Leading Off: What to do about generative AI right now: A leaders guide

McKinsey and Salesforce share a history of collaboration helping organizations accelerate digital transformations. This powerful combination allows McKinsey to bring together the strengths of its Salesforce experts, AI, and tech to help clients move from strategy all the way through implementation and impact. Finally, companies may create proprietary data from feedback loops driven by an end-user rating system, such as a star rating system or a thumbs-up, thumbs-down rating system. OpenAI, for instance, uses the Yakov Livshits latter approach to continuously train ChatGPT, and OpenAI reports that this helps to improve the underlying model. As customers rank the quality of the output they receive, that information is fed back into the model, giving it more “data” to draw from when creating a new output—which improves its subsequent response. As the outputs improve, more customers are drawn to use the application and provide more feedback, creating a virtuous cycle of improvement that can result in a significant competitive advantage.

• The model answers complex questions based on a prompt, identifying the source of each answer and extracting information from pictures and tables.
• Overall employment in low- and middle-wage occupations has fallen from prepandemic levels, while occupations that pay more than $57,000 annually added about 3.5 million jobs.
• The first represents instances in which companies use foundation models largely as is within the applications they build—with some customizations.
• 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.
• Instead, they can partner with generative AI vendors and experts to move more quickly.

These organizations also are using AI more often than other organizations in risk modeling and for uses within HR such as performance management and organization design and workforce deployment optimization. In addition, the advent of generative AI, which helps to accelerate, automate, and augment human tasks, can potentially drive the transition of traditional consumer companies into software entities. Consumer and retail organizations are anchored on many functions where generative AI’s impact is projected to be felt most heavily, including marketing, sales, and customer operations. As a result, the annual productivity impact of generative AI on the sector is projected to be $400 billion to $660 billion, among the highest of all industries. This expectation only raises the already-high stakes of staying ahead of the technology curve for consumer and retail players.

Individuals as workers, consumers, and citizens

As with AI in general, dedicated generative AI services will certainly emerge to help companies fill capability gaps as they race to build out their experience and navigate the business opportunities and technical complexities. Existing AI service providers are expected to evolve their capabilities to serve the generative AI market. While generative AI will likely affect most business functions over the longer term, our research suggests that information technology, marketing and sales, customer service, and product development are most ripe for the first wave of applications. As demonstrated in the use cases highlighted above, technical and talent needs vary widely depending on the nature of a given implementation—from using off-the-shelf solutions to building a foundation model from scratch.

But what’s interesting is to look at the holistic ecosystem beyond the consumer and think about the technician that services your car when you bring it to the dealer. Gen AI has the ability to guide the technician, to identify the problem and quickly pinpoint how to solve that problem. Consumer companies can distinguish themselves from tech-native enterprises by focusing on the ability for talent to see the tangible impact of their innovations. This would largely impact high earners like knowledge workers and could add “trillions of dollars in value to the global economy,” McKinsey said. September 7, 2023McKinsey and Salesforce have announced a new collaboration to accelerate the introduction of trusted generative AI for sales, marketing, commerce, and service.

AI-related talent needs shift, and AI’s workforce effects are expected to be substantial

Banking, a knowledge and technology-enabled industry, has already benefited significantly from previously existing applications of artificial intelligence in areas such as marketing and customer operations.1“Building the AI bank of the future,” McKinsey, May 2021. It is expected to increase efficiency and productivity, reduce costs and create new opportunities. Gen AI is already being used to develop personalized marketing campaigns, generate creative content and automate customer service tasks. It can help creators to iterate faster, from the brainstorming stage to actual development.

CPG and retail industry product managers need to deeply understand ever-changing consumer habits and preferences, as much of their job is to optimize the digital touchpoint and make the transitions among multiple channels seamless for customers. Embedding software into organizational culture requires, first and foremost, that companies outline a clear vision for their software business. That means explaining how the value proposition and strategy will impact customer experience, growth, and talent—and communicating this perspective consistently across internal and external forums. According to McKinsey’s 2022 Voice of Consumer Organizations Survey, managers at high-performing consumer companies are 1.6 times more likely to say their digital agenda is integrated into business units rather than siloed in an IT organization. Investing wisely in software across the entire value chain, from initial customer interactions to internal corporate functions, can help consumer packaged goods (CPG) and retail companies meet these rising expectations.

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.

But before ChatGPT, which by most accounts works pretty well most of the time (though it’s still being evaluated), AI chatbots didn’t always get the best reviews. GPT-3 is “by turns super impressive and super disappointing,” said New York Times tech reporter Cade Metz in a video where he and food writer Priya Krishna asked GPT-3 to write recipes for a (rather disastrous) Thanksgiving dinner. Until recently, machine learning was largely limited to predictive models, used to observe and classify patterns in content. For example, a classic machine learning problem is to start with an image or several images of, say, adorable cats. The program would then identify patterns among the images, and then scrutinize random images for ones that would match the adorable cat pattern. Rather than simply perceive and classify a photo of a cat, machine learning is now able to create an image or text description of a cat on demand.

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. 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.

Generative AI is here: How tools like ChatGPT could change your business

Previous generations of automation technology were particularly effective at automating data management tasks related to collecting and processing data. Generative AI’s natural-language capabilities increase the automation potential of these types of activities somewhat. But its impact on more physical work activities shifted much less, Yakov Livshits which isn’t surprising because its capabilities are fundamentally engineered to do cognitive tasks. Retailers can create applications that give shoppers a next-generation experience, creating a significant competitive advantage in an era when customers expect to have a single natural-language interface help them select products.

In particular, model outputs must be verified, much as an organization would check the outputs of a junior analyst, because some large language models have been known to hallucinate. RMs are also trained to ask questions in a way that will provide the most accurate answers from the solution (called prompt engineering), and processes are put in place to streamline validation of the tool’s outputs and information sources. Our research has shown that such tools can speed up a developer’s code generation by as much as 50 percent. It can also help in debugging, which may improve the quality of the developed product.

Industry impacts

Eventually, when leaders are completely confident in the technology, it can be largely automated. The development cost comes mostly from the user interface build and integrations, which require time from a data scientist, a machine learning engineer or data engineer, a designer, and a front-end developer. Costs depend on the model choice and third-party vendor fees, team size, and time to minimum viable product. The bank decided to build a solution that accesses a foundation model through an API. The solution scans documents and can quickly provide synthesized answers to questions posed by RMs. Additional layers around the foundation model are built to streamline the user experience, integrate the tool with company systems, and apply risk and compliance controls.