen both short-term efficiency-driven improvements and long-term breakthrough development. The research shows that AI not only boosts innovation output but also changes the way firms manage people, data, and research systems, helping them compete in fast changing markets.

The peer reviewed study, titled “How Does Applying Artificial Intelligence Influence Firms’ Ambidextrous Innovation Performance? Evidence Obtained from Chinese A-Share Listed Firms” and published in Sustainability, examined how AI adoption affects two types of innovation that companies must balance for long term survival: exploitative innovation linked to refinement and improvement of existing products, and exploratory innovation linked to new technologies and future markets. The authors reviewed detailed annual data from more than 3000 Chinese firms across a decade.

The findings point out a major shift. AI is no longer a supporting tool that improves narrow tasks. It is becoming a structure shaping force that changes how companies learn, search, allocate resources, and build capabilities. The study shows that companies that use AI more deeply perform better in both forms of innovation, although the strength of this effect depends on the amount of data a firm has and how it uses it.

AI strengthens both exploitative and exploratory innovation

The study shows that firms applying AI report higher levels of exploitative innovation. These activities focus on improving ongoing operations, refining existing goods, and upgrading production systems. Such innovation helps firms survive intense competition by improving efficiency and reducing waste. AI strengthens this by giving companies sharper search tools and faster access to relevant information, allowing them to identify flaws or chances for improvement more quickly.

At the same time, AI increases exploratory innovation. This form of innovation stands at the center of future growth and involves searching for new knowledge, building disruptive technologies, and designing products for future markets. Compared with exploitative innovation, this requires bold moves, greater investment, and tolerance for uncertainty. The study shows that AI’s ability to process complex data and reveal new patterns encourages firms to look beyond current constraints and attempt new development paths.

Together, these two effects show that AI helps solve a long lasting challenge for firms: how to balance improvement today with breakthroughs tomorrow. Traditional firms often struggle because both forms of innovation draw from the same limited pool of resources. The study shows that AI enhances overall efficiency, releasing resources that allow firms to support both further refinement and future exploration.

One of the major mechanisms behind this dual improvement is the boost AI brings to research efficiency. The study finds that AI strengthens the ability of firms to collect, process, and combine internal and external information, cutting the cost and time tied to early stage research activities. Better prediction tools also reduce the risk of wrong decisions. As a result, firms can develop more ideas, test them more quickly, and produce more patents with the same level of investment.

Another key mechanism is the shift in workforce structure. AI takes over repetitive and low skill tasks, which allows companies to hire more knowledge driven workers. The share of highly educated employees rises when firms adopt AI. These employees then engage in higher value tasks, producing more stable and stronger innovation output.

Data resources shape the strength and direction of AI’s impact

Since AI depends heavily on data for training and predictions, the data a firm owns becomes a direct factor that shapes AI’s effectiveness. The study shows that firms with richer and more complete data resources enjoy stronger gains from AI in exploitative innovation. When firms have detailed records and structured data, AI can scan existing processes and detect optimization chances more easily. This makes it easier to deepen improvements and strengthen short term competitive advantages.

However, the same data richness produces the opposite effect for exploratory innovation. Firms with large stores of structured data tend to gain less from AI when trying to enter new fields or explore unknown domains. This happens because AI performs best when it can learn from existing patterns. When data strongly reflects the past, AI tends to reinforce what firms already know instead of pointing toward new opportunities. Over time, this supports path dependence and reduces the appetite for risks. The more firms rely on known data, the harder it becomes to break away and move into unfamiliar territory.

This dual effect reveals an important boundary for the use of AI. While AI can strengthen both short term and long term innovation, the strategic value of this impact is linked to how firms manage and refresh their data. Firms that depend too much on old data may unintentionally restrict their ability to discover new knowledge. The study suggests that firms that wish to maximize exploratory innovation should collect varied external data and avoid letting internal databases become overly rigid or narrow.

Slack resources, industry competition, and AI maturity create further differences

The study also shows that the relationship between AI and innovation changes based on the characteristics of each firm. These differences help explain why some firms gain large innovation advantages from AI while others see limited progress.

Firms with more slack resources carry extra capacity that allows them to support both routine innovation and risky exploration. For such firms, AI leads to stronger gains in both types of innovation because they can withstand failures and invest in longer cycles. For firms with limited spare resources, the effect is different. AI mainly improves exploitative innovation while helping little in the way of exploratory work. Firms facing financial pressure tend to focus AI on short term tasks that improve cash flow, avoiding long term research that carries high uncertainty.

The level of industry competition also shapes how firms apply AI. In highly competitive environments, firms rely more heavily on improvements that bring immediate returns. Because of this, AI mainly helps exploitative innovation, while exploratory innovation remains limited. In less competitive sectors, firms have more space to experiment. AI then supports both types of innovation because leadership teams can pursue both short term stability and long term advancement.

Another difference comes from a firm’s starting point. The study divides firms into those with strong AI foundations and those with weak ones. Firms that already possess AI-related patents and mature systems do not receive major gains from further AI use. Their advantage comes from deeper integration rather than simple application. In contrast, firms that begin with weak foundations see strong improvements in both types of innovation when they start using AI. This shows that the first wave of AI adoption brings the largest gains in innovation performance.

A new framework for innovation strategy in the AI era

The study presents a clear path for firms seeking sustainable growth in a fast-shifting digital economy. It shows that AI is not only a technical upgrade but a major force reshaping how companies organize knowledge, allocate resources, and build future capabilities.

The evidence suggests that firms should treat AI integration as a strategic investment. Those seeking short-term performance gains should strengthen internal data governance and improve the quality of their internal datasets. Those committed to long-term breakthroughs should widen the variety of data they use, refresh their databases more often, and avoid rigid structures that trap them in past patterns.

When it comes to workforce management, the study highlights the need for more knowledge-driven staff. Firms must prepare for a shift in skill demand as AI reduces the need for routine labor and increases the need for creative and strategic roles. Firms that fail to strengthen their talent base may miss the innovation benefits linked to AI.

Another key message is the importance of maintaining enough slack resources. Firms that operate under strict financial constraints gain little from AI for long-term exploration. Leaders should therefore plan resource buffers to support both near-term and far-term innovation demands.

Industry regulators and governments should design policy tools that support both data circulation and structured use of AI. The study suggests that efficient data markets, clear rules over data ownership, and protection for experimentation can help firms gain more balanced innovation outcomes from AI.