The Knowledge Employee in the age of Artificial Intelligence
The age of artificial intelligence(AI) has evolved machines into digital humans who can converse, read, write, draw, paint, view(see), review, code, predict, prescribe and even innovate now with multiple generative AI models available. Much of this has been the forte of knowledge workers who represent the intellectual capital of organizations.
Knowledge workers are used to finding solutions to challenges faced in a dynamic and changing work environment(including work from home or a future gig environment), processing, storing and using knowledge, whether tacit or explicit, to the benefit of the organizations they work in. With the advent of ChatGPT, LLMs(large language models) are being increasingly deployed in organizations to assist in daily workload delivery.
LLMs are pretrained generative AI models capable of communicating like humans in their natural language by understanding the intent and context of the interaction. These models can be improved by fine tuning their parameters, reinforcement through human interaction or through retrieval augmented generation (RAG) systems which are contextual to an organization or domain. Their ability to process and act on vast inputs at speed gives these machines immense power to increase productivity and efficiency which every organization is trying to leverage. In fact, we are now gravitating towards SLMs (small language models) which are less complex and easier to maintain in their specific domains. However, it is not utopia yet, because they still fall short as they are not privy to the tacit understanding of knowledge workers acquired through formal education, critical thinking and years of learning on the job. We are not yet at the crossroads, where they can replace knowledge workers since all tacit knowledge can never be made explicit. If we ever reach a future where this becomes feasible aka science fiction today, then responsible AI will keep the machine controls with the knowledge workers to forestall such eventuality!
However, knowledge workers need to enhance their skills to leverage AI models in their work to increase their own productivity by automating the repetitive tasks as they compete with fellow knowledge workers using AI. These models are also enabling them enormously in their pursuit of acquiring higher and diverse knowledge.
Time journey of a knowledge employee
AI models stand to impact knowledge workers differently depending on their years of experience. The younger lot in early stages of acquisition of tacit knowledge can leverage AI more effectively while the experienced knowledge worker will gain less.
However, there are different challenges here, especially for the leadership in organizations since culture flourishes through the drive of youngsters while being channelized by seniors. If for a moment we assume that AI does start impacting the quantum of people entering the knowledge industry, it will start impacting culture. Right culture propels growth which no organization can compromise on. Thus there needs to be a fine balance(easier said than done) as know all machines or sentient machines may never see the light of day!
Impact on Organizations
Leadership will find it difficult to function without adequate knowledge of AI models. Strategic AI usage and right prioritization across the organization will form an important part of any business strategy.
Besides, there is an ethical dilemma of violation of intellectual property(IP) rights which AI engines could inevitably end up with and the additional conundrum on how to detect such occurrences and enforcing compliance. This potentially opens a pandoras box with serious ramifications for patent owners and the enforcement agencies.
Looking into the crystal ball, one foresees a future served by a hybrid workforce comprising of humans and AI assistants. The role of knowledge workers will see a redefinition as these digital humans partake in their work and newer application areas emerge.
Human resource policies would need to be recrafted with responsibilities clearly assigned, as while the AI tools are fast and easy they are not always accurate. Experimentation with these AI models will increase leading to varied levels of success and failure. The policies would also need to be tailored to encourage this experimentation and innovation and not strangle it. The right culture would need to be created for this so that we move on and not wait when failures seemingly stall us while celebrating wins whenever they happen. The future of the knowledge worker will remain secure and be measured by the extent of innovation in the times to come with its impact on both organizations and the society at large.
In conclusion we look forward to an interesting era of faster growth for all and a better world to live in with AI models helping knowledge workers in:
- Augmented intelligence
- Boosting cognitive capabilities through workload sharing with gen AI models
- Improving learning and the pace of learning
- Innovation and creation of newer differentiated solutions
- Avoiding proliferation of unnecessary or unproductive work
- Workflow automation
- Decision support
There are concerns as well in terms of potential job displacement(though jobs with newer skills will also get created) or bias and fairness of AI algorithms or the loss of human touch. Team dynamics will change and comprise of knowledge workers leading a team of humans and digital humans and owning KRAs(key responsibility areas) of these machines as well. HR policies and guidelines would need to be understandable so that the digital worker can self- regulate easing the burden on the knowledge worker.
The knowledge workers with enhanced AI skills will still reign supreme with results reflected both in the company’s stock and in a better way of life for us all!