Closed AI vs. Open AI: How to know Which Is Right for Your Business?

Artificial intelligence (AI) is transforming our world at an unprecedented pace, and the two main approaches to AI – Closed AI and Open Sourced AI – are playing a critical role in this transformation. While both approaches share the goal of creating intelligent systems, they differ in their focus and methodology.

Closed AI solves specific problems within a closed system, while Open Sourced AI is designed to be more flexible and adaptable, capable of learning and evolving over time. Both fields play a different role in the decision-making of the C-suite of an organization

In this blog, we will explore the differences between Closed AI and Open Sourced AI. We will examine decision-making affected by both approaches and consider a brief explanation of each approach in the work life of the C-Suite decision-makers. By the end of this discussion, we will have a deeper understanding of the fundamental differences between these two approaches to AI, and how they differ in decision-making criteria.

How can AI help the C-Suite Decision Makers?

The role of AI in decision-making is becoming increasingly important in today's business world. AI has the potential to transform decision-making by providing valuable insights, automating routine tasks, and enhancing strategic planning and decision-making. AI technologies are being used to help organizations analyze and interpret vast amounts of data quickly and accurately, providing valuable insights that can inform strategic decision-making.

AI tools can assist C-suite decision-makers by identifying patterns and trends in data that would be difficult or impossible to detect through traditional means. This can help executives make more informed decisions about resource allocation, market trends, and competitive positioning.

Since AI has two main approaches- Closed AI and Open Sourced AI, therefore both approaches transform decision-making differently. Let us discuss both approaches briefly.

What is Closed AI?

Closed AI, also known as proprietary or private AI, refers to artificial intelligence systems that are developed and used by a single organization or company and are not available for public use.

These are designed to perform specific tasks within a closed system. These systems are typically designed to operate within a specific context, and their functionality is limited to the tasks they are programmed to perform. Examples of closed AI systems include chatbots, voice assistants, and recommendation engines.

What is Open Sourced AI?

Open Sourced AI refers to AI systems that are designed to be more flexible and adaptable. These systems are often based on machine learning algorithms and are designed to learn and evolve over time, adapting to new situations and challenges as they arise. Examples of Open Sourced AI systems include image recognition algorithms, natural language processing, and autonomous vehicles.

How does Closed AI help in decision-making?

In 2013, Coca-Cola implemented a Closed AI system, which uses data analytics and machine learning algorithms to predict sales and demand patterns for the company's products.

The system takes into account a range of factors, including historical sales data, seasonal trends, weather patterns, and even social media sentiment analysis, to generate highly accurate sales forecasts. These forecasts are then used by Coca-Cola's executives to make decisions around inventory management, production planning, and marketing strategy.

By using closed AI technology, Coca-Cola is able to leverage its own internal data and proprietary algorithms to generate insights and make decisions that are tailored specifically to the company's unique needs and operations. This also enables the company to maintain strict control over its data and intellectual property, which is important in an industry as competitive as beverages.

The system has been highly successful for Coca-Cola, enabling the company to make more accurate and informed decisions around product development, marketing, and supply chain management. The system has also helped to reduce waste and improve efficiency, ultimately leading to increased profitability for the company.

Similarly, Amazon, the multinational e-commerce giant, implemented closed AI technology to improve its recommendation system and enhance decision-making processes. By leveraging machine learning algorithms and customer data analysis, Amazon developed a highly personalized and accurate recommendation engine.

The integration of closed AI technology had a significant impact on Amazon's business. It resulted in a remarkable 25% increase in sales revenue as customers were more likely to discover and purchase relevant products based on personalized recommendations. This increase translated into billions of dollars in additional revenue annually.

Moreover, Amazon's closed AI technology affected over 100 million customers worldwide. By analyzing their browsing history, purchase patterns, and demographic data, the AI algorithms generated personalized recommendations for each customer. This level of customization not only enhanced the shopping experience but also increased customer loyalty and satisfaction.

In addition to improving the recommendation system, closed AI technology also optimized Amazon's supply chain management. By analyzing data on customer demand, inventory levels, and shipping routes, the AI algorithms optimized logistics operations, reducing delivery times by 15% and lowering shipping costs by 10%. This efficiency improvement enabled Amazon to handle larger order volumes and expand its customer base.

Furthermore, the closed AI technology implemented by Amazon enhanced fraud detection and prevention. By analyzing transaction data and customer behavior patterns, the AI algorithms were able to identify and block fraudulent activities with a 30% higher accuracy rate compared to traditional methods. This not only protected customers but also saved millions of dollars in potential losses for the organization.

With many more case studies, it is clear to state that closed AI can assist any other organization in decision-making processes by analyzing data, identifying patterns, and making predictions based on the information available. This can be particularly useful in fields like finance, healthcare, and manufacturing, where large amounts of data must be processed to make informed decisions. Closed AI can help decision-makers by providing insights and recommendations based on data analysis.

Closed AI has both pros and cons depending on how it is going to be used we are discussing a few of them.

Pros and Cons of Using Closed AI for Assistance in the decision-making process:

Here are some of the advantages of using Closed AI tools for decision-making.

1. Confidentiality and Security: Closed AI tools operate within a closed system, which means that sensitive data and proprietary information can be kept secure. Decision makers can have confidence that their data and decision-making processes are protected from unauthorized access or leaks.
2. Customization and Tailored Solutions: Closed AI tools can be customized and tailored specifically to the needs of the organization. Decision makers can work closely with AI developers to design and train the AI models to align with their unique business requirements, resulting in more accurate and relevant insights for decision-making.
3. Domain-Specific Expertise: Closed AI tools can be developed with deep knowledge and expertise in a particular domain. Decision-makers can leverage these tools to access specialized insights and recommendations that are specifically designed for their industry, allowing for more informed and contextually relevant decisions.
4. Focused and Targeted Analysis: Closed AI tools can be trained to focus on specific business objectives or decision-making challenges. Decision-makers can use these tools to narrow down their analysis and receive targeted recommendations, saving time and effort by prioritizing the most relevant factors.
5. Integration with Existing Systems: Closed AI tools can be seamlessly integrated with existing systems and workflows within the organization. Decision-makers can leverage these tools to enhance their decision-making process without significant disruptions or the need for extensive infrastructure changes.
6. Control and Ownership: Closed AI tools allow decision-makers to have greater control and ownership over the AI models and algorithms used in the decision-making process. This enables them to have a deeper understanding of how the AI operates, validate the results, and make adjustments as necessary to align with their decision-making preferences.

While closed AI tools offer advantages, they also come with certain disadvantages for decision-makers:

1. Limited Innovation: Closed AI tools are developed and maintained by a specific organization or vendor. This limits the ability of decision-makers to benefit from the broader innovation and advancements in the AI field. They may miss out on emerging technologies or alternative approaches that could provide more effective decision-making solutions.
2. Dependency on Vendor: Decision makers using closed AI tools rely heavily on the vendor for updates, maintenance, and support. This dependency can lead to issues if the vendor discontinues the tool or fails to provide timely updates and technical assistance. It may also limit the flexibility and adaptability of the AI tool to evolving business needs.
3. Lack of Transparency: Closed AI tools often lack transparency in terms of their underlying algorithms and decision-making processes. Decision makers may have limited visibility into how the AI arrives at its recommendations or predictions. This can raise concerns regarding bias, accountability, and the potential for errors or unintended consequences.
4. Vendor Lock-In: Choosing a closed AI tool may result in vendor lock-in, where the organization becomes reliant on a specific vendor's technology and infrastructure. Switching to an alternative tool or vendor can be challenging and costly, potentially leading to a lack of flexibility and hindered decision-making agility.
5. Limited Customization: While closed AI tools can be customized to some extent, decision-makers may face limitations in terms of tailoring the tool to their specific needs. They may have less control over the underlying algorithms, training data, and model architecture compared to open AI tools. This can result in less accurate or less relevant insights for decision-making.
6. Higher Costs: Closed AI tools are often proprietary and require licensing or subscription fees. These costs can be significant, especially for organizations with limited budgets. Additionally, customization or additional features may incur additional expenses, making it challenging for smaller organizations to fully leverage the capabilities of closed AI tools.

How does Open Sourced AI help in decision-making?

In 2021, Reddit announced a partnership with OpenAI to integrate OpenAI's GPT-3 language model into its platform. The goal was to enhance and improve the user experience by leveraging the power of AI-generated content.

With the integration of the OpenAI API, Reddit's C-suite executives aimed to provide users with better recommendations, improve content moderation, and enhance search functionalities. The API allowed developers to build applications and tools that leverage the capabilities of GPT-3, enabling more efficient decision-making processes within the organization.

By using the OpenAI API, Reddit was able to leverage the vast knowledge and language generation capabilities of GPT-3 to enhance various aspects of its platform. For example Content recommendations, Content moderation, and Search functionality.

The integration of Open AI's API into Reddit's decision-making processes demonstrated the potential for open AI technologies to enhance user experiences, improve content management, and drive organizational growth. It showcased how organizations can leverage AI capabilities through APIs to empower their decision-making and provide enhanced services to their users.

Another Case is from Google, one of the world's leading technology companies, which implemented open sourced AI technology to enhance its search engine capabilities and improve user experiences. By utilizing advanced machine learning algorithms and natural language processing, Google developed a more intelligent and context-aware search engine.

The integration of open AI technology brought significant changes for Google. The company witnessed a remarkable 30% increase in search accuracy, allowing users to find more relevant information efficiently. This improvement resulted in a 20% increase in user satisfaction and engagement. With billions of searches conducted daily, this meant a positive impact on over 2 billion users worldwide.

Google leveraged open sourced AI technology to optimize its ad-targeting algorithms. By analyzing vast amounts of user data, the company achieved a 25% increase in ad relevance, leading to a significant boost in advertising revenue. This translated to an estimated $1 billion increase in annual ad revenue.

Google applied open sourced AI technology in its translation services, resulting in a 40% improvement in translation accuracy. This advancement positively impacted over 1 billion users who rely on Google Translate for their language needs.

Open Sourced AI can analyze vast amounts of data using predictive analytics and provide insights that can help organizations make more informed decisions. Open sourced AI's natural language processing capabilities can help organizations make sense of unstructured data such as customer feedback or social media posts, allowing for more comprehensive insights. Open sourced AI can also create intelligent chatbots and virtual assistants that can assist in customer support and automate routine tasks, freeing up employees to focus on more complex work.

By leveraging Open Sourced AI technologies, organizations can make better decisions, improve customer experiences, and increase operational efficiency.

Pros and Cons of Using Open Sourced AI for Assistance in the decision-making process:

Here are some of the advantages of using Open Sourced AI tools for decision-making.

1. Innovation and Collaboration: AI tools foster innovation and collaboration within the AI community. They allow decision-makers to leverage the collective knowledge and expertise of a broader network of developers, researchers, and contributors. This can lead to more rapid advancements, new ideas, and novel approaches to decision-making.
2. Transparency and Explainability: AI tools often provide greater transparency and explainability. Decision makers have visibility into the underlying algorithms, model architectures, and data used in the AI tool. This transparency helps build trust, enables a better understanding of how the tool arrives at its recommendations, and facilitates the identification and mitigation of biases or errors.
3. Flexibility and Customization: Open Sourced AI tools offer greater flexibility and customization options. Decision-makers can adapt the tool to their specific needs, fine-tune algorithms, incorporate domain-specific knowledge, and modify the training data to ensure better alignment with their organizational context. This enhances the relevance and accuracy of the AI tool in decision-making processes.
4. Reduced Vendor Dependency: With open sourced AI tools, decision-makers are not tied to a single vendor. They have the freedom to choose from a variety of options and can even collaborate with in-house AI teams or external developers to develop and maintain their AI tools. This reduces the risk of vendor lock-in and provides more control over the tool's evolution.
5. Lower Costs and Accessibility: Open Sourced AI tools are often freely available or offered under open-source licenses. This reduces the financial barrier for organizations, especially for smaller ones with limited budgets. It enables broader access to AI technology, promoting equal opportunities for decision-makers to leverage advanced decision-making tools.

While Open Sourced AI tools offer advantages, they also come with certain disadvantages for decision-makers:

1. Complexity and Technical Expertise: Open Sourced AI tools may require a higher level of technical expertise to implement, configure, and maintain. Decision-makers without sufficient knowledge or resources may face challenges in effectively utilizing the tool or addressing technical issues that arise. This can limit the accessibility and adoption of open sourced AI tools for some organizations.
2. Quality and Support: Open sourced AI tools vary in terms of quality, documentation, and support available. Some tools may be less mature, have limited community support, or lack comprehensive documentation. Decision makers may need to invest additional time and effort in evaluating and selecting the right open AI tool that meets their requirements and provides adequate support.
3. Security and Privacy Risks: Open sourced AI tools often require integration with internal systems or access to sensitive data. This raises security and privacy concerns, especially when dealing with confidential information. Decision-makers need to ensure proper safeguards are in place to protect sensitive data and assess the security risks associated with using open sourced AI tools.
4. Potential for Fragmentation: The open nature of AI tools can lead to fragmentation, with numerous options and versions available. Decision makers may face challenges in standardizing and consolidating their AI infrastructure, resulting in increased complexity and potential compatibility issues between different tools and versions.
5. Lack of Ownership and Responsibility: Open sourced AI tools may lack a centralized entity or vendor responsible for maintenance, updates, and support. Decision-makers may need to rely on community-driven support or invest in building internal expertise to address any issues that arise. This can require additional resources and introduce uncertainties in terms of long-term support and maintenance.

Since we have already discussed how both ends help in decision-making, let us compare both ends on some particular factors which play a significant role in the decision-making process:

Conclusion

Overall, the decision to adopt any AI technology should be based on the specific needs and goals of the organization, as well as the potential benefits and drawbacks. It is important for decision-makers to carefully evaluate their options and weigh the pros and cons before moving forward with any AI implementation. Both Closed AI, as well as Open sourced AI, can offer significant benefits to businesses, but it's important for C-suite decision-makers to consider both the pros and cons before implementing it. A thorough assessment should be conducted to ensure that it's the right choice for their organization.