With technology at its heart, the fusion of human talent and automation is the recipe for growth. However, a mere mention of automation conjures up scenes of a robot uprising and the fear of job losses. That fear is misplaced. Technology enables more jobs than it eliminates. A Gartner report on Artificial Intelligence pegs the net increase of jobs to over two million by 2025. That includes not just software engineers but a variety of positions that will train Artificial Intelligence systems to recognize objects, human activity, among others.
That said, technology advances do pose the possibility of job loss in the short term. In the long term, however, the question becomes one of job transformation. If machines take over the mundane tasks of running a network, what is the human role?
Take the domain of human resources as a case in point. Artificial Intelligence systems can drive new business growth, while HR leaders and managers can leverage the Artificial Intelligence-powered systems to augment decision-making in recruitment and retention. Likewise, when Artificial Intelligence-powered chatbots utilize natural language processing techniques to provide human-like interactions for daily activities, it frees up HR’s precious time to focus on more human elements. Again, Artificial Intelligence systems can do sentiment analysis on the mood of employees and suggest timely action to help retain talent. Such systems provide personalized recommendations on the learning and development programs of employees and in effect improve productivity in a targeted way.
Machines in a live environment
So, there’s a key human function in the transition from augmented work to autonomy.
Coming to more hardcore tech, there’s a strong move towards Self-Driving Network (SDN2). Similar to a self-driving car, SDN2 is the next frontier — an autonomous network that is predictive and adaptive to its environment. It simultaneously increases economies of scale and efficiencies, while decreasing operating costs and delivering an optimized and customized quality of experience inexpensively to the end-user.
But, again, the first thing that comes to mind with “self-driving” and automation is job losses. Not quite if we note what the head of Orange Business Services, Thierry Bonhomme, says: “We are running out of competent staff – we are finding it hard to recruit.” Here’s a list of opportunities SDN2 can actually create, once deployed:
- Service creation, service “mash-ups” — human creativity is required to keep the service catalogue fresh, appealing and relevant.
- Specification of intent: Human analyses of what a service should aim for; what is the definition of an optimal network, etc.
- Providing automation scripts for the actions that an SDN2 system needs to carry out.
- Monitoring the Artificial Intelligence and tweaking it for correctness, or optimality.
Human experience, expertise and, perhaps, intuition will be needed in the “augmentation” mode. How this will play out will depend on many factors, least of all, technology. Social aspects, skills and regulations are key. Deployment scenarios will also be significant. We expect the primary mode of operations to be augmentation rather than autonomy until operators deem that the technology has reached an acceptable level of accuracy and trustworthiness, giving them time to adjust to SDN2.
Man and machine
The journey to the highest level of automation is intermediate steps to augmentation, where human and machine cooperate in running the network efficiently and effectively than either can on its own. Today, significant time is spent in poring over past logs than focusing on improving future customer experience. Repetitive manual tasks are error prone and are not great career builders.
Take, for example, a scenario where a network security organization displays 10,000 messages on a potential security breach. What do employees do? They can handle them manually by using human intuition and expertise to determine which ones to prioritize. They can have a machine analyze and prioritize the messages and present them with the top threats to dispatch. Or, they can build a machine fully capable of prioritizing the messages and taking the right actions for each. The third is an ideal option, but the second is an improvement over the first and, therefore, is a more intermediate and preferred course until we have reached an acceptable level of accuracy and trustworthiness.
Good. But what about widening skill gaps between traditional and machine-augmented work? It brings us to the central idea: Learn, unlearn, and reskill to meet the need for new training, new types of experts, and other endless possibilities. The transition from manual to augmentation to autonomy will need progress on several fronts where humans need to reskill and build machines. Humans then need to incorporate intelligence into machines to take over manual, and repetitive tasks. The subsequent stage is to build algorithms to perform inferences and take necessary actions. All this needs more human capital than that is available – but one that is skilled at more creative work than repetitive and manual work.
The way ahead
An estimated 40% of IT professionals in India must upskill to nurture the country’s digital reality. With Artificial Intelligence expecting to add $957 billion to India’s GDP by 2035, we will face a demand-supply gap of more than 200,000 data science, analytics, Artificial Intelligence, Machine Learning and robotics professionals by 2020.
Augmenting technology will encourage employees to see new opportunities to upskill and grow. As Artificial Intelligence systems take over network supervision, the human resources it replaces can upskill in data analytics to create insights for business growth. That is more P&L role than a mere IT service.