Gartner’s predictions keep IT leaders informed and ahead of rapidly unfolding generative AI technology developments.
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Generative AI technologies will evolve quickly over the next four years.
Technologies underpinning generative AI (GenAI) have been progressing at an unprecedented pace, thanks, in large part, to enormous investments from large technology companies and research labs. In fact, GenAI seems to be immune to the overall slowdown in venture capital investment, while well-funded startups continue to emerge and mature.
The speed at which GenAI technologies are emerging poses significant challenges for IT leaders tasked with staying abreast of industry developments.
Three key predictions for the future of GenAI technologies
Looking at all four layers of the generative AI technology stack — infrastructure, models, AI engineering tools and applications — Gartner offers the following predictions.
GenAI prediction No. 1: Demand will increase for domain-specific GenAI models
Although general-purpose models perform well across a broad set of applications, demand for GenAI is rising in many sectors. Combined with increased availability of high-performing and commercially usable open-source large language models, there is an appetite for domain-specific models. By 2027, more than 50% of the GenAI models that enterprises use will be specific to either an industry or business function — up from approximately 1% in 2023.
Domain models can be smaller, less computationally intensive, and lower the hallucination risks associated with general-purpose models.
Plan for the need to deploy and manage multiple domain-specific GenAI models to support a variety of use cases. But before you build your own, look for off-the-shelf, domain-specific models you can train or tune to accommodate your enterprise needs.
GenAI prediction No. 2: Synthetic data will solve a variety of existing data challenges
Development of synthetic — i.e., artificially generated — data supports systems where real data is expensive, unavailable, imbalanced or unusable because of privacy regulations. By 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023.
Introducing synthetic data into models enables organizations to simulate environments and identify new product development opportunities, especially in highly regulated industries. It also enables fast prototyping of software, digital and hybrid experiences.
Focus on the use of synthetic data in areas that directly correlate to business growth, such as the development of customer segments, journeys and experiences and training of machine learning models.
GenAI prediction No. 3: Sustainable GenAI will focus on energy-conserving techniques
The rapid adoption of generative AI tools has made the negative environmental impact of GenAI, which the public and governments are calling out, an immediate concern for business leaders.
Minimizing the energy and resources required for AI training and development is critical. Renewable energy and infrastructure, via green computing techniques, for both on-premises and cloud services will be customized for AI. By 2028, 30% of GenAI implementations will be optimized using energy-conserving computational methods, driven by sustainability initiatives.
Control costs for energy-optimized compute resources by diversifying your suppliers, pursuing composable architecture and edge operations for GenAI in each jurisdiction of operation, and using high-quality renewable energy during training to mitigate its impact on your sustainability goals.
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