SI Industry Embraces AI, From Business Models to Competitive Benchmarks

As generative artificial intelligence (AI) spreads, changes are being felt across the systems integration (SI) industry. As AI adoption expands across internal operations and the building and operation of core customer systems, AI is becoming a factor that strengthens the competitiveness of existing SI businesses. Accordingly, customer demands are also moving beyond whether to adopt AI to questions of how much actual work can be reduced, how it will be linked to existing systems, and whether the investment can be justified.

In this process, the importance of the long-accumulated capabilities of SI companies is also increasing. The background is that they have closely handled work processes and data structures during customer system building and operations. To apply AI in a company’s internal operations, it is necessary to select tasks for AI use, define the required data, and design links with existing systems such as ERP, SCM, MES, and CRM, so the role of SI companies with relevant experience is expanding.

As AI-generated images spread, SI companies are first introducing AI into their own work processes before proposing it externally. More companies are also applying AI across their internal operations before making AI proposals to customers.

The scope of AI application now extends to proposal writing, market research, cost estimation, contract review, system design, development, testing, quality management, and project management. AI use within SI companies goes beyond simple task automation.

The effects of this internal use of AI are tied to project costs and execution competitiveness. Because labor costs account for a large share of SI business, whether the time required for development and testing can be shortened is also linked to project profitability.

An NDS official said AI ultimately comes down to a productivity issue. The official also said that labor costs make up a significant portion of SI project costs.

The official said NDS intends to address labor-cost issues by securing productivity through vibe coding. NDS sees vibe coding as a means of improving productivity in this respect.

Changes are becoming visible quickly in development work. AI is taking over repetitive tasks once handled by developers, such as coding, testing, and documentation, and is driving changes in how projects are carried out. As a result, the amount of work that can be processed per person is likely to increase, and the time required for the same project may also be shortened.

These productivity changes are also affecting the existing SI business structure. In traditional SI, project size was judged by the number of people assigned and the length of the project, and its structure was centered on "man-months." However, as AI reduces the time needed to handle the same tasks, it is becoming increasingly difficult to explain project value solely by the number of developers assigned.

An Asiana IDT official said the spread of AI coding assistants is reshaping the benchmarks for SI competitiveness. If the traditional standards of competitiveness were the number of personnel assigned and man-months, the redefined standards are shifting toward the use of AI solutions and automation tools. As evaluation factors for this shift, the official pointed to the ability to build higher-quality systems more quickly and stably.

With the spread of AI, the competitive focus of SI companies is shifting from simple system building to problem-solving capabilities. SI companies are now moving beyond implementing the functions requested by customers and toward identifying on-site problems and proposing AI-based solutions.

The capabilities now needed in this changing environment are said to include understanding customer operations, understanding data structures, and designing integration between AI and existing systems. At the same time, the importance of consulting capabilities and architecture capabilities that support these efforts is also growing.

A Hanjin Information and Communication official said the direction of strengthening the capabilities of engineers across the company lies not in acting as simple developers but in performing the roles of architects and consultants. The official also said the company is pursuing business structure improvements, avoiding labor-intensive SI, and aiming to enhance value through intellectual property-based SI business.

A Shinsegae I&C official said AI is a key turning point that expands existing system-building businesses to the next level. The official added that while the stable building and operation of customer-requested systems used to be the important factor, what matters now is understanding customer business data and processes, identifying areas that can be made more efficient with AI, and the potential to generate actual business results.

The importance of domain expertise accumulated over the long term by small and mid-sized SI companies is growing. That is because SI companies with experience building systems for specific domains already understand the work flows, data, and operational constraints of each industry. Companies with experience in aviation and logistics systems understand workflows such as flight operations, ground handling, and vehicle dispatch; companies with retail experience understand where data is generated and where inefficiencies occur in store operations and sales processes. Companies with experience in manufacturing sites understand production and equipment data, while companies focused on public-sector projects understand constraints such as network separation and security environments.

It is explained that this accumulated know-how leads to the ability to design and propose implementation plans at the AX promotion stage. An Asiana IDT official said the center of traditional SI was simple labor input and passive building. The official added that the core of the AX era lies in consulting capabilities for proposing AX implementation plans.

The official also said the competitive focus in the SI industry is changing rapidly. The shift is moving away from simple price competition toward high-value consulting and vertical solutions. This means that industry competition is moving from a price-centered standard to one centered on consulting and industry-specific solutions.

As access to AI technology rises, the value of industry experience is expected to increase. While AI technology itself is becoming rapidly commoditized, the importance of capabilities closely tied to on-site operations and work processes is also coming into focus.

A GS ITM official said AI technology is being rapidly popularized as commercial models, open source, retrieval-augmented generation (RAG), and agent technologies advance. However, the official said deep understanding of the customer’s business context is needed, data meanings must be defined, and AI must be designed to operate safely within business processes.

The official said these tasks are difficult to replace. Accordingly, separate from the spread of AI technology, the distinction of capabilities to design and apply AI to fit the customer environment is expected to remain important.

In this flow, AI is spreading across the existing SI business as a whole, and its application ranges from proposals and development to implementation and operations. Accordingly, the criteria for judging AI performance in the SI industry are presented as needing to consider not only revenue from new AI businesses, but also the impact on project execution productivity and the impact on proposal and contract competitiveness.

Domestic IT service companies said it is difficult to separate AI performance into a distinct revenue line, but that its impact is expanding on the ground. A Kolon Benit official said the company does not separately tally AI contributions, but it is feeling the expansion of AI-related business. The official added that inquiries about related projects are increasing and that related contracts are also rising.

A Shinsegae I&C official said AI contribution is a factor that raises the competitiveness of the overall existing IT service business rather than short-term revenue from a specific business. The official also said AI helps attract new customers and strengthens proposal competitiveness. In addition, the official said it is helping secure a foundation for mid- to long-term growth.

A GS ITM official said it is difficult to clearly distinguish standalone AI revenue from existing SI business. The official added that while the market is still at an early stage, AI is driving stronger proposal competitiveness and stronger contract-winning competitiveness. The official also said it is driving internal productivity innovation.

An Asiana IDT official said the company views AI contributions in the first half not as a simple revenue figure, but as a period of building foundational infrastructure deeply embedded in all IT businesses. The official added that AI is no longer just a new technology option. The official also said AI is being applied as an essential element across individual system-building and operations businesses.

Hanjin Information and Communication said its AI business is in an active investment phase aimed at preparing for future growth and maximizing technological value, but added that it has entered the early stage of generating full-fledged profits. The company also said AI project orders continue to be secured in security-sensitive industries such as enterprise-level companies and hospitals.

NDS said interest in adopting AI at the government level is rising, and that cases in which AI-related items are included in project orders are clearly increasing. The company also expects the related business to expand gradually and said it is preparing personnel and technology in advance to respond.

Source: IT DAILY · Yang Seung-gap
Original: https://www.itdaily.kr/news/articleView.html?idxno=241296

References

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