AI needs us: Why human oversight is crucial for realising AI’s potential
In the numerous debates currently raging around the future of AI, one element is often missing: the role of humans. Contrary to some beliefs, AI is not fully autonomous. It thrives under human oversight, which is essential for optimising its performance and realising the benefits that business leaders hope for. From pinpointing the most effective use cases for AI to managing AI models in production environments, human involvement is indispensable for success. Here's how.
Human oversight: Exploring and validating AI
AI adoption in Asia Pacific trails behind the global average, with only 15% of organisations in the region fully prepared to deploy and harness AI. Infor's latest study, How Possible Happens, also reveals that only just over one-tenth of APAC organisations use digital technology to automate repetitive, low-value tasks. One barrier is the identification of AI use cases, with many decision makers struggling to distinguish between valuable and incidental applications.
To overcome this challenge, businesses need to tap into human knowledge and experience of AI. Viewing AI adoption as a dynamic, human-driven process tailored to specific business needs is key. After all, human judgement is critical for evaluating AI's value and identifying and prioritising applications based on organisational requirements.
The language surrounding AI integration should emphasise this human-centric approach. Specifically, AI should be portrayed as a tool that empowers employees. It should be seen as a means to augment human capabilities, requiring human guidance and input, rather than as a replacement for the workforce.
AI providers should proactively engage with customers to explore potential applications, initiating discussions, identifying client pain points, and demonstrating how AI solutions can effectively address these challenges.
Human direction: Driving efficiency and service innovation
The classic IT adage, 'garbage in, garbage out,' is particularly pertinent for AI. If the data used to train or operate a model is poor, the outcomes will be similarly lacking. Data scientists and engineers are crucial in ensuring data acquisition, cleansing and lifecycle management for AI applications. Their responsibilities include evaluating data quality, identifying appropriate datasets, and reviewing data governance practices. Should the data be substandard, AI experts may suggest improvements in data cleaning, structuring, or governance before proceeding.
Even in production environments, human oversight is crucial for directing and controlling AI applications. For example, in process optimisation, AI and machine learning can reveal insights that humans can leverage to enhance efficiency. This scenario is a reversal from the training phase: AI now provides the data, while humans process this information by interpreting and acting on it to refine processes, innovate services, and enhance decision-making. We are fast approaching a world where humans and AI agents collaborate seamlessly to boost efficiency, but with humans still very much in the driving seat.
Human responsibility: Maintaining and monitoring AI
Many AI projects struggle to advance beyond the proof-of-concept stage, often due to insufficient maintenance resources. This underlines the need for continuous human involvement, not only to prevent model decay but also to guarantee the long-term success, adoption and scalability of AI initiatives. Change management is also a crucial part of its success.
For instance, human oversight is indispensable to ensuring that AI models maintain their accuracy and relevance over time. This is especially vital in complex, evolving environments where data inputs and conditions can change.
Similarly, human experts play a crucial role in monitoring the performance of AI models by identifying and addressing emerging issues that could hinder functionality or accuracy.
Human context: European innovation and challenges
Although the majority of AI technologies used today are developed in the US, European researchers make a substantial contribution to their development. With the EU AI Act, the bloc is also a regulatory leader and playing a crucial role in ensuring the responsible deployment of AI technologies.
However, regulatory and administrative challenges can hinder innovation. In this context, human employees are crucial, with regulatory experts playing a vital role in helping businesses meet their compliance obligations while fostering an environment that supports AI development. Additionally, human oversight is essential to ensure the ethical development and deployment of AI, safeguarding both innovation and integrity.
Human-centric AI
AI holds great promise, but its models require care. Only with human oversight can this technology fulfil its potential to deliver the efficiencies promised. Furthermore, when humans maintain control, AI becomes a powerful tool that not only enhances their capabilities but also optimises workflows and fosters greater innovation.
