As we enter an era where technology could amplify human potential to unprecedented levels, ensuring trust and accountability in AI has never been more important. Innovation in AI, and generative AI in particular, offers enormous opportunities to address the challenges and problems humans face, but business leaders must proceed with caution.
To address issues like bias and ensure AI delivers real value, the approach to AI tools must be human-centric. Business leaders have an important role to play in how they deploy this technology, both for their organizations and the world at large: AI must be at the service of people, aligned with and embodying individual and organizational values, goals and aspirations.
For AI to be responsible, new systems must be human-centred, inclusive, transparent and accountable. AI systems must be subject to constant oversight and feedback from stakeholders, and they must also be designed to work alongside human expertise, rather than replacing it. AI is really the key to improving the way all humans interact with technology, but a responsible approach is required to get the most out of it.
Vice President of Solutions Consulting at ServiceNow.
Building AI to solve problems
Generative AI creates value if it solves specific problems for specific people, and this should guide the design of AI software. By designing AI software for “people,” such as those working in marketing, HR, or finance, humans can be placed firmly at the center.
Business leaders need to make sure they understand what each role or persona expects from AI, whether it’s easy-to-use, frictionless experiences, or increased agility and productivity. For example, in the case of a developer, this could be code completion, or in the case of an agent, case summarization, which can translate more broadly to everything from IT support to customer service to HR.
By taking this human-centric approach and focusing on the real challenges employees and customers face every day, AI-powered solutions can deliver real value across the organization. Continuous feedback from employees and customers helps shape AI solutions that deliver consistent, lasting value and, most importantly, technology that is fair and open for everyone to use.
Breaking down barriers
Inclusion and accessibility are key to improving how humans interact with technology. The ability for multilingual and multimodal AI models to understand anything in multiple languages will be an important way to break down barriers for people working with AI. AI models of the future will be able to understand multiple inputs, such as videos and images, meaning easier access to technology regardless of language and location, and enabling teams to work together across the globe.
AI-powered translation software can also ensure that international teams can enjoy seamless communication across the company. For agents, this means that customer and employee issues can be resolved faster in real time, regardless of the original language in which the problem was raised. For everyone, however, this means that help and understanding can be provided in a more equitable way than before.
Leveling the playing field
Generative AI interfaces, along with low-code and no-code applications, can enable business users to actively participate in application development. This not only accelerates digital transformation efforts by enabling business users to create basic digital workflows, but can also boost productivity and job satisfaction as text-to-code helps anyone create new applications with simple text inputs.
Generative AI frees up highly skilled developers to focus on more mission-focused applications. For such developers, AI “buddies” will simplify coding and building flows – this rapid automation can in turn help organizations reduce IT delays and drive innovations. For smaller organizations, text-to-code and low-code can be game-changers.
Diversity and bias
Bias in AI is a real and ongoing problem that affects everything from credit scoring to job applications to the images generated by AI systems. Business leaders need to consider the real impact of the technology on the people who use and interact with it, and also think carefully about the impact of any AI system on society as a whole. This social and environmental impact must be considered at every stage of the development of an AI product.
Both the teams that train, test, and use these systems and the data sets used in training must reflect the diversity of society, to ensure that the tools avoid errors such as bias. Business leaders must work to ensure that AI technology meets the widest possible range of needs.
It’s important to acknowledge the downsides of using AI early on and be transparent about them. This, in turn, can lead to meaningful conversations with customers about how to manage the challenges, rather than simply wishing them away. To give just one example, it’s impossible to completely get rid of AI bias, but by taking a careful approach to AI training and the teams hired to deal with it, business leaders can recognize and mitigate the problem.
A brighter future
By putting humans at the center of AI development and taking a transparent, inclusive, and accountable approach, business leaders can build AI systems that truly improve not only their organization but the broader society around them.
Business leaders must ensure that teams and data sets are diverse and that “humans are in the loop” through 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, goals, and values, business leaders can be confident that they can create a brighter future.
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