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The sea of opportunities brought by AI is beyond imagination. Knowing what it is, and what it is not, has become critical.
Many still think of AI as something coming up in future.
Meanwhile AI is everywhere around us.
Think of Google Search for instance. Millions of people all over the world have created web pages and linked those web pages to each other.
And all that knowledge is then harvested by the Google technology so that when you type a question in the Google search bar, the answers you get are often surprisingly intelligent.
Also, there’s no need for a fingerprint when you look into the camera to unlock your Smartphone.
Netflix recommends movies to you that are similar in some way to the previous ones you watched.
And so on.
The list is so long.
In fact, these are all examples of artificial intelligence (AI) in our everyday life.
Why artificial?
Well, because it doesn’t occur naturally. It’s achieved with computers trying to mimic human intelligence.
In most cases, humans work together with computers to achieve results that each of them on its own, cannot achieve.
Hence the core questions:
- how can people and computers be connected, such that, collectively they act more intelligently than any person, group or computer has ever done before? and
- how can this human-computer system get better with time?
Every AI system seeks to address these two questions in some way.
Have you started asking any these questions in your organization?
Well, don’t worry if you haven’t. I’m about to help you demystify artificial intelligence, and you’ll walk away with ideas about where this technology enables digital transformation.
You’ll also walk away with clarity that AI won’t eliminate jobs the way people think, but that even if it does, people will probably get more interesting ones.
So what’s Artificial Intelligence?
Artificial intelligence can really be difficult to define. But let’s try to break it down.
The word “artificial” is more straightforward, meaning something that doesn’t occur naturally.
By contrast, “intelligence” has been defined in many ways.
One good definition, by the psychologist Howard Gardner, focuses on problem-solving: “intelligence is the ability to solve problems, or to create products, that are valued within one or more cultural settings”
Professor Malone of MIT Sloan School of Management, and one of the world’s leading thinkers in this field, defined AI as: “machines acting in ways that seem intelligent”.
Let’s settle with this last definition.
Now note that I’ll be using “machine” and “computer” interchangeably.
No need to complicate.
By the way, we know that machines won’t be able to do anything if humans do not program them.
In fact, humans are responsible for creating software, selecting applications, fixing problems, and performing other actions that machines can’t do.
So artificial intelligence can also be thought of as some form of collective intelligence that results from sharing tasks between computers and humans.
Now, let’s rephrase the two questions we asked above like this.
- What tasks should computers do and what tasks should people do? and
- How can this human-computer system improve over time?
A good rule of thumb will be to let each do what they are best at.
So who is best at what?
It turns out that machines do better than humans when it comes to remembering large amounts of data. And people are better than machines at interacting flexibly with other people.
Let’s clear the air with the Google Search example.
With Google search, people create content on websites and link them together. Then machines remember vast amounts of contents and help people get the content that is relevant to their interest.
So you see that Google Search didn’t replace librarians who did similar functions in the past.
Instead, the Google service enabled a vastly more creation and searching of knowledge, and created new jobs for searching, content creation, and advertising.
So it turns out, in this case, that humans working together with machines can deliver even better outcomes than each of them working alone.
And I hope this should start to reassure you that even if AI replaces some jobs, it will create even more better ones.
Three types of Artificial intelligence
1. Machine Learning
Stitch Fix is a company that uses machine learning to predict the clothing that customers will like.
Customers fill out style surveys, provide measurements, and send in personal notes.
Then machine learning algorithms digest all of this unstructured information to send recommendations to the company’s fashion stylists.
And they then select five items from a variety of brands to send to the customer. Customers keep what they like and return anything that doesn’t suit them. And the system learns and gets better.
That’s machine learning in action.
One of the hardest parts of using computers is that you have to give them explicit instructions on what they should do.
This becomes almost impossible when the task is complex – like figuring out which dress someone will like.
So machine learning technology solves this problem by designing computer programs that instead try to learn from experience.
2. Natural Language Processing (NLP)
Have you heard of sentiment analysis?
A computer program reads reviews on what people are saying about a product or brand on Facebook or Twitter. And then tells if the overall sentiment is positive or negative.
Also, a chatbot on a website receives a request from a user, interprets it, and determines the appropriate response that will solve users’ problems.
These are examples of Natural Language Processing.
The thing is – computers understand structured languages like python or java. But human activity uses highly unstructured languages like English, French, Spanish or Chinese.
And it’s surprisingly very hard to get computers to understand and generate these unstructured languages.
So Natural Language Processing is that part of AI that’s focuses on getting computers do this kind of work.
Computers can now understand natural language, either as text or speech, they can generate natural language, either as text or speech, and they can put the two together to converse with other people, either in text or in speech.
Common examples are Siri, Alexa, Google Assistant, or Google Translate.
3. Robotics
What comes to your mind when you hear robotics?
Do you picture a human-like robot that can walk, talk, and do most of the things humans do?
In fact, most of what you saw in films and TV about robots is fiction, and not true of real robots.
Real robots are designed for specific purposes, like transporting people, cleaning home, or assembling products in a factory.
And they therefore do not look like humans. For instance, the robot in a self-driving car doesn’t look like a driver sitting in a driver’s seat. Instead, it’s a bunch of sensors, motors and computers embedded invisibly throughout the car.
Organizations that automate manual tasks for robots to execute, potentially reduce errors and lower costs while improving the efficiency and quality of their operations.
In many settings, the most successful outcomes have been achieved through robots and people working together, each doing the tasks that are easiest for them to do.
Robots are also gaining ground in software environments, in what is called Robotic Process Automation.
For instance RoboAdvisors automate the investment process in the financial world.
In other industries you also find diagnosis and treatment systems, automated customer service agents, automated threat intelligence and prevention systems, as well as fraud analysis and investigation.
The Value that Artificial Intelligence brings to business
It’s not enough to adopt AI technology.
Most organizations think they’re undergoing digital transformation just because they implemented AI technology into one of their processes.
Without a clear strategy on how AI technology fits into the digital transformation roadmap, the organization will most likely end up among the 70% of companies that fail with digital transformation in the world.
Porter told us that any given organization pursues any of the following strategies:
- Cost leadership strategy
- Differentiation strategy, or
- Focus strategy
So how do we align AI with strategy to create value?
AI can help you cut costs
Does your organization want to be a low cost producer of some product or service?
AI can cut costs by automating tasks initially done by people.
Just like chatbots do.
Computers don’t get tired. They don’t go on vacation. And they don’t make errors like humans. So they’re best for routine tasks.
And they can do routine tasks cheaper then humans too, while humans focus on more important ones.
So try to answer these questions.
- Can AI help to improve productivity by automating routine tasks?
- Can AI help to automate processes to reduce errors?
- Can AI help to eliminate costly suppliers, and open you to a more competitive business model?
AI can help you differentiate in quality
Or does your organization want to become unique on dimensions that are important to customers, like quality?
For instance, IBM Watson uses artificial intelligence to help doctors diagnose patients. Machine learning algorithms learn from vast amounts of past data collected from patients, and medical literature.
That’s AI helping to improve the quality of the health system.
AI systems can help you transform customer experience by learning from large amounts of customer data to know more about their needs.
You could ask the following questions.
- Can AI help you reduce human error in critical customer-facing processes?
- Can AI inform your product development process with key insights generated from customer data?
AI can help you personalize products and services
AI can be your best friend when you want to target a narrow segment of customers.
Your business may decide not to try pleasing all customers with a single product.
That’s a focus strategy.
To serve such a segment, means giving them offers that are customized to their needs.
Think of the recommendation systems of Netflix, and that of Amazon.
They give every user that impression of, “I know what you’re looking for”.
And they all use AI to achieve this.
So you could ask the following questions.
- Can AI help to transform your business model, and focus your offers on a narrow segment of customers?
- Can you use AI to give a personalized experience to each customer, like Netflix does?
And to wrap it up…
Your AI initiative should align with your organization’s digital transformation strategy.
But in some cases you may be able to use AI strategies in ways different from your larger organization’s strategy. Or you may see where AI could support a whole new strategy for your company.
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