Essential Guide for Data Leaders: Scaling Generative AI

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Moving Beyond the Honeymoon Phase of Generative AI

After nearly two years of excitement and exploration surrounding generative AI (gen AI), companies are beginning to transition from the initial infatuation to a more pragmatic approach focused on creating tangible value. The latest McKinsey Global Survey reveals that 65% of organizations across various sectors are now utilizing gen AI regularly, a significant increase from the previous year. This surge in adoption is accompanied by rising investments, driven by the belief that early adopters are paving the way for cost reductions and increased profitability. However, many companies are still grappling with the challenge of realizing significant impacts from their gen AI initiatives.

The Need for Strategic Integration

To keep pace with rapid innovation, data executives are drafting strategies to integrate gen AI into their existing technology frameworks. While many organizations have initiated pilot projects, the journey toward full-scale implementation requires more than just technical integration. Companies must develop comprehensive gen AI operating models that ensure their technology investments yield measurable business results. An operating model outlines how people, processes, and technology work together to deliver value to customers and stakeholders, encompassing everything from staffing and compliance to technology development.

The Role of Chief Data Officers

Chief Data Officers (CDOs) are often at the forefront of creating these operating models, tasked with aligning technology, personnel, and processes to harness the potential of gen AI. However, data leaders frequently encounter two common pitfalls when establishing these models:

  1. Tech for Tech’s Sake: This trap involves investing heavily in gen AI without a clear business objective, leading to tools that are underutilized and disconnected from real-world applications.

  2. Trial and Error: Many organizations experiment with various gen AI projects in an uncoordinated manner, which can be particularly detrimental in fast-paced sectors like technology and retail. In contrast, industries such as agriculture and manufacturing may have the luxury of time to strategize their deployments.

The Urgency for Deployment

The urgency to deploy gen AI creates a unique opportunity for data executives to advocate for operating models that prioritize data at the core of the organization. When CDOs and their executive allies are ready to define a gen AI operating model, they should consider several foundational steps to ensure their frameworks address risk, governance, security, and compliance.

Designing a Component-Based Operating Model

Given the rapid evolution of gen AI, organizations should adopt a component-based operating model. This approach allows companies to systematically integrate new gen AI components into their existing architecture while aligning with business objectives. By enabling incremental changes without overhauling the entire tech stack, organizations can remain agile.

For example, a leading European bank successfully implemented 14 key gen AI components, allowing it to address 80% of its core use cases within three months. This component-based strategy not only streamlined development but also ensured that resources were focused on high-impact areas. Despite its advantages, only a small percentage of organizations have adopted this model, highlighting a significant opportunity for growth.

Structuring Talent Teams

When building a gen AI operating model, defining the core team is crucial. Organizations can either extend existing data or IT teams by equipping them with new gen AI skills or create a distinct gen AI team. Each option has its pros and cons.

Extending existing teams may seem easier, but it can lead to slower rollouts due to the integration of gen AI into broader technology platforms. Conversely, establishing a separate gen AI team allows for rapid iteration and innovation, particularly in regulated industries such as healthcare and finance.

Prioritizing Data Management

Effective data management is essential for successful gen AI implementation. Without a robust data organization, gen AI applications struggle to access and process the necessary information. Many enterprises face significant challenges in data utilization, including issues with model reusability, accessibility, and quality. Therefore, a comprehensive data management and governance strategy should be integral to any gen AI operating model.

Organizations can tackle the complexities of unstructured data—comprising over 80% of total data—by prioritizing specific domains based on business needs. This targeted approach allows for more manageable and actionable data governance, ensuring that gen AI systems have access to high-quality data.

Embracing Decentralization

As organizations become more adept at managing data, they may choose to shift from a centralized to a decentralized approach for gen AI development. This evolution can occur in stages:

  1. Centralized Gen AI: Initially, companies may centralize gen AI capabilities within specific domains to build expertise and control costs.

  2. Federated Gen AI: As proficiency grows, organizations can adopt a federated model, where business units take on more responsibility for data processing and integration into their workflows.

  3. Decentralized Gen AI: Ultimately, some organizations may fully decentralize gen AI capabilities, allowing each domain to create tailored applications that meet their specific needs.

Unifying Teams Through Common Infrastructure

Regardless of the chosen model, it is crucial to maintain a unified infrastructure that supports all gen AI tools. A centralized IT team should oversee the development of a common technology framework, ensuring that all business units adhere to security and compliance standards while fostering innovation.

Emphasizing Risk and Compliance Governance

The deployment of gen AI introduces heightened risks, including misinformation and data leaks. Therefore, every gen AI operating model must incorporate robust risk and compliance governance. Companies should conduct thorough risk assessments to identify potential threats and establish a governance plan that includes monitoring and compliance measures.

A structured approach to risk management involves identifying new risks, classifying gen AI tools, deploying a tiered approach to risk mitigation, and ensuring that all stakeholders are informed about safe gen AI practices. This proactive stance not only safeguards the organization but also positions it to adapt to evolving regulations.

Conclusion

As organizations transition from experimentation to implementation, the creation of effective operating and technical models is essential for successful gen AI deployment. By prioritizing data organization and governance, companies can facilitate rapid rollouts and prepare for a future where decentralized development becomes the norm. With a well-coordinated strategy, organizations can harness the full potential of gen AI to thrive in today’s competitive landscape.

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