Artificial Intelligence

Is Artificial Intelligence relevant to our Business?

“Data science and AI seem to be on-trend right now. Our business would like to benefit from the application of data science and Artificial Intelligence. The problem is that our organisation hasn’t aligned its digital capabilities strongly enough to take advantage of the benefits data science and AI provide. Also, what part of our organisation is this most applicable to and how can we make ourselves ready for this?

____ Why should we look at this?

Data science and AI offer organisations to the opportunity leverage their data for automating manual processes, back office functions, customer service, and to assist or even make certain decisions. 

Organisations wishing to benefit from AI automation, often hit the wall because the quality of their data won’t support the required capabilities. In fact, according to a recent study published in Forbes, nearly 80% of data science effort, is spent on data quality-related issues (see figure below).

____ The IMS approach

 Our approach marries data science with data management, enabling the focus of data science to be on the development of better performing predictive models, rather than on remediating data. We recognise there are many areas that can be enhanced through the application of data science and AI. We focus on the key outcomes to drive value from any initiative.

The IMS consulting team includes numerous Data Scientists, who are able to develop predictive and prescriptive models that provide advice to humans, or automate decision making.  IMS’s data science capabilities bring together multiple data science modalities and approaches to provide predictive insights that can be used to improve decision making. 

These includes:

  • Development of bespoke data models to integrate disparate data from multiple data sources into single view / source of truth

  • Implementation of classification algorithms (including Decision Tree, Random Forest, Naive Bayes etc.) to enable categorisation and segmentation of groups of customers / assets / products  etc,

  • Implementation of recommendation engines (including collaborative and content based methods) to assist with operational processes.

    ___ Examples of recent IMS work include

  • Asset management reliability centered maintenance models that determine whether to replace or maintain network assets such as pipes, roads or powerlines.
  • Personalised customer experiences - the use of recommendation engines to determine the next best action or conversation with a customer or citizen e.g. offering them a renovation loan because they’ve just had a child; advising insurance customers to move their vehicles to avoid a potential hail storm.
  • The use of decision trees to determine whether to pay an invoice based on contractual terms.
  • Chatbots for accessing information - beyond customer service, IMS has developed a natural language data access framework, enabling bots to interpret questions into various kinds of information requests, including:


    • 1. Requesting for reports or forms (e.g. application form)
    2. Requests for data (e.g. how many employees work here?)
    3. Requests for information (e.g. when does the library open?)
    4. Undertake a transaction (e.g. pay rates, apply for a loan)

    Chat bots interact with users in natural language, and can be trained for non-english speaking interactions as well. 

Questions You Might Have

  • How realistic is it to deploy AI for our organisation?
  • What sort of investment is required to establish a robust AI function?
  • Which areas of an organisation are best suited to benefit from data science?
  • Are there any prerequisites before starting on the data science / AI journey?
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Potential realised.