IIA’s Research and Advisory Network (RAN) clients leverage battle-tested frameworks and an exclusive network of over 150 active practitioners and unbiased experts to plan, prioritize, and execute strategic enterprise data and analytics initiatives. We regularly check the pulse of trending topics for the RAN community and facilitate critical conversations in virtual roundtable format for peer-to-peer exchange.

In a recent roundtable discussion with IIA RAN clients, data and analytics leaders from diverse industries gathered to share their challenges and decision-making process to building or buying generative AI (GenAI) models for enterprise analytics. While the simple answer to the question of “build or buy?” is a blend of both, this discussion revealed key issues every company is grappling with, from leveraging hybrid models for GenAI production to security and governance and internal coordination challenges.

This discussion was moderated and summarized by Mina Meman, Director for Client Success, IIA. Here’s the key takeaways.

1. The Hybrid Approach

The prevailing trend among organizations is the adoption of hybrid models that combine internal development efforts with external partnerships. By leveraging the expertise of external partners while retaining control over core processes, organizations can achieve a balance between innovation and stability in their AI initiatives.

2. The Data Quality Challenge

Data quality and regulatory considerations serve as fundamental pillars shaping AI investment strategies. In sectors such as healthcare, regulatory requirements and data privacy concerns pose unique challenges compared to industries like retail. Organizations must navigate these complexities by implementing robust data governance frameworks and leveraging advanced analytics tools to glean actionable insights from their data assets.

3. Foundational Models and Partnerships

The success of enterprise-level GenAI lies in the collaboration between organizations and leading services that provide foundational models. Whether partnering with OpenAI, Google, Microsoft, or other industry leaders, organizations benefit from access to cutting-edge technologies and expertise. However, the choice of partnership must align with organizational objectives and values to ensure a mutually beneficial relationship.

4. Security and Governance

Amid escalating cybersecurity threats and regulatory oversight, security and governance are essential considerations in AI adoption. By prioritizing security and governance, organizations hope to mitigate risks and build trust among stakeholders.

5. Measuring Success

Success in AI adoption extends beyond technical implementation and includes factors such as speed of deployment, enhanced security, and tangible value creation. Change management initiatives, coupled with upskilling efforts led by learning and development departments, serve as key catalysts for organizational transformation. By aligning AI initiatives with broader business objectives and measuring their impact, these organizations may demonstrate tangible value and drive continuous improvement.

6. Internal Coordination Challenges

Coordinating between business and technical teams presents inherent challenges, particularly in environments where decentralized decision-making leads to internal competition for AI initiatives. Effective communication and cross-functional collaboration are essential to overcome these coordination challenges and drive alignment across the organization.

7. Stakeholder Education

Educating stakeholders about the nuances and risks of GenAI is critical for fostering awareness and buy-in across the organization. Distinguishing between traditional AI and generative AI, as well as addressing potential challenges such as hallucination in generated outputs, requires tailored education and communication strategies. By empowering stakeholders with knowledge and insights, these organizations hope to foster a culture of innovation and responsible AI adoption.

8. Investing in Talent

At the heart of successful AI adoption lies the investment in skilled talent and continuous learning initiatives. Data scientists play a central role in translating AI concepts into actionable insights and driving innovation. Initiatives such as webinars, structured courses, and hands-on training programs facilitate broader organizational understanding and adoption of GenAI technologies.

In Summary

As organizations embark on their GenAI journey, navigating the complexities of adoption requires a strategic, multidimensional approach. By embracing hybrid models, prioritizing security and governance, and investing in talent and stakeholder education, organizations can unlock the transformative potential of GenAI while mitigating risks and driving sustainable growth. As data and analytics leaders chart the course toward an AI-powered future, thoughtful decision-making, collaboration, and continuous learning will be essential drivers of success.

Do you want to join the conversation? Seats are limited and reserved for C-level data and analytics leaders or equivalent at mid- to large-sized enterprises. Learn more about IIA’s RAN community and contact us to RSVP for future roundtables here.