What is generative AI?

Generative AI is a form of artificial intelligence that focuses on creating new and creative text, data or media. It leverages complex algorithms to learn from vast datasets and autonomously generate new content that resembles human-produced creations.

Generative artificial intelligence is not entirely new, but recent advancements in deep learning models and natural language processing (NLP) have significantly accelerated its development and popularity. According to McKinsey, about 80% of current AI research investments focus on generative AI.

Traditional AI vs generative AI: Key differences 

Unlike traditional AI systems, which are primarily used to process data and detect patterns, gen AI has more advanced capabilities. Here’s a breakdown:

How does generative AI work? 

Generative AI works by detecting patterns from existing data and leveraging this acquired knowledge to create novel content or outputs. The key stages include:

• Gathering a large dataset of examples related to the type of content AI will generate, such as images, text, music etc. 
• Cleaning, preprocessing and structuring the data to make it suitable for training.
• Choosing a generative model architecture, often using variations of neural networks such as generative adversarial networks (GANs) or variational autoencoders (VAEs). 
• Feeding curated data into the model and training it to learn the underlying patterns and correlations.

Following the training data phase, the generative AI model can create new content by starting with random information or an initial input. Here’s an example using chatGPT: 

Benefits of generative AI

Thanks to its ability to generate creative and personalized content, assist in data analysis and automate tasks, gen AI brings benefits such as:

• Enhanced creativity: Generative AI’s ability to create content autonomously opens up new realms of creativity. It can generate compelling narratives, artwork and music, allowing artists and writers to expand their creative horizons and experiment with novel ideas. In the creative arts, for example, musicians can use generative AI to compose original pieces and experiment with new music genres.
• Time and cost efficiency: By automating content writing, generative AI saves valuable time and reduces production costs. Businesses can rapidly create content at scale, freeing up resources for other essential tasks. 
• Personalization: Generative AI enables personalized content creation, tailoring experiences to individual preferences and enhancing user engagement. For example, by analyzing previous customer interactions, AI tools can generate custom email marketing content that speaks directly to the recipient’s needs and pain points.

🤖 Can AI content rank on Google? Read our guide to AI content vs human content.

Challenges of generative AI

Gartner lists “AI risk management” as a top data and analytics trend for 2023. Businesses need to stay ahead of potential pitfalls associated with AI deployment because:

• Human oversight remains essential as the accuracy of AI-generated information can vary. If trained on biased datasets, generative AI may produce content that reflects those biases.
• The ownership of AI-generated content raises questions about copyright and intellectual property rights.
• The potential misuse of AI capabilities poses ethical concerns (for example, deepfake technology could lead to misinformation and online deception).

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Generative AI use cases

According to Accenture, three out of four C-level executives are worried their organizations risk going out of business by 2025 if they don’t move beyond experimentation to aggressively deploy AI. 

If you are looking to introduce or expand your use cases of AI in marketing, consider leveraging this technology to:

• Generate articles, blog posts, product descriptions and social media content.
• Provide instant responses to customer queries. 
• Summarize lengthy documents, articles or reports.
• Automate data analysis to identify trends and streamline decision making. 
• Make correlations and pull out key insights from presentations.
• Analyze user preferences and behavior to recommend products or services. 

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Gen AI in SaaS: How our clients are using it to make their products smarter

We’ve seen plenty of success stories of technology startups that have integrated generative AI not only into their processes but also into their own products. 

Here’s how two of our clients are using generative AI in SaaS to make their platforms smarter:

Generative AI in customer service 

AI-driven chatbots have had a profound impact on customer service. But there’s much more artificial intelligence can do for the call center industry. 

That’s the case with Pathlight, a performance management platform that uses generative AI to capture and analyze customer conversations. 

With Pathlight, call centers can become more efficient and scaler operations by:

• Summarizing customer interactions
• Automating data analysis 
• Surfacing hidden insights

Pathlight’s AI-powered features also include customized coaching and guidance for each agent — key to improved decision making and performance.

Generative AI for business intelligence 

Now let’s look at the example of Box, a cloud platform for document storage, management and collaboration. 

Two-thirds of Fortune 500 companies trust Box for content management. To help enterprises make the most of their data and increase productivity, Box AI is able to answer questions about documents, summarize reports, draft content and more. 

Generative artificial intelligence is here to stay

In the months to come, expect to see generative AI reshaping businesses at an even more accelerated pace. 

You won’t want to stay behind. 

If you need help navigating these changes and making the most of AI to accelerate your B2B SaaS growth, shoot us a message. ✍️