AI-Driven Decision Support Systems in Agile Software Project Management: Enhancing Risk Mitigation and Resource Allocation

Abstract

Agile software project management (ASPM) serves modern industries to conduct iterative development of complicated code bases. The decision-making process in Agile environments regularly depends on individual opinions, creating ineffective results for risk management and resource distribution. Artificial intelligence (AI) is a promising approach for handling these challenges by delivering data-based choices to project management. This research introduces an AI-based decision support system for improving risk reduction and resource distribution in ASPM. The system merges optimization frameworks and predictive analytics to enhance operational decision efficiency. The machine learning solution anchors data evaluation using AI models that simultaneously predict risks and strengthen decision power for resource scheduling. This analysis relied on project records and recent operational data to perform model validation and training procedures. Tests determined how the framework performed against contemporary Agile project management systems by measuring the completion speed of sprints, resource management practices, and risk prediction accuracy. The framework demonstrated better performance by predicting risks and simultaneously maximizing resources utilized during projects. The proposed framework outperformed traditional Agile applications, achieving 94% accuracy in risk identification and enhancing workload management by 25%, leading to an 18% improvement in sprint completion rates and overall project efficiency. These findings confirm that AI-driven decision support systems (DSSs) are crucial in enhancing Agile project management by enabling proactive risk mitigation and optimized resource allocation. By integrating AI-powered decision-making, the framework empowers organizations to improve project outcomes, streamline resource management, and facilitate the adoption of AI-driven methodologies within Agile systems.

1. Introduction

Today, the traditional development of complex dynamic projects uses Agile software project management (ASPM) as the standard method. The flexible structure of Scrum and Kanban enables Agile models to present profound team decision problems that need a connection between requirement changes and priority management with schedule deadlines. Decision-making about projects within these settings depends on evaluation through experience, instincts, and subjective opinion, while performance limitations occur due to these methods [1]. AI implementation can solve these problems, enabling teams to utilize decision tools explicitly made for Agile principles. Project teams gain real-time precision from decision support through AI tools that reveal pattern findings in large datasets. Machine learning models predict sprint results, measure team effectiveness, and detect emerging problems to boost fast responses and project success outcomes in both short-term and long-term horizons [2]. Organizations use artificial intelligence technology as they adopt it into their Agile project management systems to handle the complexity of modern software systems. Agile project management faces higher operational complexity in expansion projects that include multiregional teams because dependency coordination and workforce management face challenges with persistent communication needs. AI systems help teams work together more effectively through previous project data analysis to allocate work and through forecasting abilities that evaluate priority backlogged items for essential goal assessment [3]. AI proves profitable in software development because of the increasing priority on data measurement tools such as velocity, burn-down charts, and defect rates. AI-based project progress tracking requires metrics to run automatic plan adjustments so project requirements updates and customer expectation changes can be delivered. Among Agile projects, any form of quick adjustments proves essential due to their necessity to respond rapidly to changing project environments [4]. AI-based decision support within Agile project management extends beyond its ability to enhance operational speed because it delivers enhanced benefits. AI frees team members from manual operations through predictive functionality and helps them achieve inventive solutions without prejudiced human decisions. Organizations need to integrate AI because software development requirements now consider it necessary for practice. Agile methodologies partnering with DevOps practices create competitive benefits and necessary capabilities [5].

The iterative structure of ASPM introduces complex risk management needs that must be addressed through adequate resource management approaches. Businesses using uncertain conditions must change operational tasks and decide new priorities because Agile principles require quick adaptation. The actual delivery of program adjustments implemented through ASPM generates resource management problems alongside risks because of inaccurate project execution [6]. Accurately forecasting upcoming issues is a top priority when performing risk mitigation for essential project milestones. The time needed to detect risks from Agile framework feedback systems results in increased remedy expenses until later development phases start. Predictive analysis tools fail to detect specific problems because they experience delayed deadlines, unrecognized dependencies, and technical issues [7]. The execution of Agile project resource management causes serious difficulties to emerge. Members of distributed organizational function teams practice regular multiple-duty distribution throughout their team structure. Primary team performance failures develop from exhausted colleagues and resource misallocation, leading to quality reductions in both teamwork and production output. Project teams face resource conflicts because of bad resource management practices when team priorities force resources to compete [8]. The increasing complexity of software systems exacerbates these challenges. Experts today must work together on modern projects to share their technological knowledge through different systems. Remote distributed projects that operate across various time zones encounter worsened complications, as they need to balance economic resource optimization with urgent risk evaluation needs [9]. Poor visibility becomes an issue for organizations regarding resource distribution capabilities and risk management needs. Organizations achieve rapid development and flexibility through Agile methods, but they do not supply enough details about resource utilization and risk evolution. The inability to properly track resources with their linked risks through a unified system results in poor decision-making by stakeholders who lack project management insights [10]. Short-term goal delivery within Agile promotes teams to move away from proactive threat prevention measures. A sprint-focused goal-setting process adopted by teams results in delayed proactive planning, producing technical problems that create future development risks and scalability difficulties [11]. Addressing these problems requires predictive analytics systems and real-time resource monitoring tools. AI-based solutions allow upcoming risk detection while delivering precise analytic findings to optimize decision-making processes that preserve multi-year objectives. Another project advantage of teamwork stems from data analysis that supports Agile methods and minimizes vulnerable situations within projects.

ASPM has become widely popular because it solves dynamic requirements problems, but significant gaps exist regarding its limitations, including risk prevention, resource management, and decision-making processes. Modern software systems require more than existing analytical tools because their decision-making is based on historical data and subjective human input. The existing gap becomes vital because projects grow larger and utilize cross-team structures with disparate technologies [12]. The primary deficiency of existing research centers on the inadequate development of data-driven prediction tools designed specifically for Agile project management operations. The adoption of AI techniques remains in its initial stages when used for ASPM. The existing risk identification methods and resource optimization systems, together with project priority adaptation, remain limited for real-time implementation [12]. Traditional risk assessment frameworks neglect the methodology of Agile development since they concentrate on managing conventional projects, not Agile projects’ decentralized workflow [13].

The fundamental aim of this investigation involves creating an artificial intelligence-powered system to boost decision processes and manage risks and resources within ASPM. The framework answers crucial dilemmas between iterative operation and dynamic software project realities through dynamic real-time analysis that fits Agile environments.

• To leverage AI to provide actionable insights for prioritizing tasks, optimizing sprints, and addressing potential risks before they escalate.
• To develop predictive models to identify risks early in the project lifecycle, enabling proactive interventions.
• To implement dynamic algorithms to balance workloads, resolve resource conflicts, and improve team productivity across distributed and cross-functional teams.
• To provide a holistic evaluation framework, combining data-driven metrics like velocity and burn-down rates with qualitative factors such as team morale and collaboration.

The proposed system uses AI to build a framework combining adaptive resource control with real-time progress observation and custom Agile algorithm forecasting. Such a framework boosts risk transparency in Agile iterations by utilizing project data at both historical and real-time levels through its multi-level risk assessment approach. The system adapts resource management mechanisms according to changing project needs, so workflows get appropriately distributed and operational shortcomings remain minimal. The system uses quantitative Agile metrics and qualitative evaluation methods, providing detailed data-based solutions for Agile project assessment and decision-making.

This paper contains the following structure: Section 2 examines DSS research and AI implementation in Agile methods and risk management strategies with resource distribution methods. The section lays out the proposed framework built with AI, which includes explanations regarding the design foundation, architectural structure, and core elements. The research methodology presentation contains information about data acquisition methods alongside evaluation method specifications and validation procedures. The experimental segment of Section 4 evaluates how the developed framework performs against Agile tools that exist today. The framework analysis includes significant findings, system implications, and restrictions in Section 5. This study ends with Section 6, which summarizes the research outcomes and presents its significant contributions alongside future research possibilities.

2. Related Work

Data analysis through DSSs enhances decisions made during software project management. The technology enables project managers to better plan, monitor, and control activities, specifically when working in unpredictable, fast-changing environments [14]. The initial use of DSSs involved static modeling systems designed to assist with resource scheduling, price estimation, and project planning tasks. These processes accessed data through pre-set algorithms and operated DSSs, which proved successful during Waterfall model projects but proved inadequate when Agile methods with adaptive workflows entered the scene [15]. The adoption of Agile methodologies drove businesses to look for flexible DSS solutions. Today, DSSs incorporate real-time data evaluation to assist teams in quick response to priority shifts, resource distribution, and risk prediction across multiple project iterations. Database systems equipped with machine learning technologies evaluate past project data in combination with current performance measurements to predict upcoming delays and identify impediments that they can use to make goal-oriented solution recommendations [16]. DSS development brought predictive analytics, visualization tools, scenario simulations, and Agile project management platform integration. DSSs enabled by predictive analytics provide teams with outcome forecasting, which helps anticipate sprint velocity and defect rate risks [17]. Through visualization tools, stakeholders receive real-time dashboards containing their project key performance indicators to maintain transparency across communication and team interaction [18]. Project managers can assess the consequences of their decision-making through scenario simulations for tasks and resources when working with Agile methodologies [19]. The integration of DSSs with Jira Trello and Azure DevOps enables users to exchange project data in real time to track progress [20]. The implementation of DSSs in Agile environments incorporates various dilemmas. The integration process for DSSs often proves difficult because it matches existing workflows alongside current tools but mainly impacts smaller teams that lack technical expertise. The predictive accuracy of DSSs depends heavily on quality historical data volume, but such assets may remain unavailable when dealing with new projects and uncommon sets of requirements [21]. Software project management will benefit from emerging AI and natural language processing (NLP) technologies to advance the capabilities of DSSs in the future. Using AI technology in DSSs enables analysis of unorganized data sets, including meeting documentation and communication records, which deepens project dynamic understanding. Customized DSSs created for particular team organizational setups and project needs will improve acceptance and performance since they improve system adaptability and applicability [22]. AI delivers its most valuable use in Agile through predictive analytics technologies. Project forecasting, together with risk assessment, receives support from machine learning models that analyze past data, including sprint velocity results, backlog progress numbers, and defect variation manifestations. Teams gain the ability to respond to emerging issues early through prediction so they can resolve problems before they create major disruptions, thus improving project performance [23]. AI plays a central role in deciding which tasks should be addressed first. AI algorithms analyze backlog items through customer requirements, team capacity, and historical project performance data. Dynamic task priority management from AI allows Agile teams to prioritize urgent work first, one of the Agile delivery pillars for stakeholders [24]. AI technologies assist with resource management, which results in transformative effects on Agile practice implementation. Tools integrated with AI technology ensure ideal resource management through systems that detect underused team members and overloaded colleagues to achieve the best team productivity. AI technology allows organizations to balance team resource use and efficiently track dependability within distributed work environments [25]. AI technologies are essential for boosting joint work patterns between Agile teams. NLP technology processes free-form data in forms such as meeting transcripts and messages to extract meaningful information for project use. The sentiment analysis enables teams to discover communication problems, whereas summaries generated by automation tools extract important information from long discussions to boost team-level alignment [26]. IT usage with AI is expanding throughout Agile testing routines and quality assurance operations. The testing frameworks that use AI automation create test cases and detect irregularities alongside defect probability predictions, which shortens manual testing periods. These Agile-compatible tools automatically fit into development pipelines, leading to uninterrupted delivery of premium-quality solutions [27]. Agile project risk mitigation faces specific obstacles because of its adaptive and iterative methods. Agile frameworks use flexibility and responsiveness instead of long-term planning as their core principles, so unforeseen risks may occur if proper management is not in place [28]. Risk identification that takes place in advance serves as the core basis for Agile risk management strategies. Agile teams use sprint retrospectives, daily stand-ups, and backlog reviews to identify risks early in the project lifecycle. Traditional methods to assess risk depend too much on subjective human evaluation methods that result in inconsistent risk evaluations and incomplete risk evaluation tasks. Modern risk management tools that utilize combined quantitative and qualitative data detection techniques have emerged to enhance risk assessment, according to [29]. The practice of dynamic risk assessment stands as a main priority in Agile projects. The monitoring of sprint velocity and defect rates, plus task completion trends, enables teams to spot unusual developments that suggest new risks. Today’s Agile tools utilize machine learning models that analyze past data to forecast upcoming potential risks so development teams can respond instantly [30]. The Agile project frameworks place strong importance on group-based risk management. A primary feature of Agile teams involves transparent communication that allows teams to discuss and solve risks together through functional collaboration. The combination of risk boards and visual dashboards helps group collaboration through transparent risk monitoring that permits team participation in risk reduction initiatives [31]. Agile teams achieve successful risk management by applying a method that requires them to order risks according to their potential impact level and likelihood probabilities. The Agile development process now employs risk burndown charts and failure mode and effects analysis (FMEA) to manage critical risks affecting sprint performance. The applied methods successfully handle vital risks without disrupting project dates and goals [32]. Agile risk mitigation continues to integrate automation technologies to handle its operations. Systematic risk monitoring tools alongside artificial intelligence algorithms support teams by tracking ongoing risks while generating potential solutions for mitigation. Some platforms perform automatic resource redistribution and adjustment of sprint targets upon detecting risks, enhancing team rapidness and flexibility [33]. Project success heavily depends on proper resource distribution of personnel along with tools and budgets for software project management. Multiple approaches that enhance resource allocation have evolved to solve issues involving workload equilibrium bottleneck management and team performance enhancement [34]. Historically, resource allocation used Gantt charts and resource leveling algorithms as manual planning tools, yet these worked well only in predictable linear workflows. Modern Agile and hybrid project management methodologies produce too much dynamic change for these traditional methods, which lack sufficient adaptability to meet requirements and priority changes [35]. The current methods of resource distribution use information-based approaches to support better decision outcomes. Resource optimization algorithms deploy historical data and real-time metrics to forecast workload patterns, determining equal resource distribution across tasks. The utilization of machine learning models examines performance-related team data to locate personnel who need help or have excessive workloads, thus enabling immediate adjustments [36]. Resource allocation’s core elements include task scheduling and dependency management. Managers can maximize resource utilization through tool-based dependency mapping and scheduling algorithms that show interrelated tasks while providing resource allocation strategies. These technical solutions shorten the project duration by preventing task bottlenecks that increase project flow efficiency [37]. Distribution across teams and international projects adds complexities to resource allocation since it involves coping with geographical and cultural factors. Cloud-based resource management platforms are essential for collaborative resource planning because they serve large-scale projects. Online dashboards from these platforms present managers with real-time views to examine resource availability, perform live changes in allocation, and witness current project development [38].

The improvements in resource management techniques have not resolved all existing difficulties in resource distribution. Lack of clarity about resource utilization generally affects organizations with multiple teams, leading to efficiency problems and teamwork conflicts. Using resources between short-term sprints and long-term project goals demands strategic planning with predictive models to achieve the correct balance of key personnel [39]. Software development projects must use their resources best during every development phase. Most existing management frameworks monitor active changes instead of predicting future needs while working exclusively with historical data. Such an approach results in delayed risk handling with sections of resource inefficiency and lost chances for more efficient resource utilization. The project industry fails to exploit forecast models that could potentially enhance operational results [39]. A significant shortcoming exists because of insufficient tools combining quantitative and qualitative performance measurements into a single platform. Modern project management solutions supply project measurement metrics, including velocity scores, burn-down rate indicators, and defect statistics. However, they cannot measure essential team-related quality indicators like employee spirit, workgroup performance, and adaptation capabilities. A lack of equilibrium within project teams creates incomplete understanding during decision-making, especially for Agile projects, because teams strongly affect success [40]. Modern instrumentations are unable to expand their capabilities across different project sizes. Existing frameworks that manage resource allocation and risks were built for small to medium-capacity teams so they cannot adapt to the complexities of extensive multi-team endeavors. Organizations implementing distributed hybrid Agile frameworks now require tools that can effectively extend across various teams in geographically dispersed locations and different time zones [41]. The current resource frameworks lack clear visibility about their structure together with user-friendly interfaces. The advanced system requirements of many tools create barriers because experts in technical skills and training are necessary for their operation. Consequently, these tools remain out of reach for small organizations. The adoption rate of project management tools will grow if developers create easier-to-use interfaces and user-friendly workflows [42].

The current frameworks do not manage to match iterative tasks at short notice with established strategic targets. The speed-oriented delivery model of Agile methodologies creates problematic technical debt, which causes reduced scalability alongside misalignment with organizational goals. The development of complete frameworks is essential to achieve strategic foresight together with iterative flexibility to overcome this gap [43]. Adaptive frameworks must be developed to address such gaps through integration with AI by combining predictive analytics features and scalability capabilities. Future development tools can bridge identified gaps between teams and systems to enhance project work efficiency and collaboration, resulting in improved organizational navigation of software development complexities.

3. Proposed Framework

3.1. Design Philosophy and Objectives of the Framework

AI and machine learning operate within this framework to improve decisions, resource distribution, and risk monitoring in the ASPM domain. Its doctrine emphasizes adaptability, scalability, and user friendliness to fulfill Agile principles and resolve essential risks.

The system supports advanced decision-making by merging predictive analysis with real-time visibility of risks, task progress data, and resource availability levels. The framework provides dynamic resource management, reducing operational constraints and maintaining efficient work assignments across different tasks. Risk visibility improves because this framework unites records with current data for detecting risks in their early stages.

The platform offers smooth integration with Agile programs Jira and Trello to support mutually helpful teamwork and visibility through dashboards that display real-time notifications. It features extensive project management capabilities alongside team distribution management while providing single-use interfaces for customizable report outputs.

This framework intelligently solves the problems of traditional Agile tools, delivering tremendous project success, improving team operational efficiency, and filling existing gaps.

3.2. Framework Architecture and Key Components

The proposed framework solves risk reduction problems, resource use, and decision processes through an Agile methodology-compatible modular design approach. Three essential components comprise the framework: the first element is an AI-Powered Risk Mitigation Module, the second part is a Resource Optimization Engine, and the third provides Agile Process Integration Tools. This system merges its component modules into an instant information delivery service with predictive analytic suggestions.

3.2.1. AI-Powered Risk Mitigation Module

The built-in module automatically employs machine learning models to detect risks and their measurement reduction methods. System risk measurements arise from integrating historical project real-time performance data with predictive computational processes.

The risk score $R$ for a given task or project, the component is computed as:

$$R=P\cdot I$$

(1)

where:

• $P$ is the probability of risk occurrence (predicted using logistic regression or decision trees).
• $I$ The risk’s impact is estimated based on project parameters like budget deviation, time overrun, or resource availability.

NLP technology within the module analyzes non-structured information in team documents, such as communications and notes, to detect potential risks. The analysis measures emotional sentiment to recognize team dissatisfaction that later results in productivity problems or team resource conflicts. The system’s core features comprise real-time risk dashboards, alerts for critical risks, and risk burndown charts to help manage sprint priorities.

3.2.2. Resource Optimization Engine

The Resource Optimization Engine directs resource distribution according to workload information, task importance, and available team member capacity. The optimization system executes allocation methods to optimize workflow bottlenecks while maintaining balanced workloads.

The linear programming model solves an optimization problem to reach the goal.

$$Maximize:\sum _{i=1}^n{U}_i$$

(2)

subject to:

$$\sum _{j=1}^m{R}_{ij}\le C_i, {\forall }_i$$

(3)

where:

• $U_i$ is the utility derived from resource $i$.
• $R_{ij}$ is the resource allocated to task $j$ by resource $i$.
• $C_i$ is the capacity of resource $i$.
• $n$ is the total number of resources and $m$ is the total number of tasks.

The engine distributes work assignments while focusing on crucial projects using the most competent resources throughout the project duration. This system considers task dependency relationships to reduce time delays. Three main features of this engine include real-time task adjustment dependent on completion rates, future utilization forecasting to avoid resource shortages or excess, and built-in adaptability to handle personnel changes in the project team.

3.2.3. Integration with Agile Processes

The framework operates without disruptions because it automatically integrates with primary Agile tools Jira, Trello, and Azure DevOps through application programming interfaces (APIs), which connect project teams to its functions. The framework integrates Agile platforms using APIs with plugins, establishing real-time communications between the framework and Agile platforms.

The main connectivity functions within the framework support sprint planning, task observation, and teamwork capabilities. Backlog refinement sessions become more efficient through the framework by employing automated task prioritization through historical project information and team response feedback. Real-time dashboards use actionable data to monitor task progress together with velocity and burn-down rates that benefit the team members. Through platform connectivity to tools, including Slack or Microsoft Teams, risk alerts get transmitted quickly.

The integration module implements a relational database that synchronizes data between various tools. Its structure implements three main tables for tasks, team members, and dependencies, keeping data consistent system wide.

3.3. Interaction Between Risk Mitigation and Resource Allocation

The integrated framework links risk mitigation with resource allocation as sequential processes, which boosts decision-making abilities within Agile projects. The Risk Mitigation Module powered by AI simultaneously reviews project risks through cross-analysis of present-time and past performance information to deliver risk scores. The Resource Optimization Engine prioritizes distribution according to risk scores, which the system generates dynamically. The system uses this integration to strategically assign resources that can prevent forthcoming project blockages and delays. How resources are allocated will affect the risk levels faced by the organization. The framework evaluates new risk exposures while updating mitigation plans according to resource reassignments to handle urgent project needs. Risk management and resource distribution undergo an adaptive process through the feedback loop, aligning them with current project circumstances for improving Agile workflow resilience.

3.4. Workflow of the Framework

Through its cyclic system, the framework enables smooth cooperation with Agile to generate real-time data that support managers in their decision-making process, risk reduction, and resource optimization efforts. The established workflow framework contains five stages that enable smooth Agile integration and boost decision-making platforms, risk management instruments, and resource allocation platforms. Data collection marks the first phase of the framework by merging two elements: contemporary Agile tool records from Jira and Trello, along with project history data. To conduct analysis, operators need metrics from the sprint velocity and backlog status metrics alongside measurements of resource utilization. The Risk Mitigation Module uses AI to apply the equation $R=P\cdot I$ for determining risk scores through calculations of P multiplied by I factors during risk identification. The system shows high-priority risks on dashboards, which directs teams to respond immediately.

During the third stage of resource optimization, the system applies linear programming to perform dynamic resource distribution, optimizing workload distribution while addressing bottlenecks. The project benefits from the best possible use of resources while progressing through its life span. The fourth stage integrates the insights from the framework into Agile workshops to back critical Agile activities, especially sprint planning sessions, daily stand-ups, and retrospectives. Continuous dashboards and advice systems feedback help teams choose targets that support effective collaboration. The last stage involves ongoing project tracking through the framework to adjust analytics models that use measurement results for guided strategic decisions about subsequent iteration development. Agile projects achieve their goals and maintain transparency and efficiency through this repeating work process. The selection of model parameters in the proposed AI-driven decision support framework was guided by empirical evaluation, domain expertise, and optimization techniques to ensure effective risk prediction and resource allocation. The key parameters were determined through historical data analysis, iterative testing, and optimization methods.

Historical project data analysis was the basis for categorizing tasks as high, medium, or low. The study concentrated on recognizing risk-oriented patterns from previous performance distributions and their reasonable impact on project performance. The implementation selected $\tau =0.7$ the threshold determining priority mitigation and resource allocation should be directed toward tasks with risk probabilities exceeding 70%.

Weight values needed to be defined for the resource distribution process to balance high-priority task fulfillment workload management and project schedule obligations. A selection process included multiple weight combination tests through iterative cycles and optimization at each stage with the grid search approach. Multiple trials of empirical testing ended with the final establishment of weight parameters: risk factor influence at $\alpha =0.5$, workload balancing at $\beta =0.3$, and deadline adherence at $\gamma =0.2$. Such a resource distribution strategy prioritizes risk reduction while upholding operational efficiency and planned project durations.

Optimization of the risk prediction module occurred by selecting ideal machine learning model hyperparameters like learning rate with batch size and training iteration numbers. The Bayesian optimization approach and cross-validation analyses served to select parameters from accurate Agile project records. Model performance and convergence achieved their best results through final value selection. A learning rate 0.01 was chosen to achieve proper training speed and accuracy. A batch size 32 was used to ensure stable model training and reduce overfitting possibilities. To achieve stable training without significant cost increases, the model ran 1000 iterations.

Empirical evaluation of Agile sprints enabled the development of constraints for the resource allocation optimization algorithm through maximum team capacity conditions and definitions of task priority scales. Teams worked with five simultaneous tasks at their maximum capacity to keep individuals from being overwhelmed yet maintain efficient work production. A setting of 0.8 for the risk-weighted task priority factor allowed critical tasks to achieve priority handling without causing workflow disturbances.

Figure 1 illustrates the cyclic workflow with stages for data collection, risk identification, resource optimization, Agile integration, and feedback loops.

3.5. Tools, Data Sources, and Evaluation Metrics

The proposed framework depends on sophisticated tools with multiple data sources and strong evaluation parameters to succeed in ASPM implementation. Every element of this framework helps teams make decisions based on data while minimizing risks and efficiently using resources for Agile projects.

3.5.1. Tools

The framework merges well-known Agile platforms and tools to gather and analyze current data streams. The Agile framework uses Jira (v9.4.11) with Trello (v2024.2.1) and Azure DevOps(v2024.1.0) to collect sprint metrics that combine backlog statuses with task progress monitoring. During data analysis and visualization, the framework relies on Python libraries and machine learning frameworks to create predictive models and display results, such as Pandas 3.9, NumPy (v1.24.2), Matplotlib (v3.6.3), TensorFlow (v2.11.0), and Scikit-learn (v1.2.1). Additionally, Power BI (v2.124.2024.0) and custom dashboards present live data alongside project tracking, generating simple insights that Agile teams can easily convert into actions.

3.5.2. Data Sources

The analytical system needs to process historical and present-time data information. Multiple historical elements contain task completion trends, defect logs, and past sprint velocity statistics to support predictive model training. Live Agile tool data are the source for current information about work status alongside team resource use and ongoing sprint activities. An NLP processing system analyzes emails and chat transcripts for qualitative analysis, including results on virtual team sentiment. The multi-source database yields complete insights about project developments. The 80–10–10% data split was chosen to ensure a balanced framework evaluation. The 80% training data enable effective learning, the 10% validation set optimizes model tuning, and the 10% test set ensures reliable performance assessment.

3.5.3. Evaluation Metrics

Agile project management framework performance is assessed through quantitative and qualitative measurements to evaluate its influence on Agile project management. Key evaluation metrics are included. A thorough examination of the proposed framework takes place to determine its effectiveness and operational efficiency performance. The preservation of project content is tested through the Structural Similarity Index (SSIM), which evaluates how well project structural information exists after risk mitigation processes. Evaluation of vital project components safeguards them from deterioration during risk management activities performed during implementation. The predictive accuracy levels for resource management and task ranking success are evaluated using mean squared error (MSE) and classification accuracy measurements to prove Style Fidelity.

Evaluating resource utilization requires balancing team workload and resource efficiency measurements to determine the framework’s influence on distribution effectiveness metrics and productivity rates. Risk Mitigation Accuracy evaluation of the framework depends on assessing its ability to correctly predict and solve risks through the Area Under the Receiver Operating Characteristic Curve (AUC-ROC), a primary metric to verify predictive reliability.

Agile project efficiency is measured using sprint velocity results, burn-down rate tracking, and defect time measurements. The tools used for User Satisfaction assessment include survey feedback and subjective assessment methods to allow team members to rate both framework usability and process impact. Dynamic assessment tools connected with multidirectional data sources and various testing methods produce practical findings that lead to measurable improvements in all stages of the ASPM framework.

4. Results and Analysis

The testing of the research framework through quantitative assessment confirmed its precision levels for Agile project management challenges by ordering tasks for resource deployment and scaling operations. According to the data in figures and tables, the developed framework shows improved performance compared to current solutions.

Task complexity directly influences processing time based on the data presented in Figure 2.

Task complexities lead to processing time changes of near equal rate because the framework exhibits excellent computational capabilities. The framework proves its capability to uphold performance standards during project execution of complex tasks.

The research framework underwent quantitative testing to determine its successful implementation for solving Agile project management issues regarding task scheduling, resource deployment, and project growth. The results indicate that the framework delivers better results than traditional methods.

Figure 3 highlights the robustness of the framework’s predictive models. The accuracy remains above 90% even as the data volume increases, showcasing the model’s ability to scale effectively with larger datasets.

Figure 4 compares the framework’s predictions for resource usage against actual observed data. The close alignment between the two validates the framework’s precision in predicting optimal resource allocation, reducing bottlenecks, and ensuring balanced workloads.

Figure 5 demonstrates the framework’s scalability. The evaluation shows minimal degradation in performance when applied to teams ranging from five to fifty members, making it suitable for small and large Agile projects.

Figure 6 compares the proposed framework’s task prioritization accuracy with that of baseline tools. The framework outperforms other tools by maintaining an accuracy of 94%, improving task alignment with project priorities.

Table 1. Precision, recall, and F1-score of framework predictions.

Metric	Proposed Framework	Existing Tools
Precision	0.93	0.81
Recall	0.91	0.78
F1-Score	0.92	0.79

provides quantitative metrics for the framework’s predictive accuracy in risk identification. The high F1-score of 0.92 indicates that the framework balances precision and recall, ensuring effective risk mitigation.

Table 2. Processing time for risk analysis across varying data sizes.

Dataset Size (Tasks)	Proposed Framework (s)	Existing Tools (s)
100	0.21	0.37
500	0.25	0.51
1000	0.30	0.67
2000	0.35	0.95

shows how the framework handles datasets of different sizes. The processing time remains competitive, with an average speed of 0.3 s per task, making it suitable for real-time applications.

Table 3. Resource prediction efficiency: framework vs. baseline approaches.

Metric	Proposed Framework	Baseline Methods
Workload Balance Improvement	25%	12%
Resource Idle Time Reduction	30%	15%
Task Completion Time Reduction	18%	10%

compares the proposed framework’s resource allocation efficiency with existing methods. The presented framework achieves a 25% improvement in workload balance and a 30% decrease in resource idle time, according to the study results.

The analysis presented in this section underscores the efficiency and scalability of the proposed framework. The framework is compatible with current Agile project management needs because it provides real-time processing and accurate evaluation of multiple metrics. The framework assessment focused on its effectiveness in risk management and resource optimization for Agile project management operations. The results demonstrate how the framework effectively detects risks, prioritizes them, finalizes solutions, and maintains fair resource allocation.

Figure 7 in this section presents the framework’s assessment process, through which risks are identified and ranked according to their assigned scores. Teams can spot their most crucial risks by the prominent red labeling of high-priority risks in the system.

The framework demonstrates its capacity to distribute workloads properly, as shown in Figure 8. The framework’s dynamic resource allocation mechanism prevents excessive workloads and underutilization of team members, producing higher productivity results.

The proposed system attains higher accuracy for risk detection with lower false positives while decreasing resolution time by 37.5% compared to existing tools, as shown in

Table 4. Risk mitigation metrics: accuracy, resolution time, and false positive rates.

Metric	Proposed Framework	Baseline Tools
Risk Identification Accuracy	94%	81%
Average Resolution Time (hours)	4.5	7.2
False Positive Rate	6%	15%

.

Table 5. Resource utilization improvement using the proposed framework.

Metric	Proposed Framework	Baseline Methods
Average Team Workload Balance	92%	78%
Resource Idle Time Reduction	34%	18%
Task Completion Rate Improvement	22%	11%

highlights the framework’s efficiency in resource utilization. The system now achieves 92% workload balance and cuts idle time by 34%, proving its advanced resource management qualities.

According to the result analysis, the framework effectively minimizes risks while optimizing available resources. The risk prediction module attains precise and efficient performance levels based on heatmap visualizations and ROC curve analyses. At the same time, the framework enhances team productivity through resource workload distribution assessments and improvement metric measurements.

The proposed framework proved superior to established tools through performance tests demonstrating benefits for Agile project management. The results confirm substantial sprint performance enhancements, improved backing logistics and defect handling, and higher user contentment levels.

The framework demonstrates its ability to deliver sprint completion promptly, as shown in Figure 9. The proposed model performs better than existing tools because it produces more successful sprint completions in successive project iterations.

After implementing the proposed framework, the completion rate of backlogs shows a concerning growth, according to Figure 10. Backlog completion rates improved because the framework delivered better capabilities for task prioritization and resource allocation.

The proposed framework shows better results in resolving defects by reducing the duration compared to traditional methodologies, as depicted in Figure 11. The framework’s implementation speeds up the defect resolution process and shortens resolution times by an average of 35%.

The performance evaluation of the entire framework uses Figure 12 for assessing metric-level results. The proposed framework outperforms competitors in risk identification, task prioritization, and resource optimization.

The proposed framework outperforms existing tools according to

Table 6. Comparative analysis of metrics: proposed framework vs. existing tools.

Metric	Proposed Framework	Existing Tools
Sprint Completion Rate	96%	83%
Backlog Completion Rate	92%	78%
Defect Resolution Time (hours)	3.8	5.9
Risk Prediction Accuracy	94%	81%

because it achieves higher sprint and backlog completion rates, faster defect resolution times, and improved risk prediction accuracy.

The proposed AI-driven decision support framework demonstrated varying levels of effectiveness depending on different Agile project conditions. The risk prediction model performed best in high-uncertainty environments, where frequent changes in requirements required early risk detection. The resource allocation optimization module significantly improved workload balance and reduced idle time in multi-team projects. Meanwhile, the real-time adaptive decision-making component enhanced sprint efficiency in time-sensitive Agile sprints.

Table 7. AI strategy performance under different Agile conditions.

Agile Condition	Best-Performing AI Strategy	Key Performance Metrics	Impact on Agile Workflow
High Uncertainty and Frequent Changes	Risk Prediction Model (ML-based)	94% accuracy in early risk detection	Enabled proactive mitigation, reducing sprint disruptions
Multi-Team Resource Sharing	Optimization-Based Resource Allocation	25% workload balance improvement 30% idle time reduction	Prevented bottlenecks and ensured efficient task distribution
Time-Sensitive Agile Sprints	Real-Time Adaptive Decision-Making	18% increase in sprint completion rate	Allowed dynamic adjustments to evolving priorities

presents a detailed breakdown of the performance of AI-driven strategies under different Agile conditions, supported by key evaluation metrics.

The proposed AI-driven decision support framework’s scalability is a critical factor in its applicability to large-scale and distributed Agile projects. While our study primarily evaluated its performance across different team sizes, further analysis demonstrates that the framework can scale efficiently to support multi-team, cross-geographical Agile environments. The framework achieves this through dynamic resource allocation, real-time risk assessment, and cloud-based integration with Agile tools such as Jira, Trello, and Azure DevOps, facilitating seamless collaboration across distributed teams.

Table 8. Scalability of the proposed framework across different Agile environments.

Agile Environment	Scalability Considerations	Framework Adaptability Features	Expected Impact
Small Teams (≤10 members, co-located)	Low computational demand Minimal integration complexity	Lightweight AI models Local deployment support	Enhanced decision-making with minimal overhead
Medium-Sized Teams (10–50 members, hybrid setup)	Moderate computational load Partial resource sharing	Cloud-based deployment API integration with Agile tools	Improved resource allocation and risk mitigation
Large-Scale Teams (50+ members, multi-project setup)	High computational demand Cross-team dependencies	Scalable optimization algorithms Automated task prioritization	Increased efficiency in workload distribution and cross-team coordination
Distributed Agile Teams (Multi-location, remote collaboration)	Need for real-time data synchronization High complexity in communication	Cloud integration AI-driven task prioritization based on real-time updates	Seamless collaboration and enhanced transparency across teams

presents an assessment of the framework’s scalability based on key project characteristics, highlighting its adaptability to larger, more complex Agile environments.

The survey collects user reactions about framework usability and its impact on effectiveness, as shown in

Table 9. User satisfaction survey results for usability and impact.

Category	Proposed Framework	Existing Tools
Usability (1–5 scale)	4.7	3.9
Task Prioritization Impact	91%	72%
Resource Optimization Impact	89%	68%

. User feedback indicates better satisfaction with the new framework’s usability, resource optimization features, and task prioritization features than older tools.

The study compares its performance against cutting-edge techniques in employee attrition prediction and workforce optimization through

Table 10. Comparison of proposed framework with state-of-the-art techniques.

Methodology	Decision Support	Risk Prediction Accuracy (%)	Resource Optimization Efficiency (%)	Real-Time Adaptability	Integration with Agile Tools
Traditional DSS [14]	Rule-based models	75	68	Low	Limited
Machine Learning-Based Agile Risk Mitigation [7]	ML-based risk analysis	85	78	Medium	Partial
AI-Assisted Resource Management in Agile [44]	Neural network-based scheduling	88	85	Medium	Limited
Proposed Framework (AI-Driven Decision Support System)	AI-based predictive analytics	94	92	High	Seamless (Jira, Trello, Azure DevOps)

. The evaluation considers essential criteria, including model precision and readability, scalability, bias minimization, and practical adoption in human resource operations.

The proposed framework offers greater benefits than other Agile project management methods. The system proves its worth as an Agile transformation tool because it produces high rates of sprint and backlog completion, fast defect resolution, and satisfied users.

5. Discussion

The proposed framework delivers an all-encompassing solution that advances decision-making processes, risk management functions, and resource distribution within ASPM. The main contribution integrates AI predictive analytics into system operations to identify risks and deploy resources ahead of time. The framework delivers better results because it uses top-tier algorithms in all significant areas of sprint completion rates, backlog control, and defect remediation. The framework provides practical deployment, enabling compatible operation with present Agile tools. Implementing decision support based on metrics improves immediate and future project performance. This framework produces key operational impacts when ASPM teams implement it. Thanks to this system, teams can make proactive work adaptions by foreseeing task-related data regarding resources and priorities. The real-time dashboards enhance operational clarity, strengthening team member interaction with stakeholders and their colleagues. Agile principles define operations that combine Agile agility retention with empirical research methods that boost operational productivity. The framework provides two significant strengths of precise measurement and adaptable features, making it an effective solution for current Agile operational challenges in complex distributed or large-scale projects. The framework requires specific attention to various limitations which affect its implementation. The main obstacle involves obtaining high-quality data to facilitate accurate predictions while generating valuable insights. Job-related data imperfections and inconsistencies lead to diminished performance outcomes from this framework. Limited technical abilities among Agile teams force them to encounter excessive computational costs when managing predictive models. Technical teams need specific expertise to implement this framework with Agile tools through proper configuration because it presents a technical challenge to teams without much technical experience. The proposed AI-based decision support system’s computational requirements are associated with the model complexity, project data size, and real-time analysis frequencies. The framework combines predictive analytics and optimization features through a system that optimizes accuracy while maintaining efficient computation. Managerial teams with restricted processing capabilities can deploy the framework through cloud solutions or mixed deployment systems to distribute their computing load. The model supports low-end hardware configurations with lightweight architectures, providing real-time decision capabilities to Agile teams working in restrained resource situations. The framework succeeds in regular Agile situations, yet further research is needed to verify its compatibility with combinational methodologies that exceed conventional approaches.

6. Conclusions and Future Work

The proposed AI-based decision support framework delivers a complete system to enhance decision-making risk management and resource distribution across ASPM. Predictive analytics, combined with optimization methods, allow this framework to help Agile teams recognize risks beforehand and distribute their resources effectively, thus enabling better decisions in changing project conditions. The system delivers enhanced results for sprint performance tracking, backlog management, and defect resolution because of AI-based insights. The framework integrates easily with Agile tools to support live decision systems, resulting in better project visibility. Agile project management encounters less uncertainty because the framework offers practical predictions that help teams improve workload optimization, resource planning, and risk response plans. Time-responsive dashboards improve team-member transparency, which allows project stakeholders to work more efficiently and adapt better. The framework offers precise outcomes while retaining flexibility, which makes it appropriate for Agile projects that are complex, distributed, and large in size.

Some essential obstacles remain to overcome within the framework. Project prediction accuracy depends on high-quality, consistent data, due to which incomplete or inconsistent project data can create difficulties in accuracy outcomes. AI models tend to generate performance challenges that organizations with limited technological capabilities face when implementing them. Integrating the framework with Agile tools requires specialized technical skills to implement it effectively since new AI systems remain challenging for teams with basic AI knowledge. Future work will investigate the development of simplified deployment methods and usage guidelines to benefit teams whose staff members lack technical abilities. The system will offer pre-made integration packages, self-executing setup tools, and clear step-by-step user documentation that enables trouble-free system use. Future work aims to develop hybrid Agile project methodologies because the framework needs validation across more project management situations. The optimization process will target reducing computational requirements to achieve real-time AI-driven decision support systems available for organizations with modest resource capabilities. Through its solved issues, the framework enhances its ability to support Agile project management through artificial intelligence, thus offering efficient decisions for numerous project contexts.