We are already seeing new technology emerging and replacing traditional jobs across many industries—in manufacturing and automotive, for example
Artificial intelligence (AI) and machine learning (ML) are among the most disruptive technologies when it comes to reshaping the enterprise. New capabilities and applications of AI and ML are being realised every day and will permeate the workforce sooner than we think. However, the implementation of AI and ML in practise doesn’t necessarily mean that the number of jobs will decrease or that long-anticipated effects on employment may not come to fruition. Instead, technology can be used to augment human capabilities and improve efficiency levels rather than replace workers.
Where AI and ML will provide the most impact
Data-driven companies making in-roads in digital transformation are applying AI and ML to build real-time analytics infrastructures that offer valuable insights, which confer a competitive advantage. These infrastructures can not only intelligently process large amounts of data, but also reduce the time it takes to do so. Smart capabilities are then applied to tasks such as data cleansing and suggesting new combinations of data, which would be laborious to do manually. Tasks like these were previously handled by the IT department, which can now add value by enabling IT staff to devote their time to higher level tasks that require critical thinking. It also allows business leaders to focus on the application of these improved insights.
The emerging ‘modern’ workplace
Over the past decade, mobile and remote working has disrupted the traditional workforce. AI and ML will be even more disruptive. Forward-looking companies should embrace the opportunities that new technology offers and proactively look at both creating new roles and addressing existing gaps to maximise the benefits of AI and machine learning. Rather than job eradication, there will be a subtle shift to more in-demand skills. Expect to see the emergence of customer insight centres of excellence. The competition for data engineers will also accelerate as smart capabilities in data analytics become more commonplace.
How AI affects the democratisation of data in an enterprise
Advanced data mining coupled with improved user-friendly tools will help to further democratise data. This will also have a compelling effect on culture. ‘Self-service’ analytics has gained popularity because it enables users across different levels and departments to act on insights and better serve their customers and partners. Historically, when organisations embarked on this approach, they struggled to implement a complete analytics solution that met the needs of their business users, executives, and the IT department. This is where AI can be beneficial—by improving the user experience and increasing adoption and interaction. A ‘one size fits all’ solution simply does not work in practise, and neither does a patchwork of point solutions that inevitably creates data silos, complicated workflows, and lagging user adoption. The key to resolving this starts with the user, to ensure that they are interacting with the technology in an optimal way.
Maximising ML and AI in the enterprise
Every organisation will have different needs and pressure points, so the key to optimally leveraging AI and ML is to examine those critical areas where this new technology can be beneficial and implementing where appropriate. We’re currently in the era of ‘Digital Darwinism,’ where organisations risk extinction if they don’t adapt to the pressures of the market. However, the ones who can adopt new technologies to transform the customer experience and discover and deliver on new business models will ultimately be the disruptor that reaps the biggest rewards. This harsh reality highlights the need for organisations to embrace new technology intelligently, focusing on where it can be used to complement existing skills and replace missing ones.
Looking to the future
Over the last year, smart assistants such as Amazon’s Alexa and Google Home have gained widespread adoption. In the coming year, we will see more organisations embrace this new technology to transform how they do business. Software with enabled voice capabilities and natural language processing has the potential to improve productivity and efficiency through changes to workflows. For example, employees can recall important documents through voice commands rather than lengthy search procedures.
Going beyond human-to-machine interaction, AI and ML will also be used to intelligently analyse data generated from outside the central organisation—through IoT connected devices, for example. This enables insights to be combined with existing data sets to create a new, enhanced perspective.
As technology replaces in situ skills, new roles will be created that require critical thinking and cannot yet be completed by a machine. Upskilling existing staff will become more of a priority to be able to fill these roles, as new technology inadvertently worsens the skills gap. Business leaders also need to step back and take a holistic view of where they can effectively use technology and understand that simplistic lift-and-shift approaches will only scratch the surface of possibilities that emerging technology offers.
About the Author
Hugh Owen is the Senior Vice President in charge of Product Marketing for MicroStrategy. A 17 year industry veteran, he is responsible for the marketing of MicroStrategy’s Enterprise Software offerings for Analytics, Data Discovery, Big Data, Mobile, and Cloud. He has been with MicroStrategy since 2000 in various roles. Mr Owen received a Bsc (Hons) degree in Business from The University of Bath, England.
