Despite the fearmongering of robots taking over the world (let alone stealing our jobs), it will be some length of time before machines can complete the breadth of tasks that humans can. For the time being, automating the human mind is out of reach; however, we can leverage intelligent algorithms to support and automate certain levels of decision making. Our focus is thus on augmentation – human and machine working in collaboration.
Here are three predictions for the relationship of the human analyst and the algorithmic machine, which is always developing:
The Need for Human Expertise Will Continue
Although algorithms and AI can automate an increasing number of labour-intensive tasks, they are not yet able to complete complex tasks such as persuading or negotiating and cannot generate new ideas as efficiently as solving problems. Thus, jobs that require a certain level of creativity and emotional/social intelligence are not likely to be superseded by algorithms any time soon. It’s likely job titles such as entrepreneur, illustrator, leader and doctor will stay human for now.
In addition, acting upon the intelligence of machines will still require a human in many cases. Sophisticated algorithms may predict high risk of a cancer, but it is the doctor who will relay that information to a patient. Self-driving cars may move us from point A to point B, but it is the human that will be the ultimate navigational influence, deciding the destination of the journey and changes along the way.
As these applications of intelligent machines develop, the most advanced technology companies have kept their human support teams. When there is an issue with automated processes, the fixing is often carried out by a human. The need for human expertise when dealing with smart machines is essential: The new systems being developed require updates, corrections, ongoing maintenance, and fixes. The more we rely on automation, the more we will need individuals with the relevant skills to deal with the complex code, systems and hardware. This created a raft of new careers, disciplines, and areas of expertise not existing today.
Human Skill Sets Will Sync With Machines
In the future, machines will automate a range of tasks, hence survival will belong to those who are most adaptable and learning agile. Digitalization and algorithms are creating a new interface between the worker and end goal. Humans are now faced with dashboards providing indicators of machine performance. Interpreting, understanding, and acting upon the data in this dashboard becomes the task of the future. A new interface is emerging for the human.
This new interface drives a change in the skill sets required. In order to adapt to the possibilities that artificial intelligence creates, businesses globally will have to hire a multitude of individuals who are data and digital savvy, as well as understand how to interact with machine interfaces. We will see the continued rise of new teams with data and analytical expertise to create and manage the intelligent algorithms of the future.
Not only has machine intelligence opened up new jobs and departments within businesses, but it’s also created the requirement for completely new organizations and business models. Siemens is an example of a traditional rail industry transforming from selling trains to becoming a key service provider for guaranteed, on-time transportation that just happens to use trains. The need for data and analytical expertise is only likely increase as analytical automation grows: autonomous vehicles will still need mechanics, as well the self-driving systems within the vehicle.
A new Role Will be Established for Analysts and Business Users
As we embrace AI and deep learning algorithms that automate the detection of insights, we must not lose sight of the importance of the analyst who deploys the algorithm and the user who consumes the insights.
Analysts explore data, generating new ideas, being creative, and solving problems by using algorithms. Machine learning and AI models can harness complex data and make more accurate predictions, but it is still the human analyst that will make the decisions on what type of data to feed the algorithm, which algorithms to deploy, and how best to interpret the results.
As algorithms create more and more predictions, can we automate all decision-making? Is there a danger that this automation will become a crutch for business users – allowing human judgement to be overlooked? It is crucial that business user are equipped to understand the value of human judgement and how to manage algorithms making questionable decisions.
Looking to get started and become a leader and entrepreneur in your industry? If CIOs want to take the lead in introducing AI to their organizations, they should begin to identify which business processes have cognitive bottlenecks, need fast accurate decisions, or involve too much data for humans to analyze. These are the areas that can be positively impacted by human analysts leveraging algorithmic machines.
It is the human in the loop that will help businesses globally harness the potential of automated machine intelligence. A partnership between human and machine will create unbounded value at speed and scale.
View Part 1 of this blog post.