Adopting generative AI requires a deep understanding of this rapidly evolving technology. Much of this work falls to CTOs and tech-focused personnel. But CEOs and other business leaders also make decisions that control costs, speed innovation, and help ensure generative AI meets your organization’s goals.
Which aspects of generative AI merit your attention? Several key areas will determine the cost and the value generative AI delivers.
Choosing a Foundation Model
You probably know the basic facts about generative AI models: foundational models (FMs) serve as a starting point for building generative AI applications, and large language models (LLMs) are FMs trained on vast amounts of data and text that users can interact with in natural language.
Generative AI involves two core phases: training, when the model is learning from curated data, and inference, when the model is using what it has learned to analyze, recognize, and respond.
Choosing FMs has a big impact on cost and the capabilities of your generative AI applications. There is no one-size-fits-all approach to choosing a model, so it’s important to evaluate their capabilities rigorously and collaboratively to balance price and performance.
Your organization can select from an ever-increasing array of models based on criteria such as latency (response time), scalability, and suitability for your specific needs. This decision often involves stakeholders from higher management, line-of-business departments, and technical experts. Experimenting with multiple models and performing thorough evaluations can help your team make an informed technical and business choice.
Approaches to Model Customization
There are many techniques for model customization: the process of training a model for your specific use case or domain. Customization is an important business decision, since these techniques vary in cost and complexity—and affect the accuracy and utility of your generative AI applications. Fine-tuning modifies the model to make its responses more relevant, while retrieval-augmented generation (RAG), a simpler and more cost-effective technique, optimizes the accuracy of a model’s output by retrieving curated data from external data sources without modifying the model.
Data as a Differentiator
Integrating your data with your generative AI applications via customization helps transform a generic application into one that truly knows your organization. Your data improves the model’s accuracy by helping it understand your company’s processes, products, customers, and terminology. That shows customers and other users that you know them and their preferences, creating value and building a competitive advantage.
Customization techniques such as RAG help your model draw from diverse data stores to provide the accurate, relevant results and personalized recommendations that users need, and quickly.
The fine details about data are the domain of CDOs, CTOs, and data scientists, but business insight helps ensure your data serves as a crucial differentiator. You may need to determine whether your organization needs to invest in upgrading its data infrastructure, to make it more suitable for fueling your generative AI applications. The condition and availability of data can greatly affect the relevance of results, the success of your generative AI applications, and the cost of implementation.
Risk Mitigation
Every new technology comes with risks. Mitigating the risk of generative AI means implementing technologies and employing techniques that help ensure security, privacy, and responsible AI to protect your organization financially, protect your brand reputation, and preserve customer loyalty.
Security can never be an afterthought. You need to protect your data from the start. Your customers rely on your vigilance with their information, and any privacy breach is a violation of their trust.
Your organization’s technical team can guide techniques that help reduce all types of risk. Context grounding is one customization technique that checks the output of your model against verifiable sources of information, helping weed out bias, reduce hallucinations, and build trust. Implementing effective guardrails, and testing results from your generative AI applications against your defined policies, helps ensure accurate, relevant, and unbiased results.
Risk mitigation does much more than protect your organization. By 2026, organizations that implement transparency, trust, and security in their AI models will see a 50% improvement in adoption, achieving business goals, and user acceptance, according to Gartner.
Exploring Costs Holistically
Cost is a multilayered issue. Executives need to ask about the financial impact of key technical decisions: the choice of model, how the model is customized, the anticipated volume of user interactions after you scale. Business leaders need to consider all costs beyond the model itself, including customization, testing, and data preparation. During inference, when the model is deployed and in use, other factors come into play: more interactions raise the cost, particularly if your application is customer-facing and available on the internet.
Be the Voice of Value
Even seemingly arcane technical decisions can affect the value generative AI delivers to your organization, its people, and its customers. As you explore generative AI, remember why so many organizations are adopting it: to create value.
Value can have multiple meanings: higher revenue, better customer experiences, breakthrough innovation. But asking one question at every stage of your generative AI journey—“What is the business value here?”—can help keep your organization on track.
Business leaders who use their curiosity to explore AI with their tech-focused colleagues will be better prepared to create a viable generative AI roadmap and guide their organization from initial experiments to production-grade applications that deliver significant value at scale, and at the right cost.
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