Generative AI: 7 Steps to Enterprise GenAI Growth in 2023

When Priya Krishna asked DALL-E 2 to come up with an image for Thanksgiving dinner, it produced a scene where the turkey was garnished with whole limes, set next to a bowl of what appeared to be guacamole. For its part, ChatGPT seems to have trouble counting, or solving basic algebra problems—or, indeed, overcoming the sexist and racist bias that lurks in the undercurrents of the internet and society more broadly. Finally, it’s important to continually monitor regulatory developments and litigation regarding generative AI. China and Singapore have already put in place new regulations regarding the use of generative AI, while Italy temporarily. But generative AI only hit mainstream headlines in late 2022 with the launch of ChatGPT, a chatbot capable of very human-seeming interactions. Both relate to the field of artificial intelligence, but the former is a subtype of the latter.

For companies embracing the technology, employees view AI as a constructive force. An overwhelming majority of respondents (92%) say AI is having a positive impact on their work and more than one-quarter (26%) call AI a “miracle.” Two-thirds say AI is already saving them time, and 61% say it helps them work faster. Many people think of generative AI as a tool that allows them to use their own words to ask questions or generate copy and images—both of which it does remarkably well. However, it also has incredible potential to transform our personal and professional work—helping us access, consume, and utilize the untapped information that floods our inboxes and languishes in archives. The online survey was in the field April 11 to 21, 2023, and garnered responses from 1,684 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures.

AI language models are rife with different political biases

Of those respondents, 913 said their organizations had adopted AI in at least one function and were asked questions about their organizations’ AI use. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. In 2023, the rise of large language models like ChatGPT is indicative of the explosion in popularity of generative AI as well as its range of applications. The emergence of consumer-facing generative AI tools in late 2022 and early 2023 radically shifted public conversation around the power and potential of AI. Though generative AI had been making waves among experts since the introduction of GPT-2 in 2019, it is just now that its revolutionary opportunities have become clear to enterprise. The weight of this moment—and the ripple effects it will inspire—will reverberate for decades to come.

In terms of basic intelligence, text-to-image models are no smarter than the language-generating AIs that underpin them. Tools like GPT-3 and Google’s PaLM regurgitate patterns of text ingested from the many billions of documents they are trained on. Similarly, Yakov Livshits DALL-E and Stable Diffusion reproduce associations between text and images found across billions of examples online. The neural network is then trained to reverse that process and predict what the less pixelated version of a given image would look like.

The History of Generative AI

The ML scientists work on solutions for the known problems and limitations, and test different solutions, all the while improving the algorithms and data generation. We all admire how good the creations coming from ML algorithms are but what we see is Yakov Livshits usually the best case scenario. Bad examples and disappointing results are nothing interesting to share about in the most popular publications. Admitting that we are still at the beginning of the generative AI road is not as popular as it should be.

They were most enthusiastic about lead identification, marketing optimization, and personalized outreach. In this visual Explainer, we’ve compiled all the answers we have so far—in 15 McKinsey charts. We expect this space to evolve rapidly and will continue to roll out our research as that happens. To stay up to date on this topic, register for our email alerts on “artificial intelligence” here.

Yakov Livshits
Founder of the DevEducation project
A prolific businessman and investor, and the founder of several large companies in Israel, the USA and the UAE, Yakov’s corporation comprises over 2,000 employees all over the world. He graduated from the University of Oxford in the UK and Technion in Israel, before moving on to study complex systems science at NECSI in the USA. Yakov has a Masters in Software Development.

Generative AI models typically rely on a user feeding it a prompt that guides it towards producing a desired output, be it text, an image, a video or a piece of music, though this isn’t always the case. Generative AI models are increasingly being incorporated into online tools and chatbots that allow users to type questions or instructions into an input field, upon which the AI model will generate a human-like response. It wasn’t until late May that Microsoft’s president, Brad Smith, disclosed that it had built its “advanced AI supercomputing data center” in Iowa, exclusively to enable OpenAI to train what has become its fourth-generation model, GPT-4. The model now powers premium versions of ChatGPT and some of Microsoft’s own products and has accelerated a debate about containing AI’s societal risks. In a paper due to be published later this year, Ren’s team estimates ChatGPT gulps up 500 milliliters of water (close to what’s in a 16-ounce water bottle) every time you ask it a series of between 5 to 50 prompts or questions. The range varies depending on where its servers are located and the season.

The models do this by incorporating machine learning techniques known as neural networks, which are loosely inspired by the way the human brain processes and interprets information and then learns from it over time. Diffusion models are neural networks trained to clean images up by removing pixelated noise that the training process adds. The process involves taking images and changing a few pixels in them at a time, over many steps, until the original images are erased and you’re left with nothing but random pixels.

What are the benefits and applications of generative AI?

Generative AI is a powerful tool for streamlining the workflow of creatives, engineers, researchers, scientists, and more. The TQ is how we build and demonstrate our understanding of transformative technologies and how they deliver on the promise of technology and human ingenuity. From the C-suite to the front line, employees at all levels will need to develop a TQ to drive successful reinvention.

The field accelerated when researchers found a way to get neural networks to run in parallel across the graphics processing units (GPUs) that were being used in the computer gaming industry to render video games. New machine learning techniques developed in the past decade, including the aforementioned generative adversarial networks and transformers, have set the stage for the recent remarkable advances in AI-generated content. Generative AI is a new buzzword that emerged with the fast growth of ChatGPT. Generative AI leverages AI and machine learning algorithms to enable machines to generate artificial content such as text, images, audio and video content based on its training data. As you can see above most Big Tech firms are either building their own generative AI solutions or investing in companies building large language models. Generative AI models use a complex computing process known as deep learning to analyze common patterns and arrangements in large sets of data and then use this information to create new, convincing outputs.

Generative

Cem’s work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple. Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years.

• Data are based on 47 countries, representing about 80% of world employment.
• • A trove of unstructured and buried data is now legible, unlocking business value.
• The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters.
• That’s optimistic, to say the least—many experts believe that today’s AI will never reach that level.
• Early implementations of generative AI vividly illustrate its many limitations.

This is based on the assumption that automated work hours are reintegrated in work at today’s productivity level. Two scenarios are shown for early and late adoption of automation, and each bar is broken into the effect of automation with and without generative AI. The addition of generative AI increases CAGR by 0.5 to 0.7 percentage points, on average, for early adopters, and 0.1 to 0.3 percentage points for late adopters. In the overall average for global growth, generative AI adds about 0.6 percentage points by 2040 for early adopters, while late adopters can expect an increase of 0.1 percentage points. Data are based on 47 countries, representing about 80% of world employment. Since the release of ChatGPT in November 2022, it’s been all over the headlines, and businesses are racing to capture its value.

Experts have said it can make sense to “pretrain” an AI model at a single location because of the large amounts of data that need to be transferred between computing cores. “They’re building them as fast as they can,” said Steve Gaer, who was the city’s mayor when Microsoft came to town. Gaer said the company was attracted to the city’s commitment to building public infrastructure and contributed a “staggering” sum of money through tax payments that support that investment.