## 1 Introduction  
The rapid advancement of technology has led to unprecedented business growth through mergers and acquisitions ([PERSON] et al., 2018) [1]. This growth necessitates the development of effective optimization techniques to ensure success and sustainability [2, 3]. This makes optimization a key driver for business success and sustainability strategies [2, 3]. Challenges such as the lack of real-time information, absence of value chain optimization, shortage of integrated systems, ineffective communication from decision-makers to individual workers, and isolated pockets of information prompt the need for business optimization [4].  
Organizations create a plan to achieve their objectives guided by their business strategy. The success of these objectives relies on defining clear business objectives, which are translated into key success factors using key performance indicators (KPIs) which can be calculated at different business levels, including operational, tactical, and strategic ones. Well-formulated and visualized KPIs have been shown to encourage productive business engagements and continuous improvement. However, traditional KPIs are designed using process-driven automation techniques that rely on strict rules, instructions, procedures,computations, and configurations set by industry experts and stakeholders. This approach makes KPIs inflexible, passive, and unintelligent, as they cannot adjust to changes in the business environment and cannot proactively manage performance.  
Therefore, this research is aimed at inventing a model to supplement traditional KPIs by looking at business data, identifying relationships and trends, and creating new smart KPIs (SKPIs) that dynamically adjust themselves as business evolves and data change. To achieve this, this study uses a data-driven automation model that leverages cross-functional KPIs (CFKPIs) generated within an integrated ecosystem. The CFKPIs are then validated for correlation and suitability using machine learning (ML) techniques, namely, [PERSON]'s test of sphericity and the Kaiser-Meyer-Olkin (KMO) test of sampling adequacy. This study further uses ML to implement decision engines and predictive analytics. In addition, artificial intelligence (AI) techniques of exploratory Factor Analysis (FA) were employed to identify data patterns, correlations, and significance levels of CFKPIs. This, in turn, helped to create SKPIs tailored for business optimization. To achieve this, the following research objectives were formulated:  
The first objective was to create a ground-breaking model that serves as a complementary tool to traditional KPIs.  
The second objective was to develop and implement mechanisms within the model that enable SKPIs to dynamically recalibrate in response to shifting business dynamics.  
The third objective was to evaluate the practical application and effectiveness of the data-driven (DDA) model.  
This study holds great importance in advancing the field of business optimization by introducing a new approach to KPIs. By achieving the aims and objectives of this study, the dynamic and adaptive nature of SKPIs contributes to the creation of more resilient and responsive business strategies. This ensures competitiveness and sustainability in an ever-evolving business landscape.  
The remainder of this article is structured as follows: Section 2 provides an overview of related studies on automation categories and techniques, key performance indicators, and business optimization. Section 3 introduces the methodology that utilizes statistics, ML and AI techniques, to devise an innovative model that produces intelligent KPIs for business optimization. Section 4 presents the research results, where the effectiveness of the model is evaluated through a case study. Section 5 discusses the research results, and Section 6 concludes this study with implications, recommendations, and potential areas for future research.