As we enter an era where technology could amplify the potential of human beings to unprecedented levels, ensuring trust and responsibility within AI has never been more important. AI innovation, and in particular generative AI, holds enormous opportunity to tackle challenges and problems for humans, but business leaders must tread carefully.
To deal with problems such as bias, and ensure AI delivers real value, the approach to AI tools needs to be human-centric. Business leaders have an important role to play in how they implement this technology, both for their organizations and the wider world: AI should be in service of people, aligned with and embodying individual and organizational values, goals and aspirations.
For AI to be responsible it means that new systems need to be human-centered, inclusive, transparent and accountable. AI systems need to be subject to constant oversight and feedback from stakeholders, and must also be designed to work alongside, rather than replace, human expertise. AI truly holds the key to improve how all humans interact with technology, but a responsible approach is required, to get the most from it.
Building AI to solve problems
Generative AI creates value if it solves specific problems for specific people, and this should inform the way AI software is designed. By designing AI software for ‘personas’, such as those working in marketing, HR or finance, humans can be placed firmly at the center.
Business leaders should ensure they understand what each role or persona wants from AI, whether that be user-friendly, frictionless experiences, or greater agility and productivity. For instance, in the case of a developer, this could be code completion or for an agent, case summarization, which can translate more widely to everything from IT support to customer service to HR.
By taking this human-centric approach and focusing on the real challenges which employees and customers face every day, AI-driven solutions can deliver real value for the whole organization. Continuous feedback from employees and customers helps to shape AI solutions providing consistent and lasting value, and most importantly, a technology which is fair and open for all to use.
Breaking down barriers
Inclusivity and accessibility are central to improving the way human beings interact with technology. The ability of multilingual and multimodal AI models to understand anything in multiple languages will be an important way to break down barriers for the people who work with AI. Tomorrow’s AI models will be able to understand multiple inputs such as video and images, meaning easier access to technology, regardless of language and location, and enabling teams to work together across the world.
AI translation software can also ensure that international teams can enjoy seamless communication across the enterprise. For agents, this means that customer and employee issues can be resolved faster in real-time, regardless of the original language the issue was raised in. For everyone though, this means help and understanding can be given in a more equitable way than previously.
Levelling the playing field
Generative AI interfaces, alongside low-code and no-code applications can enable business users to be ‘hands-on’ with developing applications. This not only accelerates digital transformation efforts by empowering business users to build basic digital workflows, but can also boost productivity and job satisfaction, with text-to-code helping anyone create new applications with simple text inputs.
Generative AI frees up highly trained developers to focus on more mission-centric applications. For said developers, AI ‘companions’ will simplify coding and the construction of flows: this rapid automation in turn can help organizations reduce IT backlogs and drive innovations. For smaller organizations, text-to-code and low-code can be game-changing.
Diversity and bias
Bias in AI is a real and ongoing problem, affecting everything from credit scoring to job applications to the images generated by AI systems. Business leaders have to consider the technology’s real-world impact on the people who use it and interface with it, and also think carefully about any AI system’s impact on society as a whole. This societal and environmental impact should be considered at every stage of AI product development.
Both the teams which train, test and use such systems, and the datasets used in training should reflect the diversity of society, to ensure that the tools avoid pitfalls such as bias. Business leaders must work to ensure that AI technology serves as wide a range of needs as possible.
It's important at an early stage to acknowledge and be transparent around the trade-offs that come with using AI. This in turn can spark meaningful conversations with customers about managing challenges, rather than simply wishing them away. To take just one example, it's impossible to get rid of AI bias entirely, but by taking a careful approach to AI training and to the teams hired to deal with AI, business leaders can recognize and mitigate the problem.
A brighter future
By putting the human at the center of AI development and taking a transparent, inclusive and accountable approach, business leaders can build AI systems which truly enhance not just their organization but the wider society around them.
Business leaders must ensure that teams and datasets are diverse, and keep the ‘human in the loop’ with constant feedback from all stakeholders. This collaborative approach will be key to developing safe and accessible AI, with continuous improvement driven by insights and feedback from real-world performance. By centering AI on human needs, human goals and human values, business leaders can be sure they can create a brighter future.
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