Generative artificial intelligence (AI)

Generative AI language models have gained popularity across the globe.
GenAI has the potential to catalyse the reinvention of organisations.
Whether by identifying opportunities for growth or delivering cost efficiencies, GenAI is going to necessitate the imagination of organisational value, and operating models will be transformed as a result.

AI Unplugged - Where AI meets business, innovation, and the future

In this video, we bring together two leaders, Joe Atkinson, Chief Global AI Officer, PwC and Manpreet Singh Ahuja, TMT Leader and Chief Digital Officer, PwC India—to discuss how AI is reshaping the future of work and industries.

39%

of India CEOs said generative AI had been adopted across their respective companies in the last 12 months

70%

believe Gen AI would significantly change how their companies create, deliver and capture value over the next three years

57%

of respondents believe a wider adoption of Gen AI could improve the quality of products or services

^{Source: PwC's 27th Annual Global CEO Survey, 2024}

Generative AI (GenAI) is a type of artificial intelligence that can create original content and is moving this field of innovation forwards significantly towards human-like interactions.

It uses deep learning models trained on large bodies of source material such as billions of lines of text scraped from across the internet to formulate new content based on human prompts and complex predictions of relevant responses and structure.

This is a watershed moment for the application of AI, as models which appear to give human-like responses without domain-specific training meet the ability to respond in natural language and other expressive mediums like images, soundand video.

What is generative AI?

Generative AI (GenAI) is a type of artificial intelligence that can create original content and is moving this field of innovation forwards significantly towards human-like interactions.

It uses deep learning models trained on large bodies of source material such as billions of lines of text scraped from across the internet to formulate new content based on human prompts and complex predictions of relevant responses and structure.

This is a watershed moment for the application of AI, as models which appear to give human-like responses without domain-specific training meet the ability to respond in natural language and other expressive mediums like images, soundand video.

What is Gen AI?

Where can I use Gen AI?

How PwC can help?

Generative AI (GenAI) is a type of artificial intelligence that can create original content and is moving this field of innovation towards human-like interactions.

It uses deep-learning models trained on large bodies of source material, including billions of lines of text scraped from across the internet to formulate new content based on human prompts and complex predictions of relevant responses and structure.

This is a watershed moment for the application of AI, as models which appear to give human-like responses without domain-specific training meet the ability to respond in natural language and other expressive mediums like images, sound and video.

Create

Content generation
Code-assist

Discover

Conversational AI
Deep retrieval
Sentiment analysis

Summarise

Summarisation
Classification

At PwC, we can help integrate generative AI models that aligns with your organisational priorities and maximise the value generated from the technology. A comprehensive assessment of your organisation's current capabilities, data infrastructure, and business objectives can be conducted to assess how AI can help your firm. We can also design and implement generative AI solutions tailored to your specific requirements. Our team of experts can help in identifying use cases, defining project scope, and developing a roadmap for implementation.

We seek ways to empower organisations to unlock the full potential of generative AI and drive innovation, improve efficiency, and foster competitive advantage. We combine technical expertise with industry insights to deliver tailored solutions that address your specific needs and drive measurable business outcomes.

Four key pillars of a Generative AI strategy

Getting Started with Prompts

Prompts are the foundation of Generative AI tools, including ChatGPT. How well you prompt defines the value you receive. You can use ChatGPT or BingChat to create a prompt for you. Post that, you can copy and paste it into other tools like Midjourney or Blender. Just make sure to follow the latest guidelines for the prompt style needed by each tool.

Be Specific

Generic prompts produce generic results. Being more specific and concise will generate better results.

Act as if

Asking GenAI to behave like a person, project or object can help AI to tailor answers accordingly.

What to add in output

Being specific about the output like code, stories, report or summary etc. can produce better results.

Using dos and don'ts

Telling AI what you want to add and what not to add in the response can save time and improve the output.

Use examples

Using an example of the output text that you want to be displayed helps set a reference point and generates better results.

Consider tone

Providing GenAI with information on audience and tone provides more context and further improves the output.

Providing constraints

Providing specific guidelines and instructions helps in greater control of the output and provides more relevant results.

Build on prompts

With GenAI, you do not have to be right on the first prompt itself, building prompts guides the AI towards desired output.

Sector specific GenAI use cases

Wealth management journey - Generate a roadmap for an individual's retirement plan. The plan will take the individual's picture, show how it might look like at retirement age, overlay it with the person's retirement goals, and create a series of steps to achieve it
Sales Staff/ Agent support - Recommendation engine to give specific nudges and product recommendations based on customer persona and needs
Contact Centre Optimization - AI chatbot for customer query management. Call center agent support catalog to create repository of information customer specific information

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PwC's Engineering, Procurement & Construction Gen-AI solution - Designed to help business teams build solutions across report generation, legal contract analysis, project management and chatbot creation for EPC clients
Gen-AI led Control Tower - Provide visibility and actionable decision points for monitoring and controlling the movement of goods, vehicles, and information across the supply chain network
Claim Management - Manage claim processing through Gen-AI by identifying fraudulent patterns, communicating with customers through chatbots and generate automated claim documentation

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Personalized marketing at scale - This can be a game changer for early adopters of Gen AI, especially at the higher end of the product spectrum, to create hyper-personalized offers for customers that can significantly improve the outreach and enhance conversion of leads. Applicable for consumer, retail and hospitality.
Customer service excellence - Gen AI powered bots (including voice assistants) can revolutionize customer experience for inquiries, turnaround time, complaints, personalized planning for improving customer satisfaction which leads to a significantly higher lifetime value. Mining insights from interactions for proactive actions can actually enhance new product development
Market share intelligence - Using Gen AI to proactively scan for leading indicators for potential sales erosion across data from sales, pipeline, demand, market research and macro factors can help marketing & sales team to act in advance for protecting market share. This can be a game changer for market leaders where marketing analysts fail to stop hidden patterns in advance.

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Gen-AI for Standard Operating Procedure (SOP) document generation: Utilizing multiple documents like regulatory documents -FDA/ WHO, equipment manufacturing manual to generate and update SOPs quickly through Generative AI
Regulatory Intelligence Gathering and Investigation Support: Scan guidance and documentation issued by regulatory authorities and highlight potential impacts to the organization. Also Identify discrepancies or errors in financial documents and other records during audits or investigations
Promotional Content Creation, Review, & Deployment and Content Personalization: Creation of net new content to support marketing teams in developing collaterals for product marketing using flyers and posters. Augment sales force with promotional sales pitches to support HCP conversations.

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Procurement Data Analyzer - LLM / NLP based analysis of procurement function master data and transaction data - to help analyze and eliminate common anomalies. This tool would use contextual and text similarity for material & supplier master data cleanup and de-duping. Its other important feature will be master information augmentation based on textual inputs on all their suppliers and material categories captured from communications and digital news sources
Inventory Watchtower - Conversational Interface - Enabling manufacturing operations analytics for CXO dashboards from ad-hoc analysis to detailed data and summarization capabilities. The solution will look to build such an interface for data integrated across modules related to Production Planning / MES / IoT data platforms – with focus on OEE, productivity and plan deviations
Breakdown Response Co-pilot - While condition monitoring and preventive maintenance is run using numerical analysis, a lot of the breakdown maintenance root-cause analysis depends on understanding the context of the situations, that is captured as textual information across various communications and ticketing systems. The Gen-AI led solution will captures the wealth of information resident across operating manuals, condition monitoring data, plans and schedules, equipment maintenance history and maintenance ticketing information to build a corpus of knowledge that can will be accessed for quality analytics at scale – where the power of the LLM is leveraged to look for patterns that escape the human eye/comprehension.

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Sector-specific use cases

Contact center analytics

Transcribe audio files into text
Detect topic of the call, identify missed selling opportunities, detect sentiment
Curate KPIs for better insights

Market pulse

Allow senior management to query unstructured notes of salespersons
Answer market related questions e.g. competitive pricing, positioning, product launch

Resume analytics

Matching of a given resume with job description and evaluation of the candidate’s profile basis parameters such a work experience, past company/industry, educational qualification, etc.

Discharge summary data extraction and analysis

Created a discharge summary data extraction and analysis system leveraging a large language model.
Parses discharge summary files to extract key performance indicators (KPIs) such as medical issues and treatments undergone.
Utilises insights into potential disparities, aiding in the improvement of healthcare delivery and outcomes.

Drug discovery and development

GenAI used to analyse vast amounts of biological and chemical data to accelerate the drug discovery and development process. By applying machine learning algorithms, GenAI can identify patterns and relationships in data-related to disease mechanisms, drug targets, and compound properties.
Helped researchers in identifying potential drug candidates, predicting their efficacy, and optimising their properties.
Utilised in virtual screening of large compound libraries, reducing the time and cost required for experimental testing.

Clinical trial optimisation

GenAI analysed historical clinical trial data to optimise trial design and patient recruitment.
By analysing various factors such as patient demographics, disease characteristics, and treatment outcomes, GenAI can identify potential trial participants, predict patient eligibility, and optimise trial protocols.
Improved patient recruitment and retention, and increasing the chances of successful trial outcomes.

Predictive maintenance

GenAI can be used to analyse data from various sensors and devices installed in infrastructure systems such as bridges, roads, or buildings.
By applying machine learning algorithms, GenAI can identify patterns and anomalies in the data to predict potential failures or maintenance needs.
Enables infrastructure managers to proactively address issues before they become critical, reducing downtime and improving overall system reliability.

Traffic management

GenAI can analyse real-time data from traffic cameras, sensors, and other sources to optimise traffic flow and reduce congestion.

By analysing historical traffic patterns, GenAI can predict future traffic conditions and suggest alternative routes or timing adjustments to minimise delays.
Improve the efficiency of transportation networks, reduce fuel consumption, and enhance overall user experience.

Energy optimisation

GenAI can analyse energy consumption patterns in infrastructure systems including buildings or power grids.
By identifying energy usage trends and patterns, GenAI can provide recommendations on optimising energy consumption, reducing costs, and improving sustainability.
This can include suggestions for adjusting HVAC systems, optimising lighting schedules, or identifying energy-saving opportunities in power distribution networks.

Knowledge base Q&A

Developed a multilingual document retrieval and search solution based on the RAG architecture.
Employs a large language model for deep search capability, providing top three most relevant documents (with summary from relevant sections on user query) for user queries in over 25 languages.
Offers both keyword-based search without the language model and deep search using the model with enhanced accuracy and relevance.

Intelligent production support

Gen AI-based real-time support for production workers with guidance and troubleshooting
AI-driven helpbots to drive line changes, improve efficiency and 24/7 support

Vehicle design evolution for automotive

Optimise vehicle designs for enhanced aerodynamics, fuel efficiency and safety
AI-based algorithm to design variations in a click of a button, reducing need for physical prototypes

Personalised marketing at scale

This can be a game changer for early adopters of Gen AI, especially at the higher end of the product spectrum. This can create hyper-personalised offers for customers that can significantly improve the outreach and enhance conversion of leads. Applicable for consumer, retail and hospitality.

Customer excellence

Gen AI-powered bots (including voice assistants) can revolutionise customer experience for inquiries, turnaround time, complaints, personalised planning for improving customer satisfaction. This leads to a significantly higher lifetime value. Mining insights from interactions for proactive actions can actually enhance new product development.

Market intelligence

Using Gen AI to proactively scan for leading indicators for potential sales erosion across data from sales, pipeline, demand, market research and macro factors can help marketing and sales teams to protect their market share. This can be a game changer for market leaders where marketing analysts fail to stop hidden patterns in advance.

GenAI use cases

Customer Service

Contact Center Analytics

Transcribe audio files into text
Detect topic of the call, identify missed selling opportunities, detect sentiment
Curate KPIs for better insights

Strategy / Sells

Market Pulse

Allow senior management to query unstructured notes of salespersons
Answer market related questions e.g. competitive pricing, positioning, product launch

HR

Resume Analytics

Matching of a given resume with job description and evaluation of the candidate’s profile basis parameters such a work experience, past company/industry, educational qualification, etc.

Wealth management journey

Generates a roadmap for an individual's retirement plan. The plan will take in the individual's wealth management picture, show how it might look like at retirement age, overlay it with the person's retirement goals, and create a series of steps to achieve them.

Sales staff/agent support

Provides a recommendation engine to give specific nudges and product recommendations based on a customer’s persona and needs.

Contact centre optimisation

Provides an AI chatbot for customer query management. A call center agent support catalog can be created to host a repository of customer-specific information.

Customer Service

Contact Center Analytics

Transcribe audio files into text
Detect topic of the call, identify missed selling opportunities, detect sentiment
Curate KPIs for better insights

Strategy / Sells

Market Pulse

Allow senior management to query unstructured notes of salespersons
Answer market related questions e.g. competitive pricing, positioning, product launch

HR

Resume Analytics

Matching of a given resume with job description and evaluation of the candidate’s profile basis parameters such a work experience, past company/industry, educational qualification, etc.

GenAI for data management practice

There are various applications of GenAI that can be leveraged for data management practice spanning across code creation to code understanding and beyond…

Code generation

Use simple language instructions to generate code. Code generation for specific tasks, functionalities depending on coder’s requirements

Code repository search

Create a knowledge repository of codes and perform deep search using LLMs

Code conversion

Convert from one programming language to another with minimal efforts

Documentation

Create MoM’s and extraction information from Notes, e-mails and SOPs to create BRDs

Synthetic test scenarios and data

Generate boundary conditions or data unseen by systems to perform testing

Code documentation and understanding

Add comments, descriptions to codes and improve understanding of legacy codes

Code generation

Use simple language instructions to generate code. Code generation for specific tasks, functionalities depending on coder’s requirements

Code repository search

Create a knowledge repository of codes and perform deep search using LLMs

Code conversion

Convert from one programming language to another with minimal efforts

Documentation

Create MoM’s and extraction information from Notes, e-mails and SOPs to create BRDs

Synthetic test scenarios and data

Generate boundary conditions or data unseen by systems to perform testing

Code documentation and understanding

Add comments, descriptions to codes and improve understanding of legacy codes

Code generation

Use simple language instructions to generate code. Code generation for specific tasks, functionalities depending on coder’s requirements

Code repository search

Create a knowledge repository of codes and perform deep search using LLMs

Code conversion

Convert from one programming language to another with minimal efforts

Documentation

Create MoM’s and extraction information from Notes, e-mails and SOPs to create BRDs

Synthetic test scenarios and data

Generate boundary conditions or data unseen by systems to perform testing

Code documentation and understanding

Add comments, descriptions to codes and improve understanding of legacy codes

How PwC can help

At PwC, we can help integrate generative AI models that aligns with your organisational priorities and maximise the value generated from the technology. A comprehensive assessment of your organisation's current capabilities, data infrastructure, and business objectives can be conducted to assess how AI can help your firm. We can also design and implement generative AI solutions tailored to your specific requirements. Our team of experts can help in identifying use cases, defining project scope, and developing a roadmap for implementation.

We seek ways to empower organisations to unlock the full potential of generative AI and drive innovation, improve efficiency, and foster competitive advantage. We combine technical expertise with industry insights to deliver tailored solutions that address your specific needs and drive measurable business outcomes.