Generative artificial intelligence (AI), is a trending topic that has emerged in the past five years, and the interest around it has grown rapidly ever since. The recent push into generative AI by companies such as Google and Microsoft has further raised the spotlight on the topic. But for some, the question remains, what is generative AI and what is its purpose?
First off, let’s define the term:
Generative AI refers to a type of artificial intelligence (AI) capable of creating new data, such as images, videos, text, and music, that have never been seen before.
A 2022 McKinsey survey highlighted that AI adoption has more than doubled over the past five years, and investment in AI is swiftly increasing. Unlike other types of AI that are designed to solve specific tasks or problems, generative AI algorithms learn from large amounts of data and then use that knowledge to create new content that is similar to the training data.
There are several different types of generative AI algorithms, these are the top three:
- Generative Adversarial Networks (GANs): a type of neural network that consists of two parts: a generator and a discriminator. The generator creates new data, while the discriminator distinguishes between real and fake data.
- Variational Autoencoders (VAEs): a type of neural network that learns to encode and decode data in a low-dimensional space, which can then be used to generate new data.
- Autoregressive models: a type of model that generates new data by predicting the next value in a sequence based on the previous values.
Now let’s unpack why making generative AI actionable in the enterprise can be tough:
- Data Quality & Availability
Generative AI models require large amounts of high-quality data to learn from, and often the data may need to be made available or of better quality. In addition, cleaning and preprocessing the data can be time-consuming and labor-intensive.
- Algorithm Complexity
Generative AI algorithms can be complex and difficult to understand, making it hard to fine-tune the models for specific use cases. Finding the right algorithm for the given use case and even training the model effectively can be challenging.
- Resource-Intensive
Generative AI models require significant computational resources, such as specialized hardware and software. This can be expensive to acquire and maintain, making it difficult for smaller enterprises to leverage this technology.
- Ethical & Legal Concerns
Generative AI models can be used to create fake content such as deep fakes, which can lead to ethical and legal issues. Enterprises must be mindful of the risks of generative AI and ensure they use it responsibly.
- Lack of Expertise
Building and deploying generative AI models requires expertise in data science, machine learning, and domain-specific knowledge. Finding and hiring qualified personnel with the correct skill set can be challenging.
- Lack of Integration
Integrating generative AI models with existing enterprise systems can be challenging. It requires expertise in data storage, data analysis, and visualization tools to ensure that the models are effectively integrated.
That said, generative AI has the potential to drive significant value in enterprises by automating tasks, improving product design, and generating new business opportunities. If done right, the juice is worth the squeeze.
Based on our current and past engagements, here’s the step-by-step process we see working when it comes to making generative AI actionable in enterprises:
- Identify Use Cases
Enterprises need to identify specific use cases where generative AI can be applied to create value. Some examples include creating product designs, generating synthetic data for training machine learning models, and developing new marketing materials.
- Build A Team
Enterprises need to build a team of experts who are skilled in working with generative AI. This team should include data scientists, machine learning engineers, and domain experts who can guide the development of generative AI models within the enterprise.
- Collect & Clean Data
Generative AI models require large amounts of high-quality data to learn from. Enterprises must collect and clean data from various sources to build effective generative AI models.
- Choose Appropriate Algorithms
There are several generative AI algorithms, and choosing the appropriate one for the use case is crucial. Enterprises need to consider factors such as the type of data, the complexity of the problem, and the availability of computational resources.
- Integrate With Existing Systems
Generative AI models must be integrated with existing enterprise systems to make them actionable. This includes integrating with data storage, data analysis, and visualization tools.
- Monitor & Optimize
Enterprises must continually monitor and optimize their generative AI models to ensure they perform as expected. This includes monitoring the quality of the generated data and optimizing the algorithms to improve performance.
All companies are different, but this high-level plan is what we are seeing the most innovation-driven organizations enact as they pursue their generative AI journey. The sooner enterprises can leverage the power of generative AI, the faster they will drive value via new business opportunities, and improve their operations overall.
In the next article, we will dive a bit deeper into generative AI by exploring the topic of synthetic data, and the value that it unlocks for financial services and insurance firms in the age of rapid AI and ML adoption.
