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Introduction of AI for Managers

6th Apr 2020 Rod Makin AI

Being a baby boomer, and working with, and for really smart people, I am constantly introduced to new industry buzz words. 

So, when terms like Artificial Intelligence (or AI), Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (or NLP), or others are introduced in conversations, I am reminded when I request an explanation in layman’s terms, that ‘the more things change, the more they stay the same’ – many of the underlying fundamentals of IT and of business remain the same.

This article is aimed at Business Managers.  The objective of this article is to breakdown and simplify what is referred to as ‘Artificial Intelligence’, by explaining what it is, why it is now gaining increased interest, and where and how you can get started to address your current business challenges.

 

What is AI?

Artificial Intelligence (or AI) is really a broad concept covering many technologies.  It has become like a thing, just like ‘the Cloud‘, the ‘Internet’, or ‘Digital Transformation’.

The simplest definition of AI I have identified is the following:

AI is a collection of different technologies that can be brought together to enable a machine to act with intelligence - to learn and make decisions. 

What many companies are calling AI today, is not necessarily so.  A true AI system is one that can get smarter and more aware through learning, allowing it to enhance its capabilities and its knowledge over time.

Current everyday applications of AI, you may be aware today include:

  • Smart assistants, including Siri and Alexa
  • Facial recognition technology
  • Personalised recommendations on platforms such as Amazon and Netflix
  • Self driving cars, for example Tesla.

 

What are the common AI technologies?

Artificial Intelligence is an umbrella term encompassing a number of technologies.  The following, while not an exhaustive list, are the key ones driving today’s conversations: 

  • Machine Learning - driven by large amounts of data - the use of statistical tools to analyse and learn from that data
  • Deep Learning - A subset of machine learning but with more layers – the objective is to mimic the human brain
  • Computer Vision – used to identify and classify objects—enabling the computer to react to what they ‘see’
  • Natural Language Processing (NLP) - helps computers understand, interpret, and manipulate human language

 

What is driving the AI hype?

While AI is still in its infancy in delivering business outcomes, there has been a significant increase in technology and AI enablers. Today’s AI applications are making use of:

  • with the advent of the Cloud, virtually unlimited processing power and greater levels of computational efficiency
  • huge amounts of data resulting from the reduced cost of storage, and
  • the emergence of open source frameworks -  publicly accessible software which is ready-to-use

 

What areas should your business be looking at?

Process Automation – to address inefficiencies

AI technologies are extending the business process automation possibilities, providing greater opportunities to:

  • save time and money by automating and optimising routine processes and tasks
  • increase productivity and operational efficiencies
  • make faster business decisions based on outputs
  • avoid mistakes and human error.

 

Business Scenarios

Your manual business process is reviewing unstructured data (such as a CCTV video) and manually counting objects (eg: people, vehicles, animals), assessing an object (eg: looking for a defect in a product) or identifying an event (eg: falling over, moving in or out of an area).

 

Using Computer Vision and Machine Learning, the same video can now be analysed, objects and events can be identified, and actions (eg: notifications, reporting) can be made in near real time. 

 

 

Augmentation – extending the capability of your workforce

AI can augment your current workforce by extending the capability of your workforce – capabilities such as judgement, decision making, and analysis, enabling the ability to make faster and more informed business decisions based on outputs.

 

Leveraging AI, specifically Machine Learning, can make your workforce smarter by making your data smarter. AI can provide insights (because it sees patterns, similarities, and anomalies), particularly with large volumes of data.

 

Use Case – Medical: Using AI can now predict which patients are likely to have a stroke or heart attack.

Use Case – Call Centre: Using AI  can now predict call volumes in call centres for staffing decisions.

Use Case – Utility: Using AI to predict power usage in an electrical-distribution grid.

Use Case – Marketing: Using AI to understand buyer behaviour and demographics to target and engage audiences in the right way.

 

Key to making your data smarter, is engaging someone who can identify how your data can be used to achieve your business goals. This someone, a data scientist, will assist with designing data modelling processes, creating algorithms and predictive models to extract the data the business needs, then helping to analyse the data and share insights.

 

Augmentation – providing better experiences for your customers

AI can provide your business the capability to provide better experiences for your customers, providing greater opportunities to:

  • use insight to predict customer preferences and offer them better, personalised experience
  • mine vast amount of data to generate quality leads and grow your customer base
  • increase revenue by identifying and maximising sales opportunities.

 

Use Case – Customer Service: Using AI to gauge a customer’s tone of voice, enabling businesses to respond more quickly and effectively to customer concerns, complaints and queries.

Use Case – Customer Service: Using AI to make personalised product or service recommendations on an e-commerce site.  E.g. Netflix, Pandora, Amazon

 

Summary

So, you want to start, or continue, your AI journey.  How do you identify opportunities?  How do you select and deploy the right technology?

  1. Identify your biggest opportunities for delivering value
    1. For Process Automation: List out the three most time consuming, repetitive or manual processes
    2. For Augmentation: List out the key insights you are after, or the key improvements you would like to make to your customer’s experience
  2. Prioritise – identify those that have largest impact on the delivery of operational processes, decision making or customer experience
  3. Engage a trusted advisor on use of AI technologies (from your internal IT or an external partner) – ask for advice on how best to use AI

 

Why talk to a trusted advisor on the use of AI?

The reason for talking to a trusted advisor is the possibilities for AI are increasing every day.

 

Who is your trusted AI advisor?