Tag Archives: Editorial board

Automation and AI – What does the future of work look like?

Author: Steve Dale BIR Editorial Board Member

Our news and activity streams are buzzing with articles, blogs, analyst reports and social media hype around the topic of “AI”. It’s a fairly loosely defined topic that covers an enormous spectrum of disciplines, from big data and predictive analytics, to machine learning, natural language processing, automation and robotics. Depending on who you listen to, it’s either the most important technological breakthrough since the invention of electricity, or it heralds the end of civilisation as we know it! Extreme scenarios are most certainly fantasies and should be discounted. The most likely outcome is neither extremely negative nor extremely positive.

What tends to focus our attention are the stories about how AI and “intelligent” machines are replacing roles, jobs, or even professions. What is the real truth behind these stories?

There is no doubt that workplace automation is becoming more widespread, and today’s AI-enabled, information-rich tools are increasingly able to handle jobs that in the past have been exclusively done by people (including tax returns, language translations, accounting, even some types of surgery) – automation is destined to have profound implications for the future world of work.

McKinsey recently reported that 30 percent of activities for 60 percent of occupations are now technically automatable.

Recent advances in robotics, machine learning, and AI are pushing the frontier of what machines are capable of doing in all facets of business and the economy. Physical robots have been around for a long time in manufacturing, but more capable, more flexible, safer, and less expensive robots are now engaging in ever expanding activities and combining mechanization with cognitive and learning capabilities—and improving over time as they are trained by their human co-workers on the shop floor, or increasingly learn by themselves.

Massive amounts of data that can be used to train machine learning models are being generated, for example through daily creation of billions of images, online click streams, voice and video, mobile locations, and sensors embedded in the Internet of Things. The combination of these breakthroughs has led to spectacular demonstrations like DeepMind’s AlphaGo, which defeated a human champion of the complex board game ‘Go’ in March 2016.

New milestones are being achieved in numerous areas, often with performance beyond human capabilities. In 2016, for example, Google’s DeepMind and the University of Oxford applied deep learning to a huge data set of BBC programs to create a lip-reading system that is more accurate than a professional lip-reader.

There are numerous examples of how machine learning is being used to augment human decision making in healthcare, aircraft maintenance, oil and gas operations, recruitment, insurance claims processing and law. There is barely a sector that is not engaged in some way in exploring the use of AI and automation technologies to improve productivity or accuracy.

One of the more practical roles for AI over the past few years has been to automate administrative tasks and decisions. Companies typically have thousands of such tasks and decisions to perform, and it was realized that if they could be expressed in a formal logic, they could be automated. A key feature of this type of automation is machine/deep learning and robotic process automation (RPA) – which, contrary to its name does not involve actual robots; it makes use of workflow and business rules technology to perform digital tasks.  The technology makes it relatively easy to automate structured digital tasks that involve interaction with multiple information systems.

So, what does all of this new technology mean in terms of jobs? Most analysts are agreed that whilst many routine tasks and functions – both physical and cognitive – are being automated, this does not necessarily mean that we are heading for mass unemployment as the machines take over. Perhaps one of the most extensive research programmes into the impact of AI on jobs and skills has been undertaken by Nesta. It has published its findings in the report:  The Future of Skills: Employment in 2030. Well worth a read. The report highlights that:

  • skills that are likely to be in greater demand in the future include interpersonal skills, higher-order cognitive skills, and systems skills.
  • the future workforce will need broad-based knowledge in addition to the more specialised skills that are needed for specific occupations.
  • dialogues that consider automation alone are dangerous and misleading since they rarely take account of globalization, an ageing population and the rise of the green economy.

Perhaps the last word on where AI and automation is having (or will have) the most impact should go to Gil Press at Forbes, who identifies the sectors and functions as follows:

  1. Customer Self-Service: Customer-facing physical solutions such as kiosks, interactive digital signage, and self-checkout. Improved by recent innovations such as better touchscreens, faster processors, improved connectivity and sensors. A prime example is the experimental Amazon Go convenience store.
  1. AI-Assisted Robotic Process Automation: Automating organizational workflows and processes using software bots.
  1. Industrial Robots: Physical robots that execute tasks in manufacturing, agriculture, construction, and similar verticals with heavy, industrial-scale workloads. The Internet of Things, improved software and algorithms, data analytics, and advanced electronics have contributed to a wider array of form factors, ability to perform in semi- and unstructured environments, and the “intelligence” to learn and operate autonomously.
  1. Retail and Warehouse Robots: Physical robots with autonomous movement capabilities used in retailing and/or warehousing. Amazon deploys this technology throughout its warehouses.
  1. Virtual Assistants: Personal digital concierges that know users and their data and are discerning enough to interpret their needs and make decisions on their behalf.
  1. Sensory AI: Improving computers ability to identify, “understand,” and even express human sensory faculties and emotions via image and video analysis, facial recognition, speech analytics, and/or text analytics.

He goes on to say:  “There is no question that we will continue to see in the future the same disruption in the job market that we have witnessed in the last sixty-plus years of computer technology creating and destroying jobs (like other technologies that preceded it). The type of disruption that has created Facebook and Tesla. Facebook had a handful of employees in 2004 and today employs 20,000.  Tesla was founded in 2003 and today has 33,000 employees. Whether AI technologies progress fast or slow and whether AI will continue to excel only at narrow tasks or succeed in performing multi-dimensional activities, entrepreneurs like Zuckerberg and Musk…will seize new business opportunities to both destroy and create jobs. Humans, unlike bots and robots (now and possibly forever), adapt to changing circumstances.”

One thing we can be sure of: the rate of change will continue to accelerate, and if we wish to remain relevant in our chosen professions, we need to identify and refine the skills that can’t easily be automated. Whether that’s a shrinking or expanding environment remains to be seen.

Knowledge Management – Don’t Forget The SME’s!

Guest blog post by Stephen Dale of Business Information Review Editorial Board

The research paper by Cheng Sheng Lee and Kuan Yew Wong in the December of issue of Business Information Review raises a number of interesting points that deserve wider discussion. The research focused on the effectiveness of knowledge management techniques in Small to Medium Enterprises (SME’s) in Malaysia. Though the scope of the research is limited to one geographic region, the findings could – and should – be tested against a wider and more international cohort.

According to the research paper, in Malaysia, SME’s account for up to 98.5 percent of the total number of businesses and contribute up to 33.1 percent of GDP. They employ 57.5 percent of the total workforce.

To offer some comparison, UK, SME’s account for over 99.8 percent of the total number of businesses, they contributed over half of UK outputin 2013 (GVA) and employ 48 percent of the total private sector workforce. The EU average SME contribution to GDP is 55 percent.

It is clear from this data that SME’s make up a significant, and growing, contribution to the UK and European economies. It seems quite odd, therefore, that so little research has been undertaken into how knowledge management strategies and techniques have been utilized within and across this sector.

The Cheng Sheng Lee/Kuan Yew Wong research gives us some insights that could be tested against a wider geographic sample of SMEs. Some key points from the research as follows:

The literature research identified that the size of an organization affects its behaviour and structure (Edvardsson, 2006; Rutherford et al, 2001) and how it influences the adoption and implementation of KM (Zaied et al, 2012).

SME’s should not be perceived as homogenized groups. They themselves can be categorized according to relative size, e.g. micro, small and medium, which can influence the way that KM is implemented.

In terms of human capital, medium-sized businesses (SMEs) focus more on codification strategies (explicit knowledge) whereas micro-sized businesses (SMEs) are more dependent on socialization strategies.

An obvious point, but reinforced by the research – the need for better infrastructure, such as tools, office layout, rooms etc. increases as the organizations grows.

Knowledge Maturity is a key attribute that should be monitored measured. The value of an employee will increase in terms of their contribution to the success of the organization as they progress from beginner, intermediate and advanced staged of KM maturity. Clearly the impact of an employee leaving without an effective knowledge transfer process will be more keenly felt by a small organization. [NB. This is not an excuse for large organizations to treat this is a lower priority!]

Company size does make a difference to KM performance measurements. A number of factors are proposed, e.g. impact of high turnover, limited resource redundancy in smaller organizations, smaller organizations will likely prioritize implementation processes over performance measurements etc.

KM performance measurement (KMPM) is still new for SME’s, as the majority of analyst reports and case studies remain focused on large organizations, with a mindset that SMEs do not need or are not ready for KMPM.

Overall, this is an excellent piece of research, and highly recommended reading, which despite it’s limited sample size and geographic boundary, gives some very useful insight into how KM is being implemented across SME’s. Reassuringly it shows that a growing number of SME’s see KMPM as vital to the growth and success of their business.

The paper is also a wake-up call to academia, research, analyst and consultancy organizations in that we need for more definitive and comprehensive studies in this field, to embrace UK, Europe and other key industrial and economic zones.

To finish with a quote from the authors: “Enough with large organizations; SMEs should not be neglected as they play a major role in a country’s economic growth”. Who could disagree?