Generative AI – not just a chatbot?

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Is generative AI just another technology fad? Or can organisations make it go the extra mile?  

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In today’s era it is rare to encounter someone who has not heard of generative AI. The hype surrounding the launch of ChatGPT has spotlighted generative AI for its remarkable capabilities, such as generating content and images and analysing data. Organisations worldwide are rushing to implement the technology into their business models in the hopes of increasing revenue, operational efficiency and productivity. But can generative AI really live up to all the hype? NashTech experts George Lynch, Head of Technology Advisory and Ramaraju Indukuri, Senior VP and AI Practice Leader, share their thoughts on the emerging technology, where they see the opportunities and challenges.  

Is generative AI another technology fad? 

Generative AI has stirred momentum across organisations and industries worldwide. In a remarkably short time, it has become the world’s most talked about and tested technology, with the launch of generative AI platforms such as ChatGPT spotlighting its capabilities for businesses and the everyday user. In 2022 we saw ChatGPT alone grow to one billion users within just the first three months of its the launch, an indicator of its impressive capabilities and receptiveness of society. The debate has already begun worldwide on how organisations can best harness the power of generative AI for value creation. In a report by Deloitte, 74% of organisations are testing generative AI in their business models. Yet, amidst all the hype, we have found ourselves questioning, is generative AI just another technology fad or can it truly take society into a new era?  

On November 14th NashTech’s leading experts George Lynch, Head of Technology Advisory and Ramaraju Indukuri, Senior VP and AI Practice Leader shared their two cents on generative AI in an insightful panel discussion hosted by NashTech. Among the topics discussed were the potential use cases of generative AI, where our experts see its potential and where organisations should take caution when implementing the technology into their operational models. 

Accelerating value creation cross-industry

Generative AI poses a number of practical applications for organisations looking to increase their competitiveness in the market and the value of their offerings. We are already witnessing its applications across industries, from automating the claims process in the insurance industry, personalising promotional materials in marketing and introducing new avenues for customer service in the retail sector, generative AI is changing the way that individuals interact with technology in their day-to-day lives.  

Lynch shared some example cases where we see our clients dabbling in generative AI: 

Personalisation in retail  

Lynch describes generative AI as a ‘nirvana’ for marketing. For retail and ecommerce, generative AI presents the opportunity to create new customer experiences through advanced personalisation and analytics, contributing an anticipated $400-$660 billion annually for retailers. Intelligent generative AI-powered chatbots can provide customers with a greater shopping experience by finding requested items on demand, providing instant customer resolution and tracking orders across its dispatch journey. But beyond increasing customer service touchpoints, generative AI provides a leap forward in the world of personalised marketing. Leveraging generative AI and machine learning for advanced analytics, organisations will be able to deliver personalised promotional discounts and offers based on an individual’s unique historical data, such as former purchasing behaviours and search intent.  

Read more about how generative AI is transforming the retail sector:    


Claims processing in insurance  

According to BCG, generative AI will lead to a 20-30% reduction in loss-adjustment expenses for the insurance industry. 

In the insurance sector, Lynch explained the potential of generative AI for streamlining claims processing. Through advanced Natural Language Processing (NLP) capabilities, generative AI can efficiently analyse images, pictures and incident descriptions of submitted claims, generating reports on demand and reducing time spent by employees on manual processing. But generative AI doesn’t just help offload employee time to more valuable activities, it can also aid in fraud detection when assessing claim eligibility. Insurance organisations will be able to analyse claims against former customer profiles, historical data and market intelligence, analysing the probability of a fraudulent claim and flagging suspicious activities for further human investigation. Thus, increasing claim accuracy and essentially reforming claims processing altogether. 

Student wellbeing in higher education  

Over the next three years, 47% of learning management tools are expected to be powered by AI – Edgeucating 

The education sector faced strife last year with generative AI platform ChatGPT disrupting the validity of student assessment processes, demanding rapid and new strategies for accurately assessing student performance. But this is just one side of the story. Generative AI has the potential to enhance student experience and satisfaction through personalised learning. By analysing student performance against class type, behavioural data, learning speed and style, generative AI can provide personalised content and learning materials to maximise the success of its students. But that’s not all. Lynch explained that generative AI has potential even across the more human-centric role, such as intelligent wellbeing for students, demonstrating the sophistication of its model. The Large Language Model (LLM) will be able to understand tones and cues from its student conversations, using contextual understanding to answer mental health related questions and provide relevant help resources and guides.   

Balancing the dangers of generative AI  

The potential of generative AI is endless for organisations. But despite its realm of possibilities, our experts emphasise the importance of careful planning to maximise the returns of its implementation. 


Data privacy and legislations   

Ethical and responsible AI usage and regulations has captured the headlines of governments and regulatory bodies worldwide. According to our 2023 Digital Leadership Report, eighty-eight percent of organisations believe that AI requires stricter regulation. This sentiment was echoed by Indukuri who emphasised the importance of testing and generative AI model transparency during the development process. Indukuri warned organisations who are experimenting with generative AI about the ramifications of potential future governmental policies, advising organisations to create documentation of AI processes for model transparency in the event that governments require sharing of test data for governance and evaluation of the models that organisations are building.   


Legal ramifications 

Another danger for organisations pertains to the legalities of AI. Internet-facing generative AI models such as ChatGPT retrieve their data from public sources. The real danger for organisations, Indukuri explains, relates to the legal and intellectual property implications of its output. While legalities surrounding IP and copyrighting still remain unclear at this point, all outputs by public-facing generative AI platforms like ChatGPT should be carefully scrutinised to avoid raising of legal cases down the line. What’s more, organisations need to take caution when sharing sensitive company data with models like ChatGPT that may be shared with other users or prone to data leakage. 

Governance of generative AI usage has come late to the party” 

George Lynch, Head of Technology Advisory at NashTech

Model accuracy 

A significant concern for organisations is how accurate their generative AI model is. Naturally, the data output quality is dependent on the initial training data that is fed into the model. Thus, when the training data is biased or incorrect, there runs the risk of generating skewed or incorrect outputs altogether. This is important Indukuri explains, because biased outputs reflect on the organisation that is providing it, potentially risking an organisation’s reputation, which as we know, impacts the overall performance and profitability of a business. 

Preparing for generative AI implementation  

The stir surrounding generative AI has led to organisations seeing the possibilities that generative AI can yield; increased revenue, innovation, operational efficiency and productivity. Yet there is a danger in chasing the ‘shiny’ new technology trend. Without careful preparation and a sufficient implementation roadmap, organisations can miss the mark in producing the results they require and making the technology work effectively for them. In the Nash Squared Digital Leadership Report 2023, an overwhelming seventy-five percent of organisations reported feeling unprepared for generative AI implementation, with data privacy and regulation among top concerns. To reap the benefits of generative AI, organisations need to prepare both technically and culturally for its shift.  

As with any digital transformation, organisations need to encourage their organisation to embrace technology as part of its growth strategy, since promoting trust among employees is a significant factor for successful integration of technology and people. But what other factors do our experts believe organisations should consider for success? 

  1. Evaluating and understanding potential use cases: prior to implementation, it is important to understand how generative AI will help organisations deliver against their strategic goals and which use cases can be solved by the technology. Organisations should consider, ‘is the technology solving the right business pain points?’. Is it helping the business move forward with efficiency, productivity or growth? Can the more classical traditional AI model like machine learning solve the organisation’s problems? 
  2. Integration with existing business processes and systems: how will the generative AI model interact with existing technologies and processes and how can it streamline the business to operate more efficiently? Complete overhaul of existing infrastructures can lead to a lengthy, costly and laborious transformation initiative. Developing a well-thought-out integration roadmap will reveal any gaps with legacy systems and if additional resources are required. 
  3. Empowering and training of relevant employees: employees need to understand the changes at hand and have supporting change management initiatives to effectively encourage its adoption. An additional team of AI experts may need to be hired to support the new AI initiative. 
  4. Developing a data-governance programme: regular monitoring and assessment of the generative AI model is necessary to ensure that outputs are accurate, unbiased and of high quality. This will aid in ensuring responsible AI usage and compliance with future AI regulations.  

To find out more about what factors your organisation should consider when implementing generative AI, speak with one of our experts today. 

Kickstart your AI journey with NashTech 

With its endless possibilities and opportunities, it’s clear that generative AI is here to stay and will continue to evolve in capability and accuracy over the next several years. As models continue to become smarter and more sophisticated, organisations need to get a handle of their AI environment and strategy. At NashTech we can help organisations to navigate their AI journey. Our AI experts have experience delivering transformational AI projects for clients, building successful strategies that deliver exceptional results. 

Kickstart your generative AI journey and stay on top of the every-changing technology trends today. 

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