At a time where all businesses are becoming technology businesses, having a team in place that can help with digital transformation is key. Despite this, a recent study by the Open University Business Barometer has revealed that three in five senior business leaders surveyed report that the skill shortage has worsened over the last year.
We are irretrievably losing vital skills because many organisations keep their expertise locked in silos. This means valuable business acumen, skills and expertise are lost forever when the last cohort retires. When economic uncertainty forces businesses to lay off staff, it can mean they permanently lose the skills needed to rebuild and recover. Skills shortages or immigration restrictions can also have a devastating effect if companies become over-reliant on a narrow pool of human talent.
When vital skills are limited to just a few people, companies and even countries are on the back foot, unable to scale skillsets in response to new opportunities and threats. For example, Brexit could mean the number of exports requiring veterinary certification will soar by 325%, yet Britain has a severe shortage of vets. Codifying human expertise is therefore useful for getting around the skills gap and preventing knowledge-silos. A new science to decision-making has revealed a four-step formula to expert decisions including starting with a ‘blank slate’ and identifying the common drivers behind barriers to corporate objectives. For example, AI algorithms can now replicate the decision-making process involved in route planning, enabling this to be automated and bringing greater consistency and reliability to the process. Crucially, algorithms can draw on live data from across departments to ensure their planning decisions always draw on the most up-to-date information.
For instance, a route-planning algorithm could draw on live weather or commuter data to adjust planned timetables and routes in light of changes. This would create more adaptive, customer-friendly train services that seamlessly shift in response to changing conditions to produce optimal customer outcomes. It would also enable human intelligence to be applied to transport problems in a consistent and reliable fashion with all of the latest information instantly neurologically available.
The growing skills gap is causing multinational giants to turn to philosophy, mathematics and linguistics to capture and codify human expertise so it can be widely reproduced and taught to other humans and even software algorithms. It is vital to find a way of catching and categorising human skills so that organisations can quickly scale their pool of expertise to meet new demands even during a skills shortage. The key is to create familiar, user-friendly methods of explaining, recording and visualising the reasoning behind everything from insurance claims decisions to checks on meat imports.
Philosophy, linguistics and mathematics have given us methods for visualising and codifying how we organise information to make decisions. So-called ‘knowledge engineers’ can now use a kind of ‘mind-map’ to encode the logic and rationale behind expert human decisions so human thinking can be reproduced by AI algorithms and by other humans. Mind maps replicate the way knowledge is structured in the human brain and form a new way of allowing humans to pass on their knowledge to machines and to each other.
The ability to reproduce human thinking with machines enables human expertise to be spread and scaled at speed across a company or a country. The process of capturing and codifying human expertise and experience for AIs not only allows machines to reproduce human judgements but also enables the secrets behind expert decisions to be explained and taught to other human employees, helping up-skill the existing workforce. This makes limited human resources stretch further and enables organisations to rapidly respond to increased demand and manpower shortages.
Scientists have devised a way of finding common patterns among decision criteria that are enabling Artificial Intelligence systems to replicate the most talented bankers or lawyers and automate everything from fraud prevention to financial trading decisions. Trying things in this new approach can be unnerving but it is also exciting. The more open, creative and enthusiastic the people involved are, the more successful and fun a project can be.
Scientists are again starting with a ‘blank slate’ and are prepared to try new techniques. Each part of the process should be examined to determine whether it is needed, or whether it gives an opportunity to create a new, more concise and cohesive process around the decision-making. Depending on the complexity of the decision-making there may be more or different steps, and each analyst might have a different take on the best approach. However, the higher level steps can be boiled down by simply starting with identifying what the goal of the knowledge map is, the decision or decisions it is making and all possible outcomes. The logic behind the affecting factors is analysed to find patterns, before being categorised into differently behaving logic, revealing the optimal threshold for decisions. This is all then translated into a format a knowledge engineer can read for building the logic into an AI system, like the architectural drawings of a building.
Tackling the skills gap is imperative, but it can seem impossible where there is simply not enough expertise remaining. Capturing and codifying human expertise is therefore an essential means to improving the skills gap, and unlocking knowledge silos which started it in the first place.