Artificial intelligence has reached a tipping point: generative AI is so powerful and intuitive to use, it’s poised to revolutionise how work gets done. Conventional AI is advancing too, delivering ever greater productivity and new revenue streams. If well deployed and with caution, both conventional and generative AI can drive sustained outcomes today and transformation tomorrow.
PwC has long been an AI leader and a first mover, helping organisations use AI to reimagine their business models while protecting underlying data, developing skills and building trust in AI systems. Below are four questions asked most by business leaders. We hope our answers help you too move forward with greater confidence.
1. What is generative AI?
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 is a subset of AI. It is a type of deep learning that can understand, analyse and create content. Since it often works on plain language commands, applications developed with Generative AI can be remarkably easy to use. Think Chat GPT or Google’s Bard.
The business-relevant content that generative AI can analyse and generate includes:
- Software code for business processes
- Text, images, audio and video that are high quality
- Data analysis
- Transcriptions, translations, summaries and analysis of business documents, phone calls and meetings
- Virtual simulations such as digital twins and metaverse spaces
2. What are generative AI’s business applications today?
Innovative organisations are expected to leverage Generative AI to optimise 80% of their knowledge work. Applications include:
Customer service. Cut costs through automation and enable self-service that actually satisfies rather than irritates through true personalisation and rapid, accurate responses to questions and concerns.
Automating high-volume tasks. Whether it’s processing insurance claims, meeting payrolls, creating “first drafts” of software code or technical writing, you can automate much of the tedious, repetitive knowledge work that humans currently do.
Provide people with insights. Generative AI’s ability to read, listen to, synthesise and analyse text and voices can give your teams a start on the information they need from things like contracts, invoices, customer feedback, corporate and government policies.
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.
3. How does generative AI work?
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.
The foundation models are generally trained on truly vast quantities of data — hundreds of billions or even trillions of data points. They are all designed to include mostly or exclusively open-source data. These models, which are the source of generative AI’s often remarkable accuracy, can represent the collective wisdom of the internet.
In addition to publicly available data, organisations can fine tune these models on their own data sets. 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 be of high quality and accuracy that can improve over time. This is because of all the data it learns 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. And organisations can introduce AI governance and human supervision to optimise AI generated content.
4. How can I accelerate AI and generative AI in my company — while building trust?
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.
- Implement responsible AI to build trust. Responsible AI offers frameworks, templates and code-based assets for your AI usage and data governance to be ethical, secure, compliant and robust. A responsible AI framework is a powerful tool to instil trust in both conventional and generative AI.
- Act to operationalise AI at scale. AI offers its greatest benefits when you use it at scale. For generative AI, that may require changes in your approach to model development and deployment, your technology architecture and skills.
- Recognise new ROI opportunities. Boost your bottom line with AI strategies that capture indirect costs (such as new burdens on specialists and the new oversight needs of generative AI) and indirect benefits (such as improved employee and customer experiences).
This article was originally published on Tech Effect. If you would like to learn more about using AI within your organisation in Australia, please contact Jahanzeb Azim.
