In artificial intelligence, one specific technology is getting the spotlight: generative AI. Since its breakthrough in Autumn 2022, Gen AI has gained more and more popularity as new tools are launched. Here is everything you need to know about it.
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Why Generative AI is gaining the spotlight in the AI Landscape
2023 will be remembered as Generative AI’s breakout year. The latest annual McKinsey Global Survey on the current state of Gen AI confirms it: Generative AI market size is skyrocketing, and every company is rushing to adopt the latest technology.
The organizations that have already successfully embedded AI in their business processes have been the first to explore Gen AI’s potential, outpacing competitors and aiming at a significant competitive advantage.
A brief history of Generative AI and its current fields of applications
Generative AI has been an active field of research since the 1960s, building on deep learning, which appeared in the 50s. In the 1990s and 2000s, computer processing capacity has substantially grown, allowing for enormous improvement in deep learning. However, only years later, Generative AI saw its major breakthrough with the introduction of GPT (Generative Pre-trained Transformer) models. In 2018, the first version of GPT was created by OpenAI. It was way more limited than the one we currently use, but it paved the way for more advanced models.
Since the release of ChatGPT in November 2022, Generative AI models have quickly spread and have been adopted by a vast and varied number of people. Both businesses and private citizens are now using Gen AI for work and, in their free time, experimenting with its creation capabilities.
Among all AI services, Generative AI seems to offer the most value. The public is stunned at its ability to create visuals, text, and audio files in seconds, and since many tools are free, more and more people are experimenting with them. How this will impact industries across the globe is still unclear: the only thing we know for certain is that Gen AI services have the potential to be disruptive.
What are the most common Generative AI applications?
Generative AI use cases are various and vary across industries and functions. New applications are tested daily, and each business should consider its goals, problems, and weaknesses to determine where Gen AI could be the most beneficial. For this reason, far-sighted executives are turning to AI consulting services to get support for their AI strategy.
In general, Generative AI is mostly applied to the same functions as overall AI:
- Marketing and sales (14% of companies using AI);
- Product & service development (13%);
- Service operations, especially customer care and back-office support (10%).
According to the same research, these three areas and software engineering could deliver about 75% of the total annual value from Generative AI use cases.
Why companies should think about an AI strategy today
“Right now, every company, no matter the size or industry, should think about an AI strategy.” (Harvard Business Review).
Although it is generally agreed upon that AI is now essential for business growth, many businesses still fail to go beyond the experimental stage. Adopting Generative AI models comes with many risks and challenges, like data protection, privacy, and intellectual property concerns. Moreover, there is now a need for specific technical knowledge and specialized talents, which not all organizations can afford or find.
But there is no denying that Generative AI is causing a huge paradigm shift and transforming the business landscape. Forecasts predict that advanced tools could change up to 70% of our work today and unlock trillions in value. Learn more- Download Gen AI primer. The world is undergoing big changes and becoming increasingly competitive: those who can successfully implement Generative AI today will have much better chances of staying ahead of the curve.
What is Generative AI, and why is it gaining prominence?
Generative AI is a type of Artificial Intelligence fueled by deep learning and able to generate new data (images, videos, text, or other media) based on patterns and features learned from existing data.
What are some examples of practical applications of Generative AI in various industries?
Gen AI is already disrupting the creative industries thanks to its ability to generate images, music, text, and videos. In marketing and social media, copy posts can be written with text-to-text tools like ChatGPT and Bard. In the medical field, Gen AI designs proteins for medicines.
What are the main benefits and potential challenges/ethical concerns of Generative AI?
Embedding Generative AI in business practices can offer many advantages, like cost reduction, increased efficiency, and task automation. Challenges and ethical concerns exist, mainly regarding data security, intellectual property, and deepfakes.
How Generative can AI solve complex business processes?
Executives must design integration strategies to align Generative AI use cases with the company’s goals and objectives. Incorporating AI requires diligent planning and execution to guarantee a smooth implementation. Best practices include:
- Defines clear objectives
- Analysis of IT systems
- Understanding the differences between AI technologies
- Foster a culture of continuous learning among employees
- Monitor performance and make strategic adjustments as necessary.
What are some outstanding examples of Generative AI-driven innovations in 2023?
- Microsoft Bing is an advanced search engine incorporating AI technology to improve online searches.
- Jasper Campaigns can create multichannel campaigns for your brand that start with a concise and respectful tone and position.
- Cleo is a money app designed to help individuals manage their finances.
- Boost.ai revolutionized the world of chatbots by implementing natural language processing and custom immersion training. It works easily on platforms like Slack and Teams.
- PEDAL is an AI-powered platform that supports oncology physicians in predicting a specific tumor’s drug response with unparalleled accuracy.
What are the skills and resources required for effective Generative AI usage?
Effective Generative AI usage requires technical skills, domain knowledge, and appropriate resources.
Technical skills include:
- Mathematics and statistics
- Machine Learning
- Deep Learning
- Data Handling
Domain knowledge spans across specific fields of expertise and understanding of ethical issues.
Needed resources include:
- Computational power
- Access to quality data
- Generative AI libraries
What is the future outlook for Generative AI?
Generative AI is here to stay and will only get bigger. Its market size is expected to increase over 15 times in just seven years, from $43.87 billion in 2023 to $667.96 billion in 2030.
The effects of generative AI use cases on business functions will vary depending on the industry. High-tech, financial, medical, and pharmaceutical industries are predicted to be most affected; marketing, customer operations, and R&D are the areas where Gen AI will have the greatest impact (source: McKinsey).
How do you stay updated on the latest Generative AI trends?
Adapting generational AI should involve understanding the latest trends, developments, and tools to remain relevant and excel at it. The most effective and safe approach for designing a viable AI strategy is to rely on AI ML consulting services. Executives can also keep themselves informed by studying scientific publications, attending conferences, and reading from reliable sources, like Tredence’s blog.