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3 Bold and Actionable Predictions for the Future of GenAI – Part 1 of 2

• Gartner’s predictions keep IT leaders informed of and ahead of rapidly unfolding generative AI technology developments.

• We offer foresight into the rise of domain-specific models, use of synthetic data and mitigating the environmental costs of GenAI.

By Lori Perri. Gartner. April 12, 2024

Big Picture

Generative AI technologies will greatly evolve in the next four years.

Technologies underpinning generative AI 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, and well-funded startups continue to emerge and mature.

Looking at all four layers of the generative AI technology stack — infrastructure, models, AI engineering tools and applications — Gartner makes five predictions for how generative AI will evolve in the coming years. We preview three, along with their implications for your organization, here.

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.

• 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 LLMs, there is an appetite for domain-specific models.

• 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.

By 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023.

• Development of synthetic — i.e., artificially generated — data supports systems where real data is expensive, unavailable, imbalanced or unusable because of privacy regulations.

• 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 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.

By 2028, 30% of GenAI implementations will be optimized using energy-conserving computational methods, driven by sustainability initiatives.

• 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 for both on-premises and cloud services will be customized for AI.

• 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.

“GenAI is being embedded into a broad range of business applications and as the underlying models become multimodal, it can enable richer and more intelligent automation workflows. This will also allow GenAI models to become more autonomous and better reflect the environments they’re trained on.”

3 things to tell your peers

1 With continued investment from large technology and research companies alongside enterprising startups, GenAI will continue to make huge strides in the coming year.

2 Gartner predictions — and