MDBS Invited to Speak at IBM Solutions Day

MDBS was invited to participate in this event, where Chairman Simon Chang presented a keynote titled “From General to Specialized: Building Truly Valuable AI for Industries.” He shared key trends and practical insights on enterprise AI implementation.

AI development has evolved from a stage of technical demonstration to one focused on real-world application and value validation.

In the early stages, enterprises adopted AI primarily to improve efficiency and enhance creativity. However, during actual implementation, companies began to notice a clear gap: general-purpose AI is good at generating responses, but lacks execution capability.

Simon Chang pointed out three recurring challenges enterprises face when implementing AI:
1. Difficulty integrating with complex workflows
2. Data compliance risks
3. AI remaining at the conversational level without real execution capability

These challenges have become the main barriers preventing AI from moving from concept to real deployment.

The market is now showing a clear shift—from Generative AI to Agentic AI, which is capable of executing tasks. This transition has become a key trend in industry development.

According to Simon Chang, the real challenge for enterprises is enabling AI to integrate with existing systems and actively participate in operational workflows.

AI competition has shifted from model capability to integration architecture and deployment capability. By leveraging modular models, rule-based orchestration, and hybrid cloud/on-premise deployment, enterprises can move AI beyond conversation into execution.

Taking IBM Orchestrate as an example, its skill-based integration framework connects systems such as ERP, CRM, and collaboration tools, allowing AI to become part of business processes. Its low-code architecture further reduces adoption barriers, enabling rapid deployment and validation.

AI Is Already Delivering Value Across Industries

AI applications have demonstrated real-world impact across multiple industries:
• Finance: Integrating market data, trading decisions, and account opening processes to enhance end-to-end service efficiency
• Investment:Combining chart analysis, financial reports, and real-time news to support more comprehensive decision-making
• Manufacturing: Applied to knowledge retrieval and document understanding, extending to order and production management
• Enterprise Operations: Integrating HR systems, including payroll, attendance, and administrative processes, reducing manual workload

Overall, AI is evolving from isolated tools into integrated, process-driven systems.

Governance Builds Trust in AI

In highly regulated industries, governance is critical.

IBM watsonx provides traceability and explainability, ensuring transparency in AI decision-making and strengthening enterprise trust.

Three Stages of Enterprise AI Adoption

Simon Chang outlined three key stages in AI adoption:
• Identify – Find the right entry point
Start with highly repetitive and logic-driven processes to minimize initial risks
• Pilot – Validate on a small scale
Use PoC (Proof of Concept) to verify feasibility and guide decision-making
• Scale – Expand across the organization
Extend successful use cases to more departments and scenarios

This structured approach has become a widely adopted best practice for effective AI implementation.

In other words, the real competitive advantage does not belong to companies that adopt AI, but to those that can operate AI continuously and generate real outcomes.

MDBS has long been dedicated to enterprise AI integration—from process analysis and system integration to deployment and validation—helping companies implement AI step by step.

Being invited to IBM Solutions Day not only reflects MDBS’s practical experience but also highlights a new stage in the evolution of enterprise AI applications.