Generative artificial intelligence (GenAI) is transforming the banking sector, bringing new opportunities for innovation and efficiency. With the promise of adding between 200 and 340 billion dollars to the annual value of the global industry, the strategic implementation of this technology could transform the way banks and financial institutions operate.
However, choosing the right operating model is critical to making the most of this technology. In this article, we’ll explore the best practices for selecting and implementing the ideal Generative AI operating model for your business.
Understanding the operating model for GenAI
An operating model refers to the way a company structures and manages the integration of technology into its operations. The choice of this model is crucial, as it directly affects the efficiency and success of the implementation of Generative AI. A well-selected model can guarantee:
- Operational efficiency: a well-structured model ensures the efficient allocation of resources and effective coordination between different departments and systems within the organization.
- Flexibility and adaptation: the flexibility of the operating model is essential to ensure that AI will adapt to technological changes and new market demands.
- Risk management: a suitable operating model helps to minimize the risks associated with adopting Generative AI, such as integration failures and security issues.
Operational models for implementing Generative AI in the financial sector
The model adopted by a business can vary from a centralized approach, where one department controls Generative AI, to a fully decentralized model, in which different areas of the company have the autonomy to do so.
Highly centralized model: In this model, the management and coordination of Generative AI is centralized in a specific team, offering control and consistency. This approach allows for the uniform development of skills and the definition of clear guidelines. However, there can be a disconnect with the business units, which can make it difficult to integrate the technology with the specific needs of each area.
Centrally led model, executed by business units: Here, the centralized GenAI team leads strategy and development, while the business units are responsible for executing the solutions. This model facilitates integration and support throughout the company, promoting closer collaboration between the parties involved. However, the need for approval from the business units can result in delays in implementing the technology.
Business unit-led model with central support: In this model, the business units lead the implementation of Generative AI with centralized support for resources and guidelines. This facilitates the adoption of the technology and aligns the solutions to local needs. However, coordination between different units can be challenging, and there can be variations in the development and application of the technology between the different areas.
Highly decentralized model: Each business unit or department is responsible for its own Generative AI initiatives. This model offers great flexibility and customization, allowing each area to adapt the technology to its specific needs. However, there can be challenges related to integration and coordination between the different systems and processes, as well as a possible lack of access to best practices and centralized knowledge.
Each approach has different benefits and challenges. However, in the financial sector, most institutions prefer a centralized model, as studies show that 70% of companies that have adopted this model have advanced in their use of technology, compared to only 30% of those that have opted for a fully decentralized model.
Criteria for selecting and evaluating the GenAI operating model
Choosing and implementing a Generative AI operating model for banks and fintech requires a careful analysis of several areas, taking into account internal and external aspects, such as:
1. Alignment with strategic objectives
- Definition of goals: Establish clear objectives for the implementation of AI, such as improving operational efficiency, developing new financial products, or innovating existing processes.
- Needs analysis: Identify the organization’s specific needs in terms of the resources, technology, and capabilities required for successful implementation.
2. Assessment of the operating model’s capacity
- Resources required: Assess whether the model can support the scale and complexity of implementing Generative AI. This includes the availability of specialized talent, adequate technological infrastructure, and necessary data.
- Flexibility and scalability: The model must allow for adjustments and expansions as the technology and the organization’s needs evolve. The ability to integrate new functionalities and adapt to market changes is essential.
3. Integration and compatibility
- Compatibility with legacy systems: Check that the GenAI technology is compatible with the organization’s existing systems. It may be necessary to update or adapt old systems to ensure efficient integration.
- Interoperability: Ensure that Generative AI can interact and communicate effectively with other technologies and platforms. Developing interfaces and integration protocols may be necessary to ensure smooth operation.
4. Security and privacy
- Data protection: Implement strict measures to protect data from unauthorized access and leakage. Use advanced encryption, strict access controls, and frequent audits to ensure data integrity.
- Regulatory compliance: Make sure your AI implementation complies with privacy and data protection regulations, such as the GDPR. Compliance is essential to avoid penalties and maintain customer trust.
5. Talent management and training
- Recruitment and retention of experts: Attract and retain highly qualified professionals in GenAI and data science. Collaborating with academic institutions and investing in continuing education programs can help ensure the availability of specialized talent.
- Continuous development: Promote the continuous development of staff skills to keep up to date with the latest innovations and best practices in AI. Training and certification programs are key to preparing staff for new challenges.
6. Evaluation and continuous adjustment
- Monitoring and measuring performance: Establish metrics and performance indicators to evaluate the effectiveness of Generative AI. Use this information to identify areas for improvement and adjust solutions as necessary.
- Feedback and iteration: Collect feedback from users and stakeholders to continually refine AI solutions. Creating feedback channels and carrying out periodic reviews are crucial to ensuring the ongoing relevance and effectiveness of the technology.
Ensuring the success of Generative AI in the financial sector
To maximize the potential of this technology, financial institutions must consider the pace of innovation, their organizational culture, and the evolving regulatory environment. Adaptive decisions are essential to unlock new opportunities while facing the challenges of adopting emerging technologies.
Success depends on continuous learning, strategic adaptation, and a well-defined operating model. Companies like Luby, with extensive experience in financial software, help implement robust Generative AI solutions, ensuring sustainable results over the long term.
