making, Strategy.

Most businesses depend entirely on Cloud Computing (CC) and Artificial Intelligence (AI) technologies to safeguard and manage resources. Since every company owner wants to automate their activities, the need for

great flexibility and scalability while being relatively easy to administer. With time, people started interacting with different technologies increasingly, becoming dependent on new developments. The CC industry is growing daily to achieve automated procedures that aid in improving operations, saving expenses, and accelerating corporate development [1], [2].

According to current predictions, the worldwide public CC industry is expected to rise at a Compound Annual Growth Rate (CAGR). According to Shift research, eighty-one percent of businesses are using cloud infrastructure, which is expected to increase in the coming years. This circumstance makes it evident that to compete in today's market, most organizations are becoming digital and introducing cutting-edge CC technology. Information Technology (IT) is thus essential for efficiently arranging corporate structures and developing future market strategies [3], [4].

With the invention of novel innovations and the rising demand for digital services, the IT industry has seen tremendous growth in recent years. The industry is expected to expand as technology becomes more pervasive in our everyday lives. Therefore, it is essential that the business closely monitors the factors impacting the cloud system [5], [6].

intricate and multifaceted real-world issues [7], [8]. Using MCDM techniques, decision-makers may compare and contrast several options based on endless criteria, then choose the option that receives the most incredible score while considering the requirements. Generally speaking, the development of MCDM techniques aims to tackle two critical difficulties in decision-making [9], [10]: 1) determining the significance of the choice criteria, and 2) ranking or prioritizing a set of options about the requirements. A significant development in management science and related subjects has been the development of MCDM techniques [11]-[14].

I. Identify the critical barriers of AI and cloud systems in implementation with IT industry systems.

II. Identify the critical strategies for overcoming these barriers.

III. We developed an MCDM methodology named the Evaluation-based on Distance from Average Solution (EDAS) method to rank the alternatives.

The EDAS method is an MCDM methodology used to run the alternatives from various options. This study used this method to rank the key strategy to overcome the barriers to implementing AI and cloud systems in the IT industry [15]-[18]. Fig. 1 shows the steps of the EDAS MCDM method.

Phase 1. Build the decision matrix.

The decision matrix is built between a set of criteria in the key barriers and a set of strategy to overcome the barriers to implementing AI and cloud systems in the IT industry.

a11 ⋯ a1n

A = [ ⋮ ⋱ ⋮ ] , i = 1,2, … , m; j = 1,2, … , n. (1)

am1 ⋯ amn Phase 2. Compute the average solution.

∑i=1 aij

Phase 3. Compute the positive and negative distance.

The positive and negative distances from every alternative are computed for each beneficial and non-beneficial criterion as:

max (0, (aij − Vj))

ij

max (0, (Vj − aij))

ij

max (0, (Vj − aij))

ij

max (0, (aij − Vj))

ij

Phase 4. Compute the criteria weights.

Phase 5. Compute the weighted Pij and Nij.

Phase 6. Compute the weighted normalized Pi and Ni.

max(WPi)

max(WNi)

Phase 7. Compute the appraisal score.

i i i

This section introduces the results of the EDAS method for identifying key strategies of the key barrier in implementing AI and cloud systems in the IT industry. Ten key barriers are used in this study, as shown in

Phase 1. Build the decision matrix.

The decision matrix is built between a set of criteria in the key barriers and a set of strategy to overcome the barriers to implementing AI and cloud systems in the IT industry. The decision matrix is built by using Eq. (1).

Phase 2. Compute the average solution.

The average solution is computed by using Eq. (2).

Phase 3. Compute the positive and negative distance by using Eqs. (3)-(6). The cost criterion is negative. The positive and negative distances from every alternative are computed for each beneficial and non-beneficial criterion, as shown in Tables 1 and 2.

Phase 4. Compute the criteria weights as shown in Fig. 3. The technological barriers have the highest weight.

Phase 5. Compute the weighted Pij and Nij.

The weighted positive and negative distances are computed by using Eq. (7) and Eq. (8) as shown in Tables 3 and 4.

This study changes the criteria weights under different cases to show the rank of alternatives under different cases. We proposed 11 cases in criteria weights. In the first case, we put all criteria in equal weight. In the second case, we put one criterion with 0.12 weight; the other criteria are equal, as shown in Fig. 5. Then, we used the EDAS method to rank the alternatives under different cases. Fig. 6 shows the appraisal score under different cases. The results show the rank is stable, as shown in Fig. 7.

This study proposes a decision-making model for identifying key barriers when implementing cloud systems and AI in the IT industry. We proposed an MCDM method logy to rank the alternatives. The EDAS method is an MCDM method used to rank the options. Ten barriers and ten strategies are used in this study. The mean method is used to complete the criteria weights. The technological barrier has the highest weight—the EDAS method starts with the decision matrix between criteria and alternatives. Then, the positive and negative criteria are computed. Then, the weighted normalized positive and negative criteria are calculated by multiplying the criteria weights by the positive and negative criteria. The cost criterion is negative, and all other criteria are positive. The EDAS shows that alternative 4 is the best and alternative 6 is the worst. The sensitivity analysis is conducted to show the stability of the results. There are 11 cases proposed in this study. The first case has equal weights; in the other case, we put one criterion with 0.12 weight, and the equal criteria are equal. The results show the rank of alternatives under different cases is stable.

No financial support was provided by any external entity for this study.

The authors confirm that there are no competing financial or personal interests that could influence the outcomes or interpretations of this research.