AI is here and it’s here to stay. Reactions were, as with any major change, vocal and polarized. From the likes of Stephen Fry who called for kicking artificial intelligence out of art, to corporations who have embraced the opportunity to cut costs and become even more efficient. How will the new workforce partner with a coworker who doesn’t get sick, never needs to rest, and has the answers to all the questions in 0.2 seconds?
So, what exactly is AI?
Artificial intelligence is computer software that can accomplish humanlike tasks like learning, planning, and problem-solving. Not all AI software applies to most businesses, and some will be more vulnerable to this change than others. Some of them are creative industries like marketing, graphic design, customer support, and IT. SEO experts are already reconsidering their position in the new digital landscape, and the transition has only begun.
So, where can we apply the vast potential of AI when it comes to everyday workflow? Anywhere where large numbers of data need to be analyzed, systemized, and delivered, and where pattern recognition across data can result in some sort of comprehensive output.
Some of the most standard uses of AI are machine learning, cybersecurity, customer relationship management, internet searches and personal assistants.
Machine learning – that a machine can learn
Machine learning is a way for computers to learn from data and make predictions or decisions without being explicitly programmed. Imagine teaching a computer to recognize cats: instead of telling it exactly what a cat looks like, you show it many pictures of cats and let it figure out common features on its own. This process is similar to how our brains learn from experience. For example, in spam email filtering, a machine learning algorithm can analyze thousands of emails to learn patterns associated with spam, enabling it to identify and filter out new spam messages without explicit programming. In essence, machine learning enables computers to learn and improve from experience, making them more adaptable and capable of handling complex tasks.
Cybersecurity: Hacker’s worst nightmare
To fight robots we need robots, the Terminator movies taught us that years ago.
Nowhere is this more true than in cybersecurity. AI-like elements are already used across the web to analyze websites, and Google uses crawlers on this website as we speak. AI systems can identify cyberattacks, and stand on eternal guard against any suspicious behavior in the algorithm. You would need an army of humans to check and double-check huge chunks of data, while AI can do it in a heartbeat. These systems can also adapt over time by continuously learning from new data, staying ahead of evolving threats. Additionally, machine learning helps in the development of predictive models, allowing cybersecurity professionals to anticipate and proactively address potential vulnerabilities before they are exploited.
Customer relationship management
Most CRM management programs help you manage data while still needing a lot of hand-holding. People need to monitor and update these servers and it can become a painstaking job of constant input, input, input! AI can not only fill in some gaps, but it can also learn what worked best in the past an automize at least part of that drudgery, and do it effortlessly at scale. Let’s say you are a mobile carrier with millions of users. The system could utilize machine learning algorithms to analyze customer data and predict individual preferences. This could result in personalized product recommendations based on a customer’s usage patterns, leading to more targeted marketing efforts that could yield higher conversion rates then using a human designed offer to a customer segment, which is the usual way of doing things. Additionally, machine learning can be applied to predict and prevent customer churn by analyzing historical data to identify patterns associated with customers likely to switch to another service provider. This information allows the telecommunications company to proactively engage with at-risk customers and offer targeted incentives to retain their business.
Research
Even the way we Google things could change. The world’s most popular search engine is already showing us the most popular answers first and gives us snippets of conversations on Quora and Reddit instead of letting us click our way through it’s endless library of data. It’s easy to imagine how if you type in “Paris” on your computer, if you are a frequent flyer and a lover of cheese you will be shown the beauties of the French capital, while a fan of Paris Hilton will see loads of pink bags and the newest gossip. On the other hand, imagine software that can read any scientific article on the web in all the world’s languages and make a comparative analysis of the latest discoveries in cancer treatment, sort it, and send it like a newsletter to the world’s universities. Now, that’s useful!
Where there is data – there will be AI
Name a repetitive process based in the digital world, and AI is right around the corner.
Lawyers, salespeople, and customer support agents find themselves constantly responding to the same questions. The AI transition could take months, years or decades, and who knows what those periods of progress will bring. Until then it is crucial to strike a balance between humans and machines and continue cherishing the people on our teams who make everyday goals for us possible because no matter how much AI we integrate into our business models, at their core there will always be amazing people.
