Untapped Potential: Overcoming Generative AI Challenges in 2024

Struggling to unlock the true value of generative AI? You’re not alone. Discover the top challenges and how data orchestration can unleash its potential for your business.

For companies looking to harness generative AI in their organization, it’s common to feel a tension between its looming potential and the struggle to turn it into something with real business value. A Gartner survey revealed that the primary obstacle to AI adoption, as reported by 49% of survey participants, is the difficulty in estimating and demonstrating the value of AI projects. That means nearly half of people trying to prove AI’s value are coming up short. Despite this, when executed properly, generative AI presents massive potential to completely transform businesses and creates many possibilities for improving scale and efficiency across various industries. 

To achieve successful generative AI efforts, AI models must be powered by rich proprietary datasets and operational data streams to build use case-specific applications for business needs. It also requires scalable and reliable data workflows that keep the models running with the most up-to-date data. Without operational workstreams, generative AI cannot be successful. To achieve maximum ROI on generative AI efforts, businesses must combine generative AI with insight-rich, proprietary data assets and operational data streams to build differentiated applications that reflect their unique business needs and drive real competitive advantages.

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Top generative AI challenges in 2024

So – where to start? First, looking at some of the most pressing generative AI challenges for tech leaders today is important. Some of the main issues include: 

• API outages and rate limits: Disruptions in external APIs are common challenges derailing data flow and model training processes.
• Constantly changing tools and APIs: In the generative AI space, new opportunities arise every day, requiring data orchestration processes to be adaptable and flexible. 
• The need for dynamic pipeline structures: Data is the most valuable asset in generative AI. Delivering the right data at the right time places high demands on data pipelines.
• High demands for compliance and data lineage: For compliance purposes, it’s essential to track which data was used in training AI models and have a clear data lineage. 
• Confidence in cost-effective scaling: As AI projects grow, orchestration platforms must handle increased computational demands and maintain reliability to avoid disruptions in critical processes. Given the rising need for computation, cost-effective resource optimization is crucial from both business and environmental perspectives. 

As highlighted by the above challenges, generative AI involves complex, resource-intensive tasks that must be carefully optimized and repeatedly executed. The orchestration of data-related tasks ensures that computational resources are used efficiently, workflows are optimized and adjusted in real-time, and deployments are stable and scalable. This orchestration capability is especially valuable in environments where generative models must be frequently updated or retrained based on new data or where multiple experiments and versions must be managed simultaneously. By incorporating tools for workflow management and deployment and scaling capabilities, teams will no longer need to worry about managing infrastructure and can focus instead on data transformation and model development, accelerating the deployment of generative AI applications and enhancing their performance.

Leveling up data needs by orchestrating generative AI workflows

To orchestrate generative AI workflows and data pipelines that power the most valuable insights for your business, organizations must align on the requirements needed for efficient data orchestration. Orchestration considerations and requirements in 2024 include: 

• Dependable Data Delivery: One of the biggest challenges in building AI applications is integrating proprietary data across the enterprise into training, tuning, and RAG models. This process is not one-off but continuous to keep models grounded in the latest business context.
• Reproducibility, Resilience, and Scalability: As users move from prototype to production, they expect their generative AI apps to be reliable and performant and for the outputs they produce to be trustworthy.
• Evaluation, Experimentation, and Explainability: From embedding generative models to vector databases and testing frameworks, the generative AI stack is evolving quickly. At the same time, customer expectations of generative AI applications are also increasing. To stay ahead, development and AI teams must continuously evaluate and experiment with different technologies and techniques while making model outputs explainable.

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Generative AI success and positive results take time and care to produce. Orchestrating data workflows and operationalizing data streams are important pieces of the wider generative AI picture that cannot be overlooked. When rich data sets are continuously cost-effectively flowing through optimized data pipelines, teams will start to experience the ROI of generative AI bets. 

As we’re in the early stages of generative AI developments, the technology is likely in its infancy of reaching the full breadth of its potential. To ensure your early generative AI bets perform to their maximum potential, data, and AI leaders must ensure that models are powered by rich proprietary datasets and operational data streams to empower domain-specific insights.