I
Imran Chaudhri
Guest
AI is swiftly finding its way into enterprises, impacting various processes and workflows across industries. Increased business efficiency and effectiveness, enhanced decision-making, quality results and reduced costs are just some of the benefits generative AI brings to companies.
With the advent of ChatGPT, the term “generative AI” has become increasingly popular and is gradually being incorporated into a wide range of fields and industries. Generative AI is a technology capable of generating content based on a user’s input and request, otherwise known as a "prompt." A 2023 Gartner survey highlights that this technology will profoundly impact businesses, their competitiveness and their operating models. In this highly technological and data-driven business landscape, generative AI is emerging as a transformative force for enterprises. Incorporating generative AI in your business will have substantial benefits, including innovation, efficiency and enhanced business operations. For these benefits to be realized, though, you must address some of the key challenges that generative AI presents for your enterprise organization. Using Generative AI and a semantic layer in an enterprise data system solves these challenges, provides even bigger benefits for your enterprise and revolutionizes how businesses harness data. Here, we will explore the benefits of merging generative AI with enterprise data systems and how this can amplify your business operations.
The lack of human reasoning and understanding of generative AI’s models, leads to their inability to understand and misinterpret a user's question. This can result in the creation or invention of incorrect answers. These types of errors are also referred to as hallucinations. Hallucination is a term used to describe the phenomenon where the generative AI model produces outputs that are incorrect and don’t correspond to the data they are trained on or to any known pattern. In fact, generative AI confidently states incorrect data 15–20% of the time (Datanami, 2023). The hallucination issues will never completely disappear, but companies can reduce them by using semantic data platforms that can contextualize and harmonize the data into the correct canonical model for the AI. Companies should be cleaning, curating and modeling their enterprise data before the AI even looks at it to ensure that these hallucinations are reduced, and that the data required is significantly less. Simply put: Greater context will improve the probability of predicting the correct answers.
Generative AI models are unpredictable and have the tendency to produce outputs that are meaningless, irrelevant or inaccurate for the context. There is also the possibility of producing results that reflect biases encoded in the training data. This can be a problem, especially if the generated output is used for critical business decisions.
Enterprises can increase the accuracy and trustworthiness of the answers they get by combining generative AI with their enterprise data systems. Generative AI's ability to act upon specific private semantic data allows enterprises to gain insights unique to their organization. The merging of generative AI with MarkLogic and Semaphore allows enterprises to benefit from semantically tagged data that acts as an associative memory of generative AI, allowing natural language questions to be asked against the most pertinent private data. By consuming and processing private or proprietary data, the generative AI model gains a deeper understanding of the company's products and services, customers and internal processes. Because the enterprise updates and retrieves this data in real time, the generative AI system gets access to this fresh data, helping to solve the problem of the "training data cut-off,” as older and outdated data become less relevant. In fact, search relevancy tuning can be adjusted to prioritize more decent data and only feed that to the generative AI system for its consideration.
Generative AI models are complex, and the accuracy of the final output is often questionable. Connecting generative AI to a data platform creates more transparency and allows you to reference and analyze specific URIs from the private data that were provided to the generative AI to generate the answers. Additionally, the generative AI can be instructed to provide you with references to any URIs that it used. This makes the system easier to troubleshoot and allows you to easily track and analyze the actual prompt sent to the generative AIs, creating human-readable audit trails needed in regulated environments. These saved answers can also be reused to reduce the load on the generative AI systems, as regenerating/re-predicting already known answers is much more resource intensive and costly than a simple relevancy-based search retrieval. The answers can then be used to further train the generative AI systems.
Generative AI is trained on substantial amounts of data, but when it comes to private and enterprise data, enterprises must be extra cautious as using this data for generative AI training creates privacy concerns. For example, users who were not meant to have access to a particular data set might be able to recreate it by interacting with generative AI systems that were trained on it. Also, it has been shown that most data submitted to vector databases can be recreated from just the embeddings alone. Organizations can manage the data generative AI is provided to generate its answers, and thus improve their data security. With document level or even element security, enterprises can ensure their AI will only consume data that is allowed by the users’ roles or queries' rules. This means that retrieved data will be tied to role-based access control (RBAC) or query-related security directly associated with the user's zero-trust access privileges or the data’s own security metadata settings. In turn, this ensures that the generative AI short-term memory (or tokens) will never receive unauthorized data. By doing this, companies can ensure that their AI systems are compliant with enterprise governance, lineage and provenance standards—meaning they operate within defined parameters, company policies and procedures. All of this protects enterprise data.
Good generative AI results rely on the data they are trained on—as such, the quality of data is foundational for the quality of the generative AI’s results. The integration of enterprise data with data management platforms benefits businesses through enhanced data quality. Companies can integrate processes such as data harmonization, data deduplication and data mastering to ensure data consistency across diverse sources and minimize the amount of redundant data fed to the model. By aggregating and analyzing data to identify data biases in the generative AI’s responses, companies can improve data quality throughout the data lifecycle.
The implementation of generative AI in an enterprise typically requires substantial resources, technical expertise and manpower. The scalability of the solution could become an issue because of the increasing complexity of data and the changing requirements a business may have. MarkLogic and Semaphore can solve this problem by providing a scalable and secure knowledge data platform that can store all the enterprise information. Plus, when a company needs to change their generative AI model, they will not have to re-index and/or regenerate their data—minimizing from-scratch work. Integrating generative AI models into existing systems should be a well-planned initiative, and companies should be focused on building on a solid enterprise data architecture instead of a one-off for an AI activity.
A recent report from McKinsey (2023) reported that enterprises using AI systems in their operations are experiencing cost reductions and improved operations. Generative AI can be implemented for different use cases across industries and lead to cost-savings. By automating mundane tasks and improving some business processes, businesses can free up staff to focus on more critical business-related activities.
Incorporating generative AI into your business is not just a technological advancement, it is imperative for success—and more companies are seeking ways to use the technology. It enables you to stay competitive, enhance your business operations and leverage your workforce more efficiently.
Embrace generative AI in your enterprise now and position your organization for a future of innovation and growth. If you are looking at AI systems, especially LLMs or generative AI, be sure to explore the combined power of MarkLogic and Semaphore.
Get started with incorporating your business data with generative AI today.
Continue reading...
With the advent of ChatGPT, the term “generative AI” has become increasingly popular and is gradually being incorporated into a wide range of fields and industries. Generative AI is a technology capable of generating content based on a user’s input and request, otherwise known as a "prompt." A 2023 Gartner survey highlights that this technology will profoundly impact businesses, their competitiveness and their operating models. In this highly technological and data-driven business landscape, generative AI is emerging as a transformative force for enterprises. Incorporating generative AI in your business will have substantial benefits, including innovation, efficiency and enhanced business operations. For these benefits to be realized, though, you must address some of the key challenges that generative AI presents for your enterprise organization. Using Generative AI and a semantic layer in an enterprise data system solves these challenges, provides even bigger benefits for your enterprise and revolutionizes how businesses harness data. Here, we will explore the benefits of merging generative AI with enterprise data systems and how this can amplify your business operations.
Key Business Benefits of Integrating Your Enterprise Data with Generative AI
1. Reduced Hallucinations in the Generative AI’s Outputs
The lack of human reasoning and understanding of generative AI’s models, leads to their inability to understand and misinterpret a user's question. This can result in the creation or invention of incorrect answers. These types of errors are also referred to as hallucinations. Hallucination is a term used to describe the phenomenon where the generative AI model produces outputs that are incorrect and don’t correspond to the data they are trained on or to any known pattern. In fact, generative AI confidently states incorrect data 15–20% of the time (Datanami, 2023). The hallucination issues will never completely disappear, but companies can reduce them by using semantic data platforms that can contextualize and harmonize the data into the correct canonical model for the AI. Companies should be cleaning, curating and modeling their enterprise data before the AI even looks at it to ensure that these hallucinations are reduced, and that the data required is significantly less. Simply put: Greater context will improve the probability of predicting the correct answers.
2. Improved Reliability and Trustworthiness of the Generative AI’s Outputs
Generative AI models are unpredictable and have the tendency to produce outputs that are meaningless, irrelevant or inaccurate for the context. There is also the possibility of producing results that reflect biases encoded in the training data. This can be a problem, especially if the generated output is used for critical business decisions.
Enterprises can increase the accuracy and trustworthiness of the answers they get by combining generative AI with their enterprise data systems. Generative AI's ability to act upon specific private semantic data allows enterprises to gain insights unique to their organization. The merging of generative AI with MarkLogic and Semaphore allows enterprises to benefit from semantically tagged data that acts as an associative memory of generative AI, allowing natural language questions to be asked against the most pertinent private data. By consuming and processing private or proprietary data, the generative AI model gains a deeper understanding of the company's products and services, customers and internal processes. Because the enterprise updates and retrieves this data in real time, the generative AI system gets access to this fresh data, helping to solve the problem of the "training data cut-off,” as older and outdated data become less relevant. In fact, search relevancy tuning can be adjusted to prioritize more decent data and only feed that to the generative AI system for its consideration.
3. Transparent and Auditable Generative AI Outputs
Generative AI models are complex, and the accuracy of the final output is often questionable. Connecting generative AI to a data platform creates more transparency and allows you to reference and analyze specific URIs from the private data that were provided to the generative AI to generate the answers. Additionally, the generative AI can be instructed to provide you with references to any URIs that it used. This makes the system easier to troubleshoot and allows you to easily track and analyze the actual prompt sent to the generative AIs, creating human-readable audit trails needed in regulated environments. These saved answers can also be reused to reduce the load on the generative AI systems, as regenerating/re-predicting already known answers is much more resource intensive and costly than a simple relevancy-based search retrieval. The answers can then be used to further train the generative AI systems.
4. Data Security and Compliance with Enterprise Standards
Generative AI is trained on substantial amounts of data, but when it comes to private and enterprise data, enterprises must be extra cautious as using this data for generative AI training creates privacy concerns. For example, users who were not meant to have access to a particular data set might be able to recreate it by interacting with generative AI systems that were trained on it. Also, it has been shown that most data submitted to vector databases can be recreated from just the embeddings alone. Organizations can manage the data generative AI is provided to generate its answers, and thus improve their data security. With document level or even element security, enterprises can ensure their AI will only consume data that is allowed by the users’ roles or queries' rules. This means that retrieved data will be tied to role-based access control (RBAC) or query-related security directly associated with the user's zero-trust access privileges or the data’s own security metadata settings. In turn, this ensures that the generative AI short-term memory (or tokens) will never receive unauthorized data. By doing this, companies can ensure that their AI systems are compliant with enterprise governance, lineage and provenance standards—meaning they operate within defined parameters, company policies and procedures. All of this protects enterprise data.
5. Enhanced Data Quality
Good generative AI results rely on the data they are trained on—as such, the quality of data is foundational for the quality of the generative AI’s results. The integration of enterprise data with data management platforms benefits businesses through enhanced data quality. Companies can integrate processes such as data harmonization, data deduplication and data mastering to ensure data consistency across diverse sources and minimize the amount of redundant data fed to the model. By aggregating and analyzing data to identify data biases in the generative AI’s responses, companies can improve data quality throughout the data lifecycle.
6. Scalability and Integration with Existing Systems
The implementation of generative AI in an enterprise typically requires substantial resources, technical expertise and manpower. The scalability of the solution could become an issue because of the increasing complexity of data and the changing requirements a business may have. MarkLogic and Semaphore can solve this problem by providing a scalable and secure knowledge data platform that can store all the enterprise information. Plus, when a company needs to change their generative AI model, they will not have to re-index and/or regenerate their data—minimizing from-scratch work. Integrating generative AI models into existing systems should be a well-planned initiative, and companies should be focused on building on a solid enterprise data architecture instead of a one-off for an AI activity.
7. Innovative Cost-savings
A recent report from McKinsey (2023) reported that enterprises using AI systems in their operations are experiencing cost reductions and improved operations. Generative AI can be implemented for different use cases across industries and lead to cost-savings. By automating mundane tasks and improving some business processes, businesses can free up staff to focus on more critical business-related activities.
How to Integrate Generative AI with Your Enterprise Data
Incorporating generative AI into your business is not just a technological advancement, it is imperative for success—and more companies are seeking ways to use the technology. It enables you to stay competitive, enhance your business operations and leverage your workforce more efficiently.
Embrace generative AI in your enterprise now and position your organization for a future of innovation and growth. If you are looking at AI systems, especially LLMs or generative AI, be sure to explore the combined power of MarkLogic and Semaphore.
Get started with incorporating your business data with generative AI today.
Continue reading...