The benefits of implementing artificial intelligence (AI) into a company’s operations are becoming 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 in any business, no matter their size or industry.

In practice, however, this is easier said than done. When implementing AI into their operations, organisations must ask some difficult questions about their budget, such as which AI solutions are the most appropriate, and crucially, how they will ensure a strong return on their investment.

Although AI is improving in its scope and standard at a rapid pace, creating a bespoke AI solution that perfectly matches the needs of a company can still incur significant costs and resources.

As such, some decision-makers might struggle to reconcile these costs with the possible challenges of implementing AI; for example, a potential lack of compatibility and adaptability. This is perhaps why less than 15% of businesses are choosing to use AI at present.

That said, AI is becoming more affordable, thanks to the developing Application Programming Interface (API) economy in the AI arena. Crucially, this enables firms across all sectors to acquire AI solutions ‘off-the-shelf’, reducing the costs and resources that in-house AI development entails.

As such, what benefits can APIs deliver businesses, and how exactly does ‘off-the-shelf’ AI work?

How APIs drive off-the-shelf AI

Essentially, APIs help different forms of software, such as applications or programmes, 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 such as opening times, reviews and contact information and to display these on your screen.

Gradually, APIs have been harnessed by low- and no-code platforms to democratise technology that would normally require costly expertise to create and operate. To draw a parallel, only individuals with prior coding experience could operate computers in the past, until APIs like Microsoft Office were created.

Today, practically anyone can use a computer without having even the most basic coding or programming skills.

APIs are now doing the same for AI. With this in mind, off-the-shelf AI can be defined as third-party solutions that help businesses include AI features in pre-established web products, such as a mobile apps or company websites, to make them more ‘intelligent’.

Organisations can access cutting-edge algorithms and AI software to create their own solutions, without allocating a significant portion of their budget and resource to Research and Development and in-house development.

Artificial Intelligence for all

Current prices put the cost of developing a custom AI solution at an estimated cost of $20,000 to $1,000,000. On top of this, recruiting an in-house AI-professional or data scientist would increase spending by another $94,000 a year on average.

While these costs provide only a rough estimate, these fees are simply not viable for the majority of smaller firms and SMEs.

However, the emergence of low- and no-code platforms in the AI arena means that businesses can now effectively drag and drop the AI ‘building blocks’ that they would like to integrate within their web products.

This method of command, that doesn’t require coding ability, means that unique and tailor-made AI solutions can be created at a fraction of the cost, and without the need for building a new team from the ground up.

A secondary benefit, and one that further reduces costs, is that the management of the AI model can be outsourced to a third-party company which supplies on-the-minute access to AI specialists. Consequently, the need for in-house expertise is minimised even further, although access to this knowledge internally can be a valuable asset for firms that can afford it.

With the above in mind, it is no surprise that low-code approaches are becoming increasingly popular, with the market for development platforms expected to hit over $45 billion by 2025.

Navigating implementation challenges

Like any nascent technology, AI has its drawbacks. It is therefore vital that business leaders are aware of the potential challenges that they may face along their implementation roadmap.

Naturally, not all AI solutions will be suitable for every business, and a one-size-fits-all approach might not deliver the results or value for money expected from off-the-shelf AI.

Organisations should note that some off-the-shelf vendors fall short when it comes to adaptability and compatibility with legacy software. Business leaders should therefore factor in pre-existing technology into any decisions, ensuring that it is possible to integrate their chosen solution into their wider operations without significant obstacles.

For example, if a business requires AI to automate highly specific tasks, it can be difficult to adapt the AI programmes without purchasing the source code from the third-party vendor and hiring a data scientist to make the necessary tweaks to customize the solution.

Likewise, while a solution might look promising at face value, the algorithms might quickly become redundant as the software around it changes or its responsibilities increase in complexity.

Why APIs mean efficiency

That is not to say that API models do not bring significant benefits to business. Certainly, many are already unlocking the benefits of AI in this way.

A particularly promising use-case is AI’s ability to automate the mundane and time-consuming tasks that many workers currently have to carry out manually.

Low-code platforms, for example, offer firms a low-code approach to automation, allowing managers to coordinate staff, data and systems on one simple dashboard. This simplifies cumbersome data input and cybersecurity tasks, with AI taking on the grunt work, leaving staff with more time to work on bigger picture projects.

Likewise, Machine Learning (ML) and Natural Language Processing (NLP) are ripe for automation, providing businesses with some food for thought when it comes to how they manage their corporate knowledge. These solutions ‘read’ and analyse pieces of text, drastically cutting down the time spent on data analysis or mining.

By using a simple voice or text command, AI can aid data analysts when sourcing specific figures and statistics in large data sets quickly.

Indeed, the speed at which AI can complete tasks is perhaps one of the biggest advantages in the corporate world, and its efficacy will only become more agile and accurate as the technology matures.

Ultimately, business leaders will always be wary of investing significant amounts of money and resources into new technology – AI is no different. However, the organisations able to successfully leverage AI models and solutions can expect greater innovation and productivity than ever before.

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