Читать книгу Artificial Intelligence for Asset Management and Investment - Al Naqvi - Страница 22

Based on my work, the American Institute of Artificial Intelligence offers a model for transforming a company to the fourth stage. This model is based on the strategic factors discussed above, which serve as the underlying assumptions. For instance, the model assumes that management views the firm as a complex system composed of interconnected capabilities, where each capability has an individual role and a collective role. Secondly, the model proposes companywide adoption of the scientific method to run the company.

As shown in Figure 1.1, the vertical dimensions of the model are based on the value chain of a firm. The model shown here is for a general investment firm, but it can be realigned and reconfigured in accordance with the unique nature of the firm (e.g., private equity or wealth management). Each value driver of the value chain has a specific goal. For example, the goal of alpha and risk is to generate alpha while managing risk in a firm. The goal of cost optimization is to decrease costs across the company. Note that the value drivers are not mapped as departments (e.g., operations, marketing, or sales). They are listed as capability areas. These capability areas can affect one or more departments. It is important to recognize that the functional departments–centric models—leftover from the twentieth-century bureaucratic organization—are no longer deemed necessary in the modern organization. I will explain that in Chapter 2. The capability areas are more consistent with the strategic goals of the entity, and it is assumed that each capability will tie in to one or more functional area organizations. Most importantly, the vertical dimension of strategic cohesiveness represents the strategy of the firm.

FIGURE 1.1 The AIAI Core Model

The horizontal dimension is composed of the scientific method, and it represents the operational excellence and execution potential of a firm. The scientific method is adopted to implement an industrial-scale enterprise machine learning approach for managing each function. The core processes of the scientific method include six competency areas of design, data, modeling, evaluation, deployment, and performance. Each one of those machine learning competency areas is independent of the vertical capability areas of the value chain. These competency areas are geared toward designing and developing machine learning solutions at an industrial scale.

When combined, the firm becomes a factory where AI artifacts are developed, deployed, nurtured, managed, and decommissioned at the end of the life cycle. Each of the artifacts plays a role in creating value for the business and is designed to be efficient and effective. The efficiency and effectiveness are determined in comparison to human performance or the performance of another machine and compared to the goals of the firm and the state of the competition and technical potential in the marketplace.

With both strategic excellence and operational excellence, firms are operated as research and science organizations—with every functional area transitioning to science-centric management. Thus, terms such as sales science, marketing science, human resources science, and supply chain science refer to the transformed organizations that are driven and led by data-centric planning and execution.