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AI in 2023 has reached a tipping point: Generative AI is so powerful and easy to use, it’s poised to change business models and revolutionize how work gets done. It may soon reinvent entire industries. Conventional AI is advancing too, delivering ever greater productivity and new revenue streams. If well deployed, both conventional and generative AI can drive sustained outcomes today, transformation tomorrow and trust every step of the way.
PwC has long been an AI leader and a first mover, helping organizations use AI to reimagine their business models while protecting underlying data, developing skills and building trust in AI systems. Now that generative AI is maturing, we’re scaling up our AI investments and services. Based on this experience we identified some of the top questions business leaders may have about AI today. The answers can help you chart a trusted course for AI in your organization.
We usually define AI as computer systems that can gather information from the digital or physical worlds, draw conclusions, then make smart choices and act on them. Generative AI does all that and goes one step further. It is a type of deep learning that can create content. Since it often works on plain language commands, it can be remarkably easy to use.
The business-relevant content that generative AI can generate includes:
As it matures, generative AI’s applications will likely extend to almost every area of business. We’ll see workplaces and internal operations transformed and new business models created as skilled people instruct well-supervised generative AI models to create content. Just look at some of its more valuable and accessible business applications.
At PwC, for example, we’re already using generative AI to turn large volumes of data into richer insights and recommendations for our clients. We’re also using it more and more to find efficiencies and cost and time savings for ourselves and our clients.
Like conventional AI, generative AI runs on models — sets of algorithms that are trained with human help to produce desired outputs. Based on this training, the AI attempts to predict the better answer to the prompt or command given.
For example, you could say, “Listen to all the calls to our help desk in the last 24 hours and identify the three biggest complaints.” If the AI model was well trained, its output would likely be accurate because of all the data it had already learned from: other human voices, other complaints, other human language in general.
Generative AI’s answers aren’t always perfect, but they can be remarkably helpful, in part because of its differences with conventional AI.
To use AI and generative AI to deliver near-term outcomes, transformative innovation and increased trust is not an easy task. Three guidelines can help.