Top 10 Generative AI Technologies to Augment Enterprise Content Workflow

Introduction

In today's digital age, the volume and complexity of content generated by enterprises have grown exponentially. To cope with this challenge, organizations are turning to generative artificial intelligence (AI) technologies to automate and streamline their content workflows. 

These innovative tools leverage machine learning algorithms to generate high-quality content, saving time and enhancing productivity. 

According to a McKinsey survey from 2022, the adoption of AI has more than doubled in the last five years, and people are investing more and more in AI-related technologies. It's pretty evident that generative AI tools like ChatGPT and DALL-E, which focuses on AI-generated art, have the potential to shake up how many jobs are done.

But, here's the catch: we don't really know the full extent of their impact yet, and we're still unsure about the risks involved. It's an exciting time, but there's still a lot to explore and understand! View article by McKinsey here. 

In the below points, we will explore the top 10 generative AI technologies that are revolutionising the enterprise content workflow. However, before that, enterprises should be aware of the risks and challenges associated with these technologies as well. 

Risks of Generative AI 

Generative AI platforms that are trained on public data, tend to ‘hallucinate’ a lot. In other words, they tend to incorporate information, facts or figures that might be incorrect or not factual. 

This happens because Generative AI technologies are non-deterministic. They are not rule based, and rather are to be used as a starting point for content creation, not as the end product. 

Public LLMs, for example, pose significant risks for enterprises in terms of privacy, security issues, confidential data leakage, and ‘off-brand’ content. 

Often, when employees or teams try to use public LLMs to generate marketing content, they realize that the content would actually hurt rather than help. Organizations then often have to get it rewritten by a writer, who deep-dives and transforms the content into something that is usable by marketing teams. 

To deal with these challenges that public LLMs pose, the Enterprises are now starting to explore custom-trained private LLMs. An example of this is BloombergGPT, a 50B parameter model trained in the domain of finance.

However, this comes with significant infrastructure and R&D costs. At Superteams.ai, we are exploring these possibilities in collaboration with partnering organizations who seek to explore this space. If you are looking to explore this for your business, feel free to reach out to us at https://superteams.ai/demo. 

Top 10 Generative AI Technologies

Even though the technology is in its early stages, a lot of businesses are already exploring the possibilities of plugging it into their workflow. We outline these below, so that as a business you are aware of the tools and technologies that are at your disposal to generate content for your use-cases. 

Natural Language Generation (NLG)

NLG systems use AI algorithms to transform structured data into human-like narratives. These tools generate coherent and contextually relevant written content, such as reports, summaries, and articles. 

They serve as a potential time-saving measure for writers who have to create case-studies, market research documents, competitive landscape analysis and more.

Image and Video Captioning

Generative AI models can analyze visual data and generate descriptive captions automatically. Image and video captioning technologies enhance content accessibility, improve SEO, and provide additional context to visual assets. 

Image and video captioning eventually helps with SEO, and is the task that’s often cumbersome and forgotten by creators. 

Text Summarization

Text summarization algorithms extract essential information from lengthy documents and generate concise summaries. 

These AI-powered tools enable enterprises to quickly review and digest large volumes of information, facilitating decision-making processes and content curation.

However, the key to use text summarization effectively is to ensure the right prompt is used. Furthermore, the content writer / creator would need to study the summary to ensure that the AI hasn’t ‘hallucinated’ and incorporated data that was missing from the original text.

Content Personalization

Generative AI technologies can dynamically personalise content based on user preferences, behaviour, and demographics. 

The way this works is, the business fine-tunes the AI model on their private data and other domain-specific content. Once that’s done, it can provide details about the customer’s choices and preferences, and create content that’s unique a particular customer or target customer segment. 

Creative Writing Assistance

Generative AI tools can assist content creators by suggesting creative ideas, generating catchy headlines, and providing writing prompts. 

These technologies help writers overcome writer's block, enhance their productivity, and improve the quality of their content. Often, when faced with a deadline, LLM technologies can help with rephrasing and conjuring multiple title options, which can help the writer generate more ideas.

Language Translation

AI-powered language translation technologies can automatically translate text from one language to another, enabling enterprises to reach global audiences effectively. 

Generative AI models trained on vast multilingual datasets provide accurate translations, reducing the time and effort required for manual translation. This works well for languages that the model has been trained on. For instance, most public LLM models can generate translated text in popular European languages.

Here, challenges arise when companies try this with Indic languages or any other language which the LLM hasn’t been trained on. In such scenarios, businesses are better off going the traditional route of hiring translators who can assist. 

Chatbots and Virtual Assistants

Generative AI powers chatbots and virtual assistants that can interact with users in a conversational manner. 

These AI-powered agents can provide instant customer support, answer frequently asked questions, and assist with various tasks, reducing the workload on human employees. 

For these to work effectively, the business has to aggregate their private data and documentation, fine-tune the LLM or train a custom LLM, and then build a chatbot that accurately responds to customer queries that actually helps the business, and not negatively impact its customer experience. 

Furthermore, an alternative way the AI Chatbot technology can help is through providing assistance to real human agents and assistants, who use the responses generated by the AI chatbot, rephrase them and correct them, and use them as starting points to respond to customers.

Content Enhancement

AI-based content enhancement tools analyze existing content and suggest improvements to grammar, style, and tone. 

Content enhancement technologies can help with creating stock images, opengraph images, or create other creative assets that businesses often used to incur heavy production costs with in the past.

This is an area where text-to-image and text-to-video technologies are showing a lot of promise. 

Data Generation

Generative AI models can synthesise new data samples based on existing datasets. 

For instance, Businesses often need a massive amount of customer data to test their platforms, their algorithms, or hone their customer experience by training their internal teams. 

To generate such data, generative AI can often extrapolate from existing data, and create sample training data for internal use in organizations, freeing up their resources to focus on more valuable tasks. 

Content Moderation

Generative AI technologies can assist in automating content moderation processes by identifying and flagging inappropriate or harmful content. 

These tools help maintain online safety and compliance by efficiently filtering and monitoring user-generated content. 

They can also help with grammar and language check, copyediting and proofreading, and flagging up sections which can be improved upon.

However, eventually, when it comes to final edited version, human intervention is mandatory.

Conclusion

Generative AI technologies are transforming the way enterprises handle content creation and management. From automating content generation to enhancing personalization and improving content quality, these tools offer immense potential for augmenting enterprise content workflows. 

By leveraging these top 10 generative AI technologies, organizations can streamline their content processes, boost productivity, and deliver superior user experiences in an increasingly competitive digital landscape. 

However, businesses should also be aware of the risks and challenges associated with them, and ensure they have content teams, internal, or remote, who understand these technologies, and can help businesses build a content pipeline that actually works in the long run. 

At Superteams.ai we can help Enterprises crack this new age content workflow, using writing teams, and custom created generative AI stacks, which can create long-term leverage for businesses looking to win through content.

As our experts can help craft the perfect content for your business needs, just click https://www.superteams.ai/demo and our team would give you a live walk-through how we can meet your needs.