Beyond a Buzzword: Turning AI into Real Business Value

AI has evolved into a critical technology that changes how organizations operate. However, to be truly effective, AI implementation requires clear planning and governance.

The development of artificial intelligence (AI) technology began as early as the 1950s but has grown rapidly only in the last decade. Supported by larger computing resources, more adequate e-commerce transaction data with the existence of big data, and better AI algorithms, AI has become one of the most transformative technologies for the business world. From merely technology experiments in innovation labs, AI has now become a key enabler of modern business strategy. However, this extraordinary potential can only be realized if organizations implement AI in an orderly, planned, and structured manner. Without proper governance, AI risks becoming just a technology showcase — expensive yet failing to deliver real business value.

AI: From Experimentation to Business Strategy

Many organizations begin their AI journey as experiments: building chatbots to answer simple questions, creating machine-learning-based dashboards, or trying predictive models for limited needs. Unfortunately, many stop there. AI is treated only as a “technology project” for show or proof of concept, without a clear long-term strategy.

This view must now be changed. AI is no longer just a technology experiment but has become an integral component of business strategy, much like cloud, data, and cybersecurity technologies have become the foundation of digital transformation. Properly managed AI technology implementation enables organizations to automate business processes, improve decision-making quality, create more personalized customer experiences, optimize costs, and even discover new business models.

However, to make AI part of strategy, AI systems must be designed, tested, monitored, and optimized with governance principles like any other technology and business asset.

AI Implementation Requires Solid Planning

Successful AI implementation is not determined by sophisticated technology launched randomly but by a phased, safe, and value-driven approach. Several early steps are needed before AI is implemented.

1. Set a clear vision and objectives: AI must be directed at solving real business problems and delivering measurable outcomes — for example, increasing fraud detection by 30% or accelerating customer claim processing time by 50%.

2. Start small, but with the right framework: Pilot projects should have a limited scope but be implemented with full governance standards: documentation, auditability, monitoring, and risk assessment.

3. Consider risk and compliance: Use a risk-based approach from the start. Assess the impact of AI on privacy, bias, security, and organizational reputation.

4. Build a collaborative culture: AI is not just a technology team project but requires involvement from risk management, legal, human resources, operations, and customer experience teams.

5. Ensure AI personnel competency readiness: Organizations must ensure the sufficiency of personnel skills involved in AI development and operation to ensure that AI systems are designed, developed, implemented, operated, and maintained safely.

A gradual but safe implementation prevents major failures at early stages while building internal confidence that AI truly delivers value to the organization.

Risk Management Standards and Frameworks for AI

As AI adoption increases, international standards and regulations are also evolving to ensure AI is used ethically and responsibly.

ISO/IEC 42001:2023 AI Management System (AIMS)

ISO/IEC 42001:2023 is a new AI Management System standard released at the end of 2023. This standard provides a framework for organizations to design, implement, maintain, and improve a responsible AI management system. Its principles include identifying internal and external contexts, planning AI implementation, AI objectives, AI risk assessment, AI system impact assessment, internal audit, management review, and continual improvement.

NIST AI Risk Management Framework (NIST AI RMF)

The concept of trustworthy AI, as defined by NIST, is AI that is valid, reliable, safe, transparent, fair, accountable, and privacy-respecting. These principles form the basis of many AI governance frameworks globally.

AI Governance Guide by the Indonesia Financial Services Authority (OJK)

In Indonesia, the Financial Services Authority (OJK) has released an AI Governance Guide for Indonesian Banking, emphasizing that AI must be governed throughout its lifecycle — from design, development, and use to decommissioning — considering consumer risks, financial system stability, and legal compliance.

Organizations starting their AI journey today must ensure that their programs align with these references, even when not yet legally required.

Key Enablers: Data, Architecture & Infrastructure

No AI implementation is effective without a strong foundation. At least three main enablers support structured, planned, and governed AI:

1. Data Readiness

AI is only as good as the data used. AI systems heavily depend on data sources and the availability of quality data to train and test AI models. Organizations must ensure data is high-quality, relevant, structured, clean, and privacy-protected. Without adequate data governance, AI models may fail to meet intended goals.

2. Enterprise Architecture

IT systems and business processes must be designed with AI as an integral component. Integration between systems, interoperability, and workflows supporting automation are critical. Alignment between business objectives, applications, data, infrastructure, and security also needs attention.

3. API/MCP & Cloud Infrastructure

Modern AI requires substantial computing power, access to advanced model services, and the ability to access data from various sources. Well-designed APIs or Model Context Protocols (MCP) and cloud infrastructure enable AI to evolve quickly, securely, and cost-effectively.

Investing in these three enablers accelerates AI time-to-value and allows adequate total cost of ownership management.

Relevant Use Cases

Structured, planned, and governed AI systems have proven to deliver real value across sectors, especially in financial and operational domains.

In banking and financial services, AI technology is used for:

• Fraud detection: Predictive models detect suspicious transaction patterns in real-time, preventing significant losses.

• Credit scoring: More accurate and inclusive credit risk assessments, even for previously unbanked borrowers.

• Customer service: Chatbots and virtual assistants answer customer queries 24/7 with high satisfaction.

• Risk management & compliance: Monitoring market risk, identifying regulatory violations, and automating audit reports.

Outside finance, AI improves operational efficiency through predictive maintenance, demand forecasting, and process automation — proving that well-governed AI boosts revenue and lowers costs. 

This article was published in our quarterly newsletter Valoka Volume 2 No.2, August 2025.