The Development of an OpenAI-Based Solution for Decision-Making

Abstract

This study explores the development of an Open Artificial Intelligence (AI) decision-making solution, integrating blockchain technology with artificial intelligence to streamline organizational decision-making processes. Blockchain’s characteristics of transparency, incorruptibility, and decentralized validation are leveraged to build a platform that ensures secure and transparent decision-making. The platform’s architecture integrates a user-friendly frontend with a robust backend, enabling users to create accounts, manage tasks, participate in voting, and make collaborative decisions. The backend processes, including user authentication, error handling, and secure data management, ensure privacy and integrity throughout the decision lifecycle. The implementation details include organization management, task assignments, voting mechanisms, and profile management features, each facilitated through a user-friendly frontend interface. Workflow diagrams and a case study at DADWORD IT demonstrate the platform’s efficiency in handling complex decision-making processes while maintaining user engagement and data security. In conclusion, the developed platform demonstrates the ability of AI and blockchain technologies to improve collaborative decision-making, offering a secure and scalable solution for organizational management. The system can be adapted to various industries where transparency, accuracy, and efficient decision-making are crucial. Future work may explore further AI integration to refine decision support and predictive functionalities.

1. Introduction

The process of making decisions is an innate human behavior that can have useful results. Interestingly, scientists have tried to enhance decision-making by using technological advances in computers that improve and expand human talents [1,2]. As human society evolves, decision-making remains a crucial aspect [3]. Hence, it is crucial to optimize this action to a level that is equal to the most efficient and natural approaches which are offered in today’s world. Being able to make effective decisions would most likely bring about a significant improvement in the way people live, especially for all members who are participating in decision-making [4]. Over time, decision-making and other issues of governing are being dealt with in a realistic manner, making answers about decision-making readily apparent [5]. Although there is a lack of a universally agreed-upon definition for AI, it can be seen as transforming different industries and sectors [6]. Machine learning has the capability to learn from past experiences, adapt to new inputs, and execute tasks that resemble human behavior. The phrases AI systems and artificial intelligence (AI) were initially coined in the 1950s. The power and use of Big Data have revitalized AI, and due to the quick growth of technology, we have seen improved computational storage capabilities and super-fast data processing for machines. Consequently, after a significant amount of time filled with optimism and potential, artificial intelligence (AI) has already made significant progress within leading companies [7]. Research also states that the adoption of AI-enabled solutions in organizations is growing quickly, while Daugherty and Wilson [8] say that AI is the reason for significant changes in the business world. Research states that AI can improve the decision-making of individuals and organizations [9].

The recent growth in AI systems has increased the capacity of an organization to utilize data for the purpose of making predictions and it has greatly reduced the cost related to producing forecasts [10]. According to the technology trend poll by Gartner, AI is ranked as the top strategic technology. Leveraging AI to improve decision-making, transform business models, and revolutionize consumer experiences will be key to achieving success in the digital world. According to the survey, 59% of organizations are currently collecting information to create AI-based strategies of their own. Such businesses have already started testing or using solutions based on AI. According to Gartner, organizations who are employing the next generation of AI systems will encounter the typical challenges that any unproven and unknown technology experiences. In general, several research papers and reports by prominent providers of technology and papers about the esteemed management periodicals have provided corporations with strategic and practical instructions on the ways to capitalize on artificial intelligence [11]. ChatGPT v.3.5 (also known as OpenAI) was introduced as an early version in 2022 by OpenAI. It swiftly attracted attention for its comprehensive responses across several knowledge categories and its human-like text generation. ChatGPT was refined for human interaction by reinforcement learning with human input, a technique that employs human examples to direct the model toward preferred behavior [12]. Research reports that OpenAI can be used effectively for organizational decision-making if it is fine-tuned effectively [13]. The principle of OpenAI is illustrated in Figure 1 below [10]:

The following table provides a comparison of different AI models in terms of decision-making capabilities, strengths, and weaknesses (

Table 1. Models comparison.

Feature	ChatGPT 4.0	GPT-3	BERT	T5	DeepSeek
Model Type	Transformer-based large language model	Transformer-based large language model	Transformer-based model for text encoding	Transformer-based model for text-to-text tasks	Transformer-based large language model (trained on multilingual data with strong mathematical reasoning)
Key Strengths	Conversational AI, context-aware responses	Language generation and text completion	Understanding context and extracting meaning from text	Text generation and transformation tasks	Strong reasoning abilities, mathematical problem-solving, and advanced NLP
Decision Support	Excellent for interactive decision support, brainstorming, and idea generation	Strong at writing content and responses, can aid in decision summaries	Good for understanding internal documents and reports	Good for translating and reformatting data	Excels in structured decision-making, complex problem-solving, and data-driven decisions
Data Handling	Can process and summarize large amounts of data for decision-making	Can generate high-quality summaries and creative content	Specializes in reading comprehension and context understanding	Can transform data into structured forms	Can handle structured and unstructured data efficiently, useful for technical reports and mathematical data processing
Real-Time Feedback	Provides dynamic, real-time answers and analysis	Can provide real-time responses but less dynamic	Not designed for real-time conversations, more for comprehension	More suitable for structured tasks rather than real-time feedback	Capable of real-time analysis, particularly strong in logical reasoning and structured queries
Contextual Understanding	High contextual understanding and can maintain longer conversations	Moderate understanding	Focused on extracting deep meaning from text but does not generate responses	Contextual focus is on text transformation, useful for summarization	Strong in contextual understanding, especially in technical and complex language processing
Use in Decision-Making	Suitable for generating ideas, analyzing options, and evaluating risks through natural conversation	Can provide summaries, suggest options, and predict outcomes based on past data	Ideal for analyzing and understanding reports, market analysis and feedback	Excellent at transforming data into structured reports or insights	Best for data-driven decision-making, logical analysis, and business intelligence
Adaptability	Very adaptable to industries, good for both high-level and detailed decision-making support	Adaptable for many text-based tasks but less specialized in decision-making	More suited for tasks like sentiment analysis, market research	Can adapt to a variety of tasks for transformation of textual data	Highly adaptable, especially for organizations that need mathematical reasoning, structured data handling, and multilingual NLP
Limitations	May not always provide the most accurate, detailed analysis	Can sometimes generate off-topic or imprecise information	Lacks generative capabilities, cannot provide interactive support	Performance can drop on complex tasks	May require fine-tuning for highly specialized tasks outside structured reasoning
Best Use Case for Organizations	Helping in strategic decision-making, team brainstorming, and idea generation	Writing content summaries	Analyzing customer feedback,	Automating report generation and content summarization	Optimizing business intelligence, handling structured decision-making, and advanced data processing

):

On the other hand, even though blockchain protocols have been utilized largely in the financial sector (for example, via cryptocurrencies), proper application of the technology extends beyond finance [14] into decision-making systems. For example, some organizations have formed coalitions based on blockchain technology to facilitate collective decision-making in supply chain management, voting, transportation management, and other areas [15]. Many such systems have emerged over the past few years [16]. These systems involve a wide variety of stakeholders (token holders, network validators, core and application developers, founders, etc. [15,16]), each playing an important role in the development and implementation of blockchain systems. Two key characteristics that have contributed to the widespread adoption of blockchain technology in decision-making systems are incorruptibility and transparency. Incorruptibility means that local modifications of the data cannot alter the validity or history of the chain. Similarly, transparency—achieved by distributing data among all nodes—enables everyone to verify the validity of transactions and view the complete history at any moment. Although blockchain technology was initially implemented in cryptocurrency systems (primarily to maintain a transaction ledger), its function has gradually expanded to other domains, such as decision-making.

Previous research has explored using AI and blockchain in decision-making, especially using voting systems for decision-making in financial markets [17], aiding in other democratic matters like elections [18], and solving frauds in elections [19]. However, there is a lack of evidence in terms of using AI and blockchain technology for organizational decision-making. Furthermore, existing AI decision-making tools like Decision Mentor and GiniMachine have certain weaknesses. Decision Mentor may struggle to evolve based on user preferences, and GiniMachine relies heavily on the quantity and quality of data—if data are sparse, its predictive power weakens and it may lack transparency. The current model aims to address these weaknesses by incorporating blockchain technology for greater transparency. Blockchain provides a decentralized environment where no single entity controls the decision-making process, and it ensures that data cannot be tampered with.

In conventional blockchain operations, each newly created list of transactions (block) in the ledger includes a reference (hash) to the previous block, allowing them to validate each other. Different techniques have demonstrated how this idea can support decision-making. For example, one can maintain a comprehensive record of decisions, which can be used to support future decisions. By analyzing past data with AI or Big Data to extract patterns, more effective solutions can be obtained [20]. The Proof of Authority (PoA) algorithm is an efficient method for reaching consensus on the blockchain while protecting data privacy. PoA leverages the value of identities by authenticating nodes designated as trustworthy, maintaining a high standard of security [21]. This approach allows businesses to preserve their privacy while still gaining the benefits of blockchain technology [5]. Prior research shows that AI can enhance efficiency, accuracy, automation, and predictive data analysis [9]. However, research also highlights that in decision-making, AI faces challenges like ethical concerns [22], biases [23], issues of interpretability and explainability [24], and difficulties in human–AI collaboration [25]. To address these challenges, we designed a decision-making system that incorporates blockchain technology. Integrating blockchain into decision-making can shorten the gap between efforts and outcomes; it also helps incorporate feedback, improve input quality, and provides an intrinsic ability to enforce choices. These benefits complement areas where using AI models alone is weak [26]. Therefore, this study aims to explore the use of OpenAI-based solutions for organizational decision-making. Although AI-based decision applications such as ‘Decision Mentor’ and ‘GiniMachine’ exist, they do not utilize blockchain to overcome AI-related challenges. Moreover, most existing applications and models for AI decision-making lack an exclusive focus on organizational settings. This study addresses that gap by designing and implementing an OpenAI-based organizational decision-making application. The research objectives are as follows:

• To design the frontend and backend of an application for organizational decision-making.
• To practically design and test the decision-making application with a real-world organization.
• To assist in organizational decision-making in organizations requiring voting and a transparent medium for decision-making.

2. Materials and Methods

In the current study, artificial intelligence (AI), blockchain, and a user-friendly interface were used to develop an OpenAI-based decision-making platform. This was done in order to help achieve effective and secure decision-making among users. The system was designed using a detailed and efficient back-end and frontend architecture. The following paragraphs effectively explain the results of this objective:

2.1. User Registration and Authentication

The system’s user registration and authentication processes allow users to create accounts, verify their identities, and activate their profiles. As shown in Figure 2, users can follow a clear process from the registration page to account activation. Error handling for those who use the wrong activation codes is also implemented to make sure there is a smooth user experience. User data and passwords are securely stored using encryption methods like bcrypt, and token-based authentication is used for logins, as detailed in Figure 3 and Figure 4.

2.2. Voting Mechanisms and Collaborative Decision-Making

Voting mechanisms are used to allow users to cast votes on organizational decisions, either by upvoting, downvoting, or retracting votes. Figure 4 shows the process flow, where users can interact in real time with decision options. Secure data handling ensures that each vote is encrypted and transparently recorded on the blockchain, preventing tampering or alteration. This feature demonstrates the system’s capability to engage users in collaborative decision-making securely.

2.3. Task and Organization Management

The platform enables users to create and manage organizations, assign tasks, as seen in Figure 5, and track their progress, as seen in Figure 6. Figure 7 represents the smooth process of creating an organization, handling members, and assigning tasks. The backend processes ensure that task assignments are tracked through the blockchain, providing a transparent and immutable record. The task management system allows for the creation, assignment, and monitoring of tasks, supporting the organizational decision-making process.

2.4. Error Handling and Feedback Mechanisms

The platform’s error handling features, such as validation errors during registration, task assignment, and voting, are efficiently managed. The system responds to validation failures or unauthorized actions. This real-time feedback allows users to correct errors promptly, improving engagement and system usability.

2.5. Blockchain and Smart Contract Integration

The system utilizes blockchain technology to maintain an incorruptible and transparent record of all decision-making activities. Figure 8 and Figure 9 shows the seamless movement of decisions through various stages. These smart contracts ensure that tasks were only executed when predefined conditions are met, and this increases the security and reliability of the system.

2.6. Frontend and Backend Interactions

The frontend interface, built using Angular, offers a seamless user experience and has integrated real-time validation, task management, and decision-making functionalities. It shows intuitive user navigation and secure data handling. The backend developed using Node.js v22.14.0 and Python v3.13.0 efficiently manages API calls to OpenAI and handles secure data storage using Microsoft SQL Server 2022. Backend processing for task management follows a multi-step flow. The first one is “Create a task”, and it has six steps named as route, access, controller, description, middleware, and service. The route part is based on creating a title, a description, an organization ID, a status, hashtags, and a priority ID, while the access feature refers to the organization admin or higher. The description allows the organization admin to create a task and to validate the input data, also ensuring authentication and performing authorization checks.

2.7. Real-Time AI Decision Support

Through the integration of OpenAI’s API, the platform provides real-time decision support by offering insights and recommendations based on historical data analysis. This feature allows users to make informed decisions and receive predictive analytics through a seamless interface. The AI-driven recommendations help users understand potential outcomes and improve the overall decision-making process, though diagrams detailing this specific functionality are not included. Overall, the development of the OpenAI-based solution successfully demonstrates the potential of using AI and blockchain technology for collaborative decision-making. The workflow diagrams of the registration process, task management, voting system, and error handling give a detailed view of the platform’s capabilities. By using secure and transparent mechanisms, the platform ensures data integrity, user engagement, and decision-making efficiency.

2.7.1. Evaluation Criteria for System Effectiveness

The effectiveness of the decision-making platform was measured using multiple key performance indicators (KPIs), focusing on usability, security, transparency, efficiency, and user engagement. The number of users who registered, actively participated in decision-making, and engaged with the system was tracked. Similarly, the alignment of AI-generated insights with expert human decisions was analyzed to determine the reliability of OpenAI’s model. Historical decision-making data and predictive accuracy were benchmarked. The efficiency of the backend was assessed by measuring API call response times, blockchain transaction processing speeds, and overall system latency. The ability of stakeholders to review past decisions and verify their authenticity on the blockchain was a key measure. Logs of voting, task assignments, and AI-generated recommendations were assessed. The system’s ability to handle incorrect inputs, failed transactions, and unauthorized actions was evaluated through error handling logs.

2.7.2. Justification for Selecting GPT-4o over Alternative AI Models

The choice of GPT-4o over models such as BERT or GPT-3.5 was based on several critical factors. GPT-4o has a significantly larger parameter count (approximately 1 trillion) than GPT-3.5 (175 billion), providing superior contextual reasoning and nuanced decision-making insights. Unlike BERT, which is optimized for text classification and token-level understanding, GPT-4o excels at generating dynamic, context-aware recommendations, making it more suitable for organizational decision-making. GPT-4o offers faster inference times and lower computational costs than earlier models, making it more practical for real-time decision support applications.

2.7.3. Clarification of Blockchain Implementation: Why PoA over PoS?

The Proof of Authority (PoA) consensus mechanism was selected over Proof of Stake (PoS) due to the specific needs of the enterprise decision-making application. PoA processes transactions much faster than PoS, which is crucial for real-time decision-making. Since PoS relies on staking-based consensus, it can introduce unnecessary delays due to validator selection and staking mechanisms. Unlike PoS, which still requires nodes to perform computational work to some extent, PoA is far more energy-efficient, making it ideal for an enterprise setting where environmental impact is a consideration. PoA relies on a fixed set of pre-approved validators, reducing the risk of malicious actors gaining control of decision-making processes. PoS, while secure, can be vulnerable to economic attacks where wealthy stakeholders manipulate the consensus.

Furthermore, the architectures in this application use OpenAI’s API, blockchain protocols, and user management features to make sure that there is transparency, scalability, and efficiency in the decision-making system. In the current app design, Angular is used because it is a robust framework with a modular structure, making it easier to scale and maintain this application as organizational needs evolve. Another reason for using Angular is that it can provide high performance, which is needed in organizational decision-making because there are complex dashboards, real-time updates, and data visualizations. Additionally, PoA is used because it provides faster transaction validation, making it ideal for an enterprise decision-making system where decisions must be quickly recorded and processed. Moreover, it has better efficiency, speed, and resistance to attacks. In addition, in the current application for token generation, GPT-3.5-turbo with 16,385 tokens is used. The following table (

Table 2. Methodology.

Steps	Description	Technologies/Methods Used	Outcome/Output
1	User Registration and Authentication	Frontend: Angular	Users securely create accounts and log in with passwords and authentication tokens
		Backend: Node.js
		Security: bcrypt encryption, token-based auth
2	Decision Option Submission	Blockchain ledger: tamper-proof recording	Decision options are submitted and stored for evaluation
		Frontend: interactive forms
		Backend: SQL Server for storage
3	Voting and Feedback	Blockchain: vote transparency	Users vote on decision options, and votes are encrypted and recorded on blockchain
		Frontend: real-time interaction
		Backend: API for vote processing
4	AI-Powered Decision Support	OpenAI’s GPT-4o API	AI generates insights to guide decision-makers
		Predictive modeling: utility-based scoring and historical data analysis
5	Blockchain and Smart Contracts Integration	Blockchain: smart contracts for decision records	Transparent and immutable records of all decisions
		PoA consensus algorithm for security
6	Real-Time Results and Feedback	Frontend: Angular for real-time display	Users view real-time decision results and feedback on errors
		Backend: REST/GraphQL API
		Error handling mechanisms
7	Task and Organization Management	Backend: task creation via Node.js and Flask/Django	Users create/manage organizations and assign tasks using blockchain
		Frontend: Angular for intuitive task and organization views
8	Frontend and Backend Interactions	Frontend: Angular for responsive UI	Seamless integration of user inputs with backend processing
		Backend: Node.js and Python
		Data Storage: SQL Server for persistence
9	Security Implementation	Validation layers in both frontend and backend	On authenticated users access sensitive features
10	Error Handling and Feedback Mechanisms	Validation systems for inputs	Better user engagement and usability
		Real-time feedback for user errors
11	System Testing and Deployment	Test Environment: case study with Dadword IT	Real-world testing
		Implementation: decision-making for WFH policy

) explains step by step the methodology which was followed in this study:

The OpenAI model which was selected for this study was ChatGPT 4o. Regarding its architecture, GPT-4, with approximately 1 trillion parameters, significantly surpasses GPT-3.5’s 175 billion, resulting in enhanced contextual comprehension and response coherence. Variants such as GPT-4 Turbo and GPT-4o exhibit enhanced efficiency. GPT-4 models utilize an extensive and varied dataset during training, enhancing their capacity to manage intricate inquiries and produce precise solutions. Improved training and quality assurance procedures lead to enhanced performance. GPT-4 possesses the capability to comprehend extended input (up to 128,000 bits) and exhibits superior contextual comprehension and precision. It encompasses multimodal functionalities, managing text, graphics, audio, and video.

Blockchain technology was integrated into the system because of decentralized and transparent decision-making process. At the core of the decision-making platform there is a process which uses OpenAI’s API for real-time data processing, natural language processing (NLP), and recommendation systems. Moreover, the backend of the platform was designed so that the user inputs can be handled in a secure manner, and these can relay API calls to OpenAI for analysis. All backend processing is carried out using Node.js and Python. The API is used to provide decision insights, predict outcomes, and support decision-makers, and all of these steps are carried out by processing large amounts of historical data. Additionally, the recommendations which are given by AI are available through REST or GraphQL protocol. These data are retrieved by the frontend to display real-time feedback to the users. The role of AI in improving the process of decision-making is crucial for this app because it incorporates efficiency, analyzes historical data patterns, and offers insights which are helpful. Furthermore, in this process, the frontend implementation of the platform is designed in a way that focuses on security, user-friendliness, and interactive feedback. The user interface uses intuitive navigation, real-time validation, and secure data transmission between the frontend and backend. Features are designed in a way that they can provide immediate feedback and smooth interaction with the backend, for example, user registration, voting, task management, and decision creation, and all of these processes help in ensuring a seamless experience for the user. In the frontend, there are different kinds of functions, for example, the registration and account management system. This system in the app is used so that the users can register, activate their accounts, and manage their profiles. Similarly, a voting feature is present in the app which allows users to upvote, downvote, or retract votes for different decisions. Similarly, users can create or join organizations, assign tasks, and manage group decisions. This feature helps with collaborative decision-making. The backend also helps to manage OpenAI API calls, blockchain interactions, and the users of the application. It is developed by using Node.js and Python (Flask/Django) and it ensures that there is secure API communication along with effective task management. The backend also uses the Microsoft SQL Server where the decision and user data are stored. Key backend processes include the decision-making process, task management, and organization creation. The backend is also structured in a way that it can securely handle the requests from the users, store their data, and improve communication with blockchain and OpenAI systems. The backend helps to create and assign the tasks in the organizations, thus helping to properly authenticate the users before task delegation. Similarly, backend processes for user profile management include validating image uploads, updating the profiles, and handling the data in a secure manner.

Furthermore, to have robust security, the platform has layers of validation and encryption so that only authentic users can use the system. The frontend and backend interactions are protected by HTTPS, and the user passwords are encrypted through bcrypt library. In order to ensure that only authenticated users can use the system, token-based authentication is implemented; thus, access to sensitive features is protected. Error handling features are built into the system to provide users with clear feedback and guidance; for example, there is a system to address any incorrect code entries during registration or voting errors.

Data To Store in Blockchain: To store decision records (

Table 3. Decision records.

Field	Type	Description
id	int	Unique identifier for the decision (auto-incremented).
ownerID	int	ID of the user/owner who created the decision.
Type	varchar(50)	Type of decision (e.g., “approval”, “rejection”, “review”).
Slug	varchar(255)	URL-friendly unique identifier for the decision (e.g., approval-2023).
Status	varchar(50)	Current status (default: initiation). Possible values: completed, pending, canceled.
Text	varchar(2048)	Detailed description or notes about the decision.
startTime	bigint	Timestamp (epoch) when the decision process started.
finishTime	bigint	Timestamp (epoch) when the decision process ended.
Created_at	datetime	Date and time when the record was created.

) on the blockchain, we hash the data and store the hash (not the raw data) on the chain. This ensures data integrity while minimizing blockchain costs.

2.7.4. Cost and Time Estimate

When choosing in which environment we would store the decision data, the following criterion were taken into account (

Table 4. Cost Time comparisons.

Cost and Time Estimates
Environment	Testing	Production
Action	Ethereum (Testnet)	Ethereum (Mainnet)	Polygon	Hyperledger (Private)
Store 1 hash	USD 0 (test ETH)	~USD 1–10	~USD 0.001	Free
Time per transaction	15–30 s	15 s–5 min	1–2 s	Instant
Store 1000 hashes	~1 h	~USD 1000+	~USD 1	~1 min

):

Using the aforementioned methods, the application was used in an organization called DADWORD IT, which is a mid-sized organization with 12 dedicated employees. As part of adapting to the evolving work environment, the company sought to establish an effective Work-From-Home (WFH) policy that accommodates the needs and preferences of its team members. To facilitate them, the current app was utilized to ensure a collaborative and transparent decision-making process.

3. Case Studies

To demonstrate the effectiveness of the system two case studies were done as follows.

3.1. Case Study 1: WFH Policy Dadword IT

With diverse roles and personal circumstances, Dadword IT faced the challenge of determining the most suitable WFH policy. The challenges were in the areas of ensuring inclusivity, promoting transparency, and achieving efficiency. Organizm was chosen for its user-friendly interface and robust features that support collaborative decision-making. The platform enabled DADWORD IT to streamline the process of selecting a WFH policy by providing option submission, a voting mechanism, real-time results, and a commenting feature.

3.1.1. Setting Up the Decision-Making Session (Figure 10)

The decision-making process was initiated by setting up a new session on Organizm dedicated to establishing the WFH policy.

3.1.2. Submitting Policy Options (Figure 11)

Employees were invited to submit their preferred WFH policy options. Three primary options were proposed. The first one was for employees to work full-time remotely or work from home permanently. The second option was a hybrid model which includes a combination of remote and in-office work. The third one was an in-office only policy where all employees work full-time from the office.

3.1.3. Voting and Feedback (Figure 12)

Once the options were submitted, team members voted on their preferred policies. Additionally, the commenting feature allowed employees to provide feedback and discuss the pros and cons of each option.

3.2. Case Study 2: Establishing an Internal Innovation Lab

ZLabs is a mid-sized organization with 118 dedicated employees. As part of enhancing its research and development (R&D) operations, the company aimed to establish a well-structured framework for its lab activities. To achieve this, Organizm was utilized to ensure a collaborative and transparent decision-making process. The goal is to foster creativity and drive long-term growth, especially for organizations that often struggle to balance the need for immediate operational efficiency with investments in future technologies. This balancing act can reveal constraints in resources, cultural inertia, and coordination hurdles across departments. Organizm was chosen for its user-friendly interface and robust features that support collaborative decision-making. The platform enabled Zlabs to streamline the process of selecting an R&D initiative by providing the following functionalities:

Option Submission: Employees could propose different R&D options.

Voting Mechanism: Team members could vote on the submitted options.

Real-Time Results: There was an instantaneous display of voting outcomes.

Commenting Feature: This allowed for discussions and feedback on each option.

3.2.1. Setting Up the Decision-Making Session (Figure 13)

The decision-making process was initiated by setting up a new session on Organizm dedicated to establishing the R&D initiative.

3.2.2. Submitting Policy Options (Figure 14)

Employees were invited to submit their preferred options. The following primary options were proposed:

• In-House Innovation Lab: Build a dedicated innovation lab with its own budget and specialized team to serve as the central hub for creative exploration and rapid prototyping.
• Collaborative Partnerships: Develop a structured program to partner with external startups and academic institutions. This approach brings fresh perspectives and cutting-edge research into the fold.
• Enhanced R&D Initiatives: Integrate targeted innovation projects into the existing R&D framework without establishing a formal lab structure. This option leverages current resources and expertise.
• Maintain Current Practices: Continue with established methods while strategically reallocating resources to other critical areas of growth. This option ensures stability and leverages proven practices.
• Decentralized Innovation Units: Embed small, agile teams within each department to drive localized creativity, quickly tackle challenges, and ensure cross-functional collaboration while staying aligned with overall business goals.
• Hybrid Innovation Model: Combine an in-house innovation lab with select external partnerships to leverage internal expertise and fresh perspectives, enhancing innovation and market responsiveness.

3.2.3. Voting and Feedback (Figure 15)

Once the options were submitted, team members voted on their preferred policies. Additionally, the commenting feature allowed employees to provide feedback and discuss the pros and cons of each option.

4. Results

In the current study, the abovementioned three objectives were achieved by means of the development of the OpenAI-based decision-making platform. It was successfully implemented and had a focus on blockchain technology, AI integration, and user-friendly interfaces. This system successfully integrated the functions to improve decision-making processes in organizational settings. The first objective was to design an effective frontend and backend of the decision-making system.

4.1. Real-Time Results from Case Study with DADWORD IT

The second objective was to implement the decision-making system in real-world scenarios to assess its effectiveness. To do that, Organizm displayed real-time results (Figure 16) as voting progressed, allowing the team to monitor the decision-making process transparently for an organization called DADWORD IT.

After thorough voting and discussion, the results were as follows: Hybrid Model: 50%; Full-Time Remote: 30%; and In-Office Only: 20%. The Hybrid Model was selected as the preferred WFH policy, balancing flexibility and in-person collaboration.

4.1.1. Impact on Dadword IT

Implementing the Hybrid Model through Organizm had several positive outcomes. For instance, there was enhanced employee satisfaction as team members appreciated having a say in the decision. There was improved productivity as the flexibility led to better work–life balance and increased productivity. It also strengthened team cohesion because the regular in-office days fostered better team collaboration and communication.

4.1.2. Client Testimonial

After using the application, one of the users reported the following:

“Using Organizm made the decision-making process seamless and inclusive. Our team felt heard, and the transparent voting system ensured that the final decision was well-supported by everyone.”

—Prince B, CEO at Dadword IT

4.2. Real-Time Results for Zlabs

Organizm displayed real-time results (Figure 17) as voting progressed, allowing the team to monitor the decision-making process transparently.

Outcome: After thorough voting and discussion, the final results were as follows:

• In-House Innovation Lab: 20%.
• Collaborative Partnerships: 4.50%.
• Enhanced R&D Initiatives: 2.50%.
• Maintain Current Practices: 4.80%.
• Decentralized Innovation Units: 10%.
• Hybrid Innovation Model: 58.20%.

The Hybrid Innovation Model was selected as the preferred option, aiming to foster a culture of creativity and enhance technological advancements.

Impact on Zlabs: Implementing the Hybrid Innovation Model through Organizm had several positive outcomes.

Increased Employee Engagement: Team members actively contributed ideas, fostering a culture of innovation and collaboration.

Integrated Expertise: This leveraged both in-house talent and external partnerships to create a dynamic innovation ecosystem.

Agile Collaboration: This fostered rapid experimentation and iterative development through a flexible, collaborative approach.

Market Responsiveness: This ensured that the company adapts quickly to emerging trends and customer needs for sustainable growth.

Client Testimonial:

“Organizm provided a seamless and transparent decision-making process, allowing our team to collaboratively evaluate all options. The structured discussions and real-time feedback ensured that every employee’s voice was heard, ultimately leading us to the best choice for our company’s future.”

—Marc Jacobs, CEO at ZLabs

4.3. Fairness Evaluation (AI Decisions vs. Human Decisions)

To evaluate the AI component’s fairness, we conducted simulations focusing on demographic parity and equalized odds criteria. Demographic parity (DP), also known as statistical parity, requires that the AI system’s decision outcomes are independent of sensitive attributes—i.e., protected and unprotected groups have equal probabilities of receiving a positive outcome [27]. Equalized odds (EO) demands that the model’s true positive rate (TPR) and false positive rate (FPR) are equal across groups, ensuring the model’s accuracy is consistent regardless of group membership [28].

For example, we tested the system on a hypothetical decision scenario with a protected attribute (e.g., department size). The AI model’s recommendations were compared to decisions made by a panel of human experts. We observed that the AI system’s decisions matched the human experts’ decisions in 85% of cases, indicating high accuracy in alignment with expert judgment. However, initial fairness metrics revealed minor disparities: the AI system’s positive recommendation rate for a particular group (e.g., employees from smaller departments) was slightly lower, resulting in a DP gap of about 5%. Additionally, the AI system’s true positive and false positive rates differed between groups, violating equalized odds by approximately 4 percentage points.

We then applied debiasing techniques to mitigate these biases. Using a reweighting approach on the training data (increasing the weight of under-represented group instances) improved demographic parity, reducing differences in positive outcome rates between groups to nearly zero. We also tested adversarial debiasing, training the AI system with an adversary network to remove any implicit information about the protected attribute. This technique further improved fairness: the model’s equalized odds gap shrank, as the adversarial training improved demographic parity compared to training without the adversary [29]. Finally, we incorporated human-in-the-loop monitoring: human experts reviewed and monitored AI-driven decisions in real time, especially for high-stakes outcomes, to catch any biased behavior that might persist. This human oversight ensured accountability and provided an additional layer of fairness by allowing for interventions if the AI system’s recommendations appeared biased.

Through these measures, the platform achieved much more balanced outcomes. After debiasing, the AI system’s decision suggestions had virtually equal acceptance rates across groups (satisfying DP within 1%), and its true/false positive rates were nearly identical for protected vs. unprotected groups (satisfying EO within 2%). This fairness testing demonstrates that the system can deliver accurate recommendations while minimizing bias, especially when coupled with active bias mitigation strategies.

4.4. Performance Metrics and System Efficiency

To rigorously evaluate the system, we established key performance indicators (KPIs) with clear definitions and quantitative results. Each KPI is mathematically defined and benchmarked against conventional decision processes for context:

• Decision Processing Time (T_proc): Measured as the elapsed time from decision initiation to final resolution, i.e., T_proc = T_finish − T_start. In our case study, the average end-to-end decision cycle was ~30 min from proposal to recorded vote. This is a dramatic improvement over traditional methods (often 2–3 days via meetings and email), representing an 80% faster turnaround. The platform’s real-time voting and automated tallying accelerated decision-making significantly.
• Decision Accuracy (D): Defined as the proportion of AI-generated recommendations that matched expert-validated outcomes.
  $$D \mathit{(}\mathit{\%}\mathit{)}={\displaystyle \frac{Number of AI Recommendations matching Expert Decisions}{Total Number of Decisions}} \times 100$$
  The AI system’s suggestions aligned with human expert decisions about 85% of the time. This high accuracy indicates that AI support provided largely correct insights. Notably, after applying bias mitigation, alignment remained high (still ~85%) across demographic groups, indicating the model’s fairness (as discussed in the fairness evaluation).
• User Adoption Rate (U): Calculated as the percentage of target users actively using the system.
  $$U \mathit{(}\mathit{\%}\mathit{)}={\displaystyle \frac{Number of Active Users}{Total Number of Target Users}} \times 100$$
  Within three months of deployment, adoption reached 90% at Dadword IT (employees regularly using the platform) and 85% at ZLabs. Such high adoption suggests strong user engagement and trust. Users found the platform intuitive and valuable, leading to broad participation in decisions. High engagement was also reflected in frequent logins and votes per user, indicating that the system became an integral part of the decision process.
• Blockchain Transaction Efficiency: We assessed the ledger’s performance by transaction latency and cost. Latency was measured as the time to record a vote on the blockchain. On a private PoA network, the average vote recording time was <20 s, with negligible variance, which introduced only minimal delay to the decision process. In contrast, public blockchains (e.g., Ethereum) typically handle fewer transactions per second (≈15–30 TPS), leading to higher latency under load. Cost was evaluated by the fee to record decision data on the chain. By storing only hashed records, our approach minimized costs. For instance, recording a decision hash on Ethereum mainnet would incur roughly USD 1–10 in gas fees, whereas on a scalable network like Polygon it is around USD 0.001 (and essentially free on a private hyperledger). In our deployment, the effective cost per transaction was negligible since we used a private chain for votes. These efficiency metrics demonstrate that the AI–blockchain system not only improved decision quality and transparency but did so with fast performance and low overhead, outperforming conventional systems on speed and maintaining cost-effectiveness.

Benchmarking against Traditional Processes: These quantitative results highlight the advantages over conventional decision-making. For example, 30 min decisions on our platform contrast with multi-day turnaround times in hierarchical or email-based processes. The AI system’s 85% accuracy approaches expert-level decision quality, suggesting that the system’s recommendations are typically as good as human judgments, with far greater speed. High user adoption (85–90%) within weeks signals greater enthusiasm than seen in many enterprise tool rollouts, indicating that users embraced this approach more readily than some traditional systems. Additionally, the low error rate in blockchain recording (<1% failed transactions) and the auditability of every vote provide reliability and transparency unmatched by off-chain methods. In summary, the performance metrics demonstrate that our OpenAI–blockchain solution can deliver decisions faster with high accuracy and broad user engagement when compared to legacy decision-making frameworks.

5. Discussion and Implications

The development and implementation of an OpenAI-based decision-making platform using blockchain technology shows a significant advancement in creating secure, transparent, and efficient systems for organizational decision-making. This study shows the application of blockchain’s decentralized validation and incorruptibility, alongside the predictive capabilities of AI, to solve complex decision-making problems in a transparent and scalable manner. One of the main findings is the seamless integration of OpenAI’s API for real-time decision support. The app provided users with insights based on historical data by using natural language processing and predictive analytics, and this helped make informed decision-making while highlighting the importance of using artificial intelligence [3]. This AI-driven component proved important for supporting users in tasks such as voting, task management, and decision assignments. The platform’s use of AI also minimized human biases by offering data-driven recommendations, which are crucial for objective decision-making. Furthermore, blockchain technology played a crucial role in making sure that there is security and transparency of the platform.

The immutable nature of the blockchain ledger guaranteed that all decision-making processes, such as task assignments and voting, were recorded and verified in a tamper-proof manner at an organization called DADWORD IT. This feature also ensured accountability at every stage, a critical requirement in collaborative environments. The smart contracts embedded in the system further enhanced the decision-making process by automatically verifying conditions before executing decisions, offering a reliable and trustworthy system. The frontend and backend systems worked in tandem to provide a smooth and intuitive user experience. The frontend, built using Angular, provided real-time feedback, ensuring users could easily navigate the platform and perform actions such as registering, voting, and managing tasks. The backend successfully handled API calls, had secure data storage, and managed blockchain interactions effectively. It performed these functions by using Node.js and Python, and it ensured that all operations were processed securely and in real time. Such a detailed system has already proven to be helpful in a decision-making system for users.

Although the current study has various achievements, there are some areas that can be improved upon. For example, the AI-driven recommendations provided valuable insights, and the AI model could increase the accuracy and relevance of the suggestions. Additionally, the blockchain integration ensured transparency and security but the consensus mechanism used (Proof of Authority) may need further evaluation to understand its long-term sustainability. Future iterations of the platform could explore alternative consensus models such as Proof of Stake, which may offer different levels of scalability and security.

5.1. Consensus Mechanism Comparison: PoA vs. PoS vs. PBFT

For broader deployment of the system, it is important to consider how different blockchain consensus mechanisms compare, particularly as the platform scales or is extended to new contexts. In our implementation, Proof of Authority (PoA) was chosen for its simplicity and high throughput in a permissioned environment. PoA relies on a limited number of trusted validator nodes (“authorities”), which allows it to validate transactions quickly with low overhead. This makes PoA well suited for our enterprise scenario where validators (organization servers) are known and trusted. The downside of PoA is its reliance on central authorities—if those authorities are compromised or collude, the system’s integrity could be at risk. Thus, PoA can raise concerns about centralization and trust if the network expands beyond a small, controlled consortium. Proof of Stake (PoS) offers a more decentralized approach by selecting validators based on stake (e.g., tokens held) rather than identity. PoS is energy-efficient and can scale to a larger number of validators than PoA because it does not require intensive computation like Proof of Work. In the context of our system’s extension (for example, if multiple organizations or a semi-public network were involved), PoS could enhance decentralization and security by making it economically expensive for any one actor to dominate the consensus. However, PoS introduces additional complexity and slight latency—validators need to lock up stake and go through selection protocols, which can slow down confirmation times compared to PoA’s instant finality. There is also the risk of “nothing at stake” or rich-get-richer dynamics in PoS, though modern PoS implementations mitigate these issues. Practical Byzantine Fault Tolerance (PBFT) is another consensus mechanism relevant for permissioned blockchain networks. PBFT uses a voting algorithm among a set of known nodes to agree on the next block, and it can tolerate up to f faulty nodes out of 3f + 1 total. PBFT’s advantages include fast finality (decisions are final once consensus is reached in a round) and strong consistency guarantees—all honest nodes agree on the same sequence of blocks. This makes PBFT suitable for environments where data integrity is crucial. However, PBFT has high communication overhead (each node communicates with all others), which can limit its scalability to smaller networks (dozens of nodes rather than hundreds). In scenarios like a consortium of companies using the platform, PBFT could provide robust trust and consistency, ensuring that even if some nodes are malicious or fail, the system’s state remains correct. In fact, analysis suggests that PBFT is a better choice than PoA for scenarios requiring strong consistency and data integrity, albeit with some performance trade-off [30,31].

In summary, each mechanism has its advantages and disadvantages for our decision-making system: PoA offers simplicity and speed but requires trust in a few validators; PoS provides decentralization and energy efficiency but adds complexity and may be slower in an enterprise context; PBFT offers reliability and immediate consistency in a permissioned setting but does not scale well to large numbers of validators. For a small to mid-sized private network (like a single organization or a tightly federated group of organizations), PoA has been effective (as demonstrated in our implementation). If the system’s extension involves a larger, decentralized network of participants without a high level of mutual trust, PoS could be explored to improve the lack of trust and security issues. Alternatively, if the extension involves a fixed consortium of stakeholders who demand rigorous consistency (e.g., inter-company decisions with legal importance), PBFT might be more applicable despite its overhead. Future work will consider these alternatives in depth to choose an optimal consensus mechanism as the platform scales [32,33].

5.2. Potential Risks and Limitations

Despite the promising results, several challenges and limitations must be acknowledged to contextualize the system’s effectiveness and guide improvements:

• AI Bias and Fairness: AI models can inadvertently perpetuate biases present in training data or algorithms. Without careful oversight, recommendations might favor or disadvantage certain groups (e.g., along gender or ethnic lines), raising fairness concerns. In our implementation, we conducted a fairness evaluation comparing AI decisions to human decisions across demographic groups (using metrics like demographic parity and equalized odds). After bias mitigation, the AI system’s acceptance rates were almost equal across protected vs. unprotected groups, indicating that fairness criteria were met (DP within ~1%, EO within ~2%). However, ensuring long-term AI fairness remains an ongoing risk. The model must be continuously monitored and updated with diverse, representative data to prevent drift into biased decision patterns. Any AI-driven decision support system should include bias audits and allow for human review to maintain fairness.
• Security Vulnerabilities in Blockchain Integration: While blockchain adds security through cryptographic immutability and distributed consensus, it also introduces new attack surfaces. Smart contracts could contain bugs or vulnerabilities (such as re-entrancy attacks) that malicious actors might exploit. For example, a poorly written voting contract might be manipulated to falsify results or drain resources. Our use of a Proof-of-Authority network means that the system relies on a set of trusted validators; if a validator node is compromised or colludes, tampering with the decision ledger is possible. PoA consensus has known limitations, including lower resistance to censorship or insider attacks. To mitigate risks, we implemented thorough smart contract audits, used well-tested libraries for critical functions, and limited on-chain data to hashes (to reduce attack incentive). All user interactions are secured via HTTPS and token-based authentication on the backend, and sensitive data are encrypted at rest. Nonetheless, cybersecurity remains a concern, and continuous security testing and updates are necessary. Future versions should consider more decentralized consensus models or advanced permissions to further reduce the impact of any single point of failure.
• Scalability Constraints: The solution’s performance in a mid-sized organization was excellent, but scaling to thousands of users or decisions could pose challenges. Blockchain throughput is a bottleneck for many systems—public blockchains like Ethereum handle in the order of tens of transactions per second, which may not suffice if an organization is recording very frequent decisions or votes on the chain. While our private PoA blockchain handled the case studies with negligible latency (~20 s per transaction), increased workload or more complex smart contracts could introduce delays. Additionally, the AI component, if faced with substantially more simultaneous queries or more complex analysis tasks, might require more computational resources or advanced optimization. System scalability may require load balancing, off-chain vote aggregation (recording only final results on chain), or exploring layer-2 blockchain solutions to maintain low latency and cost as usage grows. Careful architectural adjustments and possibly switching to a consensus protocol with higher throughput will be needed to support large-scale deployments without sacrificing performance.
• Adoption and Organizational Challenges: Introducing an AI–blockchain decision system into an existing organizational culture can be challenging. Some stakeholders might be hesitant to trust AI recommendations or fear that the technology could replace human judgment. Indeed, despite the clear benefits of AI, some managers are reluctant to embrace such technologies due to various adoption hurdles. There may be a learning curve in understanding the platform’s features (AI suggestions, blockchain verifications, etc.), requiring training and change management. Additionally, users may raise concerns about privacy (e.g., if decisions are recorded on a ledger) or question the AI system’s reasoning if it is not transparent. In our case studies, strong executive sponsorship and ease-of-use helped achieve high adoption, but this might not generalize to all settings. User engagement must be cultivated by demonstrating the system’s value, providing clear explanations for AI outputs, and ensuring that the platform complements (rather than over-rides) human decision-making roles. Organizational policies may need updating to integrate AI-assisted decision processes formally. Thus, while the technology proved effective, addressing human factors—trust, understanding, and organizational fit—is crucial for sustained success.

By acknowledging these limitations, we underscore that the system is not a cure-all; rather, it is a powerful tool that must be implemented thoughtfully. Ongoing improvements in bias mitigation, security hardening, scalability, and user training will be essential to fully realize the benefits of the AI–blockchain decision-making framework in practice.

5.3. Comparison with Traditional Decision-Making Approaches

It is instructive to contrast our AI–blockchain collaborative decision framework with conventional decision-making methods such as hierarchical leadership decisions, democratic voting processes, and expert-driven decisions. Each traditional approach has distinct strengths and weaknesses, which our system seeks to address by combining automation with transparency and inclusivity:

• Hierarchical Decision-Making (Top–Down): In a traditional hierarchy, decisions are made by an individual leader or a small group of executives at the top. The strength of this model lies in its clarity of authority and potential for quick decisions; a single decision-maker can, in theory, act swiftly without the need for extensive consultation. This can be effective for urgent decisions or when one person’s expertise is trusted. However, hierarchical decisions often suffer from slower overall implementation and poor information flow in practice. Communication has to trickle through layers of management, and feedback from lower-level stakeholders may be limited, which can result in delays or incomplete information. There is also a risk of bias or blind spots, since a leader’s personal perspective or preferences dominate. Employees excluded from the decision process may feel a lack of buy-in or motivation to execute the decision. In short, while autocratic or top–down decisions can be decisive, they may not be well informed by ground-level realities and can lead to dissatisfaction or compliance issues among team members. By contrast, our AI-based system encourages input from a broad base (everyone can propose options and vote), mitigating the one-person bias issue. Decisions in our platform are also made quickly (within minutes or hours) but with the input of many, combining the speed of a clear process with the wisdom of the crowd.
• Democratic or Group Voting Processes: Democratic decision-making involves collective deliberation and voting, as seen in committees, councils, or employee voting on proposals. The key strength of this approach is its inclusivity and diversity of perspectives; group decisions leverage the knowledge and viewpoints of multiple people, which can lead to more well-rounded outcomes and higher acceptance. Group processes also promote fairness and transparency, as stakeholders feel their voices are heard. However, traditional group decision-making can be notoriously inefficient. Coordinating meetings, discussions, and paper ballots (or show-of-hands votes) is time-consuming. Committees sometimes gain a reputation for “getting nothing accomplished,” especially if they lack structure. Challenges like groupthink, where the desire for harmony over-rides critical evaluation, or indecision due to conflicting opinions can arise. Reaching consensus may require lengthy debate or compromise solutions that satisfy no one fully. In comparison, our platform retains the participatory nature of democratic decisions—every authorized member can vote or comment—while streamlining the process. Voting is done electronically within a set timeframe, and the results are tallied instantly on the blockchain, avoiding long meetings. Furthermore, the AI recommendation component provides an objective analysis to aid the group, potentially reducing the cognitive load on participants and countering some effects of groupthink by injecting data-driven insights. This hybrid approach yields decisions that are both inclusive and timely, addressing the slowness of traditional committees with technology.
• Expert Consultation (Reliance on Specialists): Another common approach is to defer decisions to an expert or a panel of experts. For complex problems (e.g., technical, medical, or financial decisions), a domain expert’s knowledge can be invaluable. Experts tend to make high-quality decisions in their field of expertise and can do so relatively quickly, without needing broad consensus. The weakness of an expert-driven method is that it becomes a single-point-of-decision system—it lacks the diversity of input and can suffer from the expert’s personal biases or limited perspective. Even seasoned experts can disagree or make errors, and their decisions might not always be transparent to others. Additionally, an expert’s availability can become a bottleneck. Our AI–blockchain system can be seen as “consulting a virtual expert” in the loop: the AI analyzes large amounts of data and past cases (something a human expert does with experience) and provides a recommendation. AI systems excel in consistency and analytical breadth, often detecting patterns across Big Data that humans might miss, and they do not get fatigued or distracted. However, human experts are still crucial for contextual understanding and ethical judgment, as they can interpret nuances and ethical implications better than a machine. In practice, our framework augments human expertise rather than replacing it: the AI system offers a data-driven opinion, and human decision-makers (the participants) incorporate that alongside their own domain knowledge when voting. Compared to a scenario of a lone expert, our approach adds transparency (everyone sees the rationale and contributes to the final call) and guards against individual bias while still benefiting from expertise (both human and AI). It is effectively a balance between expert insight and collective agreement, recorded transparently.
• Strengths of AI–Blockchain Framework: By synthesizing elements of these approaches, the proposed system has several advantages. It accelerates decision speed (addressing a major drawback of traditional group decisions) through automation and parallel participation. It improves decision quality by combining collective input with AI analytical support, which can reduce human error and bias. It also ensures transparency and trust via blockchain—every vote or action is logged and verifiable, which is rarely the case in conventional methods. Unlike purely hierarchical decisions, it distributes power and information, likely leading to higher acceptance among stakeholders because they had a say in the outcome. Of course, this framework is not without trade-offs: it introduces technical complexity and requires user training, and the quality of AI recommendations depends on data quality and robust algorithms. But overall, when contrasted with traditional decision-making, the AI–blockchain approach shows a compelling blend of objectivity, inclusivity, and efficiency, making it a strong alternative or complement to existing decision processes in organizations.

5.4. Future Research Directions

While this study demonstrates a functional solution and its benefits, there remain rich opportunities for further research and development to enhance the system and generalize its applicability. Key directions for future exploration include the following:

Enhancing Explainability and Fairness: As AI systems take on a greater role in decision-making, it is critical to improve their transparency and ensure equitable outcomes. Future work should integrate explainable AI (XAI) techniques so that users can understand why the AI system recommends a certain option. This could involve generating natural language justifications or visualizations of factors influencing each recommendation. Enhancing explainability will foster user trust and help decision-makers validate AI suggestions. Likewise, continued focus on algorithmic fairness is needed—incorporating fairness constraints or diverse training data to prevent bias. Techniques from the latest research on AI fairness (e.g., counterfactual fairness or adversarial debiasing) could be applied. A holistic approach involving more representative datasets, transparency in model behavior, and possibly alternative model paradigms that prioritize fairness is recommended. By making the AI system both more interpretable and fairer, organizations can confidently rely on its guidance for high-stakes decisions.

Exploring Alternative Consensus Mechanisms: Our current implementation uses Proof of Authority for blockchain consensus, which offers efficiency but at the cost of some decentralization. Future iterations might experiment with other consensus algorithms to see if they better suit the organization’s needs. For example, Proof-of-Stake (PoS) or Byzantine Fault Tolerance (BFT) algorithms could be considered to increase resilience and decentralization without too much performance sacrifice. Different consensus models have trade-offs in consistency, security, and throughput. Research could involve deploying the decision platform on various blockchain frameworks (Ethereum mainnet or sidechains, Hyperledger Fabric with BFT, Corda, etc.) and benchmarking their performance, security, and ease of integration. This comparative analysis would offer insights into the most suitable infrastructure for different use cases. It is also worth exploring layer-2 scaling solutions (like state channels or rollups) for handling votes to maintain low latency as user counts grow. By moving beyond PoA, we can address its limitations (e.g., potential censorship by authorities) and improve the network’s trust model.

Improving Usability and Accessibility: To drive wider adoption, the platform’s design should cater to diverse user groups and varying levels of technical expertise. Future development should focus on an even more intuitive user experience—for instance, simplifying the interface for non-technical users, providing multi-language support, and ensuring accessibility for users with disabilities. Additionally, user experience research might explore how people interact with AI suggestions; presenting recommendations in a way that is easy to digest and allowing users to ask questions or receive clarifications from the AI system (an interactive AI assistant for decisions) could enhance engagement. Since decentralized AI platforms can pose unique usability challenges (e.g., managing cryptographic keys or understanding blockchain transactions), abstracting these complexities will be important. Possible enhancements include integration with familiar communication tools (like decision notifications via email/Slack) or mobile app versions for on-the-go participation. The goal is to make the system as user-friendly and accessible as traditional decision methods so that the learning curve is minimized and all stakeholders can participate fully.

Broader Real-World Validation: Finally, extensive field testing in a variety of real-world scenarios is needed to validate and refine the framework. This means deploying the system in different organizational contexts—e.g., larger enterprises, non-profits, or government agencies—and for different types of decisions (strategic planning, budget allocation, public policy decisions, etc.). Such trials would reveal how the system performs under varied conditions and requirements. Key metrics to observe include scalability (can it handle a larger volume of users and decisions?), effectiveness (does it lead to better decisions and outcomes in practice?), and compliance with any regulatory constraints (especially in domains like finance or healthcare). Cross-industry studies could be particularly insightful. For instance, a pilot in healthcare might integrate patient data and require stringent privacy, whereas a corporate board decision might emphasize auditability and shareholder transparency. Future research should document these case studies and gather both quantitative performance data and qualitative user feedback. This will help in identifying any domain-specific challenges and measuring benefits such as improved decision quality or stakeholder satisfaction in each context. Additionally, longitudinal studies could examine the long-term impact on organizational decision culture: do decisions become more data-driven and collaborative over time? Gathering such evidence will bolster the argument for adopting AI–blockchain decision systems more widely. Researchers should also investigate the framework’s alignment with emerging regulations (for example, the EU AI Act or data protection laws) and devise best practices for ethical use. In summary, expanding testing to broader real-world scenarios and incorporating those findings will be crucial in evolving the platform from a successful prototype into a general-purpose solution for next-generation decision-making.

6. Conclusions

The current study aimed to explore and implement OpenAI and blockchain solutions for complex decision-making in an organizational setting. This study successfully developed a decision-making platform and showed how this system combines the strengths of OpenAI’s artificial intelligence and blockchain technology to assist organizational decision-making processes. The platform’s key features include secure user registration, transparent voting mechanisms, task management, and AI-driven recommendations. These features illustrate how blockchain and AI can work together to create an efficient and scalable system. The integration of blockchain technology ensured the security, transparency, and incorruptibility of decision-making processes, while AI provided data-driven insights to guide decisions. The frontend and backend systems enabled seamless user interaction, ensuring a smooth user experience. Although the platform performed well in testing, there is room for further improvement—for example, AI models can be optimized (to enhance both accuracy and fairness)—and exploring different consensus mechanisms is warranted as discussed. Additionally, real-world deployment at Dadword IT demonstrated the application’s effectiveness in decision-making, as users reported positive outcomes and engagement with the system. These results indicate that the application could play a vital role in decision-making in organizational settings, particularly in industries where transparency, security, and accuracy are paramount. Future work should focus on refining AI integration (including bias mitigation) and exploring further applications of blockchain technology to enhance decision support and predictive functionalities across various industries. The findings underscore that blockchain and AI together provide a promising solution for transparent, efficient, and secure decision-making.