Over the last 5 years, the notion of Artificial Intelligence (AI) has become ubiquitous. Every software development provider implements AI (whatever hides behind this) and machine learning (ML) algorithms for almost every product and service they release.
AI is embedded into industrial robots to accurately execute complex tasks, financial software applications, autonomous cars that are loomed to impact how we drive, tiny surgery equipment, and even Apple to pick music for us—generally, AI has branded itself as an integral part almost everywhere.
Moreover, nearly half of all businesses that now use some form of AI say it helped them outperform opponents.
Businesses using AI in manufacturing, for instance, see a 12% performance improvement over those that rely solely on traditional methods.
AI tools in sales, in turn, have proven to be highly efficacious, with reports showing up to a 50% increase in leads and a 60% lessen in customer service expenses.
By the way, according to Statista, the artificial intelligence market size is expected to reach $184 billion by the end of 2024, showing an annual growth rate of 28.46%.
It would seem that with such growth, every company wishing to strengthen its position in the market is simply obliged to implement AI into its processes. But is this really so?
According to Forbes, most people don’t fully believe in AI: 67% don’t want AI to make life or death decisions in conflicts, 64% don’t like AI as a jury in a dispute, and 57% don’t want AI to fly aircraft.
People also feel that humans will do a better job in a range of activities, such as investigating corruption, voting, administering medical care, writing laws, etc.
This is largely supported by the fact that many manufacturers use AI as little more than a marketing ploy when in reality their tools do not work as expected.
So is it worth investing a good part of hard-earned capital into AI-driven apps? How to calculate whether they will pay off? And whether they will pay off at all.
What Stands Behind AI? Does AI Really Work?
At its central part, AI is powered by a few key technologies. The major one is machine learning, which gives systems the command to learn from data.
Rather than being told exactly what to do for every situation, these systems spot patterns in the tons of records and make prognoses or conclusions based on those details.
For example, machine learning can advise products based on shopping history or even notice scams in international transfers.
Next, it’s neural networks—a mechanism modeled to mimic the human brain’s works. They have layers of “nodes” that pass information along, learning more as they go to help the system, for example, recognize images or comprehend speech.
Another big part of AI is computer vision, which helps machines literally “see” images or videos.
Lastly, all of this wouldn’t ever be possible without big data, which gives AI the information it needs to memorize and improve. The more data a smart system has, the better it gets at whatever it’s doing.
What Are Artificial Intelligence Apps? How Do They Differ From Regular Apps
Basically, an AI application is a type of software that uses artificial intelligence to carry out chores that normally need human smarts.
For example, it might recognize emotions in photos, understand oral speech, or suggest items a person might like based on their past behavior.
What makes artificial intelligence apps different from regular ones is their ability to remember complex notions and adapt to certain conditions.
Regular mobile/desktop apps normally follow fixed instructions and never change. AI apps can memorize new data and make judgments on their own, showing practical cleverness.
What’s Wrong with AI Apps?
This is a point why many people refuse to invest in and develop AI software. One major issue is the miscalculations AI still makes.
In November 2021, Zillow decided to stop its Zillow Offers program and lay off 25% of its staff, about 2,000 workers, because their machine learning algorithm for predicting home prices was making mistakes.
Without going too deep, the program aimed to buy, renovate, and quickly resell homes overreached itself, which cost the company $304 million loss due to buying homes at higher prices than they could sell them for.
Zillow ended up shutting the program down as its CEO, Rich Barton, said fixing the algorithm was too risky.
Another issue is price. How much does it cost to develop an AI app? Honestly, a lot. Custom software development is always pricey both in terms of initial input and ongoing upkeep, especially if it requires a lot of data or sophisticated technology.
Finally, adding AI to your current systems can be a real pain. Getting a smart app to work with what you already have can be tricky and might need some extra know-how or tweaks.
What Businesses Can Get if Developing an AI App
Indeed, creating an AI app can help enterprises in several ways. First, it can save time by automating routine chores that normally take a long time to accomplish.
For example, an AI chatbot can answer customer questions 24/7, so you don’t need as many agents in your customer assistance department.
AI apps can also provide business prognoses and spot trends. This means businesses can get reasonable insights about what customers want and how to tweak their tactics.
Finally, AI app development can give a business a competitive edge by presenting premier services (it’s all about marketing again), whether it’s anticipating when equipment might break down or improving commercial messages to end customers.
3 Reasons Why Investing in an AI App is Totally Worth It
If you’re thinking about moving towards AI, it could totally be worth it, especially if your company suffers from the below issues. Got too many repetitive tasks that eat up all your time? AI app development can take care of those for you.
If you’ve got a lot of data but don’t know how to use it or have no time to process it, an AI app can examine it and give you valuable insights to make wiser decisions.
Lastly, if you want to offer a more personalized approach for your buyers, AI can help by giving exclusive tips and suggestions and targeted marketing.
How to Build an AI App and Reduce Costs
AI project managers often misjudge the full cost of AI systems. There are many different sides that go into thinking about the total cost of an AI project.
One of which has to do with making up brand new or acquiring your AI models. If you need a right-away and the cheapest solution, go for someone else’s already-built model. It’s already available and affordable but might lack precision and accuracy.
If you need a quick and cheap solution, go for a ready-made model. It’s affordable and easy to get but might not be the most accurate. For more precise and advanced tasks, it’s better to hire expert AI app developers, like the team at SCAND.
Just remember, it’ll take time and money from data scientists to get it right, and if you want to dive into prompt engineering and create prompts for the model, that’ll add even more time and cost.
And if you’re planning to build a RAG application or any other solution on top of it, you’ll need extra development time as well.
Though the initial investment may cost you a pretty penny, it will save you from the development headache and minimize resources in the long run.
To mitigate the budget blow, however, you can start small (create a Minimum Viable Product, MVP) and expand it if it shows satisfactory results. Of course, for every firm, how small to begin is going to be different, but in any case, it will allow you to fully control project costs.