AI is nothing without skilled human oversight
Artificial Intelligence (AI) remains hard to define. When it comes to a definition of "intelligence", context is vital and it starts with what we want the AI system to do. It is specific to the application. For example, intelligence for a search engine shouldn’t be the same as intelligence for an autonomous vehicle.
Now, with AI systems already in widespread production for more than a quarter of enterprises, businesses must ensure that employees are upskilled to effectively define and implement AI systems, and understand how to manage these systems safely in the workplace. But what does that look like in practice?
Invest in engagement, training and upskilling
The range of AI applications is vast, and there will be few that match the power of LaMDA and other such LLMs, for example, GPT-3 or OPT-175B. However, the story of LaMDA’s 'human' conversation further highlights that organizations must be mindful of how they engage with AI systems. Such conversations must be had across the workforce before misinformation, fear, or skepticism takes hold. Beyond that, organizations must also invest in greater engagement, training and upskilling around AI -- and this must be holistic.
Over the next five years, we can expect an explosion of specialized bots within the workplace; employees will be exposed to systems that can make decisions and use language in amazing ways. However, not all employees will embrace this new world, the threat of man-to-machine replacement looms large. For those whose roles may significantly change due to the implementation of automation, it will be vital to encourage the development of a growth mindset.
This is where employees are primed for AI up-skilling by presenting the future as a positive challenge and how AI skills will support their future career growth and success. Mindset will be a huge differentiator going forward, and companies that educate employees early and cultivate a positive AI culture will enjoy manifold benefits. This can include decisively identifying positive AI use cases early and clarifying how these implementations will benefit employees, for example, through reduced time on repetitive or mundane tasks.
The time saved on performing admin tasks can instead be used by employees to learn new skills and impact the business in a new, innovative way. For example, AI can take on repetitive, administrative tasks, such as reporting. However, it is then for the organization to enable their employees to replace that work with more engaging and strategic activities. And, when it comes to AI, it will not just be technical training that’s required. Employees will also need to develop new skills to help identify new business opportunities harnessing the technology and take an active role in communication around these technologies, their benefits and risks. Either way, training will be integral.
As UKRI (UK Research & Innovation) highlights, "To make a success of data and AI, organizations need to look at the full AI project supply chain. This starts with identifying a business opportunity that can benefit from AI all the way through to the validation, implementation, testing and deployment. Once the product or service has been deployed, organizations must consider longer-term adoption, maintenance, risks, governance."
To realize the benefits of AI, organizations must invest in holistic training across this chain. Leaders must be clear about what AI can and cannot do, what it should and should not do, and invest in the essential role of human oversight and understanding in making AI viable.
This investment includes ensuring learning delivers clear benefits to employees and organizations alike, providing the foundations for future-proof careers built on meaningful work.
What is clear then, is that sentience is not the goal. It is to deliver better outcomes -- for organizations, employees and society alike. That starts with engaging workforces in holistic AI learning now.
Mike Loukides is VP of Emerging Tech at O’Reilly.