If you base yourself on Hollywood, you probably have an unflattering picture of artificial intelligence (AI).
And rightfully so, killer robots have been terrifying us since the first time we went to a movie theatre on our own.
Who could possibly forget these red eyes?
But it’s 2022, and in the real world, AI and machine learning are actually our friends. A bit like R2-D2 and C-3PO, but way less retro.
Just think that 44% of firms using AI have reported a reduction in business costs in the departments implementing artificial intelligence.
But what does this implementation look like? Who’s doing it, and how can you do it, too?
That’s what we’re here to show you.
But first…
What Is the Difference Between AI and Machine Learning?
Before getting into the applications, we need to clear up a fundamental distinction between the two concepts we’ll discuss in our list: AI and machine learning.
What is Artificial Intelligence?
Artificial Intelligence, or AI, refers to any technology that replaces tasks that usually require human intelligence to complete. These could involve visual perception, speech recognition, decision-making, translation – basically any brainwork that required an actual human for 99.9% of our species’ history.
Some common examples of AI you probably use in everyday life include:
- Google Translate
- Siri and Alexa
- Your yearly Spotify Wrapped data
It’s a broad category, and it encompasses a lot of different technologies – including our next key term.
What is Machine Learning?
Have you ever heard the phrase “All elephants are grey, but not all grey things are elephants”?
The same principle applies to AI and Machine Learning, or ML.
Machine learning is a branch of AI that refers explicitly to systems that can learn from and adapt to the data being fed into them.
If you need an easy way to remember this definition, think of AI as a super-smart Cookie Monster that gets smarter and smarter with every new cookie it eats.
Any app that uses an algorithm uses ML – take Spotify as an example.
Spotify’s algorithm looks at what you’re listening to most and makes predictions about what you would like to hear next, generating new playlists and suggesting new tracks.
All clear on the difference?
Awesome sauce.
Now onto the star of today’s show, the most exciting AI and machine learning applications in use right now.
12 AI and Machine Learning Applications
1. Route planning & geopositioning
Dynamic route planning is one of the hottest ways to apply AI right now.
The most famous example is Google Maps, which uses anonymised location data from smartphones to analyse traffic on a given road. Google also acquired crowdsourced traffic app Waze in 2013 to incorporate user-reported traffic incidents in their algorithm.
This enables Google to:
- Show users areas where heavy traffic is present, and
- Calculate the quickest route to their destination on the fly
Pretty cool, eh?
And while us drivers have completely forgotten what it was like to travel pre-Google, we’re not the only ones benefiting.
Google Maps is also immensely important to local businesses.
Google Maps’ AI tools use natural language processing to allow users to make general searches in their local area, and get precisely the right results.
For instance, if you live in Perth and type in “print shop near me,” you can be sure you’ll see print shops from Perth in the results.
So, if you want to be found by locals, let this be your reminder to update your Google My Business account and master local SEO!
2. Dynamic pricing
Handling dynamic conditions is one of the major things AI is great at.
After all, it’s a freakin’ robot, and it can do everything on its own, except for clicking on an “I am not a robot” button.
As well as calculating and recalculating routes based on ever-changing data, machine learning can also be used to enable dynamic pricing.
The platform you’re probably most familiar with that implements this type of technology is Uber.
By cleverly combining driver and rider data to identify surges in demand, Uber can raise prices at precisely the right times.
Which is excellent for both Uber’s bottom-line and customers because:
- Drivers are incentivised to flood areas where there is high demand (i.e., outside nightclubs on Sunday morning at 3 AM)
- Stingy passengers are priced out of the market, reducing overall demand and ensuring that the people who really want a ride – and are willing to pay for it – get it
Uber’s ML (that’s machine learning if the acronym slipped your mind) even incorporates predictive capabilities. For instance, Uber knows you’re more likely to pay a higher price during a surge if your battery is low because you’re in more of a rush to get home, and this allows them to strategically prioritise you.
But AI-enabled dynamic pricing isn’t only relevant to rideshare companies.
It’s also applicable to other industries where fluctuating demand or supply can be remedied by changing prices, i.e., hospitality, tourism, entertainment, retail, electricity, and public transport.
For instance, an AI-enabled CRM system in retail could identify when old stock needs to be cleared out to make way for new products, and automatically notify customers of clearance sale prices. Your customers get a good deal, and you get a clear warehouse. Ideal!
3. Business analytics
Your CRM is a goldmine of sales data. Think about it – your CRM knows:
- Everything about you
- Everything about your clients
- How much you’ve talked to each other, and when, and how
- How long there was between each contact
- The amount of money they’ve spent
- And so much more
If you don’t have a strong CRM setup, all of this data is unstructured, confusing, and pretty much useless.
But with a well-designed AI integration in your CRM, you can structure your data.
Or in other words, you can model it in a way that allows you to make better-informed decisions about your business.
It would be next to impossible – or at least, extremely time-consuming – for an individual to manually synthesise all your company’s data.
Not only does AI within your CRM make the production of advanced analytics possible, but it also makes user interfaces far more intuitive. Take Zoho One’s Zia integration: it empowers you to ask questions in plain English and receive a visualisation of the answers.
For instance, if you entered “Show me monthly sales in 2018 by location and industries,” it would show you something like this:
With such easy access to this kind of information, your managers are way more likely to make the right business decisions, increasing performance and profits along the way.
And this leads us nicely to the next item in our list of AI and machine learning applications…
4. Decision support
Machine learning-enabled decision support has been around for years.
In healthcare, for example, clinical decision-making programs use machine learning to compare individual cases with a broad dataset in order to recommend avenues for testing and treatment.
This has been massively helpful to general practitioners, who see a huge variety of cases each day, as it supplements their knowledge with a database of comparison points and makes it less likely rare conditions go unnoticed.
However, decision support software is becoming increasingly complex and widespread across different industries.
Research shows that 85% of enterprises will combine human expertise with AI, machine learning, natural language processing, and pattern recognition to enable better decision-making, with the goal of achieving a 25% increase in productivity by 2026.
But no need to wait until then; many companies are already doing it.
Unilever has been using machine learning to streamline its recruitment and training processes for years, evaluating prospective employees with psychometric tests like the one below.
The consumer goods giant believes its AI recruitment method has saved them over 100,000 hours of human recruitment time in its first year alone.
A.k.a., it was well worth the cost and effort of implementing it!
5. Chatbots
We all know those little boxes in the corner of websites asking if we need help.
We might love them or hate them, but either way, they come in handy when we have a quick question we want answered in under 5 milliseconds.
And believe it or not, nowadays, they’re almost always AI and machine learning-powered chatbots.
People just weren’t fast enough at replying.
Chatbots, on the other hand, are lightning-fast, never go to sleep, and use natural language processing (NLP) to efficiently handle customer queries.
NLP sounds pretty technical (and it is), but all you have to know is that thanks to this technology, talking to robots is more like talking to C-3PO than R2-D2: no need for technical expertise or clunky shorthand.
Most chatbots are programmed to deal with basic or repetitive customer queries. The best ones use initial interactions to gather data that then helps them route queries to the appropriate human representative should that be necessary.
Chatbot technology is getting more and more sophisticated as time goes on – chatbots handled 68.9% of customer interactions from beginning to end in 2019.
Chatbots bring tremendous benefits for businesses and their customers.
For companies:
- They save time by handling simple or frequently-asked questions
- They direct queries to the relevant human representative to get them resolved faster
- This lower overhead costs and even boost revenue
And as far as the customer is concerned, you get the benefit of:
- An instant response
- 24/7 support
- Resolving a query without having to speak to anyone – which 40% of us prefer anyway
6. Fraud detection
Naturally, fraud detection is most commonly spoken about in the financial services industry, but it’s also crucial for any sales-focused business interested in speeding up transactions.
Machine learning is suited to fraud detection for several reasons:
- It’s fast. Too long a checkout process is responsible for 26% of cart abandonments, and machine learning can return an authentication request in seconds.
- It’s scalable. You don’t need to hire and accommodate new teams to run fraud checks if you have software doing it for you.
- It’s more accurate. Rules-based fraud detection systems have been around for a long time, but their rigid protocols meant that false positives were relatively common – for instance, blocking all transactions over a certain amount. ML is more dynamic and can respond to more subtle behaviours.
A prime example of ML fraud detection in action is when fraud detection software company Kount helped a client reduce chargebacks by 77%.
This project turned out to be both preventative, helping the company avoid losses, and enabling – disputed charges fell, and sales increased four years in a row.
Kount’s chargeback portal (Source)
7. Analysing technical documents
AI’s ability to automate complex processes comes in handy when analysing legal documents.
And thank the heavens. There are only a few things more boring than having to meticulously review hundreds of pages of legalese.
While AI is not yet capable of providing the full experience of hiring a human lawyer, it can undoubtedly streamline some tedious legal processes, for example:
- Legal research
- Reviewing documents
- Analysing and summarising documents
- Identifying precedent
- Projecting outcomes of litigation
This is not only relevant to legal firms, but to any business with – or in need of – a legal department.
The legal review firm AI Luminance used its software to successfully review Brexit Impact Assessment documents (sounds fun, right?) and reduce the time that would have otherwise been spent on this process by 75%.
Within two hours, the AI had pinpointed the relevant contract types and clauses in the documents and uncovered the potential risks the company was facing due to Brexit.
This did not cut out the legal process entirely, but it did identify the key areas for lawyers to look at and was able to assign workflows – eliminating huge chunks of manual work.
8. Process mining and decision automation
We know we bang on about business process automation all the time, but we really believe it’s the future of business.
And we’d rather be slightly annoying now than have to say, “we told you so” when you get crowded out of the market by more innovative companies.
Repetitive process automation (RPA) is all about achieving business improvement by automating boring processes that humans shouldn’t have to do.
It’s highly effective when it comes to sales force automation, speeding up processes like
- Contact management
- Dialling and voice calls
- Email and SMS marketing
Process mining can enable automation by identifying your business’s superfluous or clunky parts and eliminating them, creating a smooth line from A to B with no wasteful detours.
Indeed, 78% of people say that process mining is key to their RPA efforts.
Automating repetitive processes is where AI is at now – but where it’s heading is even more exciting.
AI is also increasingly being used in decision automation.
But don’t worry; this isn’t taking humans out of business altogether. It’s simply moving us one level up the chain from “repetitive manual tasks” to “logical decision-making”.
Let’s say you have to ship a bulk order of office chairs to a supplier.
You have two container types available, and you have to work out which one would be most economical to transport your order.
This isn’t a creative decision or even a particularly hard one. It’s all about the maths, and – given the right data – most people would probably make the same call.
This is exactly the kind of decision-making that AI can take off your plate, using image recognition software to work out the cheapest option.
It’s simple, easy, and leaves you free to deal with the high-value tasks you and your colleagues were hired to do.
9. AI and machine learning for copywriting
We’ve all seen really bad AI copywriting before.
It’s like when you hammer the predictive text function on your phone (incidentally, also a machine learning software) and end up with something like, “I am really excited to see you tomorrow I will go to the best day ever and ever again thank you.”
However, a handful of apps are gaining momentum when it comes to generating marketing copy, newsletter subject lines, social posts, and more.
Again, natural language processing is being put to work here, using machine learning technology to generate text from whatever information you give it.
This might be:
- A set of keywords
- A starting paragraph or excerpt
- A full article that the software then edits
For instance, Writesonic only requires a product name and a shortlist of characteristics to generate product descriptions:
There are significant pros to using copywriting software… but there are also some cons.
Take a look:
| Pros | Cons |
| AI copywriting software is often cheap and even free, making it a compelling choice if you cannot afford to hire a copywriter | Machine learning generates text based on what already exists – the originality you get from a human writer is missing |
| It saves you time and energy | Subtlety is not its strong suit, and AI writing often lacks a human touch |
| Many of the top apps produce serviceable content that, depending on its length, may only need minor edits | There’s often the need for a significant amount of editing once a text is generated, which lowers the software’s ROI |
Though AI is unlikely to replace real writers any time soon (we write it sweating profusely), it is developing into an incredible tool for generating instructional or technical copy, or even for editing existing content written by a human.
Grammarly is a good example of an AI editing tool that’s widely used by freelance copywriters and marketing agencies worldwide.
10. Customer churn modelling
It costs 5 times as much to acquire a new customer as it does to retain an existing one.
And that’s why working on customer satisfaction should be one of your top priorities if you’re interested in safeguarding your bottom line.
You should turn to machine learning, and more specifically, to the incredible level of predictive analytics it offers businesses.
Advanced churn modelling shows you how well – or how poorly – you’re doing at keeping your customers happy by unveiling all the juicy details about:
- How many of them are leaving
- When they’re leaving
- Why they’re leaving
- Complaints or dissatisfactions that triggered the exit
It also lets you dig down into specific customer profiles by using segmentation tools, where you can collect qualitative data to assist with your anti-churn initiative.
Let’s say you find that the longer a customer’s support request goes unanswered, the more likely they are to drop off and search for alternative solutions.
You could take action by implementing a chatbot to respond to and direct their queries. And you could easily convince Finance to approve the investment because they’ll love the “less wait time = less churn” formula.
Another example, if you’re using Zoho CRM, you can jump over to the transaction reports tab, see who’s in line for renewal, and then proactively contact them to prevent drop-off and show them you care about their business.
11. Biometric security
Biometric security refers to any security protocol that requires you to identify yourself by using one or more of your unique physical features.
These include:
- Facial recognition
- Fingerprint scanning
- Voice recognition
- Posture and gait scanning
It’s already commonplace in consumer technology.
Most smartphones have a fingerprint scanner, and many finance apps like Apple Pay use thumbprints to authenticate transactions.
This type of biometric Identity Access Management (IAM) is also becoming more common in B2B tech, particularly in companies dealing with highly sensitive or confidential data.
Zoho One already offers a biometric attendance-checking integration for use in meetings, and in 2018, businesses in the US and Europe were using biometrics for numerous things:
| Item secured by biometric technology | Businesses currently doing it | Businesses planning to in the next 5 years |
| Smartphone | 46% | 5% |
| Laptops | 25% | 10% |
| Tablets | 22% | 6% |
| Time clock system | 17% | 3% |
| Server room door locks | 11% | 7% |
| Other door locks in the office | 9% | 5% |
| Apps with sensitive data | 8% | 8% |
We, for one, welcome the extra layer of security, knowing full well that it could save us from dangerous hackers hell-bent on stealing our automation secrets.
12. Integration with the Internet of Things (IoT)
We’re well aware that all of the software on this list was once the stuff of science fiction.
However, in our opinion, the AI and machine learning applications really moving us into the future are ones where AI is integrated with the Internet of Things (IoT).
And yes, we also think that “the Internet of Things” might just be the coolest sounding name we’ve ever heard.
But that’s not why it’s revolutionary.
Its true power lies in its potential to build dynamic digital environments that predict and adapt to your every move, both at home and at work.
There are loads of examples of how this might work or how it’s already working for consumers.
Here’s two:
- Tesla’s self-driving car uses AI to predict the behaviour of pedestrians and other drivers based on the time and weather, and IoT to communicate with other vehicles.
- Smart electricity meters use IoT and AI to heat your home more efficiently based on your work schedule and the weather outside.
There is a myriad of applications for businesses, too.
For example, AI and IoT are being used to increase energy efficiency in Google’s immense data centres, where AI works to predict suitable operating conditions and then relies on the IoT to adjust the air conditioning in server rooms.
One word: wow.
And with this sense of awe lighting up our faces, we’re set to wrap up today’s article.
Key Takeaways
It’s time to terminate all those bad thoughts about AI.
They’re holding your business back because 21st century AI and machine learning are one of the best ways to:
- Keep up with the dynamic demands of the market (and yes, we’re singling out Millennials and Gen-Z’s)
- Get detailed insights into your business – and act on them, too
- Retain more of your customers
- Increase security and cut down on cyberattacks
- Automate tedious, life-sucking tasks
- Make way more money
With all these benefits on offer, you’re surely eager to supercharge your processes and workflows with AI and machine learning.
But, as we’re sure you can tell, it’s a complex task.
You can’t just go to Kmart and pull AI off a shelf. You need an expert by your side to help you get it just right.
And at Human Pixel, we’re here to make that happen because our speciality is marrying cutting-edge technology with the human touch. So take a look at our work, read more of our blog, and when you’re ready, contact us to discuss how AI can move your business into the future.
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