Preparing forGenerative AI Initiatives
In the rapidly evolving landscape of artificial intelligence, generative AI stands out as a transformative force poised to add trillions of dollars in value to the global economy. McKinsey Global Institute estimates that generative AI could increase the economic impact of AI by 15 to 40%, marking a significant leap in productivity and innovation.
To make progress towards gen AI, organizations must rethink and embrace new technology solutions that empower them to leverage data products effectively across various use cases.
Data product thinking leads to a network of data products that link business strategy with source data that results in impact. To accelerate Generative AI use cases, data products can be reused and applied to projects – alleviating the time needed to find, validate, and begin development of use cases.
The application of data productsfor generative AI use cases
Democratizing AI across enterprises
Generative AI represents a paradigm shift, democratizing AI capabilities across every function of the enterprise. Unlike traditional AI tools limited to specific use cases, gen AI has the potential to redefine the future enterprise by empowering every employee and engaging every customer with the right interaction.
This transition requires a comprehensive approach and rethinking data infrastructure, focusing on flexibility, scalability, efficiency, and how democratized access and the employment of data products can prepare the organization for years to come.
One Data forGenerative AI solutions
Rethinking data infrastructure for data product delivery
To support gen AI initiatives, organizations need to think beyond legacy systems and acquire new data infrastructure that can surface and utilize once-hidden data, enabling extraordinary advances across the enterprise while allowing them to plan, develop and pilot test use cases without extreme overhead and context switching.
Infrastructure should be usable by both IT and data teams and must prioritize ease of use for businesspeople to be involved in the design, development, documentation, and delivery of data products. By design, IT and data teams must emphasize access and sharing of data. New infrastructure must also consider security, intellectual property protection and a unified governance framework adaptable to for data product governance.
Applying data products in use cases for gen AI
The application of generative AI spans a wide range of scenarios, from employee training and upskilling to complex scenario forecasting and anomaly detection. Imagine a data literacy data product focused on internal upskilling of knowledge workers supporting personal and professional development across the organization.
Other use cases include automating report generation, optimizing pricing strategies, identifying process mining efficiency, personalizing customer service, and detecting security threats.
Go deeper on data products for your business or industry.
Training customized LLMs using One Data
The key to unlocking generative AI is the ability to train customized Large Language Models (LLMs) tailored to specific organizational needs. By retaining intellectual property and ensuring specificity in LLMs, organizations can innovate, collaborate, and differentiate themselves in the market.
Integrating One Data’s AI-Powered Data Product Builder into the process enables organizations to accelerate the process from plan to prototype to production.
One Data’s Business Case Builder feature allows teams to find the right components to use for their business case, plan the design, decide on connections, develop the required data products, and begin training on proprietary LLMs while maintaining control over algorithms. Data products that are used for developing the model and training as well as the final state data product enables you to accelerate the speed in which you can test, iterate, and implement your AI use case while ensuring your ability to continue developing your model over time.
Designing and developing LLMs require a step-by-step process to get to the result. Leveraging existing analytics projects, data products, and training with the right inputs requires time, testing, and review. Using One Data accelerates the speed in which you can achieve your final result.
Considerations to address governance and risks
As organizations embrace generative AI, they must also address governance and mitigate risks. This includes revamping existing governance frameworks, managing exponential growth in data sources, and ensuring compliance with regulations such as the EU AI Act. Unified governance is essential to reduce bias, model drift, privacy breaches, and intellectual property protection risks
Executive business leaders must also consider how they will develop internal swat teams focused on algorithmic auditing, documentation, and leverage tools to enhance transparency and accountability in AI projects. Internal design, development and documentation of processes and transparency into these projects for stakeholders will be critical for alignment, reviews, standardization, and auditing.
Empower human expertise
Despite the advancements in Generative AI, human expertise remains indispensable. Humans are still necessary for tasks, however they become more critical for driving strategy, curating models, and ensuring responsible AI practices.
Empowering human experts through AI governance frameworks and unified governance approaches enables organizations to strike a balance between innovation and risk mitigation. The workforce is critical, what becomes key is the ability to develop skills for the next generation.
Business value of generative AI for forward thinking organizations
Data products are the cornerstone of generative AI projects, offering immense business value. They combine vast datasets to create innovative solutions, ranging from content generation to data literacy training. These products enable personalized experiences, improving employee development, customer engagement and loyalty.
Through data-driven insights, businesses can make informed decisions, enhancing efficiency and profitability to remain competitive in dynamic markets by fostering innovation and agility.
Data products to accelerate generative AI
Generative AI presents unprecedented opportunities for organizations to drive innovation, reallocate resources, enhance productivity, and create greater value from data products.
By embracing data products as a practical and meaningful solution to help pilot and execute Generative AI projects, organizations can democratize AI, train customized LLMs, address governance challenges, and develop human expertise.
Most important for an organization is to be aware that there is a time of change, and the change will come fast. To stay on track, they need to encourage their employees to start to upskilling.
Prof. Dr. Michael Granitzer | Chairholder Data Science, University of Passau