Whats the future of generative AI? An early view in 15 charts

Financial software will generate a prose description of the notable features in a financial report. Customer-relationship-management systems will suggest ways to interact with customers. While text-generating chatbots such as ChatGPT have been receiving outsize attention, generative AI can enable capabilities across a broad range of content, including images, video, audio, and computer code. And it can perform several functions in organizations, including classifying, editing, summarizing, answering questions, and drafting new content. Each of these actions has the potential to create value by changing how work gets done at the activity level across business functions and workflows.

Speech Generation can be used in text-to-speech conversion, virtual assistants, and voice cloning. CMOs need to balance embracing generative AI solutions alongside the potential risks. Well-defined guardrails are essential, particularly around data protection, bias and intellectual property. Involving legal expertise early is vital as organizations look to manage the risks while harnessing the transformative power of the technology. In a March Resume Builder survey of 1,000 US business leaders, 96% of respondents working at organizations with a primarily remote or hybrid workforce said their firms used some form of employee-monitoring software — some of which uses AI.

What does it take to build a generative AI model?

In this example, a large corporate bank wants to use generative AI to improve the productivity of relationship managers (RMs). RMs spend considerable time reviewing large documents, such as annual reports and transcripts of earnings calls, to stay informed about a client’s situation and priorities. It’s a labor-intensive process that requires extensive trial and error and research into private and public documentation. At this company, a shortage of skilled software engineers has led to a large backlog of requests for features and bug fixes.

Our estimates are based on the structure of the global economy in 2022 and do not consider the value generative AI could create if it produced entirely new product or service categories. AI has permeated our lives incrementally, through everything from the tech powering our smartphones to autonomous-driving Yakov Livshits features on cars to the tools retailers use to surprise and delight consumers. Clear milestones, such as when AlphaGo, an AI-based program developed by DeepMind, defeated a world champion Go player in 2016, were celebrated but then quickly faded from the public’s consciousness.

Data Augumentation

Generative AI’s impact on productivity could add trillions of dollars in value to the global economy. Our latest research estimates that generative AI could add the equivalent of $2.6 trillion to $4.4 trillion annually across the 63 use cases we analyzed—by comparison, the United Kingdom’s entire GDP in 2021 was $3.1 trillion. This would increase the impact of all artificial intelligence by 15 to 40 percent.

The market for these assistants is now getting very crowded, particularly as Chinese entrants are also starting to appear. Per a story in MIT Technology Review, “Ernie Bot” from Baidu reached 1 million users in the 19 hours following its recent public launch. Since then, at least four additional Chinese companies have made their large language model (LLM) chatbot products available.

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.

Apps keep proliferating to address specific use cases

According to McKinsey’s own research, the impact of generative AI on the global economy could be monumental. The technology has the potential to unlock as much as $4.4 trillion in global productivity over the coming decades (though McKinsey has been criticized for making similarly lofty claims about other tech trends, such as the metaverse, that have not panned out). Global consulting agency McKinsey & Company and enterprise software provider Salesforce are teaming up to help businesses in the sectors of sales, marketing, commerce, and service speed up their adoption of generative AI.

The collaboration is also user-friendly, offering companies the option to “bring your own LLM” (language learning model) and prioritizing a straightforward platform where businesses can easily view data and ask questions. Salesforce also recently announced a similar collaborative AI adoption framework with IBM and its consulting arm, which is in some ways a rival to the new announcement with McKinsey. Salesforce appears to be playing “both sides” or at least offering AI adoption tools through a range of techno-consulting partners, which makes sense, as it seeks to retain its prominence as the leading CRM provider in the AI age. The company is expected to debut a wide array of new AI features at its annual Dreamforce conference this coming week. Its use cases in these industries range from automating conversations with customers, to creating personalized messages for customers, generating code, and even “generative design” which is “accelerating the process of developing new drugs and materials,” per the report.

Gen AI could ultimately boost global GDP

Automation could jump-start lackluster productivity while simultaneously easing labor shortages. One of the biggest questions of recent months is whether generative AI might wipe out jobs. Our research does not lead us Yakov Livshits to that conclusion, although we cannot definitively rule out job losses, at least in the short term. Technological advances often cause disruption, but historically, they eventually fuel economic and employment growth.

• Many of these practices are now enabled or optimized by supporting software (tools that help to standardize, streamline, or automate tasks).
• Lastly, I do think it’s important for us all, as people and as enterprises, to actually understand both the power of this technology and its limitations, so we can better assess those risks.
• In this example, research scientists in drug discovery at a pharmaceutical company had to decide which experiments to run next, based on microscopy images.
• Organizations with more resources could also customize a general model based on their own data to fit their needs and minimize biases.
• Many organizations began exploring the possibilities for traditional AI through siloed experiments.

Its out-of-the-box accessibility makes generative AI different from all AI that came before it. Users don’t need a degree in machine learning to interact with or derive value from it; nearly anyone who can ask questions can use it. And, as with other breakthrough technologies such as the personal computer or iPhone, one generative AI platform can give rise to many applications for audiences of any age or education level and in any location with internet Yakov Livshits access. While this might sound like old news, the cracks in the system a business could get away with before will become big problems with generative AI. Many of the advantages of generative AI will simply not be possible without a strong data foundation. The big change when it comes to data is that the scope of value has gotten much bigger because of generative AI’s ability to work with unstructured data, such as chats, videos, and code.

Customer operations: Improving customer and agent experiences

Addressing the need for reskilling with efforts beyond individual companies would help spread the cost, addressing the concerns of employers who might be reluctant to invest in training for employees who can subsequently leave. We estimate that 11.8 million workers currently in occupations with shrinking demand may need to move into different lines of work by 2030. Roughly nine million of them may wind up moving into different occupational categories altogether. Considering what has already transpired, that would bring the total number of occupational transitions through the decade’s end to a level almost 25 percent higher than our earlier estimates, creating a more pronounced shift in the mix of jobs across the economy. The largest future job gains are expected to be in healthcare, an industry that already has an imbalance, with 1.9 million unfilled openings as of April 2023. Automation, from industrial robots to automated document processing systems, continues to be the biggest factor in changing the demand for various occupations.