How Is Artificial Intelligence Developing In The Enterprise?

IBA Group
Mark Hillary

Artificial Intelligence (AI) has moved from science fiction into the enterprise in recent years. Many companies are using AI systems today to intelligently analyse large volumes of changing data and to notice or predict patterns. Typical business uses today include examples such as:

  • Rail operators predicting train delays before they happen because the AI system can extrapolate from small delays to predict the impact on the entire network.
  • Customer service agents being advised on how to help customers by systems that know the answer to every question a customer has asked in the past.
  • Alexa knows how to answer your question because it immediately processes your voice and determines what you are asking before creating an answer.
  • Netflix knows which movie you might want to watch because they know your past behaviour and how similar customers have also behaved.

AI really is all around us today, in the enterprise and as consumers of services. In the present environment it would now be unusual for any company to not be exploring how AI can improve their business.

But AI does have one a fundamental flaw, it is always limited to working on a very specific problem. This means that you can have a very complex system that knows everything that your customer may ask when they call for help or the system may understand how to play chess or Go, but these individual tasks are all that it can do. There is no inherent awareness of the environment around the system – although we use the term intelligence, it’s not really aware or sentient. An AI system that can play Go cannot plan the best route on a map.

This means that the system can only solve the problems it was designed for. Some might argue that this is a benefit, because it means that however good our AI systems get, they never move into the realm of awareness and all the problems that a conscious system might create.

A recent experiment by IBM has demonstrated that AI is developing rapidly though. They demonstrated how an AI system could be asked a random question and it would then have the ability to debate that subject. For example, in the video clip that I watched the system was asked if pre-school facilities should be subsidised by the government. It gave a response, arguing why subsidies are useful for 4 minutes.

This system has been pre-loaded with information on millions of subjects and objects. It’s stuffed full of encyclopedia content and research. But even with all this data it is quite an achievement to turn that into information and then a coherent argument.

Essentially this system is starting to show that perhaps an Artificial General Intelligence might be possible. It would need to be pre-loaded with an enormous amount of general data, and then would need a Machine Learning system to continue learning, but it is looking more feasible than even a year or two ago when Elon Musk started warning that we are heading for an ‘AI apocalypse’ because the machines will eventually have more intelligence than the humans.

I don’t think we will be seeing many business case studies featuring general intelligence just yet, but AI in the form we already know it will certainly be more important. AI is offering companies a chance to identify patterns and trends they could never see manually and this will be a strong source of competitive advantage in the next few years.

Artificial Intelligence (AI) has moved from science fiction into the enterprise in recent years

Business Application Support With Artificial Intelligence

IBA Group
Mark Hillary

I mentioned in my last blog that the very nature of jobs and employment is changing today because of emerging technologies such as Artificial Intelligence (AI). Tools such as AI in business application support, are fundamentally changing how corporate processes function – and this changes the skills that people in professional jobs need. This change is prevalent throughout many parts of the modern organisational structure, but the most important area of change is probably enterprise decision support.

The problem is, how do we get from here to there? Many organisations are overrun with data. They have so much that they just don’t know what to do with it all. Some have tried to focus on data analysis, creating a data-driven approach to their business, but even those who have moved in this direction need to rely on the skills of their data scientists to try turning data into information.

As data analytics evolved, it was clear that enterprises were changing from a backwards-facing approach where they analysed past events and described what happened, to an ability to be predictive – trends and patterns that help to predict future behaviour could be found. A similar change needs to take place in enterprises using AI today.

Today, the possibilities for AI-enabled decision-making are more prescriptive, with AI providing enterprises not just a look into the future, but also key diagnostics and suggestions on potential decision options and their payoffs. Such highly evolved applications of AI can help businesses make decisions that can potentially exploit more business opportunities, while averting potential threats much earlier.

This is the real opportunity for AI in the enterprise. To create the opportunity for automated decision-making, but where the system is designed to learn, unlearn, and relearn as often as required. Insights from automated AI business application support should be powerful because the system is trained to learn where insights may be found.

I believe we will be seeing many more AI business application support systems in future – its no longer a technology of the future. These are systems that are already installed and in use in enterprises globally – have you explored what AI can do for you and your decision-making processes?

Robotics And Cognitive Technologies Change Your Business Forever

IBA Group
Mark Hillary

The growth in corporate robotics feels rather like an overnight trend, but automation using robots has been changing the manufacturing industry for at least three decades. The difference today is that robotics is no longer restricted to factory production lines. Automation today is far more advanced than a machine capable of spray-painting car parts.
The reality in today’s environment is that several technologies are blending together to create new possibilities and solutions. Robotics, machine learning, and Artificial Intelligence are naturally connected because automation no longer has to just be the simple repetition of programmed bots – we can now ask the system to learn how to get better.
The IBM Watson system is a great example of this. Watson is capable of reading 800 million pages of data a second. This capacity to absorb new information constantly makes it incredibly useful for complex environments that are constantly changing. Cancer diagnosis is a good example because a traditional doctor will train for many years and then will work with patients in a hospital so their capability to absorb new research is limited. By training real doctors to work with AI systems such as Watson we can support and enhance them – allowing doctors to access a second opinion that includes knowledge of all published research.
Softbank in Japan has connected their Pepper ‘general purpose’ robot to a Watson ‘brain’ creating the possibility for intelligent assistants that actually have a physical form. It’s easy to imagine nurses treating patients with Pepper offering additional advice, or a bank advisor explaining a mortgage to a potential customer and Pepper offering further information and automatically checking compliance to legal regulations.
But this convergence of technologies is not taking place at the same speed in every company, or even in every industry. EWeek magazine recently summarised five important trends that give a good oversight on the growing importance of robotics in industry today:

  1. Most companies are not yet using Robotic Process Automation (RPA), but are noticing those that are using it; Capgemini research suggests that 39% of companies are already using RPA and many are talking of extremely positive results – such as a reduction in repetitive work and an improvement in quality. The companies that have not yet tried RPA are noticing these reports and will move quickly.
  2. RPA works best when used to create a Centre Of Excellence (COE); RPA requires a cultural change so it helps to create a mindset that you are not just automating existing tasks, rather the plan is to improve how the company works.
  3. Once companies explore RPA they deploy it everywhere; companies that have piloted RPA initiatives find that it is not just useful in the back office – automation can be deployed everywhere.
  4. Human jobs are changed, not eliminated; as with the Pepper examples, in most cases RPA enhances and improves what humans can do rather than just eliminating their roles. In research published by McKinsey, they estimated that around 90% of work functions cannot be automated 100% – the role of automation is to increase quality and productivity, not eliminate humans from the workplace.
  5. RPA plus AI will lead to new cognitive opportunities; by created automated systems that can learn we are entering a new cognitive era of business. Research by OpusCapita suggests that 81% of executives believe that this combination of RPA with AI will significantly change their business inside the next 5 years.

This highlights two extremely important – and opposing – points. Executives mostly (81%) believe that automation and AI is about to dramatically change their business, perhaps even their entire business model. However, only a minority (39%) of companies have already launched an RPA project.
It’s clear that this is where the future lies for companies across all industries so the future seems bright for service companies with expertise in both these areas. I even think that the 5-year time horizon is rather long – in my opinion this will all change before 2020. RPA and cognitive systems are about to change your business forever – are you exploring the possibilities today?

RPA Is The First Step To Data Automation, Not The Final Answer

IBA Group
Mark Hillary

Last year the analyst firm HfS Research published a cautionary article on Robotic Process Automation (RPA). They warned that the technology and outsourcing market was hyping RPA beyond what it could possibly achieve – the talk of RPA changing the world is all hype.

This should be qualified by the more nuanced comments published by HfS. They stated that the RPA boom has been hyped, but not the more general focus on business automation. So the blogs and business journals that keep on breathlessly saying that bots will transform companies need to examine just exactly what they are saying. RPA cannot exist without a wider transformation of how a company functions.

Companies today need real-time data that converges across business functions. You cannot have a marketing database that is not connected to data your customer service team is using. You cannot drive business value today without your business data being real-time and accessible from across the entire business.

The hype around bots is that all this is automatic; but if your company still relies on paper documents then how are the bots going to process that data? To create a truly automated organisation requires more thought and planning than just the deployment of a bot in the back-office.

As more processes are digitised, more value can be created. More insights can be found. More opportunities to set bots searching for trends are created. HfS has actually been saying this for a long time and they have a digital office solution called OneOffice that broadly defines how organisations need to plan operations – if they want to use the data flowing through the company.

The real message is that RPA itself is not hype. There are many excellent examples of RPA deployments that have achieved genuine efficiencies for the companies using it – including customers of IBA. However, the idea that RPA is a silver bullet that can automate your business processes, making your team super-efficient overnight, is certainly hype. RPA is just one part of a transformation to a digital business environment. It is one tool in a complete arsenal of change related to the way that data is used.

We need to see a convergence of data analytics, cognitive solutions, and RPA. Big Data is another term that is often tossed around with very little understanding, but it needs to be understood by leaders. Big Data and data convergence are not just referring to smarter ways of using data. We are talking about the complete digital transformation of organisations so that data becomes the most valuable asset.

Many people have been shocked to recently discover just how much data Facebook has on their personal likes, dislikes, and preferences. It shouldn’t be a surprise. Facebook has offered a free service for years in return for data. That data allows them to know their customers inside-out, creating advertising opportunities that the traditional advertising industry cannot match. Facebook has redefined how advertising works and major advertising companies are struggling to keep up.

Whatever your business, you need to think about how data flows from customers to you and how it moves inside the organisation. How can you leverage this knowledge to create new opportunities – even a new business? RPA is just one ingredient and can help to automate some processes, but thinking about RPA should really just be your first step towards a complete transformation of how you are using data in your company today.

AI Can Transform How Companies Interact With Customers

IBA Group
Mark Hillary

Chatbots have faced quite a challenge. Initially they were embraced, especially when Facebook championed how they could allow small companies to be available to customers 24/7 without the need for a contact centre. However, many customers and companies have also complained that they often fail to live up to the promise. The Artificial Intelligence (AI) systems are just not good enough to replicate interactions with a real person.

But a recent feature in Financial Review magazine explores how chatbots are really just the beginning of a long journey into AI. Perhaps we should review our expectations and remember that we are only just starting to use these technologies for real business solutions. Mistakes will be made, but when the technology is effectively deployed it really does work.

Take a look at the National Australia Bank deployment of a chatbot to answer customer questions. The bank focused on the most common 200 questions that customers ask and the bot can recognise all these and another 13,000 variations of the same questions. Commonwealth Bank launched a chatbot in January of this year and by the end of the year they predict that it will understand 500,000 different ways to ask about 500 different banking processes.

The bots are learning. They are applying Machine Learning principles so that every interaction with a customer becomes an opportunity to learn and improve. This is very important, because many of the executives who have been critical of AI systems have not allowed the system long enough to learn how it needs to behave.

Organisations that need to interact with their customers often, like banks, will find that an enormous amount of basic enquiries can be handled by bots and customers will prefer interacting with bots because they get immediate – and accurate – service. AI has proven beyond doubt that it is more than just a fad. The smart use of a chatbot system is the first step on a path to creating a much more automated customer experience – an experience that most customers will prefer because of the immediacy of service.

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