Unlock AI Benefits: Establish Separate Units for Optimal Impact - Essay Sample

Introduction

AI benefits are omnipotent, and it is crucial to understand that AI does not work in silos. It can be seen as a horizontal enabler of a company instead of a vertical customer-focused business unit (Constantiou & Kallinikos, 2015). It is capable of impacting the internal processes of the company while creating new products and improving the existing ones (Parry, Cohen & Bhattacharya, 2016). Therefore, according to Logg et al. (2019), companies must establish separate units that become the central enabling points of AI across the company. Horizontally enabling AI in businesses is crucial in satisfying both the employee and consumer needs, as both are highly important for strategic organizational development and management (Logg et al., 2019).

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AI can also perform well in the areas of automation, creation of new products as well as improvement of the existing products (Logg, Minson & Moore, 2019). Robotic automation has been applied in areas where tedious and repetitive tasks are involved (Ferràs-Hernández, 2018). AI, however, may not perform well in complex tasks that require a cognitive understanding of the human mind (Brynjolfsson & Mitchell, 2017). It can successfully undertake tasks that are specifically designed for it in specified environments; hence, it is, therefore, crucial that managers understand the scope and the boundaries of AI before planning such efforts (Constantiou & Kallinikos, 2015).

For instance, just like electricity touched every company around the globe, AI will eventually touch it. AI will eventually become part of the human existence in the end, and humans will inevitably embrace it fully, just like other technologies, which were invented and accepted unconditionally. Certainly, AI has the advantage of having superior properties of quantitative, analytical, and computative aspects and hence would become an important resource even in areas where many controversies have been seen as in the case of automation of industrial processes (Bettis, 2017; Constantiou & Kallinikos, 2015).

AI strategies will be different in every organization but would need to answer similar questions (Brynjolfsson & Mitchell, 2017). The core components of AI would include data, algorithms, and infrastructure that would require high levels of skill and organization (Huang, Rust & Maksimovic, 2019). The top managers of companies' and organizations' ambitions are grounded in more practical and achievable benefits (Ferràs-Hernández, 2018; Nag et al., 2007). These benefits are pursued across the three stages of cognitive development including increased productivity, strengthening the regulatory compliance through automation, and helping organizations derive meanings from large data sets (Huang et al., 2019). These factors are viewed as the driving forces of organizational development and progress. AI holds a special part in strategic organizational planning and development according to Huang et al., (2019).

Before implementing an AI strategy, the organization must be prepared for AI, and it implies that the organization evaluates and design the development processes for AI (Brynjolfsson & Mitchell, 2017). Best AI practices must be aligned including the preparation of the teams to provide business value, and the readiness of processes for the machine learning workflow (Huang et al., 2019; Logg et al., 2019). Best practices in AI allow companies to increase their competitiveness in the market, without compromising their moral values and ethics as they met customer needs and demands. They should also ensure that the AI practices follow the set rules and practices that govern its development and implementation according to Brynjolfsson & Mitchell, (2017).

Additionally, understanding the machine learning workflows is central to enabling AI in the company as it follows iterative processes that have outcomes that could be uncertain. One can use tools, for instance, AI project canvas to evaluate business potentials, but it may not guarantee specific outcomes (Parry et al., 2016). Certainly, the exploratory nature of AI makes it very hard to follow the measurements of company goals (Logg et al., 2019). Besides, diverse perspectives from teams are crucial for enabling AI strategy in decision-making (Watson, 2017). Corporate teams need to develop AI strategies that involve more functionality and would require a lot of domain knowledge to develop a feasible AI (Huang et al., 2019). Moreover, large groups of people with different roles in the corporation are often required (Brynjolfsson & Mitchell, 2017). This enables that the specific AI workflows are smooth and that any technicality faced is handled in time to evade huge cost implications.

According to Intezari and Gressel (2017), AI works best in companies as a collaborative function that utilizes trustworthy data and processes to complement a defined business strategy while aligning it with organizational values and culture. Business priorities should be the foundation of AI and would differentiate it from others through alignment of strategic priorities with the technologies that are emerging to drive the innovative scale of the company (Constantiou & Kallinikos, 2015). AI gives companies the ability to collect and process data rapidly this offering new opportunities that reshape business operations (Huang et al., 2019).

Similarly, through AI applications, businesses can find patterns and draw specific insights from data, thus, enabling the synthesis of better decisions (Parry et al., 2016). Application of AI in customer relationship management, fraud detection, underwriting, social media monitoring among others is enabling companies to better strategize the direction of the decisions to suit the company’s goals (Ferràs-Hernández, 2018). It has enabled companies to make better decisions in terms of talent development and human risk management (Watson, 2017). Also, according to Ferràs-Hernández (2018), it enables strategic managers to build a more resourceful AI that is reliable and convenient in accomplishing strategic goals and objectives of the organization.

AI also has the potential of transforming processes of identifying and developing talent necessary for organizational progress (Brynjolfsson& Mitchell, 2017). Through the utilization of data generated from questionnaires, AI could aid in pinpointing the external prospects and identify existing employees that would suit the new roles (Huang et al., 2019). Organizational strategic decisions often revolve around the development of a growth strategy that is innovative based on growing of talents, and AI could provide such data when the organization is going through specific changes (Huang et al., 2019). Talent management is crucial in the recruitment of human resources, and algorithmic capabilities of AI can provide a great insight into the type of talent that it require towards its long-term progression in management (Huang et al., 2019).

With a talented workforce, the company can gain a competitive edge in the industry in which it operates, which gives it leverage over other entities that have not adopted AI. Therefore, AI adoptions should involve partnerships between the information technology teams and entire businesses to enable it to perform maximally, hence providing needed benefits. AI ventures are successful when businesses conduct it jointly while embarking on full digital transformation (Logg et al., 2019). The business model adopted should enable transition that adjusts to the unique needs of the organization (Bader & Kaiser, 2019). Furthermore, unlocking the full AI potential means a revolution of ways of conducting business, which is only possible when all departments function collaboratively (Brynjolfsson & Mitchell, 2017). Also, according to Parry et al. (2016), AI can only solve problems identified by the organization and it requires that decision-makers collaboratively think of concrete questions and processes to be optimized and the desired outcomes to be achieved.

The literature also records that AI enables organizations to seamlessly comply with all required standards. In particular, through AI automation of compliance functions in information technology systems, companies can evade the maneuverability of human cognitive bias (Watson, 2017) and adopt machine-based robotic execution that is free of bias, mood, and subjectivity (Nag et al., 2007). Regulatory compliance has challenged companies mainly attributed to the nature of human bias (Nag et al., 2007). The objective opinions as well as differing worldviews possess challenges for people to interpret the existing rules and regulations in the same way. Certainly, this leads to conflict and a lack of transparency within the entity. However, AI algorithms do not fluid through processes like humans (Huang et al., 2019). Instead, it interprets and executes according to the literal letter of the laws according to how they are set up (Constantiou & Kallinikos, 2015). Consequently, organizations can meet all required standards efficiently.

In addition, the literature argues that strategic decisions in most companies are based on enhancing customer experience through the personalization of products and services (Logg et al., 2019). Today’s content, services, and products are designed for mass consumption (Intezari & Gressel, 2017). AI would open the possibility of customization of products and services based on the user’s individual needs, personas, wishes, and traits translating to mass personalization (Huang et al., 2019). The degree of personalization would be carried out both statically and dynamically (Brynjolfsson & Mitchell, 2017). Certainly, this would demand the use of customers’ historical information available on the internet, such as diverse websites and social media platforms. Using AI data to inform decisions making from histories is crucial in developing service and products that are highly customized as a way of improving customer experience (Parry et al., 2016). Furthermore, companies gain the ability to develop all services and products that bring value to customers, which is enabled by matching their preferences, interests, and needs to the commodities provided.

Further, research shows that asset intelligence is crucial in making strategic decisions, especially in planning (Constantiou & Kallinikos, 2015). Most companies still rely on human intelligence to interpret, sense, and anticipate information regarding company assets. AI capabilities to generate data generated from the company assets such as infrastructure, inventory, IT systems would surpass human insights as to the most critical mission for the company’s business intelligence (Constantiou & Kallinikos, 2015). Sensors that are embedded in the IoT networks, computer visions, as well as machine learning, will feed data to analytical systems in real-time (Ferràs-Hernández, 2018). AI tools that act autonomously on the resulting insights will be crucial for reconfiguring dynamic pricing, recalculating warehouse staffing projections, calibrating machines for manufacturing, and optimizing supply chains (Bolander, 2019; Logg et al., 2019).

Many researchers have agreed on the fact that strategic decisions are heavily influenced by predictions especially in market trends (Huang et al., 2019; Brynjolfsson & Mitchell, 2017; Constantiou & Kallinikos, 2015). In particular, AI predictive analytics are crucial in making informed decisions especially when predicting anticipated events through observation of da...