Curiosity and creativity have inspired our species to achieve some of human history’s most spectacular accomplishments. Right from the invention of the wheel around 3500 BCE, the printing press in the 15th century, the telephone in the 19th century, and to skip a few centuries and fast forward to now, the creation and widespread use of what is a unique technology, artificial intelligence (AI).
Key Takeaways
- Generative AI is transforming businesses by enhancing agility and personalization through the Data Cloud.
- Organizations should integrate diverse data sources to create a unified data repository for better AI model training.
- Data cleaning and preprocessing are essential for ensuring the accuracy of Generative AI outputs.
- Maintaining data security and compliance builds customer trust and supports business growth.
- Continuous retraining of Generative AI models is crucial for adapting to evolving customer needs and improving outcomes.
Here, we’ll delve into harnessing Generative AI and empowering businesses with the data cloud.
Amongst its myriad types, Generative AI has emerged as a popular and widely used frontrunner. Its growing popularity has as much to do with its ease of use as it does with the unusual, creative, and scalable ways in which it helps solve business problems.
It is common knowledge that enterprises everywhere are feeling the heat of widespread mainstreaming of technology. It is changing to better meet customers’ changing needs and anytime, anywhere needs. In being simpler and less expensive to operate and operationalize, Generative AI provides enterprises with the kind of tool to enable iterative agility and personalization. These are necessary to stand out and differentiate themselves from peers in the market.
According to a recent study[1], the generative AI market size was found to be worth USD10.79 billion in 2022. It is predicted to grow to a staggering USD118.06 billion by 2032. This represents an expansion of CAGR by 27.02 percent between 2023 and 2032.
In fully harnessing generative AI and its immense potential, organizations will need to invest in Data Cloud. This is an end-to-end cloud-based platform especially equipped to hold, categorize, and manage vast amounts of data securely. It also has a capacity for disaster recovery (DR) that can happen safely and swiftly. Without such a platform, even the smartest, most agile AI type will not do justice to its own capacities or business requirements.
Let us take a closer look at some of the best practices organizations can adopt to maximize the potential of Generative AI by leveraging the Data Cloud.
Integrating and consolidating data
One of the first steps businesses must take before they plan to adopt and start harnessing generative AI in their workflows is to consolidate and integrate data from diverse sources into the Data Cloud. Creating a unified data repository enables comprehensive model training by incorporating both internal and external data. This enhances the accuracy and effectiveness of Generative AI algorithms.
Consider, for example, the manufacturing industry. Data cloud platforms have the ability to centralize data from sensors, simulations, and manufacturing data. Generative AI models can then step in to use this data to generate and test various AI Model design options, leading to more efficient and innovative product development.
Modernizing data
Before embarking on training Generative AI models to fit a business’s industry and specific requirements, companies must also undertake data cleaning and preprocessing. By removing noise, inconsistencies, and biases from data, companies will ensure the models are trained on high-quality and reliable information. This will result in more accurate and meaningful outputs.
Take, for example, a US-based industry technology conglomerate. It was looking for ways to enhance its enterprise performance through digital transformation and modernization. Customers were complaining about long wait times and inefficient problem-solving. An AI assistant tool built on the integration of Data Cloud and Generative AI helped address these challenges. It did so by providing 100 percent automated and iterative customer intelligence on a daily basis.
Making data secure and compliant
For all its various evident advantages, leveraging smart technologies often leads to concerns around data security. By implementing strong data security protocols and ensuring compliance with relevant local and regional regulations, including GDPR, CCPA, and HIPAA, organizations can better safeguard their data. This helps maintain customer trust, a foundational requirement for business growth.
Incentivizing continuous learning
Another important best practice businesses should keep in mind when harnessing Generative AI is to ensure its models are up-to-date and relevant by continuously retraining them with new data. This approach allows models to adapt as required to changing trends. Consequently, they produce more refined and relevant outputs over time.
Consider, for example, e-commerce businesses that use generative AI to personalize product recommendations. This creates unique shopping experiences for customers. Unless Generative AI algorithms are refined in response to the changing data coming in from customer profiles, purchase history, and browsing behavior, they will not be able to generate personalized product suggestions. This refinement enhances customer engagement and increases sales or data monetization.
Prioritizing having a Human-in-the-Loop (HITL)
Along with this, employing an HITL approach will enable organizations to ensure human experts are in the loop to review and validate Generative AI model outputs. This goes a long way in helping to control and oversee all generated content. It mitigates potential biases and maintains the required levels of quality and accuracy.
Differentiating with business outcomes
Many exciting developments are taking place at the crossroads of Generative AI and Data Cloud integration. These promise to further advance business insights and deepen competitive advantages. This includes Federated Generative AI, where federated learning techniques are being extended to Generative AI. It enables models to be trained collaboratively across different data sources without sharing raw data. This is proving to be particularly useful for industries with privacy-sensitive data, such as healthcare and finance.
As businesses look at new ways to make their mark and keep and grow their customer base, integrating and harnessing Generative AI with Data Clouds will prove to be invaluable.
[1] https://www.precedenceresearch.com/insights/generative-ai-market
