McKinsey partners with Salesforce on AI adoption for enterprises

Drug researchers won’t have to do endless pre-screening of chemicals; lawyers will spend less time looking up cases; managers can pass off paperwork and instead concentrate on coaching and making improvements. These occupations will be changed by generative A.I., and all are likely to see job growth between now and 2030. By 2030, we estimate activities that account for 30% of U.S. working hours could be automated—up from 21% before generative A.I.

Research from Goldman Sachs suggests that gen AI has the potential to automate 26% of work tasks in the arts, design, entertainment, media and sports sectors. Certainly, the downsides are significant, ranging from deepfakes to the spread of misinformation on a global scale. For example, a new report claims that China is using AI-generated images to try to influence U.S. voters. Gen AI is already an excellent editor for written content and is becoming a better writer too, as linguistics experts struggle to differentiate AI-generated content from human writing. According to Sal Khan, the founder of Khan Academy, the tech can provide a personalized tutor for every student.

Intelligence as a commodity

To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP. Instead of viewing generative AI as a threat, marketing teams must learn how to leverage its potential. Human judgment will remain essential; however, generative AI opens up new opportunities for those who can skillfully wield its power. With 60% of marketers currently exploring the technology and another 22% planning to, it is evident that the industry is welcoming this transformative force. It is crucial to approach adoption carefully, addressing ethical concerns and biases to build trust and enhance, rather than damage, a brand’s reputation. Marketing professionals should leverage generative AI as an invaluable friend rather than a foe.

The goal was a swift response in a tone that matched the company brand and customer preferences. Generative AI is AI that is typically built using foundation models and has capabilities that earlier AI did not have, such as the ability to generate content. Foundation models can also be used for non-generative purposes (for example, classifying user sentiment as negative or positive based on call transcripts) while offering significant improvement over earlier models. For simplicity, when we refer to generative AI in this article, we include all foundation model use cases. Some may see an opportunity to leapfrog the competition by reimagining how humans get work done with generative AI applications at their side.

Will generative A.I. be good for U.S. workers?

Generative AI is predicted to become a $1.3 trillion market by 2032, up from $40 billion in 2022, according to a recent report by Bloomberg Intelligence viewed by Insider. According to Bloomberg’s report, the industry will likely grow at a rate of 42% per year. In this instance, generative AI can speed up an RM’s analysis process (from days to hours), improve job satisfaction, and potentially capture insights the RM might have otherwise overlooked. Unstructured data lack a consistent format or structure (for example, text, images, and audio files) and typically require more advanced techniques to extract insights. Excitement around generative AI is palpable, and C-suite executives rightfully want to move ahead with thoughtful and intentional speed. We hope this article offers business leaders a balanced introduction into the promising world of generative AI.

• Spiking demand and labor scarcity forced many employers to consider nontraditional candidates with potential and train them if they lacked direct experience.
• The findings suggest that hiring for AI-related roles remains a challenge but has become somewhat easier over the past year, which could reflect the spate of layoffs at technology companies from late 2022 through the first half of 2023.
• Using foundation models, researchers can quantify clinical events, establish relationships, and measure the similarity between the patient cohort and evidence-backed indications.
• Meanwhile, 24% said they were reading and talking about them, and 15% said their organizations had already incorporated generative AI into their business strategies.
• And even as automation takes hold, investment and structural drivers will support employment.

One foundation model, for example, can create an executive summary for a 20,000-word technical report on quantum computing, draft a go-to-market strategy for a tree-trimming business, and provide five different recipes for the ten ingredients in someone’s refrigerator. The downside to such versatility is that, for now, generative AI can sometimes provide less accurate results, placing renewed attention on AI risk management. To build such a culture, companies can work to emulate the operating model of leading technology players that use small, cross-functional teams, or pods, to address specific customer needs or journeys. In this model, pods can include employees from software development, agile coaching, data science, product management, technical program management, and user design/research. The teams are typically empowered to own a customer problem space end to end, set their own objectives and key results, and determine their own product road maps and backlogs. They are actively encouraged to base their decisions on customer data, leveraging technologies such as AI and machine learning to predict customers’ needs and deliver value.

Generative A.I., if coupled with the effective redeployment of the hours it saves, could increase U.S. labor productivity by 0.5 to 0.9 percentage points a year. Combined with all other automation technologies, the increase could be up to as much as 3% to 4% annual GDP growth. At the end of the day, calculators did not fully replace mathematical activity, and Excel improved our productivity without replacing us. Gen AI is a great addition to any activity that requires creativity and needs human interaction or leadership. And if managed correctly, gen AI can help employees be happier, more content, and focus on what they love doing. It’s really important for people to touch technology and understand its potential.

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.

All of us are at the beginning of a journey to understand generative AI’s power, reach, and capabilities. This research is the latest in our efforts to assess the impact of this new era of AI. It suggests that generative AI is poised to transform roles and boost performance across functions such as sales and marketing, customer operations, and software development.

For example, in manufacturing, roughly 36% of working hours could be affected by automation. New investments, such as the $280 billion CHIPS and Science Act, could create demand for 250,000 more jobs, and these jobs are increasingly high-tech. There will likely be fewer assemblers and machine operators, and more industrial engineers and software developers. Brings enormous potential for U.S. manufacturing in terms of higher productivity and better-paid jobs. However, to see the benefits, the sector must develop and attract a workforce with a broader set of skills. Point number two is providing clear disclaimers, explaining that this is all based solely on public knowledge plus some private, enterprise knowledge, which has a huge impact on the level of accuracy or confidence in a given answer.

Doubling Down On The Customer

The second priority is to determine which upgrades to the data architecture are needed to fulfill the requirements of high-value use cases. The key issue here is how to cost effectively manage and scale the data and information integrations that Yakov Livshits power generative AI use cases. If they are not properly managed, there is a significant risk of overstressing the system with massive data compute activities, or of teams doing one-off integrations, which increase complexity and technical debt.

Second, they may need specialized MLOps tooling, technologies, and practices for adapting a foundation model and deploying it within their end-user applications. This includes, for example, capabilities to incorporate and label additional training data or build the APIs that allow applications to interact with it. To effectively apply generative AI for business value, companies need to build their technical capabilities and upskill their current workforce. This has the potential to increase productivity, create enthusiasm, and enable an organization to test generative AI internally before scaling to customer-facing applications.

The most complex and customized generative AI use cases emerge when no suitable foundation models are available and the company needs to build one from scratch. This situation may arise in specialized sectors or in working with unique data sets that are significantly different from the data used to train existing foundation models, as this pharmaceutical example demonstrates. Training a foundation model from scratch presents substantial technical, engineering, and resource challenges. The additional return on investment from using a higher-performing model should outweigh the financial and human capital costs. While many have reacted to ChatGPT (and AI and machine learning more broadly) with fear, machine learning clearly has the potential for good.

Many companies are entering the market to offer applications built on top of foundation models that enable them to perform a specific task, such as helping a company’s customers with service issues. All of this is possible because generative AI chatbots are powered by foundation models, which contain expansive neural networks trained on vast quantities of unstructured, unlabeled data in a variety of formats, such as text and audio. In contrast, previous generations of AI models were often “narrow,” meaning they could perform just one task, such as predicting customer churn.

Companies looking to put generative AI to work have the option to either use generative AI out of the box, or fine-tune them to perform a specific task. If you need to prepare slides according to a specific style, for example, you could ask the model to “learn” how headlines Yakov Livshits are normally written based on the data in the slides, then feed it slide data and ask it to write appropriate headlines. As you may have noticed above, outputs from generative AI models can be indistinguishable from human-generated content, or they can seem a little uncanny.

Artificial intelligence (AI) is the ability of software to perform tasks that traditionally require human intelligence. As the technology evolves and matures, these kinds of generative AI can be increasingly integrated into enterprise workflows to automate tasks and directly perform specific actions (for example, automatically sending summary notes at the end of meetings). Operational KPIs should include tracking which data are being used most, how models are performing, where data quality is poor, how many requests are being made against a given data set, and which use cases are generating the most activity and value. Data leaders have a huge opportunity to harness generative AI to improve their own function. In our analysis, eight primary use cases have emerged along the entire data value chain where generative AI can both accelerate existing tasks and improve how tasks are performed (Exhibit 3). We’ve seen that developing a generative AI model is so resource intensive that it is out of the question for all but the biggest and best-resourced companies.