How can businesses apply artificial intelligence? A practical guide to AI solutions

Many enterprises are already aware of efficiency bottlenecks in their daily operations, such as inconsistent customer service response times, fragmented internal data, and repetitive tasks consuming significant manpower. These problems don't appear suddenly but gradually amplify during business accumulation and organizational growth. Generative AI and automation technologies are introduced into the enterprise's vision in this context.

However, viewing AI merely as a "plug-and-play" tool often fails to truly solve problems. This article will start from the actual needs of enterprises, explaining the feasible applications of AI in different operational scenarios, helping enterprises establish a feasible and scalable AI implementation strategy, and further exploring when enterprises need to move towards customized AI solutions.

1. Why are more and more enterprises starting to focus on AI applications?

The core reason why enterprises are focusing on AI is not the technology itself, but whether AI can help enterprises create more efficient operating models with existing resources. Many enterprises accumulate a large amount of data and processes during their growth, but this information is often locked in different systems, documents, or personal experience, making it difficult to transform it into usable decision-making data in a timely manner. As business scales up and cross-departmental collaboration becomes more frequent, traditional manual processing methods begin to prove inadequate.

The value of AI lies in its ability to quickly process large amounts of data, standardize repetitive tasks, and transform tacit knowledge into usable capabilities. For management, this translates to more stable operational efficiency; for business teams, it means reducing tedious work and focusing on high-value tasks. However, these values don't appear automatically; they depend on whether the company plans its AI application correctly. This is the core reason why companies are beginning to re-evaluate AI applications.

2. Common Enterprise AI Application Scenarios: From Operational Support to Internal Efficiency

In actual operations, the first to feel the pressure in a company are often not the innovation departments, but rather the routine support units. For example, customer service teams need to repeatedly respond to similar questions, operations staff need to compile numerous reports, and internal colleagues spend a lot of time searching for documents and policy explanations. These scenarios, while not complex, are extremely time-consuming.

In these scenarios, generative AI can play a "first-line support" role, helping businesses quickly organize information and generate initial responses or summaries. For management, the focus is not on how intelligent the AI's responses are, but on whether it can stably and controllably support existing processes. Therefore, more and more companies are integrating AI into their internal systems, rather than just for external demonstrations.

In GTS's enterprise-level customized AI solutions, AI application planning prioritizes scenarios that "do not change core processes but significantly reduce manual workload," helping enterprise users gradually build trust and usage habits with AI in a low-risk manner.

3. Three Issues Easily Overlooked Before Implementing AI

Despite enterprises' high expectations for AI applications, three key issues are often underestimated before actual implementation:

1. Unclear definition of business needs. If the specific problems AI is meant to solve are not clearly defined, it may amplify existing chaos, leading different departments to use the system in their own ways, increasing communication costs.

2. Insufficient preparation of data and systems. The quality of AI output is highly dependent on the enterprise's existing data structure. If data is scattered, inconsistently named, or lacks standards, AI can only produce seemingly reasonable but practically unfeasible content.

3. System management and security are delayed. Many enterprises underestimate the importance of permissions and security, treating AI as an independent tool rather than part of the enterprise system. In fact, access control, data confidentiality, and output traceability are indispensable foundations for enterprise-level AI applications, and these issues can easily lead to long-term risks.

These problems are not technical difficulties, but rather a matter of whether decision-makers understand the role of AI within the team. If enterprise AI applications cannot be integrated into the existing management architecture, they are unlikely to become a sustainable capability.

4. The selection process from standard tools to customized AI solutions

For most enterprises, general-purpose AI tools are often the first step in engaging with AI, quickly validating concepts and understanding the technology's potential. However, when applications begin to involve internal enterprise data, integration with existing systems, process control, and access management, the flexibility and controllability of these tools gradually become limited.

Customization doesn't mean building a model from scratch. Instead, it means enabling AI to understand and integrate with an enterprise's existing data structure, business processes, and decision-making logic. In practice, a mature approach typically involves first clarifying the actual application scenario, then gradually implementing it in a modular fashion to avoid the risks of large-scale, one-time modifications.

For example, GTS's involvement in enterprise-level AI customization development projects usually begins with business process analysis and data inventory, helping enterprises establish clear application boundaries before designing the corresponding AI architecture. This ensures that each function corresponds to specific needs, making the system scalable, maintainable, and continuously adaptable as the business grows. This is precisely why more and more enterprises are beginning to evaluate customized AI solutions.

This article, "How can businesses apply artificial intelligence? A practical guide to AI solutions" was compiled and published by GTS Enterprise Systems and Software Development Service Provider. For reprint permission, please indicate the source and link: https://www.globaltechlimited.com/news/post-id-7/