Overcoming Generative AI Disruptions in the Business Landscape

Generative AI is positioned to disrupt an array of industries, transforming established processes, workflows, and reshaping brand interactions, as well as employee and customer engagements. When we consider that some forecasts indicate that generative AI could reach a value of $909 billion by 2030, it is no surprise that the disruptive technology is a key focus area for business leaders.

Generative AI disruptions are already reshaping various industries like media, commerce, healthcare, and finance. This article delves into major AI disruptions within each sector and will provide guidance advice on how to prepare your organisation for the disruptive technology.

Generative AI Disruptions in Media

The creative sectors have been shielded from AI in the past, as earlier applications of the technology struggled with creating human-like content. However, with the emergence of generative AI, this landscape has shifted as it excels in producing creative content and is now being incorporated into the creative process. Here are some significant applications of generative AI in the media.

● Scriptwriting/publishing: No longer just used as a tool for grammar and spell checking, generative AI is being used in the creative process to enhance scripts, whether that entails editing, generating unique perspectives, or initial first drafts and plot lines.

● Directing and visual effects: Generative AI can create impressive synthetic visuals for movies, such as faces, landscapes, or objects. Additionally, it can assist in film editing by removing or adjusting elements of the picture, which ultimately saves time and money for filmmakers.

● Acting: Generative AI can be used in a multitude of ways, from de-aging actors to recreating whole likenesses and voices. This was a major sticking point in the recent Hollywood strike, which resulted in increased protections for actors and how their “digital twins” can be used.

● Video games: In addition to creating artwork and scriptwriting, generative AI has unique applications in video games. It can generate practically limitless worlds for players to discover, offering a step beyond developer-constrained procedurally generated content. Moreover, Generative AI can be used for voice acting in games and also generate dialogue for characters in real-time, allowing for more dynamic and potentially realistic responses to players compared to scripted characters and dialogue.

● Social media: Generative AI has the capability to produce content for social media, including images, videos, and written content. On the flip side, it can also be used maliciously to generate misinformation and disinformation on a large scale, using human-like text content to present false information to users.

How to Overcome Generative AI disruptions in Media

Creators and businesses must tackle the obstacles of utilising generative AI tools efficiently, all while taking into account the legal and ethical consequences. Instances of backlash have already emerged from the unauthorised use of celebrity likenesses and reproducing licensed content without consent, emphasising the importance of navigating these challenges carefully.

Achieving a balance between appropriate use will mean that creators and businesses must conduct thorough research and stay informed about regulatory and ethical frameworks. Likewise, companies will need to establish processes like governance frameworks and dedicated generative AI teams to minimise unlawful or unethical use of generative AI. This will only become important as more formal regulations concerning generative AI usage surface in the upcoming years.

Generative AI Disruptions in Commerce

Generative AI is revolutionising the worlds of commerce and marketing, helping to simplify tasks like producing compelling visuals and personalised messages. Moreover, tools driven by generative AI, such as customer recommendation systems and chatbots, are creating seamless interactions between brands and their audience. Some key disruptions include:

● Chatbots and virtual assistants: Generative AI is being used to create better conversational commerce experiences which are proving to be a step up from natural language chatbots which often don’t quite meet customer experiences. Generative AI chatbots are better equipped to provide tailored recommendations, personalised experiences and better understand customer queries.

● Content writing and media generation: One common use of generative AI is in generating marketing materials like copy, videos, and images. This is particularly impactful as an individual with generative AI could potentially produce content equivalent to that of a small team by utilising generative AI tools.

● Personalisation and product recommendations: Generative AI can generate engaging content tailored to specific audiences on a large scale. It can automate tasks ranging from product descriptions and marketing emails to providing onboarding training.

● Data analysis: Generative AI can be used to analyse and interpret text, image, and video data concurrently to gain deeper insights into opportunities to improve brand messaging and marketing.

● Customer experience: Generative AI can enhance customer experience by facilitating natural and engaging interactions with users, analysing customer data to identify trends and preferences and improving understanding of customer behaviour and needs.

How to Overcome Generative AI Disruptions in Commerce

Generative AI is a fast-growing sector, presenting challenges for adoption. The absence of established and well trodden adoption frameworks creates uncertainty for new users, but here are a few steps you can take to safely adopt generative AI:

● Define marketing goals: Understand business challenges and how generative AI can help. Consider target audience, content topics, tone, and style.

● Be open to experimentation: Encourage research and small-scale AI tool uses to explore full potential.

● Gather diverse data: Include audience, customer, content, and market research to train the model for engaging marketing materials.

● Select a model: Research and choose the best generative AI tool for specific needs. For example ChatGPT for text, GPT-4 for multimodal, DALL-E2 for images, Designs.ai for design, and Copy.ai for writers.

● Train generative AI with data: Feed the model data to learn patterns critical for content generation and marketing strategies.

● Implement generative AI: in campaigns aligned with objectives.

Generative AI Disruptions in Healthcare

Healthcare is another sector where generative AI is bringing disruption. One example is its capacity to enhance both patient and provider experiences, leading to better clinical outcomes. Additionally, the technology can lower administrative costs, speed up research and aid in developing advanced diagnostic tools. More examples include:

● Diagnosis: Generative AI can enhance the diagnosis process by accelerating it. This can involve enhancing the quality of medical images to visualise internal body structures better, automatically segmenting to pinpoint irregularities, or generating synthetic medical images to enrich existing datasets. Generative AI offers various benefits in the realm of diagnosis.

● Treatment monitoring and patient support: Generative AI can assist in summarising lengthy patient documents and offering brief summaries for caregivers. This contributes to faster patient care, enhancing comprehension and decision-making, particularly when handling vast medical literature.

● Personalised medicine: Generative AI can analyse patient data to predict diseases and generate customised treatment plans for individuals.

● Drug discovery: A historically long and challenging process, generative AI can accelerate drug discovery by creating new molecules, identifying disease targets, and forecasting clinical trial results.

● Virtual care: Similar to virtual chatbots, but with a medical twist. Virtual care can be reimagined through enhancements from generative AI. This advancement will enable personalised healthcare services and intelligent responses to patient inquiries.

How to Overcome Generative AI Disruptions in Healthcare

Engaging with generative AI within the healthcare sector will consistently present challenges, primarily because of the importance of upholding transparency and patient trust in the generative AI tools. Ensuring patient consent and data privacy is crucial and should be a top priority in any generative AI approach in healthcare. An essential aspect of this involves educating patients about the benefits and risks of AI, fostering trust, and enabling informed and ethical decision-making that goes beyond legal requirements.

Generative AI Disruptions in the Financial Sector

Generative AI is driving disruption in finance in several ways, from predicting market trends, to enhancing data security, and democratising once costly financial services. Major institutions are also using it for risk management, fraud detection, and customer service improvement. Here are a few more examples:

● Safer banking and trading: Generative AI can help identify errors, detect fraud by examining data irregularities, and enhance safety by continuously monitoring transactions.

● Understanding the human element to finance: Generative AI is being used to examine the human aspect of finance. By examining social and psychological factors alongside financial data, generative AI can aid in forecasting, and predict how societal events may influence financial markets.

● AI trading: Although algorithmic trading has been practised for a while, generative AI could represent a significant advancement in the area. Generative AI can dynamically create and refine trading strategies in real-time by analysing market conditions, news, and economic indicators continuously.

● AI forecasting: Similar to generative AI-based trading, this technology can be utilised for overall forecasting. It can predict not only individual stock prices but also offer in-depth insights into global economic trends.

● Financial recommendations: Crafting personalised financial advice messages can be time-consuming. Generation AI can help generate scalable messages in conversational language, enhancing customer experience.

How to Overcome Generative AI Disruptions in the Financial Sector

In the unpredictable financial sector, generative AI systems face difficulties in providing transparent explanations for their results. This lack of clarity can post challenges in identifying errors and ensuring accurate responses. Resolving these issues will require a combination of confidence in data security processes, such as data collation and data cleaning.

What can you do next: Navigating the costs of
generative AI landscape

If you want to keep on top of the generative AI landscape to understand how generative AI is affecting different industries and how you can prepare for the future, register to attend Generative AI Week 2024. The leading industry event that spearheads innovation and convergence across data, AI, technology, and analytics within the enterprise landscape. Themes include:

● Industry Disruption: With discussions successfully navigating business model shifts, learn how to capitalise on the transformative potential of AI, gaining a competitive edge in the rapidly evolving landscape.

● Cutting-Edge Use Cases: Delve into a diverse array of real-world practical applications through raw use cases showcasing how generative AI is revolutionising industries, and any challenges faced along the way.

● Ethical AI Frameworks: Emphasis on establishing responsible AI practices, ensuring ethical considerations are at the core of your AI initiatives.

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