How to responsibly leverage generative AI, such as ChatGPT, to drive compelling product storytelling and consumer engagement.
ChatGPT promises to revolutionize digital communication by enabling users to interact with AI that understands data, intent and context.
To reach this potential, integrating governed data into generative AI models is crucial. This integration makes data accessible, relevant, meaningful and responsible.
Many organizations are using generative AI to quickly generate content and compelling marketing copy to promote their products in their online stores and social media.
This ties closely into better and more efficient product experience management (PXM) processes. Using generative AI seems, at first glance, easy, appealing and very productive. However, when setting up so-called prompts, good data is essential to direct the algorithms to provide appropriate responses.
Responsible AI usually refers to the fact that it prioritizes ethical and societal considerations, while minimizing potential harms such as bias and privacy violations.
It involves ensuring transparency and accountability in AI systems. However, the responsible output relies on the input being governed according to certain rules. Translating this process to generative product information means data governance is required for the input in order to ensure a trusted and valid product description for the output.
AI and data governance best practices
The integration of artificial intelligence (AI) into your data management strategy requires robust data governance. Not only does this ensure ethical AI use, but it also makes regulatory compliance much easier. Here are a few best practices to consider:
- Establish an AI governance framework
- Implement data lineage and metadata management
- Use data catalogs and data pipelines effectively
In addition to the practices mentioned above, here are a few more tips about how to leverage your company’s AI initiatives responsibly, while ensuring proper data governance and compliance.
Implement ethical AI practices
One of the most challenging aspects of using vast amounts of data to train your AI is ensuring your AI systems operate fairly and without bias. As such, it’s recommended that you implement certain ethical AI governance practices, including:
- Conduct regular assessments to identify and mitigate potential biases in your data sets and AI systems.
- Involve stakeholders in your data governance strategy, with a focus on inclusivity and ethical outcomes.
- Establish clear accountability mechanisms and remain transparent through the AI development and deployment stages.
Enhanced prompt engineering is key
Generative AI, which relies on machine learning, is a powerful tool that can produce content in a matter of seconds, including product descriptions that are crucial for driving superior customer experiences.
With such powers in their hands, companies must ensure that their use of generative AI is responsible and trustworthy. The key to responsible AI lies in prompt engineering.
Prompt engineering is the process of creating inputs or prompts that guide generative AI, therefore, the quality of these prompts can greatly affect the output.
Companies must ensure that their prompts are accurate and relevant to their target audience in order to produce engaging and trustworthy content.
There are many pitfalls of using AI-generated product information indiscriminately:
- Obviously, product specifications must be accurate as any inaccuracies can lead to customer dissatisfaction and even legal trouble.
- Companies should be careful to outsource their brand to generative AI. While generative AI can produce content quickly, it often lacks the creativity and emotional intelligence of human writers. Brands that rely too heavily on generative AI risk losing their unique voice and identity.
Companies cannot rely on generative AI and chatbots as their single source of truth.
While generative AI can produce compelling content quickly, it does not have the ability to govern data. In addition to the chatbot, at least two further components are needed in the content production: (1) A single source of truth based on data governance, (2) clerical review to ensure brand compliance and approve content produced by generative AI.
Feed AI with governed data
To ensure the accuracy and trustworthiness of AI-generated content, as well as to make the clerical review easier, you need to drive the AI query with governed data.
This means that the data used to prompt the generative AI must be accurate, relevant and subject to strong data governance capabilities.
Master data management (MDM) supports governance of product data and is designed to provide a single source of truth. By interfacing with a generative AI, such as ChatGPT, you can ensure that the data used to prompt the AI is accurate and trustworthy.
Via an API and a simple configuration of the MDM, master data management and generative AI can work in conjunction to combine trusted data with the speed of content production. This allows you to produce content quickly while ensuring its accuracy and trustworthiness.
To go deeper, download our ebook on getting more value from AI with MDM to see how mastering data governance unlocks stronger AI results.
Monitor AI performance and compliance
High-quality data sources and AI data products are important, but they won’t get you far without adhering to data protection regulations, like the GDPR or the California Consumer Privacy Act (CCPA).
With that in mind, it’s recommended that you implement these key strategies to both optimize performance and ensure your data governance policies are in alignment with current regulations:
- Track relevant performance metrics like accuracy, data usage rates, overall impact to your business, costs, etc.
- Conduct regular reviews of your AI systems to ensure they comply with data protection rules and your internal data governance framework.
- Use the insights you’ve gathered to continuously improve AI models and your governance policies.
By integrating these strategies throughout the lifecycle of your AI products, you can ensure that your organization’s AI systems are both effective and compliant with relevant regulations.
Automated content creation embedded in an MDM interface with a Generate Product Description button. The AI is prompted with a product name, specifications and a product image:
Use case: A retailer wants to sell one of its best-selling products in a new market.
1) The first step is the act of defining
The product manager, Julie, needs a market-specific product description.
She will use the AI assistant for that. She defines the target criteria in the MDM to prompt the AI for suggestions. The MDM contains pre-defined prompts to query the AI in the most efficient way. These prompts contain directives, such as:
- Context
- Target audience definition (demographics, psychographics, revenue)
- Purpose
- Writing style and guidelines (tone, structure, length, word choice)
The prompt engineering is embedded in the MDM. This means the structured and quality-oriented data and processes of the MDM make it more efficient to query a generative AI.
2) The next step is the act of iterating
The AI uses the clean data from the MDM and the prompt, then returns a few suggestions. In a workflow, the product manager reviews the suggestions, refines the prompt and asks for specific quotes from local celebrities to be added. She receives new suggestions in response. She selects one and forwards it to a copywriter.
What happens is that a conversation takes place between the AI and the product manager.
3) The third step can be called refining and validating
The copywriter reviews the suggestion, refines the wording to make it more brand compliant. He notifies the data governance team of his feedback, so they can refine the pre-defined prompts.
He finds that the quote doesn't belong to the celebrity named by AI. He finds another quote, modifies the text and sends it back for final validation to the product manager.
Julie approves the final product description. To be concluded: The AI accelerates the team’s work, but an expert’s review is indispensable to refine and validate.
The benefits of embedding generative AI into the master data management platform
1. Enhanced data quality and consistency
AI needs to be asked the right question with the right data. By improving data quality and consistency, MDM empowers you to craft better prompts, leading to accurate, relevant and reliable chatbot responses.
2. Compliance and security
In a global world, trust and security are essential. MDM ensures legal compliance and data security, allowing you to confidently operate generative AI within data privacy regulations.
3. Workflow efficiency and collaboration
AI excels when integrated into everyday processes. MDM enables you to seamlessly integrate chat AI into workflows and foster cross-functional data collaboration for enhanced efficiency.
Generative AI is a powerful tool that can provide a tremendous tailwind to your product description creation, removing a great deal of tedious and repetitive work. However, in order to remain ethical and correct, the AI must be guided by a single source of truth containing governed product master data.
Challenges of integrating AI and data governance
Integrating AI and data governance can present countless issues that you’ll need to address quickly in order to ensure the most ethical and effective implementation of your AI systems. A few of the most common challenges include:
Data silos
A data silo occurs when one or some of your data assets are isolated in totally different systems or departments. Not only can this lead to data security challenges, but it can also result in data fragmentation and inefficiencies.
Bias in AI outputs
Humans are biased. In many cases, that means that there’s a high probability that our data is biased, and as a result, if you’re not careful, the AI systems trained on that data can amplify the biases in the data sets.
Scalability and performance
Scaling your AI system without compromising performance can be incredibly challenging. While some companies like OpenAI have billions of dollars to overcome this on the backend, that’s not typically a viable option for smaller companies. As such, it’s important to build your AI systems using robust data management platforms (in-house or third party) and optimizing your algorithms for large-scale use.
Regulatory compliance
Non-compliant AI systems can result in hefty fines, lawsuits and even the end of your business. It’s essential to implement ethical standards and a comprehensive data governance policy that includes transparency, regular audits and intense testing before rollout.
By understanding and addressing these challenges head on, your organization can effectively integrate data governance and cutting-edge AI. Not only can this result in added reliability, but it can also lead to more ethical and effective AI-driven outcomes.
Learn more about data governance
In any organization that collects and uses data, governance is key for accuracy, quality and decision-making. To help you ensure smooth, secure data governance, we gathered some additional resources:
- How to Implement Data Governance
- How to Build a Successful Data Governance Strategy
- 6 Best Practices for Data Governance
- Five Reasons Your Data Governance Initiative Could Fail
- The Best Data Governance Tools You Need to Know About
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