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          Is AI and data analytics out of reach for SME transport businesses?

          68% of surveyed respondents in our latest report believe that AI and its benefits for commercial transport are overhyped. Add to that the industry's concerns relating to data security, job losses, ethical use, personal privacy rights and potential liabilities arising out of the use of AI, and the conclusion must be that the adoption of AI in the transport industry is no slam dunk.

          Date: 27/09/2018

          It is clear that there is excitement about the deployment of Artificial Intelligence (AI) in the transport industry – and with very good reason. On the other hand, 68% of surveyed respondents believe that AI and its benefits for commercial transport are overhyped. Add to that the industry's concerns relating to data security, job losses, ethical use, personal privacy rights and potential liabilities arising out of the use of AI, and the conclusion must be that the adoption of AI in the transport industry is no slam dunk.

          Investment in AI technologies is not cheap for businesses. There is a strong argument that says the benefits outweigh the cost of investment – and with good supporting evidence. That argument, though, will not necessarily help small and medium sized transport businesses find the capital to invest in AI technologies. How can smaller businesses operating in the transport sector who are keen to engage with the possibilities of big data analytics, without exposing themselves fully to some of the risks inherent in AI – risks which have the potential to put them out of business?

          Perhaps the answer is a targeted programme of Augmented Intelligence. Augmented Intelligence may be defined thus:

          "An alternative conceptualisation of artificial intelligence that focuses on AI's assistive role, emphasizing the fact that it is designed to enhance human intelligence rather than replace it."

          The operationalisation of Augmented Intelligence within a smaller transport business might commence with a fairly narrow programme of data analytics targeted at specific challenges to the business, both current and upcoming on the horizon, with a mission to improve a specific aspect of business performance and/or reduce certain specific risks to the business. The operationalisation and outputs of the programme must be measurable – which ultimately translates to a number (of indeterminate digit length), prefaced by the relevant currency symbol and headlined by the word 'Savings'.

          The programme must serve to provide insightful data analytics to end-user employees of the business, on a platform that is easy to access, easy to understand and easy to use, that allows the end-user to inform their decision-making within the business. Some examples might be:

          • How profitable is my proposed new private customer aviation route/service likely to be?
          • What is the % likelihood of a specific future shipment being delivered to my customer on time?
          • For a specific dispute with a customer, should I make a payment to redress or defend to the hilt?

          There is a whole spectrum of key business decisions that can be partially informed by the use of big data analytics – to make better decisions, quicker.

          What does a business need to embark on a programme of Augmented Intelligence analytics? Some raw data, preferably of significant volume or richness. A data analyst or data scientist. A subject matter expert from within the business, of course.  A system developer is very useful. 

          The key individual required to optimise the programme is someone who both understands the statistical and mathematical disciplines of data analytics and has enough sector knowledge to be able to apply that understanding successfully into the business operations – a bridge between the hard data analyst/scientist and the products and services of the business itself. That 'bridge' individual effectively translates the business requirements of the programme to both the data analyst and the system developer and is able to keep the subject matter expert involved at all steps – keeping the expert involved and engaged is critical to the success of the programme.

          A potential output of the programme might be that the business does not have the 'right' data to embed the Augmented Intelligence into business decision-making. A waste of time, then? Absolutely not. This output is likely to lead to a programme of improved data capture by the business – which data to capture, how best to capture it at low frictional cost - with a view to revisiting the Augmented Intelligence programme at a later stage when the raw data is in a better shape.

          Small and medium sized transport businesses are integral to the industry and economy. They should not – and must not – feel or actually be left behind by the 4th Industrial Revolution.

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          Related people

          Simon White

          • Principal Consultant – Data & Predictive Analytics