Generative AI is forecasted to create 10% of all data generated by 2025, offering organizations exceptional opportunities to innovate and optimize operations.
By integrating it into your operations, you can unlock numerous advantages affecting your creativity, productivity, automation, and cost optimization.
But, because generative AI is so new, it might be challenging to figure out where to start. To help, here are our thoughts on a few steps to help you jump in.
Identification & selection
Identify the specific business needs that can be addressed by generative AI, as well as the expected outcomes and how to measure them. Assess your business’s readiness to implement generative AI, taking into account available resources, budget, and technical expertise.
Then, select the appropriate type of generative AI for your organization considering your specific use case, the type and quality of your data, and the resources needed to train and deploy the model.
Customization
Next up, customize your generative AI model. This is the depends-on-your-organization part.
Most companies are using the models as they exist today with no modification. And this is the right solution in many cases because these models have been trained on a wide range of data and can generate AI content.
However, they are not specialized for your specific tasks or domains. The output can vary in quality and is subject to their usage policies and limitations. While they’re great for companies getting started, once you have a specific use case, we recommend dedicating resources to customize the model.
Depending on your company’s resources, you can either prompt-tune the pre-trained model, fine-tune it, or train the model from scratch.
Prompt tuning, or few-shot training, is the easiest method. You give the pre-trained model task-specific context by providing it with examples of requests you might ask in the future. It guides the model toward a desired decision or prediction, so when you ask your question, it gives you an answer using the same logic.
Another option is fine-tuning, which more sophisticated companies can use. This requires improving pre-trained models using large amounts of labeled data for a specific task, such as natural language processing (NLP) or image classification. However, this can be a time-consuming and resource-intensive process. To handle fine-tuning models, you need a data science team along with data infrastructure, as well as powerful hardware and deep learning expertise.
The hardest and most expensive way is to create your own model. To do that, you would need to use one of the existing models – for instance, a large language model (ChatGPT) or diffusion model (Midjourney) – and train it from scratch. As it costs, at a minimum, $5M, only the largest companies can pursue this route.
Model management
Once you’ve customized your generative AI model, integrate the model into business processes and data. This probably involves deploying the model in a cloud service, creating custom software to interact with the model, or integrating company documents and knowledge databases.
You will also need to set up processes for data integration, governance (such as a content moderation system), as well as data entry, model output, and error handling.
Finally, track and adjust the model over time. Generative AI models can be prone to errors and biases, and their performance can deteriorate over time as real-world data changes and grows.
Regularly monitor the model’s performance and adjust it as needed. This may include retraining the model on new data, fine-tuning model parameters, or implementing new error handling and monitoring processes. As your organization’s data grows, you’ll need to scale your model to accommodate it.
Potential challenges of integrating generative AI
Integrating generative AI into business processes is not easy. In order to face the challenges of implementing AI, you need to know how to overcome them.
Challenge: Availability and quality of training data
Generative AI models require large amounts of high-quality training data. Obtaining it can be challenging, especially if data is scarce or difficult to collect. In addition, you need to constantly monitor the data quality, accuracy, and relevance, as it plays a crucial role in the model’s performance.
Solution:
- Invest in data collection and pre-processing. Allocate resources to collecting data that reflects real-world scenarios. Implement methods to clean, normalize, and augment data to improve its quality.
- Collaborate with subject matter experts. They can provide insight into data requirements and help create high-quality data sets.
Challenge: Ongoing model monitoring and adaptation
As real-world data changes and grows, generative AI models can be prone to errors, biases, and performance degradation. To prevent this, you need to constantly monitor and correct the model if it starts to produce sideways content.
Solution:
- Implement monitoring processes. These will help you track the model’s performance, identify any anomalies or deviations, and compare results to benchmarks.
- Update the model with new data. As new data becomes available, periodically retrain the model to ensure that it always remains relevant and in line with business needs.
Challenge: Resource and infrastructure requirements
As generative AI continues to develop, the demand for computational power and infrastructure grows, posing challenges for businesses. This is especially true for small companies that can’t afford significant computing resources or specialized hardware. However, cloud services can provide them with a scalable and accessible platform and allow for harnessing the full potential of generative AI.
Solution:
- Use cloud services. They offer a scalable and cost-effective infrastructure for running AI workloads. This can simplify deployment and management of generative AI models. Even without specialized hardware, businesses can use the capabilities of generative AI to improve their operations, drive innovation, and gain a competitive edge.
- Explore partnership or outsourcing opportunities. AI consulting firms can offer the expertise, infrastructure, and support to integrate generative AI into your business processes.
By seamlessly integrating AI into your company’s processes, you significantly improve speed, accuracy, and applicability. Employees and customers can focus on the core of your products and services, while time-saving occurs in supporting tasks.
In our day-to-day work, DEPT utilizes many AI tools, enabling us to more efficiently communicate ideas with clients, speak the same language and understand each other faster. Theory becomes practice, improving communication and streamlining operations.
