Businesses will necessitate a governance framework to oversee their generative artificial intelligence (AI) applications as the proliferation and diversification of large language models (LLMs) in the market are expected to rise.
As per Frederic Giron, the vice president and senior research director at Forrester, organizations will need a blend of internal and external capabilities to harness layers of knowledge effectively.
Moreover, here are three strategies for identifying the perfect AI companion for your company.
This approach may involve utilizing both paid and open-source LLMs from third-party providers like Salefsforce Einstein GPT, Claude from Anthropic, and Meta from Llama, along with integrated AI solutions such as ChatGPT from OpenAI. Companies will also develop their own AI models, such as conceptual AI, leveraging both general-purpose and specific LLMs, while managing diverse AI applications in alignment with crucial procedures, regulations, or business directives.
According to Giron, who shared insights during the research firm’s 2024 predictions presentation, this strategy will be underpinned by structured and unstructured data, with a projected uptick in the latter due to the adoption of relational AI as businesses roll out more conversational experiences for customers and employees.
Furthermore, the realms of Mathematics and business development represent distinct yet essential skills within the AI landscape.
Additionally, incorporating customer feedback and behavior into feedback loops is crucial for refining the methodology.
These prerequisites underscore the importance for businesses to establish a robust relational AI software architecture to ensure the secure and efficient utilization of these tools.
To effectively manage the data flowing into the Artificial models and ensure that the outputs comply with the company’s guidelines, this platform should integrate the application pipelines, coordinate requests to outputs, and establish the input and output gateways.
Given the intricacies of AI management, businesses may require some time to witness tangible benefits from implementation.
Forrester predicts that only 30% of Asia-Pacific enterprises will leverage the transformative potential of relational AI in the coming year. Giron identified significant hurdles in data management, quality, and infrastructure.
Moreover, there are four ways in which conceptual AI can empower the creator economy.
He highlighted that service providers are investing in expanding their business collaborations, introducing new platforms such as AI studios and design comparisons to help businesses bridge gaps and revolutionize their operational and service delivery models.
This investment will lead to more diversified pricing models and, eventually, impact business frameworks. Potential outcomes include a shift towards outcome- and solution-based pricing structures.
Furthermore, to thrive in an AI-driven business landscape, organizations must embrace a new operational paradigm.
According to analysts, 56% of businesses foresee software development and testing as the primary application for generative AI, followed closely by 48% who view generative AI as a tool for self-service data and analytics.
Dane Anderson, the senior vice president of global research and product at Forrester, asserts that conceptual AI represents the most significant technological advancement in 40 years.
Anderson positions conceptual AI following earlier “inflection point” technologies such as the advent of cloud computing in 2009 and the proliferation of wireless internet and smartphones in 2007. While private servers made their debut in 1981, the dominance of the World Wide Web emerged in the 1990s.
He further suggests that some of these advancements yielded more profound changes than others, presenting both opportunities and challenges. The analyst forecasts that stable websites will gradually phase out over the next two decades due to the rise of generative AI.
Furthermore, relational AI is projected to surpass ChatGPT’s capabilities significantly. The evolution of technology is elaborated upon here.
Users will transition towards posing questions or prompts, receiving continuously updated responses in the backend, powered by conceptual AI, tailored to enhance the interactive experience.
Search functionality, currently pivotal, will diminish in importance, influenced by these platform transformations, according to Anderson.
These substantial shifts will unfold over several years. Companies must meticulously evaluate their options and select the most suitable types to achieve desired outcomes in the short term, given the expected influx of additional LLMs into the business sphere.
Anderson suggests that more market players might start integrating relational AI features into their existing customer enterprise applications at no cost.
Leslie Joseph, the principal analyst at Forrester, laments that the value proposition for businesses lies outside the realm of LLM operations, as this sector becomes increasingly commoditized with the influx of new players.
Rather than offering standalone tools akin to ChatGPT, Joseph encourages technology vendors to embed generative AI capabilities into their products. This strategic approach, he believes, will foster an environment where relational AI functionalities are deeply ingrained in work processes, thereby reducing technology costs for businesses.
