The top five use cases for generative AI for B2B marketing

With a multifaceted approach, we can deter the spread and harm caused by AI-generated deepfakes. One of their examples includes transporting the singing voice of a woman singing in Spanish to Aloe Blacc’s face, making it look and sound as if he’s singing in Spanish. This technology could eventually allow anyone to speak any language naturally, and creating such content will become easier over time. Again in March 2023, an apparently leaked photo of Wikileaks founder, Julian Assage, was shared far and wide on social media. People who believe the photo was genuine posted their outrage but a German newspaper interviewed the person who created the image who claims he did it to protest how Assange has been treated.

• Metaphysic is also capable of processing live video in real-time, which is at the cutting edge of AI technology.
• “In the next few years the AI regulatory landscape will be transformed – which means organizations should be prepared to address how they handle IP, data, cyber, AI liability and almost every other compliance and business challenge.”
• The impact of generative AI on HR and people professionals is significant when we consider the potential for improved efficiency and cost reduction.
• P&G is looking to digitize and analyze data from its more than 100 manufacturing sites.

This gives you back the time you might spend struggling with those small but important tasks. Another area where AI can be a powerful tool for small business owners is customer communication. Add those adjectives to your prompt to help the AI tool write content that fits your brand voice.

The Potential of Generative AI Users Must Know About

As part of any AI procurement your company would also need to understand its responsibilities regarding system use and configuration, the supplier’s business continuity plan and how the unavailability of that platform would affect your business. Data must be processed in compliance with any ownership rights, legal requirements, contractual terms and company policies. Some of the key areas for legal risk management – privacy, intellectual property (IP) infringement, and other legal and commercial restrictions on data use – are discussed below. While the impact of generative AI on HR and people functions is advantageous in many ways, it’s essential to be aware of its limitations and potential risks. Despite this positive impact of generative AI on HR professionals and the people function, there are also challenges to consider, such as data security and privacy issues, as well as restrictions and potential risks. Generative AI can create virtual simulations and scenarios that mimic real-life work situations.

While a GenAI platform may be hosted internationally, under data sovereignty rules information created or collected in the originating country will remain under jurisdiction of that country’s laws. If information is sourced from GenAI hosted overseas, the laws of the source country regarding its genrative ai use and access may apply. GenAI service providers should be assessed for data sovereignty practice by any organisation wishing to use their GenAI. This can release non-public information and breach regulatory requirements, customer or vendor contracts, or compromise intellectual property.

It’s not just ChatGPT, other AI tools are Available.

Using AI, companies can churn out hundreds or thousands of options in a few hours, with full analysis of their impact on cost and schedule. Costa Group’s AI-powered pollinators are just one example of the agricultural computer vision applications in an Imaging & Machine Vision Europe article. In summer 2021, Costa Group started using Polly robots from Israeli firm Arugga AI Farming. Early results showed that the machines produced a 15% higher yield compared to manual pollination and up to a 7% higher yield compared to bumblebees. AI adoption has more than doubled over the past five years, according to a December 2022 survey by McKinsey. According to McKinsey, on average, companies are using roughly four different AI capabilities today.

It can both be about services that directly meet the audience and that are used in our internal systems and processes. Of course, we are particularly looking at audio-related AI services because sound is the focus of our overall strategy. A recent OpenAI study revealed the types of occupations which could be most affected by large language models (LLMs). Other risks recently called out by the authors of the social dilemma include the genuine risk of reality collapse, mass fakes, collapse of trust, exponential blackmail, biology automation, and exponential scams.

Yakov Livshits

Gartner predicts that more than 30% of new drugs and treatments will be discovered using generative AI models, helping the pharmaceutical industry reduce costs and even the time needed. Can you recall the “FaceApp”, which was a rage on social media platforms like Instagram a few years ago, where you can see your younger and older selves? In a more practical sense, these updates can make old documents or photos easier to read, analyze, and understand. Students can then gain a better understanding of these resources, leading to more learning. The next things to consider are, how do I decide what type of AI to invest in first and how do I test different AI solutions?

As noted previously, we have chosen to use ‘foundation model’ as the core term, but recognise terminology is fluid and fast moving. We also explain other related terminology and concepts, to help distinguish what is and isn’t a foundation model. Regulating explicable – or “explainable” – AI models is completely different when it comes to AI models that cannot be explained or interpreted; the regulatory framework will only apply to their inputs and outputs. Organisations will need to consider the level of disclosure they are required to make regarding their use of generative AI, both internally to personnel and more publicly, depending on the AI use cases. A number of existing laws and regulatory requirements, as well as laws that are on the horizon, will require disclosure of certain types of AI use.

Performance management and improvement

The company has strongly hinted that this is where its generative AI technology will come into its own. CTO Andrew Bosworth has said, “In the future, you might be able just to describe the world you want to create and have the large language model generate that world for you. CEO Mark Zuckerberg has said that one area of focus is on creating “AI personas that can help people in a variety of ways.” It’s likely that this would tie into plans to incorporate generative AI into the company’s chat technology.

By continuously analysing data streams and identifying subtle changes, insurers can proactively manage risks, prevent fraud, and mitigate potential losses. This proactive approach not only strengthens the insurer’s position but also enhances customer trust and confidence in the coverage provided. Generative AI can revolutionise this process by employing advanced algorithms to analyse vast amounts of data and identify emerging patterns and trends. AI is able to generate SEO-friendly content that includes target keywords while still providing value to the reader.

Integrating generative AI chatbots or virtual assistants can provide instant responses to customer queries, handling simple requests efficiently while escalating complex issues to human agents. By automating routine customer interactions, insurers can improve response genrative ai times, streamline operations, and allocate resources where they are most needed. By analysing patterns in large datasets, generative AI models can identify anomalies and detect fraudulent activities that may go unnoticed by traditional rule-based systems.

Regulators could themselves make use of Generative AI capabilities, helping to enhance our productivity and reduce costs for the taxpayer. At Zfort Group, we understand the potential of generative AI and are committed to helping businesses harness its power. MOSTLY AI has been a trailblazer in the generative AI field, spearheading the development of synthetic data for AI model development and software testing.