Introduction
The recent development of Artificial Intelligence (AI) tools, particularly neural networks, presents an excellent opportunity to streamline the rather laborious policy-making procedures. One sector that stands a chance of benefiting primarily from these changes is the healthcare sector, which needs less bureaucratic processes and error-free policy implementations. The attainment of quality standards in these procedures, as outlined by ISO 9000, has seemed unachievable for a long time, but the AI revolution could move humanity closer to this objective.
This disruptive marketing project plan introduces AI as a tool for change in policy-making. The suggested decision-making process will be not only error-free but also agile, responsive, and dependable, thus creating a foundation for a new future in the healthcare industry. In order to guarantee efficiency, compliance with legal standards, and adaptability, this strategy aims to demonstrate how an automated project can destroy tradition in policy-making by automating it via artificial intelligence.
Background
Context
For a long time, the organizational pursuit of excellence in the healthcare industry has proved to be a Herculean task, with human policymakers failing to meet the stringent standards and procedures outlined in ISO 9000 standards. The personnel tasked by the organizations to formulate these policies from scratch often face an insurmountable task of meeting the expectations of compliance boards due to time and resource constraints (Chandra & Mukhopadhyay, 2021). Experts who understand the organization and the industry are generally in short supply, and even if available, they are usually more focused on achieving organizational fluidity rather than the set best standard practices. This role is further cumbersome by the excessive documentation that decision-makers are expected to present when developing a new policy.
Outside Research
Numerous studies have been conducted to find out the impacts and usefulness of AI in the policy-making processes of healthcare and other industries. According to a survey by Nay & Daily (2022), AI can be consistent with human goals and values under public policy. This alignment is advantageous as this research has alluded that human experts face a myriad of problems when implementing complex procedures from scratch. A common benefit associated with using AI in decision-making processes is improved accuracy and speed (Stone et al., 2020). In the policy-making processes, the literature reveals that AI has resulted in improved decision-making due to leveraging historical data, enhanced policy formulation, policy evaluation, and its ability to address specific niches.
Purpose
The main objective of this initiative is to develop an AI system capable of drafting, reviewing, and finalizing organizational policies, procedures, and work instructions. The proposed tool will go beyond the expectations of many as it will constantly review the policies, procedures, and workflow in an organization in addition to formulating the initial plan. Its automated iterative process will help overcome the barriers caused by bureaucracies that are followed in traditional approaches. In the long run, this plan aims to set a future where AI is the cornerstone of organizational governance.
Objectives
The objectives of the marketing project plan are to:
- Ensure there is automation of the drafting process.
- Ensure there is compliance with standards like the ISO 9000.
- Reduce human errors and save time in policy implementation.
Primary Participants
Policymakers shall play an essential role in the implementation of the project as they will guarantee that the AI system adopts a strategy guided by organizational goals and compliance standards. AI tools cannot function alone and must have a skilled professional to ensure they align with a particular region’s ethos and integrity policies. Developers, particularly machine learning experts, will also be critical in the projects. They will rely on modern technologies such as Natural Language Processing and Knowledge Graphs to set up a system that meets the requirements of the policymakers. All AI-generated systems must comply with the legal requirements, and this tool must adhere to the legal standards of AI and policy-making. Legal advisors will be needed to ensure that this mandate is achieved.
Project Leader
The project’s leadership must be under people with visionary capabilities to exemplify transformational leadership traits, technological prowess, and strategic awareness. The project will be led by a project manager who will be responsible for overseeing the overall activities. A technical lead manager will manage the development side of the operations. There will be a software architect who will ensure the design of the generated automation software in the healthcare industry is functional. An experienced Human-Computer Interaction (HCI) expert must also be available to ensure that the designed system meets the useability standards.
Project Scope
In the first three months, research will be conducted to assess the current state of policy implementation, the technological landscape, and the existing legal framework. The developers will work on the project from the fourth to the sixth month. The project will be brought to life during this time, and a prototype will be generated. Its efficiency, accuracy, and adaptability shall be tested and improved between the seventh and the ninth months. Once fine-tuning is completed, the project will be implemented in the tenth and the twelfth month and a transition of practices will be initiated.
Recommended Change Methods, Tools, and Approaches and Its Application
Tools
To ensure the build system will be effective in policy-making, choosing a tool that will result in success is essential. Based on the current technologies and existing research, this project will use two necessary tools: natural language processing (NLP) and knowledge gaps. NLP is a field of Machine Learning that deals with synthesizing human-understandable language with computers (Bharatiya, 2023). Knowledge Graphs are table-like models used to represent and store information and are generally used in building search engines and virtual assistant systems. Combining these two tools will help generate a practical and relevant policy-making AI model.
The relevance of relying on NLP is its ability to comprehend human language and texts, thus training itself using previous policies set by organizations. Its importance is not only in the input since the processed information must be presented to the policymakers for approval before being passed. Knowledge graphs, on the other hand, are essential in representing natural objects, events, and situations, as well as finding out their relationships, thus leading to improved recommendation systems (Ji et al., 2021). Combined, these two tools make the automation of policy-making faster and more effective, thus relieving the workforce in the organization from laborious work.
Approach
In the implementation stage, a semi-automatically built decision model will synthesize human expertise with machines’ ability to work with large datasets. According to Lopez et al. (2022), Semi-automatically built decision models rely on inputs from existing policies generated by computers and human brains. Relying on NLP and Knowledge Graphs, previous unstructured policy decisions and recommendations, as well as the prevailing macro-and micro-economic conditions, will be analyzed to generate structured rules. The first step shall be data pre-processing, where NoSQL database applications will be used to clean and make the data in a machine-readable format. A combination of techniques, such as algorithms and data patching, will be used to extract rules from the already cleaned datasets.
Natural language expressions from the extractions will generate logical operators such as decision trees and artificial neural networks to get decision models and business rule management systems. Then, Knowledge Graphs will be integrated into the neural network system to enhance its efficacy. Finally, experts will be asked to fine-tune the model manually, relying on human inputs. This method of fine-tuning by a logical brain avoids the biases that machine learning algorithms may bring (Grunert et al., 2021). Its increased accuracy and expertise make it easily acceptable to the project’s administrative bodies, organizational managers, and legal officers.
Application
The applicability of the decision-making tool will be diverse and promote accountability and transparency of the overall policy-making procedures. The policies implemented previously will be used to make and design a decision-making model that aligns with an organization’s mission, vision, and objectives. Accuracy in this step is assured by ensuring the essence of previous decisions is not in translation and Graph’s abilities to help bring out context.
Decision models that are designed using AI enable the auditors of the models to understand the origin of a particular precedent, thus increasing transparency (Yanisky-Ravid & Hallisey, 2019). AI-based decision models also create an organization’s culture of honesty and integrity through this traceability trait. Unlike traditional policy-making processes where precedents are not required, the system can easily show their originalities, thus making their work easier.
How to Measure Results
Several key performance indicators (KPIs) will be used to assess and measure the system’s effectiveness, accuracy, and speed. These checks are critical as they help ensure that systems meet policymakers’ expectations (Buçinca et al., 2020). The first shall be the time saved in designing the policy procedures compared to the traditional bureaucratic methods. In case of technology breakdowns, wasted time when the system is under maintenance shall be treated as wastage.
The results from the system shall also be measured based on their compliance with ISO 9000 regulations. This will include aligning with international standards’ guidelines for organizations to streamline processes to ensure quality and efficiency. By following ISO 9000 and other quality standards, the AI system will meet its organizational needs and those of the customers and other relevant stakeholders.
Table 1 – Implementation Schedule.
Budget
This project is expected to cost about $200,000, which shall be used to perform an array of activities, including software development, employee training to adopt the system, payment of the implementation team, and legal consultations. A substantial amount of costs will be allocated to the software development team as this will be directly related to the success or failure of the project. The team will be willing to give extra budget to the software team should they request such with evidence on where the extra money must be used.
Some of the money will be used to finance the legal team, which is critical in mitigating the regulatory risks of designing such systems. Human capital will be essential for successfully developing and implementing the automatic AI policy-making system. Therefore, some of the budget shall be used to train policymakers, developers, and other regular stakeholders on how to design, measure, and use the system.
Sustainability of Project
Facilitating the long-term success of this project requires various sustainability measures to be taken. One of the most important measures is regularly revealing and updating the system to match the ever-changing technological landscape. Research shows that thoroughly reviewed and improved methods regularly outcompete those that take longer (Tahsien et al., 2020). Thus, by remaining committed to the maintenance of the system, it shall remain adaptable to the changing laws, technologies, and people’s choices and preferences.
Conclusion
By using AI to automate policy-making, the automated project seeks to eliminate tradition and guarantee efficiency, legal compliance, and flexibility. Research, development, testing, and implementation steps must be taken to generate the project. Additionally, a well-qualified team with competed leadership shall be gathered as discussed.
A semi-automatically built decision model where machine capabilities are synthesized with human knowledge shall be used using NLP and Knowledge Graphs tools. These systems shall be very applicable in increasing transparency and accountability in policy-making. The system’s effectiveness will be measured using key performance indicators such as time taken in policy development and compliance with ISO 9000 standards.
References
Bharadiya, J. (2023). A comprehensive survey of deep learning techniques natural language processing. European Journal of Technology, 7(1), 58-66. Web.
Buçinca, Z., Lin, P., Gajos, K. Z., & Glassman, E. L. (2020). Proxy tasks and subjective measures can be misleading in evaluating explainable AI systems. In Proceedings of the 25th international conference on intelligent user interfaces (pp. 454-464). ACM Digital Library.
Chandra, G., & Mukhopadhayay, A. (2021). Industry standards and standardization: An expert survey on opportunities and challenges. In 2021 international conference on information technology (ICIT) (pp. 284-289). Web.
Grunert, D., Göppert, A., Hort, S., Rachner, J., Grahn, L., & Schmitt, R. H. (2021). Modelling for decision-making in dynamical line-less assembly systems. Journal of Production Systems and Logistics 1 (2021), 1. Web.
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Philip, S. Y. (2021). A survey on knowledge graphs: Representation, acquisition, and applications. IEEE transactions on neural networks and learning systems, 33(2), 494-514. Web.
Lopez, V., Picco, G., Vejsbjerg, I., Hoang, T. L., Hou, Y., Sbodio, M. L., Segrave-Daly, J., Moga, D., Swords, S., Wei, M., & Carroll, E. (2022). Envisioning a human-AI collaborative system to transform policies into decision models. Web.
Nay, J., & Daily, J. (2022). Aligning artificial intelligence with humans through public policy. Web.
Stone, M., Aravopoulou, E., Ekinci, Y., Evans, G., Hobbs, M., Labib, A., & Machtynger, L. (2020). Artificial intelligence (AI) in strategic marketing decision-making: A research agenda. The Bottom Line, 33(2), 183–200. Web.
Tahsien, S. M., Karimipour, H., & Spachos, P. (2020). Machine learning based solutions for security of Internet of Things (IoT): A survey. Journal of Network and Computer Applications, 161, 1-18. Web.
Yanisky-Ravid, S., & Hallisey, S. K. (2019). Equality and privacy by design: A new model of artificial intelligence data transparency via auditing, certification, and safe harbor regimes. Fordham Urban Law Journal, 46, 428. Web.