Generative AI

Distinguishing between hype and reality of generative AI in professional contexts

In today's rapidly evolving technological landscape, the allure of generative Artificial Intelligence (AI) has captured the imagination of professionals across industries. According to a survey conducted by a leading business school, 84% of executives believe that generative AI will enable them to obtain a competitive advantage. However, only 1 in 20 companies have extensively incorporated AI into their business processes.

From generating creative content to automating complex tasks, the promises of generative AI seem endless. However, amidst the buzz and excitement, it's important for organisations to differentiate between the hype surrounding generative AI and its practical applications in professional contexts.

How is AI used in professional life?

Generative AI can use pre-existing data to create custom solutions for product development, marketing, and other areas. Generative AI frees up important resources by automating repetitive and routine operations, allowing people to concentrate on creating truly innovative results.

Here are some examples of what generative AI can do:

  • Classification: A generative AI tool can be used by a fraud-detection analyst to identify possibly fraudulent transactions by analysing transaction descriptions and customer records.
  • Editing: Copywriters can use generative AI to improve grammar and customise an article to fit their client's brand voice. Graphic designers could also use it to remove logos from pictures.
  • Synopsis: A production assistant can use generative AI to create a highlight reel from a lot of event footage. A business analyst can create a Venn diagram highlighting key ideas from an executive's presentation.
  • Answering queries: Workers in an industrial setting can ask technical questions about operational procedures to a "virtual expert" powered by generative AI. A customer can ask an AI chatbot for assistance assembling a new piece of furniture.

Benefits of generative AI

While headlines promise that using generative AI is easy and ground-breaking across the board, properly harnessing the tangible advantages of the technology is crucial for businesses. In this section, we'll explore the benefits of generative AI across many domains:

  • Enhancing creativity: Generative AI introduces a fresh perspective to the creative process, injecting it with innovation and inspiration across various professional domains. Some new AI platforms have the capability to collaborate with musicians to compose original music, leveraging human input to tailor compositions.
  • Boosting productivity: Generative AI streamlines workflows by automating repetitive tasks, freeing up resources for more impactful endeavours. For example, legal professionals utilise Gen AI platforms to expedite legal document review, identify critical clauses, and mitigate risks efficiently.
  • Personalisation and customer engagement: Generative AI enables tailored interactions, fostering customer engagement and loyalty. Retailers use generative AI to enhance online shopping experiences, exemplified by The North Face utilising AI tools to deliver personalised product recommendations.
  • Cost optimisation: Automation of tasks by generative AI reduces manual labour dependency, resulting in time and cost efficiencies. Customer service operations empowered by generative AI not only elevate the customer experience but also boost productivity. This is demonstrated by a report from an American multinational consulting firm, which indicates a 14% increase in issue resolution efficiency and a 25% reduction in employee attrition.

The reality of generative AI

On the one hand, generative AI offers unprecedented opportunities to revolutionise industries, but on the other, we cannot overlook the host of ethical and legal implications it brings. CEOs must proactively mitigate the risks posed by generative AI in order to protect their companies and gain customers' trust.

  • Bias: AI models might reinforce algorithmic prejudice because of biased training data or decisions made during model building,
  • Intellectual property (IP): The use of training data and model outputs may violate intellectual property rights by infringing on copyrighted, trademarked, or patented content.
  • Privacy: Sensitive or confidential information may be present in the data that generative models use to create new material. Such data may be misused, and using it in AI models may result in privacy violations. For example, generative AI may aid in the development and distribution of harmful information, such as deepfakes and hate speech.
  • Safety: Hackers could use generative AI to increase the speed of their cyberattacks. AI model manipulation techniques, such as quick injection, can yield unintended results.
  • Explainability: As generative AI relies heavily on complicated neural networks, it becomes more difficult to explain how outputs are generated, which raises questions about accountability and transparency.
  • Reliability: AI tools may yield inconsistent results for the same prompts, hindering users' ability to assess accuracy. Errors and inconsistencies in AI-generated information can be particularly problematic in fields like healthcare and law.
  • Organisational impact: Generative AI adoption could significantly impact the workforce, potentially affecting specific groups and local communities disproportionately.

How can Infosys BPM help implement generative AI?

Infosys BPM offers a natural language generative AI, a collection of customisable, pre-configured design frameworks, and BPM-focused solutions. The business operations platform is a component of Infosys Topaz and uses generative AI technologies to enable businesses to drive generative evolution and accelerate value creation.

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