Generative AI is a familiar idea, as businesses have deployed various AI products and services like applications, chatbots and search systems with end-users interacting with AI tools. However, modern generative AI has changed the face of multiple industries, which has had a skyrocketing effect on the demand for new and improved AI tools.
Even though such models are relatively new, they are game-changers primarily because of their ability to generate new content based on human input. This is a clear upgrade from traditional AI tools focused primarily on categorising and identifying human input.
You can use these new models in AI solutions like Chat GPT or Lensa to tell the computer to do something, and it will create upon command. This guide will look at the benefits and risks of this awe-inspiring tech and how major tech companies are jumping in on the action!
What is generative AI?
Generative AI is a set of algorithms that work on everything from audio and graphics to text, and non-technical users often see it as the “Artificial Intelligence”. It is the new phenomenon of the early 2020s, with its popularity heightened by the release of tools like GPT4, backed by Microsoft. Its functions are based on processing input data and turning it into new content, using deep learning models like neural networks that add the human touch to the output data.
A popular ML model used for this purpose is the generative adversarial network, also known as GAN, which pits two neural networks with each other to get more accurate results and data that emulates actual data based on the training data.
Some famous instances of generative AI models are Bing AI, Lensa, RankBrain, DALL-E-2 and ChatGPT, with the last one being the most popular. The new version of GPT is one step ahead, as it can process images and text.
How have the four big tech companies used generative AI?
We have already discussed how Microsoft is at the forefront of generative AI with advancements made by OpenAI‘s tools like ChatGPT. However, the rest of the three GAMA companies are not far behind in the race to create powerful AI tools that enable them to provide more personalised products and services.
The expected benefits are not limited to improving the UX experience only. These systems can also improve business operations with both back-end and front-end business processes regarding productivity, efficiency and cost benefits.
For instance, we have Google, which showed a sceptical approach to new generative AI models but depended on its tools like Rankbrain. With Amazon, we have Alexa and the improvements they are making for online businesses with advanced machine learning. Meta is trying to create a system with AI recommendations to position new content to users, rather than leaving them dependent on the accounts they follow, with new products like Reels.
The thing to note is that generative AI is the new focus for all these major tech firms and even smaller ones, who realise the long-term benefits of these models and refuse to put innovation on the back burner. To learn more about how AI will impact businesses in the future, please check out this guide!
What are the benefits of implementing generative AI in implementing business solutions?
There are several benefits to using generative AI, and it goes for both customers and internal business operations. These AI models can help create personalised products and services and improve productivity and time-saving benefits. One example of their use is customer service, where these models provide faster answers to consumer queries.
The potential for innovation and personalisation with these models is unparalleled, and it can help companies cater to a much larger customer base at lower costs. In the following section, we will further discuss the benefits of generative AI:
Higher costs translate to reduced profits for businesses of every size and industry, so looking for cost savings benefits in every solution is essential. This is where generative AI works best, as these models can streamline lengthy workflows to make them smoother and more cost-efficient.
With automated tasks, a business can cut labour costs and instead focus on hiring experienced engineers to operate and monitor the technical processes. It also makes production much cheaper, whether building products and services or creating marketing content. To learn how to upskill your tech team and make them comfortable with these solutions, check out our guide on the topic!
Innovations and efficiency
The second significant benefit of implementing generative AI models is their innovation in developing new ideas, content, products and services. These algorithms are crucial to new marketing strategies and promotional materials.
It also helps firms get out of creative ruts and can perform text and image generation in seconds, which might take a human worker several hours. This helps essential projects and regular activities move faster at efficient paces, and the result is accurate and fresh.
Even if the final result of generative AI art is not used for the campaign, it can provide new ideas that other employees can rework. With most generative AI examples, the critical objective is understanding how to talk to machines correctly by writing excellent prompts. Once you get that on the side, you can move forward with a competitive edge over other creators and businesses in the market by exercising the full power of AI models.
Improved UX and business results
The best part about generative AI models is their results and the improvements they inspire in the customer experience. A happy customer usually leads to increases in revenue, sales and profits, which are all general indicators of success.
With the right strategy on these AI models, you can improve the UX (user experience) by providing personalised services and products and a robust customer support system where your customer feels understood. Moreover, the content can be specified to cater to your consumer base specifically. Furthermore, Generative AI language models can make the results more engaging by studying other data related to the targeted demographic and age group.
While AI systems can answer customer queries with excellent efficiency and minimum human supervision, they also help improve decision-making within a firm. These tools can create data visualisations that inspire and inform executive decision-makers and help operations relate to the critical market.
What are the primary risks of adopting generative AI?
Since generative AI applications are a relatively new market phenomenon, they are not being developed on a large scale, with most businesses dependent on third-party providers for their solutions. This creates a similar solutions network across multiple businesses that could threaten their security system.
Before a company adopts any such solution, it must consider this decision’s potential benefits and drawbacks. While we discussed the benefits in the previous sections, the following section focuses on the drawbacks and risks, like data loss and potential fraud.
It is essential to consider the ethical aspects of every business operation, and AI solutions are no different. We will also discuss a few potential risks of AI in depth:
When systems wholly depend on artificial intelligence, there are several risks to reputation and sensitive data-related decisions. It is essential to cross-check any text and art to ensure that there is no risk of plagiarism or any other legal risks before the material is processed for publication on sites or as part of a campaign or product.
If two firms run the same prompt on the same solution, they might produce similar results that could pass as original for their teams but still cause conflicts with the end user’s decision-making process. So while the results might seem entirely original for now, these models need to be developed further to ensure there are no risks of inaccuracy and similar outcomes, and this shows that human workers are still crucial for final quality checks and ensuring that a business does not lose its reputation in front of clients, mitigating any potential PR disasters before they happen.
The power of these AI solutions could be used unethically by different users, including artists, writers, students, freelancers and in-office employees. There are already risks of students using tools like GPT to write their essay submissions for them, which destroys the academic purpose of their education.
Professionals might be tempted to depend too much on these tools to scale their operations and risk losing their originality and results. In addition, using these AI models to create deep fake videos and photos of different individuals could cause psychological harm and result in legal action.
Finally, we need to cover cyber security and data loss risks, primarily for intellectual property. AI has made it possible to create content and information much more quickly, but it is also much easier to steal, attack and damage different companies with malicious agendas. For example, a phishing attack that creates its messages with the help of AI-generated text or software might have a tenfold higher success rate than a normal one. This could be done by copying trustworthy individuals’ voices, writing styles and even personal knowledge. Companies like Amazon are getting ahead of this challenge and are vocally cautious about adopting third-party AI solutions.
Google and Microsoft are among the top companies that have taken steps to integrate generative AI tools into their products, but they are not the only ones and are most definitely not the last ones to do so. Every company of every size, across every industry, is looking for a way to utilise the powers of the new AI.
With such advancements coming along in the near future, now is an excellent time to shift into AI and data science for a new career and to grow as a professional. If that sounds like something for you, book a career consultation with one of our experts and get an assessment and discussion that will immediately give you actionable insights!