Companies will also have to assess whether they have the necessary technical expertise, technology and data architecture, operating model, and risk management processes that some of the more transformative implementations of generative AI will require. Others may want to exercise caution, experimenting with a few use cases and learning more before making any large investments. Some may see an opportunity to leapfrog the competition by reimagining how humans get work done with generative AI applications at their side. Such upgraded tools could substantially increase productivity.ĬEOs want to know if they should act now-and, if so, how to start. In fact, while generative AI may eventually be used to automate some tasks, much of its value could derive from how software vendors embed the technology into everyday tools (for example, email or word-processing software) used by knowledge workers. But nearly every knowledge worker can likely benefit from teaming up with generative AI. The preceding example demonstrates the implications of the technology on one job role. At the same time, generative AI could offer a first draft of a sales pitch for the salesperson to adapt and personalize. A generative AI tool might suggest upselling opportunities to the salesperson in real time based on the actual content of the conversation, drawing from internal customer data, external market trends, and social media influencer data. A specially trained AI model could suggest upselling opportunities to a salesperson, but until now those were usually based only on static customer data obtained before the start of the call, such as demographics and purchasing patterns. Imagine a customer sales call, for example. With proper guardrails in place, generative AI can not only unlock novel use cases for businesses but also speed up, scale, or otherwise improve existing ones. The downside to such versatility is that, for now, generative AI can sometimes provide less accurate results, placing renewed attention on AI risk management. 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. In contrast, previous generations of AI models were often “narrow,” meaning they could perform just one task, such as predicting customer churn. Foundation models can be used for a wide range of tasks. 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 access.Īll 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. 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. Its out-of-the-box accessibility makes generative AI different from all AI that came before it. It democratized AI in a manner not previously seen while becoming by far the fastest-growing app ever. The public-facing version of ChatGPT reached 100 million users in just two months. This article is a collaborative effort by Michael Chui, Roger Roberts, Tanya Rodchenko, Alex Singla, Alex Sukharevsky, Lareina Yee, and Delphine Zurkiya, representing views from the McKinsey Technology Council and QuantumBlack, AI by McKinsey, which are both part of McKinsey Digital.
0 Comments
Leave a Reply. |
Details
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |