Consider AI for your organisation by Dr. Hasan

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  1. A reflection of whether you think neural networks and AI might be tools you will use now or in the future and an explanation of why or why not? 

Neural networks and AI are increasingly becoming integral tools in various industries and sectors. Many organisations find that these technologies can automate complex tasks, analyse large datasets, and provide insights that might not be immediately apparent to human analysts (Manoharan et al., 2024). 

 

For instance, businesses may use AI for predictive analytics in marketing campaigns, financial forecasting, or to enhance customer service with chatbots. In the automotive industry, neural networks are critical for self-driving technology (Biswas et al., 2023).  

 

Whether or not you will use neural networks and AI now or in the future could depend on your field of work, interest in technology, or the specific needs of the projects (Manoharan et al., 2024). AI might be a valuable tool if the job involves data analysis, pattern recognition, or automation (Manoharan et al., 2024). Conversely, AI might not be as immediately relevant if the work is more hands-on or doesn’t involve data processing (Biswas et al., 2023).  

 


 

Ultimately, the decision to use neural networks and AI tools would depend on their applicability to your goals and whether they can enhance or streamline the work processes (Biswas et al., 2023). 

 




  1. A discussion of the steps you will need to take to implement a neural network or an AI initiative in your organisation (if you plan on using these tools). 

Implementing a neural network or an AI initiative in a company like NIVEA, which specialises in skin-care products, would typically involve several key steps: 

  1. Identify Objectives: Clearly define what you want to achieve with AI. For NIVEA, this could be improving product recommendations, personalising marketing strategies, or enhancing manufacturing processes (Biswas et al., 2023).  


  1. Data Collection: Gather and organise data relevant to the business objectives. This could include customer purchase history, skin type data, product preferences, etc. 


  1. Feasibility Study: Assess whether the available data is sufficient and whether AI can provide the expected benefits (Biswas et al., 2023).  


  1. Choose the Right Tools: Select appropriate AI and machine learning tools that align with the business objectives. This could involve choosing off-the-shelf solutions or developing custom models (Manoharan et al., 2024). 



  1. Build or Train the Model: Using your data, develop or train the neural network model. This step may require expertise in data science and machine learning (Biswas et al., 2023). 


  1. Integration: Integrate the AI model with existing systems, such as customer relationship management (CRM) or enterprise resource planning (ERP) systems (Biswas et al., 2023). 


  1. Testing and Validation: Test the AI system to ensure it meets your objectives and performs accurately (Biswas et al., 2023).  


  1. Deployment: Roll out the AI system for actual use within the company (Manoharan et al., 2024).  




  1. Monitoring and Maintenance: Continuously monitor the system’s performance and make necessary adjustments or improvements (Manoharan et al., 2024). 


  1. Compliance and Ethics: Ensure that the use of AI complies with legal standards and ethical considerations, particularly regarding customer data privacy (Biswas et al., 2023).  



Each step would require careful planning and execution, potentially involving cross-functional teams within NIVEA, including IT specialists, data scientists, product managers, and legal advisors. 

 

References: 

Biswas, B., Sanyal, M.K. and Mukherjee, T., 2023. AI-based sales forecasting model for digital marketing. International Journal of E-Business Research (IJEBR), 19(1), pp.1-14. 

Manoharan, G., Durai, S., Ashtikar, S.P. and Kumari, N., 2024. Artificial Intelligence in Marketing Applications. In Artificial Intelligence for Business (pp. 40-70). Productivity Press. 

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