Without a doubt, generative AI (GenAI) has been the most talked about innovation this year, and it looks like it is only going to grow in 2024 and beyond. Love it or hate it, ignoring GenAI and its benefits for organizations means losing out on a tool that is set to revolutionize the way we work.
Now, organizations are faced with a critical decision: do they build their own GenAI capabilities or opt for ready-made solutions from vendors? This pivotal choice can shape the trajectory of innovation within an organization.
In this blog, we will explore the various considerations when making the build vs. buy decision and delve into the impact of GenAI on productivity, functional excellence, and industry-specific applications.
GenAI Unleashes Innovation
GenAI transforms an organization’s knowledge and makes it accessible to employees, customers, and suppliers. It connects data to insights, unlocking lots of information in an organization and turning it into a valuable asset.
Boosting Productivity
Staying ahead of the competition often hinges on the ability to maximize productivity while driving operational efficiencies. GenAI shines in productivity use cases, automating tasks like summarizing reports, generating job descriptions, and even coding in languages like Java and Python. By automating these processes, GenAI enhances workforce efficiency, reduces human errors, and augments overall output.
To boost productivity with GenAI, you have two options. You can integrate it into existing commercial apps or opt for standalone GenAI apps. This flexibility enables customization to meet specific requirements. Companies often choose this path to overcome challenges like finding specialized AI talent and limited budgets.
However, this approach may have trade-offs, such as lower control over model governance, data security, privacy, and compliance issues. Careful consideration is necessary when deciding to build or buy GenAI for productivity enhancements.
Functional Excellence
GenAI also plays a vital role in enhancing functional excellence by automating processes, optimizing decision-making, and providing highly personalized, context-aware solutions. Companies can modify open-source models or use existing models from AI platforms to align GenAI with their business needs.
The adoption of GenAI for functional excellence is facilitated by well-harmonized institutional data, in-house talent, budget availability, and a moderate risk appetite. While it offers contextual experiences and operational efficiencies, companies need to be aware of the potential challenges, including model governance and data security.
Industry-Specific Applications
GenAI’s impact in industry-specific contexts is profound. It empowers companies to create new digital business models, design innovative products and services, and establish unique competitive advantages. Fine-tuning existing models or developing custom models are the primary approaches for industry-specific GenAI implementations.
Industries often turn to GenAI for applications such as groundbreaking drug discovery in the life sciences sector and generative material design in manufacturing. While these implementations offer tremendous value, they also come with high costs and the responsibility of managing regular model refreshes.
The ‘Buy’ Perspective
The ‘Buy’ perspective offers a way to quickly access GenAI benefits by leveraging enterprise applications with GenAI capabilities or native GenAI applications. These solutions empower businesses to harness GenAI’s capabilities without extensive in-house development efforts. However, they may lack competitive differentiation and offer limited customization.
The ‘Buy’ approach is suitable for enterprises wanting quick access to GenAI benefits, especially those with low maturity around enterprise data management and AI. It can kickstart the GenAI journey while establishing a foundation for data management, governance, and the skills needed for further GenAI development.
Fine-Tuning GenAI Models
Fine-tuning existing models is a powerful strategy that involves modifying open-source or commercial models to align with specific business requirements. This approach offers robust solutions, control over training data, and superior performance. However, it requires labeled data sets and regular updates.
Three main avenues for fine-tuning generative AI models include fine-tuning, retrieval augmented generation (RAG), and prompt engineering. Each approach has its advantages and is suitable for different use cases.
Building Your Own GenAI Model
Building your own GenAI model involves developing a foundational model using institutional data. This approach offers the highest level of control, governance, and transparency. It is ideal for organizations aiming to create unique products or services derived from proprietary data.
However, building your own model comes with challenges, including the need for a proficient data science team, high costs related to data collection and infrastructure, and a requirement for expertise in machine learning.
Converting GenAI’s potential into use cases that generate business value requires a clear-eyed understanding of current limitations and challenges. GenAI technology is powerful, but it is not fully mature and presents opportunities for misuse.
IDC’s leading-edge expertise and insight into GenAI trends, opportunities, requirements, and challenges help you elevate conversations and better engage with your customers.
Are you ready to meet the challenge? Contact us today to discuss how IDC can help you succeed with GenAI.
