What Is Generative AI?
With the emergence of Large Language Models (LLMs) – ChatGPT and others – Generative AI has gained significant momentum among artificial intelligence models over the past few months. The hype around this technology is real and merits consideration. Advancement in large language models like ChatGPT have strengthened and unlocked a myriad of generative AI capabilities increasing its appeal and unlocking opportunities to power genuine business application and use cases.
Generative AI has created a new focus on AI by catapulting assistive technology to a new stratum, positively impacting technology applications development and delivery time and empowering nontechnical users.
By virtue of being pre-trained on massive amounts of data, generative AI models deliver performance gains and accelerated time to value allowing businesses to focus on fine tuning for their specific use cases[1]. Generative AI’s ability to enhance, create and originate content, such as music, images, or text, while it interprets and manipulates pre-existing data has demonstrated that it has the potential to revolutionize industries and change the way we live and work[2].
Leaders transcending all organisations are fronting up to the key question: “How can we adopt generative AI in our organisation?” The quest to leverage generative AI for business to gain a competitive advantage is well and truly on.
Generative AI Technology Business Use Cases
Generative AI is a powerful technology that despite being in the early days of scaling, has many potential applications for businesses looking to drive growth[3]. Here are some examples companies can re-imagine their core processes with generative AI:
- Product Design: Building user applications traditionally requires writing code in a programming language that is readable by computers and provides structure for the applications logic and functionality. The ability to use natural language to communicate and allow the Ai to generate the code is ground-breaking. Generative AI powered applications / assistants can for example turns natural language prompts into code or prompts into formulas and macros for spreadsheets, enabling business users to build powerful spreadsheet-based apps. By inputting a set of design parameters, generative AI algorithms can create a wide variety of designs that meet specific criteria. Generative AI not only speeds up application development and makes it easier for new developers to start but can help companies create products that are more efficient, effective, and appealing to customers.
- Sales and Marketing: The importance of marketing to drive product / brand awareness, positioning, and emotionally connecting with the hearts and minds of target customers cannot be overstated. To achieve this clear marketing messages driven by right content are key. Traditionally, the process to create this content is time consuming and sometimes a game of luck. Step forward Generative AI – by allowing faster content creation, automating presentation formulations, video, and content editing, and optimizing social media marketers are freed to focus on more creative aspects, iterating efficiently and fine tuning content to perfection. Marketing teams can save time and resources while delivering more targeted, engaging, and effective campaigns. Sales outreach emails can be automated and personalised at scale allowing sales teams to focus on quality and personalisation without sacrificing efficiency.
- Customer Support Automation and Personalization: Customer support is vital for building customer loyalty, but providing adequate resources and staffing remains challenging for businesses potentially resulting in unnecessarily lengthy resolution times, poor quality of service and frustrated customers. Generative AI can be used to assist agents to resolve complex queries, personalize products and services for individual customers. By analysing customer data, the AI can create unique and customized products or services that meet the specific needs of each customer.
- Operations Optimization: Generative AI can also be used to optimize various aspects of a business, such as supply chain management, inventory management, and pricing strategies. By analysing large amounts of data, the AI can identify patterns and trends that can help companies optimize their operations for increased efficiency and profit maximisation.
- Regulatory Compliance: Compliance is crucial in many business sectors. For example, Data Privacy is paramount for keeping personal identifiable information secure from unauthorised access and use to avoid breaches which can bring significant penalties, damage brand reputation and cost time with legal proceedings. Using representative data to build products and services can mean risk to client privacy. Through generative AI, synthetic data can be produced from real data, or redacting and anonymising personal identifiable information within data sets to preserve privacy while keeping data realistic for testing and training machine learning models.
Summary of Generative AI Use Cases by Industry
Shared by: Kathryn Hunt
Word Of Advise
Business leaders must realise that leading generative AI use cases go beyond cost reduction and help drive employee satisfaction, customer trust and business growth. These may not be primary goals with companies focused on realizing accelerated business value, greater precision and near real-time insights into customer pain points to reduce time-to-insight discovery, faster audits to maintain assurance and regulatory compliance as well as efficiency gains in iterations.
It is important for leaders exploring opportunities to deploy generative AI to quickly identify areas of their organisation where this technology could deliver immediate impact and formulate controls to regulate and monitor the deployment. Having a steering committee with sufficient skills balance may be advisable.
Generative AI is being applied to many use cases or tested in enterprise applications to solve complex challenges and create new innovative solutions that deliver competitive advantage. Fraud detection, Predictive maintenance, Dynamic pricing, Customer segmentation and Resource allocation are benefiting from the impact large language models are having when combined with machine learning models for business benefits. With businesses seeking to stay ahead and drive growth they are already starting to take advantage of this technology to improve their operations, create new products, and enhance the customer experience.
What Next?
Embracing generative AI will not only change how the business operates, but unlock opportunities, create new market space, and deliver competitive advantage in a rapidly evolving marketplace. Business leaders need to embrace the risks associated with this ground-breaking technology’s adoption and answer the key questions: Which use cases will deliver the most value for my business? Which AI technology is best suited for my needs? Is it secure? Is it sustainable? How is it governed? And how do I ensure my AI projects succeed? Choices on how they incorporate and deploy them for their use cases. There are considerations specific to use cases and decision points around cost, effort, data privacy, intellectual property and security.
[1] Manish Goyal, Shobhit Varshney, Eniko Rozsa , What is generative AI, what are foundation models, and why do they matter?, IBM Blog, March 2023, https://www.ibm.com/blog/what-is-generative-ai-what-are-foundation-models-and-why-do-they-matter/
[2] Nicholas Deon, Top 5 Ways To Apply Generative AI To Your Business, Forbes Technology Council, March 2023, https://www.forbes.com/sites/forbestechcouncil/2023/03/08/top-5-ways-to-apply-generative-ai-to-your-business/?sh=372fccac3314
[3] Michael Chui, Roger Roberts, & Lareina Yee, Generative AI is here: How tools like ChatGPT could change your business, McKinsey, December 2022, https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business#/
