Artificial Intelligence and Mobile: a Primer For Business People

Partner Post - Fliplet Quickly create mobile apps for your business

Posted: December 21, 2016

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Fliplet, is a UK start-up that helps businesses create apps for internal use explains the eight core elements of an enterprise mobility strategy. Jamie is Sales Director at Fliplet. Having worked in publishing for seven years he’s had to grow and adapt with the constant changes in digital and has learnt to love every minute.He’s passionate about new technologies, digital media and anything that presents a physical or mental challenge… preferably both.

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So, what is AI?

Artificial intelligence, a.k.a. machine intelligence, is the development of computer applications that can process information, learn and plan in a way that’s closer to human reasoning than traditional computing.  In essence, it’s the creation of computers that can ‘think’ for themselves.

Chances are you’ve already come across some form of AI. For example, you’ve probably interacted with ‘simple AI’: the power behind virtual personal assistants such as Apple’s Siri, Windows’ Cortana and Google Now, which help users find information using speech recognition and adapt their “conversations” based on data from their previous actions. In other words, they ‘learn’ about the user.

There are five key branches to AI that you should be aware of:

Machine learning

Machine learning facilitates a “computer’s ability to learn and essentially teach itself to evolve as it becomes exposed to new and ever-changing data.”

Your Facebook News Feed is an example of machine learning. It uses your interactions with content to determine what to show you in the future. For example, if you ‘like’ a lot of posts from one particular page or friend you will see more of them in the future.

Natural language processing

NLP can be described as a way for computers to “analyze, understand, and derive meaning from human language in a smart and useful way.”

Two great examples of NLP are spam filters and language translation applications. They both “read” huge quantities of human input and learn to spot patterns in the data. That’s how Google Translate keeps getting better and more accurate through user feedback, and your spam filter learns the new language that spammers are using to get through to your inbox.

Applications are now getting better at understanding spoken word too, and responding in ways that sound more natural and less robot-like to us.

Predictive APIs (Application Programming Interface)

This is where the democratisation of AI is happening. Predictive APIs are programmes that plug into a company’s data (think Big Data) and, by processing that data they spot patterns, predict outcomes and give you recommendations on what to do next.

Predictive APIs are being built by all sorts of companies and for all sorts of purposes, but a common example in business is sentiment analysis. Your marketing department probably has access to a predictive API application like BrandWatch or Crimson Hexagon, which crawls data from social media channels to measure how people feel about your brand. Most Predictive APIs are used in companies via a MLaaS, or Machine Learning as a Service, which helps make sure that the API is evolving alongside your and your data’s needs. As a bonus, predictive APIs “offer the flexibility of deciding to host machine learning in the cloud, in-house or both, giving developers the freedom to work in the language and tools they want.”

Machine vision

Machine vision systems are “devices that capture and analyze visual information, and are used to automate tasks that require “seeing”…”

In essence, this entails the use of cameras and lenses to carry out image-based inspections for further analysis.

Context-aware computing

Gartner defines context-aware computing as a way of processing “situational and environmental information about people, places and things to anticipate immediate needs.”

For example, context aware computing might be used in a mobile app to serve you different content or features depending on whether you’re sitting in the office, commuting without a mobile signal, or near the location of a relevant business.

Mobile and AI: The perfect combo

Exciting things happen when AI is paired with business mobile apps, and tech-savvy enterprises are capitalising on the advantages it can bring.

Every single business is struggling to make sense of their Big Data, but those making strides to identify patterns are rapidly getting ahead of the competition, and that’s where AI comes in. Now, combine the power of AI mobile apps and you get the perfect combo: You are now capturing data from your clients and employees right from their most personal device at the precise time of interaction, and at the same time you’re learning from it and finding ways of acting upon it immediately.

Using machine learning technologies, enterprise apps can be optimised to bring even better results than anyone previously thought.

Bear with us while we show you a very powerful example. Suppose you have a pitching or sales presentation in the shape of a mobile application. You’ve configured a predictive API on it, and this is what it does:

  • Your potential client is spending a long time on a particular section of the presentation. They’re interacting with it and are clearly interested. Machine learning steps in to automatically pull more information about that section from your website, expanding the section so that your potential customer has even more to read about it.
  • Your potential client left the meeting but, an hour later, opens your presentation again. Your API immediately informs your sales team of their interactions and predicts whether they are interested (spending a while on the pricing page, for example) or not really (only opened the app for 5 seconds, probably a mistake).
  • Your potential client opens the app from a location near one of your foreign offices. It immediately pulls information about the foreign office’s capabilities.

This is a specific, tangible and immediately available example. It’s not science fiction, it’s just business.

Now of course there are more basic ways of using AI too. For example, apps can be configured to learn a user’s behaviour and lock themselves if they determines that the user is behaving erratically. Or it could suggest useful features an employee could be using but isn’t, based on analysis of their app use patterns.

The best mobile apps will use some sort of AI feature to maximise their capabilities, and industries of all kinds are becoming increasingly switched-on to the benefits of AI-powered apps – especially with a mobile workforce that continues to rise at pace.

According to Information Week, half of large enterprises are currently experimenting with AI projects. Here are some of the most popular business applications:

  • Predictive analytics – gaining real value from data and using it to make decisions
  • Automate manual, repetitive tasks – reducing manpower
  • Voice recognition and response
  • Automated written communications and reporting.

AI Mobile Apps and the Future of the Enterprise

The benefits of AI technology across the enterprise are far from being fully realised, so it stands to reason that there’s huge interest in AI among businesses at the moment.

Major players in the technology industry are already doing their part: Bank of America, for instance, is currently developing Erica, a “virtual assistant” that can give financial advice based on a customer’s spending patterns through the bank’s app. Gartner predicts that by 2018 the world’s top 200 companies will be exploiting what they call “intelligent apps” — it’s only a matter of time.

This movement is being boosted by tech giants including Facebook, Google, IBM and Microsoft, who are actively releasing machine learning to open source projects. Google’s own Prediction API is already being “taught” and used by thousands of developers and has extensive tutorials and documentation, ready to put to use by anyone. Right now, AI is no longer a privilege exclusive to the large enterprise: through these open source technologies smaller players can start developing their own intelligent apps.

And if you still think AI is out of your apps’ reach, consider that you might not be aware that you’re already using AI in your company. In a recent study by Narrative Science, 88% of respondents described making use of Artificial Intelligence in the workplace but only 38% of them were aware of it.

It only takes awareness for innovation to be sparked. If your company, like most, is already using some AI solutions, it’s very likely that they come with an API that’s ready to be plugged in to your mobile apps, so you could be ready to exploit its benefits at this very moment.

As businesses strive for competitive edge it’s a race to the top, and digital innovators are leading the field. In 2017, make sure you consider how AI could help you get more from your mobile audience.

At Fliplet we are at the heart of helping businesses create their own mobile apps without the need for developers or complex processes. We are excited to start plugging machine learning into apps and witnessing the sheer power of what it can do for businesses.

Would you like to see what Fliplet can do? Visit us.

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