Round table report: The impact of AI on UX

Quality Assurance + Efficiency

“The more knowledgeable inputs, the more appropriate responses” 

In the world of UX, ensuring quality assurance and efficiency is paramount to delivering exceptional products and services. While AI has revolutionised certain aspects of UX there are nuances to its application that we must navigate.

AI offers valuable insights and can automate repetitive tasks. However, as with any technology, its effectiveness is only as good as the quality of inputs and instructions provided. AI generated responses can fall short in terms of quality which emphasises the need for us to critically evaluate the outputs and make changes to the responses.

There is a tension between the opportunity for efficiency and the quality of the output, which requires careful balance. Just like your old computer science teacher said – rubbish in rubbish out. Domain expertise, precise prompting, and post-generative QA are key to getting the most from the efficiency gains

Innovation + exploration

“Create efficiencies and opportunities throughout the organisation?” 

Innovation is at the heart of progress and as technology continues to evolve, so does our exploration of potential applications. While some may fear that AI will replace human roles, it’s important to understand that AI can be a catalyst for innovation, streamlining processes, and unlocking new possibilities.

In fields like medicine, the impact of AI has extended far beyond organisational efficiency. AI tools can improve medical diagnostics, enabling rapid and accurate analysis of patient data. By leveraging these AI-powered tools, medical professionals can streamline diagnostic processes, reduce consultation times, and improve patient outcomes through timely interventions.

AI has driven an era of rapid experimentation, as organisations recognise its importance but aren’t yet sure what to do with it. This matures to hyper automation (as experiments validate ideas) as organisations recognise opportunities for efficiency gains. These in turn can become AI-driven products and services. 

Process + Governance 

“It can be a real challenge to incorporate AI into research and in particular the role of data” 

In the evolving landscape of UX, AI presents both opportunities and challenges. While some organisations recognise the potential for AI to enhance efficiency, there is a pressing need for robust processes and governance to ensure responsible and ethical use.

The abundance of data available to AI systems introduces complexities in data interpretation and ethical usage, requiring careful consideration and robust governance. Additionally, there is an inherent bias present in AI systems, derived from internet snapshots, that poses a significant obstacle to its effective use in UX design which can hinder rather than enhance clarity.

While the potential for using AI in applications like chatbots is undeniable, organisations still tend to tread cautiously in its implementation. Confidence in AI outputs is paramount before sharing with customers, through testing and validation processes we can ensure accuracy and reliability.

While many organisations recognise the opportunity for AI to bring efficiency gains, no one within the group felt confident with unchecked AI output going directly to their customers just yet. 

Accuracy + Reliability 

“Artificial Intelligence can be entirely confident and absolutely incorrect” 

As organisations delve deeper into AI integration they will have to deal with the complexities of accuracy and reliability. AI systems can be entirely confident in their outputs, but their accuracy and correctness may vary, resulting in challenges for reliability.

As an example, ChatGPT shows promise in helping with writing tasks for copywriters, but its accuracy was not without flaws with the AI tool continuously using certain words even though it was prompted not to. In translating content to other languages, inaccuracies and mistakes were observed, highlighting the importance of reviewing and checking by experts for reliability and accuracy.

A range of factors negatively impact the accuracy and reliability of AI-generated results. Data quality and relevance (data can be outdated, biased or non-representative), generalisation (AI can struggle with new data, and thus relies on generalised legacy data) and algorithmic bias (algorithms inadvertently learn biases present in their training data).

Compliance + Regulation 

“It’s the balance between delivery (led by humans) and research (where AI contributes)” 

Amid the allure of automation and efficiency, organisations must navigate the intricate world of compliance and regulation to ensure responsible AI usage. It requires organisations to establish a robust governance framework to mitigate compliance-related issues.

In industries like gambling, compliance with stringent regulations means careful considerations when using AI to ensure there is a duty of care towards customers. Despite these challenges, AI can be harnessed positively to enhance user experience. By leveraging AI tools responsibly, you could identify user behaviours and even highlight potential gambling issues.

As organisations navigate the complexities of compliance and regulation, it’s important for them to embrace responsible AI frameworks and foster collaboration between human expertise and AI driven insights. This can help navigate the regulatory challenges effectively while delivering exceptional user experiences 

Industry-specific Tools 

AI tools have the potential to span across diverse sectors, revolutionising the way industries interact with technology. It has the power to tailor interactions, streamline processes or enhance an organisation’s user engagement paving the way for industry-specific innovations.

"With lots of options for AI tools available, do you wait for a clear winner in AI before deciding which to invest in?” stated one of our experts reflecting on the challenge of navigating the AI industry. Organisations must evaluate AI tools before investing, they must weigh out the potential risks and rewards of early adoption versus cautious deliberation.

The intersection of AI and UX design offers boundless opportunities for industry-specific innovation and transformation.