Generative AI and its Transformative Impact
Upon its release in late 2022, the Generative AI system ChatGPT quickly achieved viral status across social media platforms as users shared travel itineraries, movie scripts and software code as examples of what the technology could create. The broad utility of Generative AI in its ability to write text, compose music and conceptualise digital art encouraged many to try out, play with and experiment with ChatGPT, which racked up over 1 million users within 5 days of its release.
Beyond this connection to the public consciousness however, this new wave of Generative AI systems possesses a seismic economic potential to act as a watershed in boosting productivity across various commercial and operational functions, holding the power to both transform and uproot the traditional order of entire industries. The fast- moving nature of AI development means for an organisation to maintain a competitive edge, a well-defined strategic and operational focus is required today.
What is Generative AI?
Generative AI models are based on machine learning processes inspired by the workings of the human brain, known as neural networks. Generative AI systems use neural networks which identify and effectively learn the patterns and structures within an existing set of training data (which serves as the foundation for the AI model to learn from) to produce new and original content.
GPT-3, (the precursor to and the model from which ChatGPT draws from) is an example of a foundation model, which allows users to leverage the power of natural language. ChatGPT allows users to generate an essay or write a short story, or a play based on a short text request, learnt off the GPT-3 foundation model. Similarly, models such as Stable Diffusion and Dall-E, allows users to generate images given a text input.
What Type of Content Can Generative AI Text Models Create?
Generative AI text models can be used to generate texts based on natural language instructions, including but not limited to:
- Power chatbots
- Generate marketing copy and job descriptions
- Offer conversational messaging support with zero wait time for the customer
- Search internal documents to increase knowledge transfer within a company
- Condense lengthy documents into brief summaries
- Perform data entry
- Analyse massive datasets
- Writing software/coding
How Is Generative AI Beneficial for Businesses?
1. Generative AI has the potential to transform the customer operations capability by improving customer experience and customer service representative productivity. This technology has already gained traction amongst financial services institutions, telecommunications giants and utilities companies, where the ability to automate interactions with customers using natural language has significantly increased the number of interactions with customers or queries answered, while also enhancing the quality of the interactions (particularly with less-experienced agents).
Generative AI powered bots can give immediate and personalised responses to the most complex customer enquiries, allowing customer service representatives to focus on the ‘exceptions’ in which only a human can resolve. In a more supplementary role, Generative AI can instantly retrieve company data on a specific customer, which can help customer service representatives resolve issues on initial contact, provide assistance in real time and recommend next steps in the process to reduce response times.
In a study conducted by the National Bureau of Economic Research (NBER), it was found that customer service representatives using a Generative AI tool saw a nearly 14% increase in their productivity.(1)
2. Generative AI is expected to transform the marketing and sales capability, underpinned by large scale text-based personalised communications. To heighten the personalisation of the customer experience, the technology can create personalised messages tailored to individual customer interests, preferences, and behaviours, as well as perform tasks such as producing first draft of brand advertising, headlines, slogans, social media posts, and product descriptions. The key operational benefits of embedding Generative AI includes efficient and effective content creation (reduce the time required for ideation and content drafting) and ensuring a consistent branding voice, style and format.
The increasing personalisation allows for differentiated and highly experiential brand experiences. Underpinned by the customer as the central focus, tailoring the digital experience moves away from the product and product designers as the driver of value. As the customer journey flows beyond the restrictions of the brand’s product, links to outside partners, rethinking the definitions of an organisation’s offerings and developing the underlying data and systems architecture to connect the dots in the personalised journey will be a key success factor.
3. Critical (customer) insights, which are valuable in informing the strategic direction and operational processes are often embedded in data strewn across a disparate IT network. Currently, one of the biggest challenges facing data analysts is amalgamating these large and often incompatible datasets. Many AI systems can write the code needed to understand the schemas of two different databases and integrate them into one repository. This facilitates the ability to derive analytical data collected from across the business to gain valuable insights on their customers, competitors, operations and inform decision-making to drive the value proposition of an organisation.
The above, which describes only a few examples of the power of Generative AI and the significant implications it could have for businesses and business leaders, highlights the overriding benefits of the technology’s potential – that of an uplift in productivity through automation and an increasingly targeted and personalised value proposition. It is important to note that while organisations will be changed by AI, they themselves need to change to really take advantage of the opportunities before competitors do. Realising these benefits therefore, requires a distinctly human element – to align both the internal operational processes and the operating model of the organisation to deliver the value which the customer has defined. The organisations which will ultimately win the AI race will encompass operating models that embed both digital and AI tools in their structures and processes.
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Andrew Morley – Managing Director Experienced transformation specialist, with a passion for analytics and problem solving, that excels in operating model design, lean process re-engineering, and customer experience enhancement. |
