AI Solutions for Optimal Task Planning: The Necessity and Impact of Implementation

Abstract

Amid the growing interest in the application of artificial intelligence (AI) in the business sector, it becomes increasingly important to examine both the necessity of implementing such technologies and the accompanying business transformation. This study focuses on analyzing the relevance and need for AI adoption in commercial enterprises, with a particular emphasis on its impact within the domain of project planning and management. The expected positive outcomes of AI integration are explored as a key factor driving this necessity. The analysis is grounded in existing literature, including both theoretical frameworks and empirical research, and is supported by examples from various industries.

Introduction

In recent years, the interest in implementing tools using artificial intelligence has been clearly growing in the field of innovative technologies. The compound annual growth rate (CAGR) of the global AI market for 2025-2034 is estimated at 19.2%, rising from $638.23 billion in 2025 to $3,680.47 billion in 2034. The global market for AI accelerators is forecasted to grow at a CAGR of 29.4% from 2024 to 2030. Furthermore, the market for AI communication tools is projected to grow by 30% between 2022 and 2035. The primary goal of such implementations is to enhance overall business efficiency by automating specific processes.

This trend is characterized by several distinct features, which are widely represented in existing research:

• Perception Issues: The challenges of how AI implementation is perceived by top management, mid-level managers, and regular employees.
• Integration Challenges: The tasks associated with seamlessly integrating new tools into the existing business ecosystem, which includes collaboration, communication, and network interactions.
• Impact on Task Management: The influence of implementation on traditional task management processes in a dynamic market environment, including psychological, temporal, and economic factors.
• Industry-Specific Applicability: Assessing AI's relevance in specific economic sectors and geographical markets, such as retail, new product development, and supply chain management.
• Optimal Implementation Processes: Developing the best process for implementation and business transformation to avoid disrupting established practices.
• Evaluating Implementation Effects: Assessing the effects of implementation that are linked to business transformation.
• Risk Assessment: Evaluating and mitigating risks associated with changing the current business model.
• AI for Project Management: Applying AI as a tool to improve project management efficiency and reduce operational costs.

While these studies effectively illustrate the issues, they often present only specific aspects of AI implementation rather than a complete, aggregated picture. From a business perspective, all these factors must be considered together, as each holds significant weight. Strategic management decisions regarding AI implementation can only be effective if they account for as many factors of the upcoming transformation as possible.
Research Goal and Objectives

The goal of this study is to form a consolidated, aggregated overview of the challenges of AI implementation in commercial companies, considering both management processes and industry-specific features. The focus is on the management of projects, tasks, and business processes.

The objectives are to demonstrate, based on previous research, the necessity of AI adoption and its associated challenges, considering aspects like business transformation and the specific effects of implementation in various industries.

Methodology

The methodology of this study is based on the analysis of previous works on the topic, including literature reviews, theoretical developments, and empirical research. To obtain the most complete picture, existing works were analyzed from the perspective of project and business management, as well as the specifics of individual industries. The study is structured as follows:

• Factors of Necessity and Business Transformation
• Effects of Industry-Specific Use

1. Factors of Necessity and Business Transformation

The current market environment is characterized by a high degree of dynamism, demanding rapid decision-making and adaptation. These business conditions, in turn, accelerate this dynamic even further, creating a need for the rapid creation of competitive advantages as a basis for development. From a business process standpoint, this high dynamism requires high efficiency in management, particularly in projects.

Furthermore, the complexity of business projects is increasing. Project and process management now involves handling vast amounts of real-time data. Repetitive, non-automated tasks, inefficient budgeting, and incorrect resource and task allocation can negatively impact operational results. Traditional project management methods may no longer be as effective in such an environment.

The use of LLM-based tools contributes to effective business operations in these conditions. For instance, about 15% of work tasks can be completed much faster, and with specialized software built on LLMs, this figure rises to about 56%. Other studies show that AI tools have led to a 20% reduction in task completion time, while risk forecasting has reduced project costs by 15%. The findings on project metric improvements from one such study are detailed:

• Identification of increased risks 20%
• Forecasting and elimination of negative factors 15%
• Increased accuracy of task planning 30%
• Increased accuracy of expense planning 92%
• Improvement in project results 25%

Improvement in Project Metrics as a Result of AI Application (Parekh & Mitchell, 2024)

Conversely, it is clear that if a company fails to integrate such solutions, it faces a high probability of losing its competitive position. This implementation must happen relatively quickly, as any delay can lead to customer loss and a subsequent drop in sales. However, business transformation requires significant effort in planning changes and adapting processes for innovation.

There is a certain complexity associated with business transformation for AI use. According to one assessment, up to 80% of AI implementation scaling projects in 2025 may fail. However, these studies often do not consider the use of external services, which can mitigate the following implementation problems:

1. Development Costs: Additional expenses for developing in-house AI tools can be substantial.
2. Budgeting Complexity: In-house development requires constant adjustments to the company's expense plan.
3. Support and Maintenance: The need to maintain a dedicated support and debugging team puts pressure on financial performance.
4. Easier Integration: Integrating an AI tool is simpler when its functionality is already understood, rather than being developed from scratch. A clear understanding of an existing tool's capabilities can also significantly shorten its acceptance time by top management.

When undertaking transformational changes, it is also important to recognize that AI tools are not always the best choice. A study comparing project planning for a mobile app by humans versus AI yielded interesting conclusions:

• AI may formulate tasks without necessary detail, overlooking some important aspects and risks (AI formulated 25 tasks, while a human formulated 175).
• AI might not account for non-obvious project context factors, such as market conditions.
• AI may inaccurately establish task interdependencies, their sequence, and execution time.

The authors concluded that AI serves as a good assistant for creating an initial project template that requires further refinement.

Thus, we can conclude that:

1. AI undoubtedly brings value to project and task management by enhancing resource allocation, forecasting, risk assessment, and processing large data sets.
2. Refusing to use AI may lead to the loss of market position and competitive advantages.
3. At the same time, an AI tool is not a panacea for all problems; the most effective approach appears to be targeted implementation for specific company functions (e.g., distributing work hours among employees).

2. Effects of Industry-Specific Use

When considering the necessity and benefits of AI implementation, it is crucial to examine its use within specific industries. This is important for several reasons:

• A. Specificity: Each industry has its own unique characteristics and, therefore, applications for AI capabilities.
• B. Value Variation: The value of AI implementation can differ across economic sectors. For example, the hospitality or construction sectors may require exceptional precision in resource planning and employee scheduling.
• C. Implementation Complexity: The difficulty of integrating AI tools can vary depending on the specifics of business processes.
• D. Universal Solutions: The applicability of universal solutions can differ across various fields of activity.

Financial Sector

In the financial sector, AI tools can be applied across a wide spectrum of business functions, including customer interaction, planning, automation, sales, and marketing. The dynamic external environment is particularly significant for this sector. Therefore, AI-assisted planning and decision-making are valuable applications. This time-saving advantage enhances business process efficiency and helps build a competitive edge. A study of financial institutions in Ethiopia showed a significant correlation between strategic innovation, strategic planning, and financial performance.

Supply Chain Management

The application of AI in this industry includes procurement, manufacturing, warehousing, distribution, and demand forecasting. A survey conducted among logistics industry representatives in Austria in April 2024 found that 52% of respondents already used some AI tool, and another 18% planned to implement one. According to the survey results, AI is most in-demand (already used or planned for use) in functional areas such as planning and forecasting, transportation management, and route planning. Overall, AI is most sought after for automating and improving various planning and forecasting functions, and least in the area of communication automation. The most anticipated effects from AI implementation are cost reduction, improved decision-making quality, and increased productivity.

Hospitality Sector

Hotel management is characterized by high demands for quality in room occupancy planning and optimal staff task allocation to reduce operational costs. A study on an Artificial Multiple Intelligence System (AMIS) for staff task management demonstrated significant improvements. The system's productivity is driven by AI like as Voiset, which includes real-time data processing, feedback loops, and workload balancing based on heuristic analysis. Testing showed the system reduced the average room servicing time by 50% and increased the on-time task completion rate to 99%.

By Final Performance Indicators of the AMIS System Implementation (Pitakaso, et al., 2025) On average, the system's implementation improved the company's operational activities by 21%. Room preparation time was reduced from 45 to 21 minutes, and the task completion rate increased from 91.5% to 99.2%.

Construction

Construction work requires the coordination of many elements: material delivery, technological work cycles, personnel employment, and documentation. Proper work planning is crucial to balancing resource utilization and availability. A study on the expansion of San Diego International Airport used AI to optimize construction phases while the airport remained operational. The AI model, trained on flight data and airport layouts, determined the optimal construction phases to balance costs, time, and operational downtime. The AI model produced calculations in about 1 second, compared to 15 minutes for the Simmod Pro simulator. The forecast provided various construction options with costs ranging from $8.2 to $13.4 million, demonstrating AI's effectiveness in choosing an optimal plan.

New Product Development

Developing new products requires considering a huge number of factors from both external and internal environments. Some studies show that 61% of companies using AI for new product creation launched truly innovative products and achieved higher revenues compared to those using traditional methods. However, only 29% of small and medium-sized businesses express readiness to implement AI for this purpose. A survey of 47 Irish companies revealed that the intention to use AI is highest (over 45%) in areas like idea generation, market analysis, and product design. The most valued perceived benefit of AI implementation is increased productivity (32.3%), followed by greater flexibility in responding to changes (26.9%) and faster time-to-market (25.9%).

Conclusion

The AI tools industry is relatively young, yet it is already generating significant interest from the business community, which sees opportunities to increase operational productivity and improve task planning. It has been shown how the implementation of AI solutions contributes to more effective problem-solving compared to traditional methods. In addition to optimizing operations, AI solutions are designed to create a sustainable competitive advantage in a rapidly changing market environment.

However, challenges remain, including potential management distrust of AI results and the need for substantial investments in business transformation.

Considering the above, the optimal solution appears to be the implementation of AI at the level of individual business processes and functional blocks. For example, AI tools can be used for product design, modeling different project implementation scenarios, and optimizing the distribution of tasks and resources. With this approach, as demonstrated by the results of empirical studies, there are excellent opportunities to increase the efficiency of business processes and improve the quality of market interaction.

Directions for Future Research

This paper has only examined a few industries with specific geographical characteristics. Further analysis of other industries and markets could provide a more accurate and complete picture. Nevertheless, despite potential differences, it seems effective to implement AI in universal functions up to a certain level of complexity. For instance, task planning is a vital business function in any industry, albeit with varying degrees of importance and complexity. In any case, an approach that considers AI implementation from the perspective of business value, industry specifics, and the functional characteristics of individual departments within a company appears to be optimal.