The benefits of implementing artificial intelligence (AI) in a company’s operations are increasingly difficult for business leaders to ignore.
With productivity, speed, and accuracy all likely to improve with the help of AI, these benefits can be felt across any business, regardless of size or industry.
In practice, however, this is easier said than done. When implementing AI in their operations, organizations need to ask themselves tough questions about their budget, such as which AI solutions are most appropriate and, most importantly, how they will ensure a strong return on investment.
Although AI is rapidly improving in its scope and standards, creating a bespoke AI solution that perfectly matches a business’s needs can still entail significant costs and resources.
Thus, some decision-makers might find it difficult to reconcile these costs with the possible challenges of implementing AI; for example, a potential lack of compatibility and adaptability. Maybe that’s why less than 15% of companies choose to use AI at present.
That said, AI is becoming more affordable, thanks to the development of the application programming interface (API) economy in the field of AI. Crucially, this allows companies in all industries to acquire AI solutions “out of the box”, thereby reducing the costs and resources involved in developing AI in-house.
As such, what benefits can APIs bring to businesses and how exactly does “out-of-the-box” AI work?
How APIs Drive Out-of-the-Box AI
Essentially, APIs help different forms of software, such as apps or programs, to communicate with each other. For example, when you check Google Maps to find a local restaurant, an API helps your phone communicate with a server to help you find information like hours of operation, reviews, and contact information and display it on your screen.
Gradually, APIs were leveraged by low-code and no-code platforms to democratize a technology that would normally require expensive expertise to build and operate. To draw a parallel, only people with prior coding experience could use computers in the past, until APIs like Microsoft Office were created.
Today, virtually anyone can use a computer without having even the most basic coding or programming skills.
APIs now do the same for AI. With this in mind, off-the-shelf AI can be defined as third-party solutions that help companies include AI functionality in prebuilt web products, such as mobile apps or enterprise websites, to make them “smarter”.
Organizations can access state-of-the-art algorithms and AI software to create their own solutions, without allocating a significant portion of their budget and resources to research and development and internal development.
Artificial Intelligence for everyone
Current pricing places the cost of developing a custom AI solution at an estimated cost of $20,000 to $1,000,000. On top of that, hiring an AI professional or data scientist would increase expenses by an additional $94,000 per year on average.
While these costs only provide a rough estimate, these fees are simply not viable for the majority of small businesses and SMEs.
However, the emergence of low-code and no-code platforms in the AI space means that companies can now efficiently drag and drop the AI ”building blocks” they want to integrate into their web products.
This method of ordering, which does not require coding capability, means that unique, bespoke AI solutions can be created at a fraction of the cost, and without the need to build a new team from scratch. .
A side benefit, and one that further reduces costs, is that the management of the AI model can be outsourced to a third-party company that provides instant access to AI specialists. Consequently, the need for in-house expertise is further reduced, although access to this knowledge in-house can be a valuable asset for companies that can afford it.
Given the above, it’s no surprise that low-code approaches are becoming increasingly popular, with the development platform market set to hit over $45 billion by 2025.
Meeting Implementation Challenges
Like any emerging technology, AI has its drawbacks. It is therefore essential for business leaders to be aware of the potential challenges they may face throughout their implementation roadmap.
Naturally, not every AI solution will suit every business, and a one-size-fits-all approach might not deliver the results or value for money expected from out-of-the-box AI.
Organizations should note that some off-the-shelf vendors fall short when it comes to adaptability and compatibility with legacy software. Business leaders must therefore consider pre-existing technology in any decision, ensuring that it is possible to integrate the chosen solution into their wider operations without major obstacles.
For example, if a business needs AI to automate very specific tasks, it may be difficult to adapt AI programs without purchasing the source code from the third-party vendor and hiring a data scientist to make the necessary changes. personalization of the solution.
Likewise, while a solution may look promising at first glance, algorithms can quickly become redundant as the software surrounding it changes or its responsibilities increase in complexity.
Why APIs Mean Efficiency
That’s not to say that API patterns don’t bring significant benefits to businesses. Certainly, many are already harnessing the benefits of AI in this way.
One particularly promising use case is the ability of AI to automate mundane, time-consuming tasks that many workers currently have to do manually.
Low-code platforms, for example, offer businesses a low-code approach to automation, allowing managers to coordinate people, data, and systems on a single dashboard. This simplifies tedious data entry and cybersecurity tasks, with AI taking over the heavy lifting, leaving staff more time to work on bigger projects.
Likewise, machine learning (ML) and natural language processing (NLP) are ripe for automation, offering businesses food for thought on how they manage their enterprise knowledge. These solutions “read” and analyze chunks of text, dramatically reducing the time spent on analysis or data mining.
Using a simple voice or text command, AI can help data analysts quickly find specific numbers and statistics in large data sets.
Indeed, the speed at which AI can complete tasks is perhaps one of the greatest advantages of the corporate world, and its efficiency will only grow in agility and accuracy as the technology matures. .
Ultimately, business leaders will always be hesitant to invest significant sums and resources in new technologies – AI is no different. However, organizations able to successfully leverage AI models and solutions can expect greater innovation and productivity than ever before.
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