By Sara Faatz
Implementing generative AI in digital experiences isn’t easy. Organizations should take into consideration various factors, including training models and the potential for unconscious bias, hyperautomation, security risks, technical considerations for implementation, and scalability and performance. In this article, we’ll take a closer look at each of these and discuss how organizations can manage them to fully leverage generative AI to create more compelling customer experiences.
Mitigating unconscious bias in training models
Generative AI relies heavily on the quality of its training models for content generation. A careful curation of training data is essential to ensure that it represents the diverse user base and use cases that generative AI will encounter. It is especially critical given AI’s potential to perpetuate unconscious biases, which can negatively impact marginalised groups.
According to our recent global survey, 65 per cent of organizations experience data bias. Additionally, 66 per cent stated that they are progressively relying on AI/ML for decision-making with 55 per cent expecting to further increase their reliance. However, as the dependence on AI/ML continues to grow, the majority expressed concerns about potential data bias becoming a more significant issue.
Product marketing managers and technical decision-makers must incorporate diversity into the training data, by collecting data from a wide range of sources across demographics, geography and other relevant factors. Automated tools can help detect and mitigate bias in the training data. Some companies use machine learning (ML) algorithms to identify and remove biased language from text datasets, while others employ statistical techniques to recognise and address disparities in demographic representation.
Revolutionising digital experiences with hyperautomation
Hyperautomation combines advanced technologies like AI, ML and robotic process automation to automate various tasks, from simple data entry to complex decision-making and customer service. It saves time, reduces errors and allows employees to focus on higher-value tasks. It creates personalised and engaging experiences for customers, increasing satisfaction and loyalty. It is also worth noting that the hyperautomation market is expected to witness substantial growth, projected to expand from USD 10.81 billion in 2023 to USD 26.67 billion by 2028.
However, implementing hyperautomation with generative AI requires a deep understanding of the technologies and careful consideration to avoid over-automating and losing the human touch. Best practices include starting on a small scale and automating processes in need of optimisation. It must involve all stakeholders and strike a balance between automation and personalisation. By following these practices, businesses can create efficient and human-centred digital experiences that delight customers.
Building a roadmap for responsible AI implementation
AI advancements bring improvements, but lack of regulation raises concerns about bias, ethics and human oversight. Biased data leads to unfair treatment, especially in healthcare, criminal justice and hiring. Unregulated AI allows unethical practices and data misuse, with opaque systems raising accountability issues and risks like disinformation. We need comprehensive regulations that encompasses privacy, transparency, bias mitigation and safety. The future of AI governance hinges on the cooperative efforts of governments, organizations, and stakeholders.
The EU is developing the Artificial Intelligence Act to improve regulations concerning data quality, transparency, human oversight and accountability, addressing ethical concerns and implementation issues in sectors like healthcare, education, finance and energy. Likewise, the proposed Digital India Act aims to ensure coherence in laws and regulate advanced technologies like AI, blockchain and Web 3.0, safeguarding digital citizens’ rights and interests.
Technical considerations for generative AI into digital experiences
According to Gartner, ChatGPT’s popularity has led to a substantial impact, resulting in a 45 per cent increase in AI investments among executive leaders. This growing interest is evident as 70 per cent of these executives are actively exploring generative AI and 19 per cent have already implemented it in pilot or production stages.
Selecting the right generative AI type is vital, as each comes with its own strengths and limitations. Rule-based systems use pre-defined rules but lack flexibility, while neural networks can learn and adapt but are more complex.
Businesses must follow best practices, such as defining the use case, analysing datasets for biases, selecting the appropriate AI type and creating a plan for training and monitoring. Robust security measures are also essential to protect sensitive data.
Achieving scalability and performance
According to a Deloitte survey, approximately 50 per cent of leaders identified managing AI-related risks, the lack of executive commitment, maintenance and post-launch support as the primary challenge in scaling AI.
Scalability and performance are crucial when incorporating generative AI into digital experiences. Distributed computing breaks tasks into smaller segments that are processed concurrently across nodes to enhance performance. Cloud computing offers flexible resource allocation for scalability but requires efficient resource management. Optimising performance involves efficient algorithms, data pre-processing and network architecture, along with monitoring and analysis. Best practices include investing in efficient algorithms, refining network architecture, employing caching and batch processing and using cloud computing services. Prioritising scalability and performance ensure exceptional digital experiences that satisfy users and drive growth.
Generative AI has the power to transform digital experiences, making them captivating, customized and streamlined. However, businesses must address technical and ethical challenges. By adopting a comprehensive strategy to tackle bias, hyperautomation, security, implementation, scalability and performance, businesses can use generative AI responsibly and enhance digital experiences.
The author is director, technology community relations, Progress