Gartner Experts Answer the Top Generative AI Questions for Your Enterprise

GenAI technologies hold the potential for substantial ROI, but to get the full benefits, organizations must adopt a comprehensive approach to measurement that includes both traditional and alternative metrics, invest in change management, and carefully track total costs fully.

The total cost of ownership (TCO) for GenAI initiatives can often exceed initial expectations due to hidden costs such as compliance reviews, model retraining and internal overheads. Maintain a tracker to monitor these expenses and ensure that you are capturing the full financial picture of GenAI investments.

Traditional ROI metrics, such as productivity and cost savings, may not fully capture the value of GenAI implementations. Consider alternative metrics like Return on Employee (ROE) and Return on Future (ROF), which focus on employee experience and long-term strategic benefits, respectively. 

Effective change management is crucial for realizing the benefits of GenAI. Organizations that invest in training and upskilling employees to effectively use GenAI tools are more likely to see positive ROI. This includes creating tailored training programs that match employees' AI maturity levels and addressing the specific needs of different roles within the organization.

Finally, the ROI from GenAI can vary widely depending on the specific use cases. For example, in healthcare, GenAI can streamline administrative processes like prior authorization and claim adjudication, leading to significant efficiencies and cost savings. However, the success of these implementations often hinges on the quality of data and the effectiveness of governance frameworks in place.

• Focus on augmentation, not replacement: Prioritize using GenAI to augment employee productivity rather than focusing solely on cost savings or headcount reductions. This involves enhancing decision making, improving employee experiences, and facilitating skill development. For instance, GenAI can assist in automating routine tasks, generating content and providing personalized recommendations, which can significantly enhance productivity without compromising job security.

• Identify quick-win use cases: They could include automating repetitive tasks, generating documentation or enhancing customer interactions. 

• Integrate GenAI into business processes: This involves reengineering traditional business processes to leverage GenAI capabilities, thereby creating intelligent business experiences (IBEs) that support strategic objectives. 

• Use GenAI to inform decision making: Leverage GenAI to analyze data, predict outcomes and automate routine decisions.

• Invest in training and upskilling: Build foundational skills in prompt engineering and encourage a culture of experimentation. 

• Measure and optimize impact: Track improvements in efficiency, employee satisfaction and overall business outcomes. Regularly reviewing these metrics can help organizations refine their GenAI strategies and ensure they are aligned with business goals.

Gartner clients lean on our AI Use Case Insights to explore, evaluate and prioritize over 500 proven AI use cases tailored to their industries. These are a sampling:

1. Content creation and marketing

• Use case: Automated content generation for marketing materials, social media posts and personalized customer communications

• Impact: Enhance customer engagement by creating tailored content that resonates with target audiences, leading to improved campaign effectiveness and efficiency

2. Code suggestion and testing

• Use case: Facilitates the creation and testing of code fixes or scripts using natural language input

• Impact: Improves productivity and empowers less experienced staff to implement code changes

3. Customer service

• Use case: Implementation of intelligent virtual assistants and chatbots that provide real-time support and automate customer interactions

• Impact: Streamline customer service operations, reduce response times and improve customer satisfaction by providing accurate and timely information

4. Sales enablement

• Use case: Generating personalized sales pitches and automating prospecting tasks

• Impact: Enhance seller productivity, reduce time spent on administrative tasks and improve the quality of customer interactions

5. Simulation and scenario planning

• Use case: Using GenAI for advanced simulations in urban planning, climate modeling and financial forecasting

• Impact: Create realistic simulations that inform strategic planning and decision-making, leading to better resource allocation and risk mitigation

6. Healthcare

• Use case: Drug discovery and personalized medicine through data analysis and simulation

• Impact: Accelerate the development of new drugs and treatment plans by analyzing vast datasets to identify potential candidates and optimize patient outcomes

Focusing exclusively on GenAI is a guarantee to fail. To unlock the full potential of AI, adopt a holistic approach that integrates GenAI with alternative AI techniques, ensuring that investments are aligned with real business needs and not limited by the hype cycle of any single technology.

In particular, avoid using GenAI for:

• Prediction and forecasting: GenAI models are not well-suited for tasks that require numerical predictive modeling. They lack the explicit optimization and planning capabilities necessary for valuable forecasting tasks. Alternative AI techniques, such as traditional machine learning models, are more reliable for these use cases.

• Critical decision intelligence: The models may produce inaccurate or misleading outputs that inform poor decisions. Use alternative AI techniques that provide more robust and interpretable results for decision-making processes. 

• Handling sensitive data: When dealing with proprietary, sensitive or confidential information, using GenAI — especially public instances — can introduce risks related to data privacy and security. 

• Complex problem domains: GenAI may struggle where there is limited training data or where the relationships between data points are not well understood. In such cases, simpler AI techniques or rule-based systems may yield better results.

GenAI enters the Trough of Disillusionment on the 2025 Hype Cycle for Artificial Intelligence as organizations gain understanding of its potential and limits. This phase follows the Peak of Inflated Expectations, where initial excitement and high expectations often lead to a period of disappointment as organizations begin to encounter the limitations and challenges of a technology.

AI leaders continue to face challenges when it comes to demonstrating the ROI of AI, identifying suitable use cases, encountering unrealistic expectations for initiatives, finding skilled professionals, instilling GenAI literacy, establishing governance and responding to government regulations.

Still, there is significant potential for GenAI to grow and be integrated into business processes. The focus is shifting toward understanding how to effectively implement and derive value from GenAI technologies in the long term.

The 2025 Gartner Hype Cycle for GenAI focuses on four crucial technology areas to help AI leaders identify specific technologies worthy of strategic investment.

1. GenAI models: Large language models (LLMs) remain the cornerstone of GenAI and the most mature model technology on the Hype Cycle. However, other types of models, such as open-source LLMs, domain-specific GenAI models and large reasoning models, are quickly evolving into viable options for organizations. 

2. AI engineering: AI engineering encompasses a growing ecosystem of GenAI tools and techniques that enable organizations to ensure Gen-AI-powered applications support their organization’s broader strategy.

3. AI agents, applications and use cases: AI agents embedded in SaaS and AI agent development frameworks are growing in popularity. They are increasingly targeting both business users (no code) and enterprise developers to help enable creation of custom agents. 

4. Infrastructure and enablement techniques: GenAI’s evolution depends on a combination of novel techniques and established, foundational AI practices.

According to HR leaders, GenAI is expected to disrupt approximately 37% of the workforce within the next few years. This disruption will make obsolete certain tasks and roles while simultaneously creating a demand for new skills and positions.

Contrary to popular belief, GenAI is not merely replacing jobs — but it is transforming them. GenAI is viewed as a force multiplier that enhances productivity by automating routine tasks, allowing employees to focus on the more complex and creative aspects of their jobs. This is particularly beneficial for senior employees who can leverage GenAI to be more efficient.

The rise of GenAI is also leading to the emergence of new roles, such as AI engineers, prompt engineers and data ethicists, all of which require a blend of technical and domain-specific skills to effectively integrate AI into business processes.

Organizations are increasingly focusing on AI literacy to ensure that employees are equipped to work alongside AI technologies. This includes providing training and resources to help employees understand and effectively use GenAI tools, while fostering a culture of openness and continuous learning around AI.

Gartner encourages organizations to adopt a strategic approach to workforce planning that considers the long-term implications of GenAI on job roles and skills. This involves regularly assessing the impact of AI on business operations and adapting workforce strategies accordingly.