In this article I provide guidance on developing a comprehensive coordinated strategy for deploying generative AI within an organization.
Developing comprehensive coordinated strategy for deploying generative AI
1. Define the goals of the deployment. What do you hope to achieve by deploying generative AI? Do you want to improve customer service, generate new product ideas, or something else? Once you know what you want to achieve, you can start to develop a strategy for how to deploy generative AI.
2. Assess the risks and benefits of deployment. Any technology deployment comes with risks and benefits. For generative AI, some of the risks include bias, privacy concerns, and the potential for misuse. The benefits of generative AI include the potential to improve efficiency, creativity, and innovation. It is important to weigh the risks and benefits carefully before deploying generative AI.
3. Select the right generative AI models. There are many different generative AI models available, each with its own strengths and weaknesses. The model you choose will depend on the specific goals of your deployment. For example, if you want to improve customer service, you might choose a model that can generate realistic chatbot conversations.
4. Gather and prepare the data. Generative AI models need data to train on. The quality and quantity of the data will affect the performance of the model. It is important to gather and prepare the data carefully before training the model.
5. Train the model. Once you have selected the right model and gathered the data, you can start to train the model. This process can take time, depending on the size and complexity of the model.
6. Deploy the model. Once the model is trained, you can deploy it in production. This means making the model available to users so that they can use it.
7. Monitor and evaluate the deployment. Once the model is deployed, it is important to monitor and evaluate its performance. This will help you to identify any problems and make necessary adjustments.
8. Update the model as needed. Generative AI models are constantly being improved. As new models are released, you may want to update your model to take advantage of the latest advances.
This is just a general overview of a comprehensive coordinated strategy for deploying generative AI. The specific steps involved will vary depending on the specific goals of the deployment. However, the steps outlined here provide a good starting point for developing a successful deployment strategy.
Here are some additional considerations that should be taken into account when developing a coordinated strategy for deploying generative AI:
The legal and regulatory landscape. It is important to be aware of the legal and regulatory landscape surrounding generative AI. Some countries have specific laws and regulations governing the use of generative AI.
The ethical implications of generative AI. There are also ethical implications to consider when deploying generative AI. For example, it is important to ensure that generative AI models are not used to generate harmful or discriminatory content.
The technical capabilities of the organization. The organization's technical capabilities will also need to be considered when developing a deployment strategy. For example, if the organization does not have the expertise to train and deploy generative AI models, it may need to partner with a third-party vendor.
By taking into account these factors, organizations can develop a coordinated strategy for deploying generative AI that is both effective and safe.
A sample of this strategy using a case scenario
Case Scenario: A large telecommunications company wants to use generative AI to improve customer service. The company wants to create a chatbot that can answer customer questions and resolve issues.
Strategy:
Define the goals of the deployment. The company's goals for the deployment are to:
Improve customer satisfaction by providing 24/7 customer service.
Reduce the number of customer service tickets by resolving issues more quickly.
Free up human customer service representatives to focus on more complex issues.
Assess the risks and benefits of deployment. The risks of deploying generative AI for customer service include:
The chatbot may not be able to answer all customer questions correctly.
The chatbot may generate responses that are not helpful or not in line with the company's brand.
The chatbot may be used to generate spam or other malicious content.
The benefits of deploying generative AI for customer service include: The chatbot can answer customer questions 24/7, even when human customer service representatives are not available. The chatbot can resolve issues more quickly than human customer service representatives. * The chatbot can free up human customer service representatives to focus on more complex issues.
Select the right generative AI models. The company decides to use a generative AI model that is trained on a large dataset of customer service conversations. The model is able to generate realistic and informative responses to customer questions.
Gather and prepare the data. The company gathers a large dataset of customer service conversations. The data is cleaned and prepared to remove any personal or confidential information.
Train the model. The model is trained on the dataset of customer service conversations. The training process takes several weeks.
Deploy the model. The model is deployed in production and made available to customers.
Monitor and evaluate the deployment. The company monitors the performance of the chatbot and makes adjustments as needed. The company also evaluates the chatbot's impact on customer satisfaction.
Update the model as needed. As new models are released, the company may update its model to take advantage of the latest advances.
This is just a sample of a coordinated strategy for deploying generative AI for customer service. The specific steps involved will vary depending on the specific goals of the deployment. However, the steps outlined here provide a good starting point for developing a successful deployment strategy.
What are the challenges with deploying or using generative AI within organizations in light of the cybersecurity risks?
Generative AI is a powerful tool that can be used for a variety of purposes, but it also comes with some cybersecurity risks. Here are some of the challenges with deploying or using generative AI within organizations in light of the cybersecurity risks:
Data poisoning: Data poisoning is a type of attack where malicious actors intentionally introduce incorrect or corrupted data into a generative AI model. This can cause the model to generate incorrect or harmful output.
Model theft: Generative AI models can be valuable assets, and they are often proprietary. Model theft is the act of stealing a generative AI model from an organization. This can give malicious actors access to the model's training data and insights, which can be used to launch attacks or develop their own generative AI models.
Adversarial attacks: Adversarial attacks are a type of attack where malicious actors intentionally create inputs that cause a generative AI model to generate incorrect or harmful output. Adversarial attacks can be very difficult to defend against, as they often exploit weaknesses in the model's training data or algorithms.
To mitigate these risks, organizations should take steps to protect their generative AI models and data. These steps include:
Using secure training data: Organizations should use secure training data that is free from malicious content. This can help to prevent data poisoning attacks.
Encrypting model data: Organizations should encrypt their generative AI models to protect them from theft.
Training models with adversarial examples: Organizations can train their generative AI models with adversarial examples to help them defend against adversarial attacks.
Monitoring model output: Organizations should monitor the output of their generative AI models for signs of malicious activity.
By taking these steps, organizations can help to mitigate the cybersecurity risks associated with generative AI.
In addition to the above, here are some other challenges with deploying or using generative AI within organizations:
Bias: Generative AI models can be biased, meaning that they may generate output that is discriminatory or offensive. This can be a problem if the model is used to generate content that is intended to be used by a wide audience.
Privacy: Generative AI models can be used to generate realistic text, images, and audio. This means that they could be used to create fake content that could be used to impersonate people or spread misinformation.
Explainability: Generative AI models are often difficult to explain. This means that it can be difficult to understand why a model generated a particular output. This can be a problem if the model is used to make decisions that could have a significant impact on people's lives.
Organizations that are considering deploying or using generative AI should carefully consider these challenges before making a decision.