Generative AI: The future of business… but not as you might expect.

At the recent 10th-anniversary celebrations of the AppHaus Network, I had the privilege of listening to Jonas Andrulis, the CEO of Aleph Alpha, share his insights about generative AI. In the current tech landscape, discussions about generative AI abound, with many hailing it as the ‘future,’ the ‘next big thing,’ and a transformative force for businesses. While these statements hold true, it became clear to me that the transformative power might not unfold in the revolutionary manner many anticipate.

 

With this being the ‘hot topic’ in the tech world, it’s easy to become jaded about the real impact of generative AI. In this blog, we aim to provide a fresh perspective by delving into the practical aspects of the Gen AI revolution. We’ll explore realistic use cases and, contrary to the prevailing hype, discuss why, in our opinion, it may not wield the strategic force often attributed to it.

 

What is generative AI?

Generative AI, short for Generative Artificial Intelligence, is a cutting-edge subset of artificial intelligence (AI) that focuses on the creation of new content, data, or information. Unlike traditional AI systems that are designed for specific tasks, generative AI has the unique ability to generate original and diverse outputs, such as images, text, or even entire scenarios.

Gen AI stands at the forefront of technological innovation, empowering machines to create content, images, and even entire scenarios from simple prompts. Unlike other forms of AI, which primarily involve pattern recognition and decision-making, generative AI focuses on creative outputs. This section will provide a clear definition of generative AI, highlighting its key concepts and distinguishing features from other AI types.

How does generative AI work?

At its core, generative AI employs advanced algorithms and models to understand patterns and relationships within data. Through learning from vast datasets, it can then produce new, realistic content that wasn’t explicitly present in the training data. This capacity for creativity sets generative AI apart, allowing it to contribute to various fields, including art, literature, and problem-solving.

 

This may sound like a cutting-edge concept but the truth behind how Gen AI has and is being developed is set in what makes us human. Think about how we, as humans learn. Listening, reading, doing- they can all be broken down into understanding patterns and relationships. Gen AI mimics this. It takes what it has learned and generates new content based on patterns and relationships. This is why it may have a hard time generating an image of something which is not based in reality that it is not familiar with.

How to use generative AI

Generative AI demonstrates versatile applications across industries. In content creation, it autonomously produces art, music, and literature. In business, it aids data analysis, decision-making, and customer interactions. Gen AI is valuable in design, generating images and models for architecture or product development. In healthcare, it supports diagnostics and drug discovery. It’s also employed in gaming, creating realistic environments and characters. As an innovative tool, Gen AI continually evolves, promising breakthroughs in numerous fields, revolutionising creativity, problem-solving, and efficiency across a spectrum of applications.

Examples of generative ai: Dall-E, ChatGPT and Bard

Three such examples of how Gen AI is being used already are Dall-E, ChatGPT and Bard. These examples showcase its diverse capabilities. DALL-E, developed by Open AI, generates images from textual descriptions, creating imaginative and unique visual content.

 

ChatGPT, another Open AI creation, excels in natural language processing, engaging in contextually rich and coherent conversations.

Bard, an AI language model, demonstrates creativity in generating poetry and literary works.

 

These are key examples of how Generative AI extends beyond traditional boundaries, influencing visual art, language understanding, and literary creativity, showcasing its potential to revolutionise various domains with human-like outputs.

 

What are the use cases for generative AI in SAP?

Generative AI offers compelling use cases within the SAP ecosystem, enhancing various aspects of business operations. In SAP, it can streamline data processing and analysis, automating routine tasks and optimising workflows. For customer relationship management, Generative AI can generate personalised content, improving customer interactions and engagement. In predictive analytics, it aids in forecasting trends and optimising resource allocation.

 

Additionally, Generative AI can assist in automating document creation and processing within SAP, reducing manual efforts. Its potential to generate realistic test data is valuable for testing SAP applications.

 

Overall, Generative AI in SAP contributes to efficiency, personalisation, and innovation across diverse business functions.

 

Why is generative AI important?

Generative AI holds paramount importance in the future of content generation for business, as well as other applications due to its transformative impact. Firstly, it fosters creativity by autonomously producing art, music, and content, pushing the boundaries of human imagination.

 

In business, it enhances efficiency through automation, streamlining processes and decision-making. Its ability to generate contextually relevant outputs in natural language contributes to advanced communication and customer engagement.

 

Furthermore, Generative AI facilitates innovation in fields like healthcare and design, driving progress and problem-solving. As a versatile tool, its importance lies in its potential to revolutionise how we create, communicate, and solve complex problems, marking a significant leap in the realm of artificial intelligence.

 

It’s worth commenting that the aforementioned points, whilst helping at a high level, operate on a low input-output basis. Taking what is formally a manual, monotonous and time-consuming activity and speeding up the process. This is great for the previously mentioned reasons but highlights that gen ai itself will never be capable of strategic-level activity, such as decision-making, it is merely a tool to be used in the process.

 

Benefits and limitations of Gen AI

Benefits of Generative AI:

 

• Creativity and Content Generation:

Generative AI excels in creating diverse and original content, including art, analysis, and literature. Quickly delivering these forms of content increases the efficiency of the creative process, removing the ‘umming and areing’ from the process.

 

• Automation and Efficiency:

Streamlining processes, Generative AI automates repetitive tasks, improving efficiency and allowing human resources to focus on more complex endeavours.

 

• Personalisation:

In applications like customer service, Generative AI enables personalised content creation, enhancing user experiences and engagement.

 

• Innovation and Problem-Solving:

Generative AI contributes to innovation by generating novel solutions and ideas, aiding in complex problem-solving across industries.

 

• Data Augmentation:

It helps in generating synthetic data for training models, addressing data scarcity issues and enhancing the performance of machine learning algorithms.

 

Limitations of Generative AI:

 

• Ethical Concerns:

Generative AI raises ethical issues, including the potential for misuse in creating deepfake content or biased outputs, requiring careful oversight and regulation. On the topic of careful oversight, when using AI for external content or content that will drive decision-making, it’s important to check the outputs first, as it can be wrong.

 

• Quality Control:

Ensuring the quality and accuracy of generated content can be challenging, as Generative AI might produce incorrect content, missing the context or a lack of coherence.

 

• Resource Intensive:

Training and utilising Generative AI models demand significant computational resources and expertise, limiting accessibility for smaller organisations.

 

• Lack of Understanding:

Generative AI models operate as black boxes, making it challenging to understand their decision-making processes, raising concerns about transparency and accountability.

 

• Overfitting and Generalisation:

Generative AI models may overfit the training data, resulting in outputs that lack diversity or struggle to generalise to new, unseen scenarios.

Seen above: An AI-generated image of the Bluestonex Church Offices in Oswestry. There’s still a way to go.

Expert Opinion on the effect Gen AI will have on User Experience (UX)

Our In-house Gen AI expert, SAP UX Consultant, Vikash Kumar had this to say: “Generative AI can enhance user experience (UX) by customising experiences, automating tasks, generating content, predicting user behaviour, and identifying issues. It has applications in various industries, and it creates intuitive and personalised experiences that improve UX. As generative AI technology continues to advance, its impact on UX is expected to increase. This will lead to the development of more intuitive, personalised, and user-centred experiences that will significantly improve the overall user experience. However, some challenges need to be addressed, such as balancing human creativity with AI automation, ethical and privacy concerns, and ensuring accessibility. On the other hand, there are several opportunities as well, including revolutionising the design process and expanding UX possibilities. Overall, generative AI is likely to have a profound impact on UX, transforming the way we interact with digital products and services. Designers who adopt this technology will be in a better position to create innovative and user-centred experiences in the future”.

 

The future of Gen AI

As technology continues to evolve, so does Gen AI. From improved capabilities to expanded applications, there’s a lot on the horizon. The future of Gen AI promises a transformative landscape, revolutionising industries with unparalleled creativity and problem-solving capabilities. As advancements continue, it will seamlessly integrate into daily life, optimising processes, and enhancing user experiences. Gen AI’s potential lies in its ability to adapt, learn, and contribute to diverse fields, shaping a future where innovation and efficiency thrive hand in hand.

 

As a specific example, as the algorithms behind Gen AI continue to learn, grow and mimic human thought more realistically, we will see Gen AI be able to replicate personality, rather than just regurgitating facts and figures. Humour will be included in this. This is something I have seen first-hand. A simple Gen AI algorithm was asked to look at a photo of a cat on a little replica of a president’s podium and asked ‘what would be his first policy’. The AI responded, “taking steps to improve the economy”. Bland is predictable and fails to see the humour. However, when a complex Gen AI was asked the same, it simply responded with “Declare war on the dogs”. Much more fitting and humorous.

 

So, what’s the point of that? Businesses don’t employ stand-up comedians unless it’s for the Christmas party, so what’s the use case? If Gen AI can read between the lines and respond in a way that closely resembles a personality, it can improve the user experience with chatbots, how it presents information for high-level, decision-making and personalise experiences to the specific user. In short, no longer will the employee have to do the monotonous, boring data gathering and output role, that will be there ready and it will be presented in an optimised and potentially fun way.

 

A personal note on Generative AI

As we’ve explored, the potential and applications of Gen AI in the business landscape are immense, positioning it as a key player in shaping the future of business. However, a glaring flaw emerges – its limited strategic potential.

 

While AI proves incredibly useful, it inevitably falls prey to the classic GIGO trap (garbage in, garbage out). Its effectiveness hinges solely on the quality of the data it receives. This underscores the critical need for robust data governance. To stay on point, the central message is clear: although AI can aid in strategic decision-making, the reins of decision-making should never fully pass into its hands. Despite its impressive capabilities, it merely mimics human intelligence and lacks the accountability necessary for crucial business decisions.

 

This stance dismisses the dystopian visions of a Skynet-style future propagated by technology pessimists. Instead, what lies ahead is a profound transformation and optimisation of business operations, with a significant boost in efficiency levels. Consequently, our roles as individuals in the workforce are poised to shift towards more tactical and strategic responsibilities. The only obstacle standing between the present and this imminent future is the imperative task of organising and preparing our data for this inevitable transition.

So that’s my prediction, but for a deeper look into the potential future of AI & cloud computing, click here.