What Generative AI Means for Business

Generative AI can learn from existing artifacts to generate new, realistic artifacts (at scale) that reflect the characteristics of the training data but don’t repeat it. It can produce a variety of novel content, such as images, video, music, speech, text, software code and product designs.

To identify the opportunities of GenAI, savvy organizations are creating ongoing self-service and AI literacy programs to build awareness, increase knowledge and build a dynamic, iterative process for collecting ideas and use cases in a methodical manner. 

Targeted multidisciplinary teams then use frameworks (such as the Gartner AI Opportunity Radar) to vet and juxtapose ideas based on business value and feasibility.

For stronger, faster AI outcomes, use Gartner’s proprietary AI Use Case Insights tool to explore, evaluate and prioritize over 500 proven AI use cases tailored to your industry.

A January 2024 Gartner poll confirms the interest in GenAI remains strong:

• Nearly two-thirds of organizations are using GenAI across multiple business units.
  • That’s a 19-percentage-point jump since September 2023.
• Forty percent of respondents say their organization has deployed GenAI in more than three business units.

• One in five organizations now has generative AI solutions in production.
• The primary business functions that have adopted or intended to invest in some form of GenAI solution are:
  • Customer service (16%)
  • Marketing (14%)
  • Sales (12%)
• The IT function primarily focuses GenAI deployment on the software development life cycle, and infrastructure and operations.

Understanding the benefits and risks of GenAI is important as you identify where and how it fits into existing and future business and operating models — and whether and how to experiment productively with use cases.

The benefits of generative AI include faster product development, enhanced customer experience and improved employee productivity, but the specifics depend on the use case. High-level, practical applications include:

• Written content augmentation and creation
• Question answering and discovery
• Tone
• Summarization
• Simplification
• Classification of content for specific use cases
• Chatbot performance improvement
• Software coding
• Product formulation
• Process improvements
• Analytics assistance
• Personalized recommendations

Oversight risks to monitor include:

• Lack of transparency
• Accuracy
• Hallucinations
• Bias
• Inadvertently exposing intellectual property (IP) and violating copyright
• Cyber and fraud attacks
• Negative impact on sustainability goals

Be realistic about time-to-value. Three categories of GenAI initiatives deliver ROI in three different time frames:

• Quick wins, which focus on potential productivity improvements, pay off in less than a year.
• Differentiating GenAI drives competitive advantage with time-to-value between one and two years. 

• Transformative GenAI initiatives can upend business models and markets. They tend to rely on a combination of AI techniques and often include multimodal GenAI. Because they are more complex, it usually takes more than two years to see real results.

Generative AI uses a number of techniques that continue to emerge and evolve at an unprecedented pace. 

Foremost are AI foundation models, which are trained on a broad set of unlabeled data that can be used for different tasks, with additional fine-tuning. Complex math and enormous computing power are required to create these trained models, but they are, in essence, prediction algorithms. (Also see: Gartner Experts Answer the Top Generative AI Questions for Your Enterprise.)

Since the late-2022 launch of ChatGPT, a chatbot capable of very human-seeming interactions, GenAI investment has ballooned. The market for GenAI-enabled virtual assistants and bots now includes many players. However, many GenAI technologies had already found their way to the Peak of Inflated Expectations on the 2023 Gartner Hype Cycle™ for Generative AI. 

In this environment, business leaders risk overestimating the impact and underestimating the complexity of GenAI. Gartner nevertheless expects expanded adoption and predicts:

• By 2026, 75% of businesses will use generative AI to create synthetic customer data, up from less than 5% in 2023. 
• By 2027, more than 50% of GenAI models used by enterprises will be specific to their industry or business function, up from about 1% in 2023. (These domain-specific models are much smaller than the massive GenAI models like GPT-4, and most will be built on top of AI foundation models.)
• By 2027, more than half of the selection of development assets from technology marketplaces will be performed by generative AI orchestration.
• By 2028, one-third of interactions with GenAI services will invoke action models and autonomous agents for task completion. 
• By 2028, 30% of GenAI implementations will be optimized using energy-conserving computational methods, driven by sustainability initiatives.

Open-source models are rising in prominence and aggressively competing against closed-source ones. With AI-related regulations increasing, customers may favor open-source models, which have better deployment flexibility and customizability, and enable better control over security and privacy.

Artificial general intelligence (AGI) is one highly transformational (but currently hypothetical) and contentious element of the future of generative AI. 

AGI, also called “strong AI,” can (theoretically) match or exceed human intelligence and solve problems never encountered during training. The Gartner Hype Cycle puts mainstream AGI adoption more than 10 years into the future — but short of a significant breakthrough, this could take decades or even centuries. Still, its benefits are potentially life-altering. AGI also raises considerable concerns among many stakeholders, however, stoking fears and unrealistic expectations about current AI’s true capabilities. 

While AI already displays sometimes surprising emergent behaviors that humans did not program, it’s important for business leaders to avoid prematurely anthropomorphizing AI. 

Still, AGI anticipation is accelerating the emergence of AI regulations and affects people’s trust and willingness to apply AI today. In the long term, AI continues to grow in power and, with or without AGI, will increasingly impact organizations, including the advent of machine customers and autonomous business.