Contents Overview
Introduction
Scaling Gen AI opens a door to the potential transformation of organizations around efficiency improvements, better decision-making, and more tailored experiences. Scaling across the enterprise is the challenge. And, thus, the data leader must also possess the capability to construct a strategic operating model accommodating Gen AI.
In this blog, we discuss how data leaders can scale Gen AI effectively-from building an operating model to developing collaboration across teams.
Building a Strategic Operating Model for Scaling Gen AI
An operating model that clearly aligns AI initiatives with business goals must be defined for Scaling Gen AI effectively across the enterprise. There are two options: either fit Gen AI into an existing data or IT team, or establish an especially designed AI team. Each model has its advantages. Integration of Gen AI with the existing teams ensures resource alignment, but the development of a separate team facilitates faster iteration and development outside the boundaries of the existing IT structure.
For example, a logistics company integrated Gen AI into their existing IT system but only went at a snail’s pace because they had to work within the existing architecture. Those organizations which had a differentiated AI team were able to iterate on the Gen AI components faster to at least be one step ahead of the curve.
Designing Core Reusable Gen AI Components
In order to successfully use Gen AI, organizations will need to focus on developing core reusable components. This could include scalable models, frameworks, and tools that can be functionally used across an enterprise. A task force can be established that oversees the process, ensuring IT, data, AI, and business teams all contribute.
Organizations can create component-based development models, whereby they can leverage identical Gen AI tools for myriad different applications, thus ensuring smooth processes and eliminating redundancy. Moreover, aligning similar components with strategies enables value and return on investment.
Data Management as a Foundation for Scaling Gen AI
Proper data management forms the backbone of Scaling Gen AI initiatives within any enterprise. Without robust data governance and infrastructure, Gen AI models will flounder when it comes to retrieving and processing the required information. It is important for data leaders to understand the need for structured data management since nearly 80% of company data is unstructured. Data governance protocols must be put in place such that quality control, access, and compliance checks on both structured and unstructured data are maintained.
Example: A bank-oriented application on managing unstructured data, a business category, and quality of data. This culminated in much more accurate and reliable Gen AI applications with much fewer issues of data being poorly handled.
Collaborative Scalability Approach for Gen AI
Scaling Gen AI successfully requires collaboration between IT, AI, and business teams, not just technical excellence. Open communication with clear roles can actually help the companies avoid duplication of work or disjointed deployment.
Most leading organizations use the strategy of establishing Centers of Excellence (CoE) for Gen AI. CoEs support and enable people in innovation, standardizing AI practices throughout business units.
Example: A global bank rolled out Gen AI in a federated model. This enabled business units to develop Gen AI applications that would exactly meet their individual needs for deployment, hence faster and smoother integration of Gen AI into daily workflows.
Integration of AI with existing systems
The integration of Gen AI into existing data and IT systems will prove difficult, since the technology life cycles of the different systems cannot be set out in the same timeframe. It would be necessary for data leaders to collaborate with their IT departments in synchronizing their roadmaps and establishing a common infrastructure for the AI tools that would work together for better integration.
In addition to the LLMs or orchestration frameworks built, it is essential to think about how components interact with applications already built, so that does not scale into technical debt.
For example, a telecom company tapped on the expertise of their AI team in the development of LLMs incorporated very smoothly into their technology. The type of service they then offered to clients improved and their operations became efficient.
Tools like Microsoft Azure AI and AWS AI Services demonstrate how organizations can integrate Gen AI seamlessly with existing systems to improve scalability and efficiency.
Although Gen AI has wide applicability, not all use cases present equal value. Data leaders should focus high-value use cases in customer engagement, predictive analytics, and operations optimization-those most likely to deliver real business value and improve performance.
Use Case Example: A South American telecom firm implemented Gen AI for customer engagement, and conversational AI reduced operations costs by over $80 million.
Scalability Challenges Organizations have barriers related to scalability, especially around data governance, system integration, and talent acquisition, despite the benefits of Gen AI. In fact, it takes clear change management strategies coupled with continuous upskilling of employees regarding emerging AI technologies.
Organizations should look for quick-win use cases that have an impact in the short term to build trust and garner support from stakeholders, thus avoiding the infamous pilot purgatory.
Conclusion: A Roadmap to Scaling Gen AI
Scaling Gen AI introduces huge opportunities for organizations across industries, but only through strategic means. With reusable Gen AI components, data governance at the center, and co-collaboration, data leaders can make AI across the enterprise a success. Also, strategic identification of high-impact use cases and subsequent integration with the existing systems will be critical to achieve value from Gen AI and create long-term value for businesses that stay ahead of the competition.
The road for data leaders keen to scale Gen AI is complex but full of potential – all those who do it strategically will be well-placed to win.
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