in Blog

June 29, 2023

How can generative AI transform and accelerate your internal processes


Artur Haponik

CEO & Co-Founder

Reading time:

11 minutes

Following Microsoft’s investment in OpenAI and Google’s release of Bard, many discussions have emerged about how generative AI will transform the modern workplace. [1] Even though most of these discussions revolve around generative AI’s impact on creative work, there are many other reasons business leaders should consider investing in this technology. This explains why 35% of organizations worldwide use AI and 42% are exploring the technology. [2]

For business leaders seeking to utilize this emerging opportunity, here is everything you need to know about generative AI and how you can use it to transform and accelerate internal processes.

What is generative AI?

To scale up internal processes, business leaders first need to understand what generative AI is. Basically, generative AI is a type of Artificial Intelligence (AI) that enables users to generate new content such as texts, audio, images, animation, 3D models, or code based on a prompt. Currently, the most powerful generative AI algorithms are ones that have been developed on top of foundation models trained using large quantities of unlabeled data in a self-supervised manner.

These algorithms have been ideally designed to learn from training data which comprises examples of the desired input. Generative AI algorithms can generate new content with the same traits as the original inputs by analyzing the underlying patterns and structures within the training data set. That said, generative AI is capable of creating content that is both authentic and human-like.

One of the best things about generative AI is that it utilizes unsupervised and semi-supervised machine learning techniques for training. This makes AI adoption easier and faster, even among organizations without deep AI or data science experts. Even though customization requires expertise, adopting generative AI in your organization can be done with the help of small quantities of data, prompt engineering, or examples through APIs.

Some of the most popular generative AI tools today include Google Bard, ChatGPT, DALL-E, Stable Diffusion, and Midjourney. [3] These tools make it easy for anyone to create new content in the shortest time possible or use them alongside other applications or tools.

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Types of generative AI

Generative AI consists of several machine learning (ML) and Deep Learning (DL) models, each with its own strengths and weaknesses. Each model is ideally designed to address specific challenges and applications. These generative AI models can be divided into the following categories:

Transformer-based models

Transformers are basically neural networks ideally designed for natural language processing tasks. They are mainly trained using large quantities of data to identify, track and learn the relationships between sequential data such as words and sentences.

Popular transformer-based generative AI models such as OpenAI’s ChatGPT and GPT-3.5 have been used to perform a variety of tasks, such as text generation, language translation, and even image generation.

Generative adversarial networks (GANS)

Generative Adversarial Networks (GANs) consist of two major neural networks, namely a generator and a discriminator. Both neural networks compete against each other as they aim to generate new and authentic data. The generator creates new content, such as texts, images, or music, and presents it to the discriminator to determine whether it’s real or fake.

Generative adversarial networks (GANS) scheme

Over time, the generator becomes better at generating more realistic content while the discriminator improves its ability to distinguish real content from fake content. Even though GANs have been commonly used to generate deep fakes, they have the potential to revolutionize the business industry by improving product design and content creation.

Variational autoencoders (VAES)

Variational Autoencoders (VAEs) are generative AI models that use an encoder and decoder to generate new and authentic content by analyzing patterns in a particular data set. The encoder basically takes the input data and breaks it down into a more compact form, while the decoder reconstructs the encoded data into something that resembles the original input. VAEs are mostly used for image compression and reconstruction.

Variational autoencoders (VAES)

Recurrent neural networks (RNNS)

RNNs are generative AI models designed to process data sequences such as music and texts. They usually have a recurrent connection that enables them to maintain an internal memory. This makes them suitable for various tasks such as language translation, text generation, and music composition.

Multimodal models

These models have the ability to process and understand various types of input data, including audio, images, texts, videos, and numerical data. Multimodal models can use these different forms of input data to create accurate determinations, draw insightful conclusions and make better predictions regarding real-world problems. Examples of multimodal models include OpenAI’s DALL-E and GPT-4, which accept both text and image inputs.

Read more about Domain-specific generative AI: Top 5 examples of how to use it for your business

How generative AI benefits internal processes

Here are the various ways in which generative AI transforms and accelerates the internal processes of different organizations.

Content creation

High-quality content generation is vital because it attracts and engages prospective customers. It also answers your audience’s questions, builds trust, and establishes your expertise in the respective industry. Without good content, people might never discover your business, brand, and products.

One of the best things about generative AI is that it automates the creation of high-quality, impactful, and appealing content, giving businesses a competitive advantage over their competitors. Organizations can use generative AI to create all sorts of content, including blog posts, emails, product descriptions, social media updates, and even images, to drive more traffic to their websites.

This technology can generate authentic content based on a few prompts, thus saving time and effort companies spend on content creation. By streamlining and simplifying the content creation process using generative AI, employees, and management can spend more time strategizing and attending to more important matters.

Personalization and recommendation

Personalizing your company’s marketing is important as it increases customer engagement and conversion rates, translating to higher marketing ROI. Generative AI enables businesses to analyze user data and preferences in order to generate content tailored to their respective interests and needs.

The more personalized your company’s marketing campaigns and recommendations are, the stronger your relationship with your customers becomes. This creates customer loyalty, increased conversion rates, and more revenue for the company.

Cybersecurity and fraud detection

Cybercrime and fraud are some of the biggest threats facing most businesses worldwide. In addition to affecting businesses financially, cybercrime also leads to reputational damage, stolen intellectual property, and operational disruption. Therefore, it has become increasingly important for companies to rethink how they collect, store, and handle information to ensure that sensitive data doesn’t fall into the wrong hands.

Generative AI plays an important role when it comes to strengthening a company’s cybersecurity measures. Entrepreneurs can use this technology to analyze patterns and anomalies in their large data sets in order to detect and prevent fraud in real time. Generative AI can also be used to simulate potential cyberattacks and test a system’s resilience against such exploits.

Additionally, generative AI can prove helpful when it comes to improving password and authentication security. This can be done by analyzing the company’s datasets of breached passwords and recommending stronger passwords.

Improved customer service

Generative AI can help improve an organization’s customer service in almost every way if used properly. For example, generative AI-powered chatbots and virtual assistants provide instant and accurate responses to frequently asked questions by customers. This helps improve the customer experience while minimizing costs for the organization.

Notably, generative AI can be used to learn the patterns of customer behavior and then predict their future purchases accordingly. This means that the next time the customer reaches out to the organization, these patterns can be used to provide personalized recommendations based on past purchases.

Creativity and innovation

For your business to become profitable and attain its long-term goals, you need to prioritize innovation. Remaining creative and innovative will help you stay ahead of the curve and lead your business to success while meeting the evolving demands of your customers. Generative AI can help stimulate creativity and innovation to drive your business forward.

This technology can also provide businesses with fresh ideas and suggestions to help them create better product designs, content, and concepts. Such ideas keep businesses competitive and profitable in the ever-changing marketplace.

Advanced predictive analysis and forecasting

It’s important for businesses to take calculated risks, especially during periods of economic uncertainty. In addition to being a good chance to learn, taking a calculated risk gives your business a competitive advantage. In simple terms, most people avoid risks, translating to less competition for risk-takers.

You can use generative AI models to analyze historical data and market trends to make accurate predictions regarding the demand for your company’s products and services. This helps you make more informed data-driven decisions on how to optimize your supply chain while minimizing costs.

Language translation and natural language processing

Language barriers usually pose a significant threat to businesses planning to enter into new markets. Recent reports show that about 64% of organizations believe that language barriers are hindering their plans to go international. [4] Fortunately, there is a way around this.

Various generative AI models possess advanced language translation capabilities. Businesses worldwide can use these models to translate their website content and product descriptions in real time. This helps bridge language barriers and facilitate seamless communication between companies, suppliers, retailers, and distributors.

Risks associated with generative AI

Even though many people are excited about the benefits generative AI has to offer, there are some concerns about the impact and uncertainty of this technology. The risks associated with generative AI solutions are similar to those of conventional AI. They include:

Risks associated with generative AI


Like any other Artificial Intelligence (AI) model, the data used to train generative AI models directly impacts the end results of these models. Since Generative AI’s foundation models are trained using large quantities of data harvested from the internet, the biases that exist in various platforms on the internet could be transferred to these models.

As a result, some generative AI models end up producing some biased results unintentionally. And if you don’t know the source of information, you’ll have a hard time finding any bias or inaccuracies in the output. Even though there are ways of mitigating the risk of bias, it’s up to organizations to put measures in place to address it.


Sometimes Large Language Models (LLMs) tend to produce ‘hallucination’ responses that satisfy a given prompt but are not factually correct. [5] This usually happens when the training data is insufficient or biased. These hallucinations result in inaccurate output that is far from reality or doesn’t make much sense based on the context of the prompt.

Therefore, it’s vital for organizations to have fact checkers who will ensure that all the information from generative AI is accurate. Organizations should also ensure that the data they use to train various generative AI models is correct, clean, and relevant to the respective use case.

Cybersecurity and fraud

Organizations must be aware that generative AI models are susceptible to cyber, fraud, and phishing attacks. For example, cybercriminals are using deep fake technology to scam money from organizations, access personal information and create manipulated digital content such as videos and audio. [6]

Lack of transparency

Some generative AI models are unpredictable, and the companies behind them are not fully transparent about how they train them. There are also some companies that are not in a position to explain how their models work.

Final thoughts

There is no denying that the benefits offered by generative AI models strongly outweigh the risks involved. However, it’s the responsibility of every organization to set up strong AI Ethics and Governance policies to mitigate the risks outlined above. This will clarify the design, documentation, testing, and proper use of generative AI models in the workplace.

Eventually, the ethical use of generative AI models will help transform your organization’s internal processes for the better. Striking a balance between the adoption of generative AI and human involvement is also vital for maximizing the benefits of this technology.


[1] Microsoft Confirms its $10 billion Investment Into Chatgpt, Changing how Microsoft Competes With Google, Apple, and Other Tech giants. URL: Accessed June 22, 2023
[2] Businessedit.Com. AI In Business Statistics. URL: Accessed June 22, 2023
[3] Dataconomy. com. What is generative AI: Tools, Images and More Examples. URL: Accessed June 22, 2023
[4] How Businesses Can Deal With Language Barriers. URL: , Accessed June 23,2023
[5] Bernardmarr. com. What Are hallucinations and Why Are They a Problem for AI Systems. URL: Accessed June 23, 2023
[6] Security Threats Behind Artificial Faces. URL: Accessed June 23, 2023


Generative AI