APIs or custom AI? Everything businesses need to know before taking the leap

The call to implement Artificial Intelligence (AI) is becoming difficult for businesses to ignore. Offering the promise of increased organizational productivity, speed and accuracy, some applications can be greatly beneficial to firms across a wide variety of industries and sectors.

That said, companies will naturally have some difficulty deciding on how best to implement AI, and where to achieve the best return on investment in innovative technology. Given the inherent difficulties involved in building an AI solution, finding a solution that is the perfect fit can be a mammoth task, involving great resource and even greater costs. For some, the drawbacks might even outweigh the benefits; perhaps this is why less than 15 percent of firms have implemented AI in their operations.

However, the emerging Application Programming Interface (API) economy in the AI arena is enabling a wider range of businesses to access off-the-shelf AI solutions. In short, this means AI capabilities, without necessarily having to commit to costly in-house development.

So, how can businesses reap the benefits of APIs, and how does off-the-shelf AI work?

Explaining APIs and 'off-the-shelf' AI

In short, an Artificial Programming Interface (API) helps different applications and programs to communicate with each other. For example, when you sign into a social media app on your phone, the app would communicate with an API to acquire your account details from a server and return the data back to the app, allowing you to sign in.

Increasingly, APIs have been leveraged in low- and no-code platforms to democratize technology that would usually require an expert to operate, creating a 'drag and drop' approach to programming. In the computer arena, for example, until the emergence of APIs like Microsoft Office, operating a computer was a task that only an individual with coding experience could carry out. Now, almost anyone can operate a computer without prior programming knowledge.

So, why is this relevant? Simply put, the dawn of off-the-shelf AI is setting another revolution in motion.

These third-party solutions enable businesses to add AI features to their pre-existing applications, or other web products, to make them more 'intelligent'. They provide organizations with a collection of algorithms with which they can build AI models without costly spend on R&D and in-house talent.

For instance, a small business without access to hordes of customer data could use AI-powered APIs to personalize their website, online store or customer self-service portal based on the location, time zone or habits of customers. As a result, firms can improve their customer service overnight simply by installing software to carry out the task.

Democratizing Artificial Intelligence

Currently it is estimated that the cost of a complete custom AI solution can be anywhere between $20,000 to $1,000,000, whilst recruiting an in-house data scientist could cost around $94,000 a year. Whilst costs can obviously vary a great deal, these figures show that integrating AI often prices out firms who do not have the budget to develop a custom solution from scratch.

Fortunately, off-the-shelf AI APIs can empower more businesses to leverage AI, with the low-code/no-code movement playing an integral role. Namely, businesses can use low-code/no-code platform interfaces to develop their web products by simply dragging-and-dropping AI 'building blocks' to construct a tailored product.

Using methods of command that do not require coding, businesses remove the need to hire an AI team, as well as significantly reducing the time it would take to develop unique algorithms. Indeed, this is an increasingly popular approach, underscored by the predicted growth of the low-code development platform market -- which is due to reach over $45 billion by 2025.

Another benefit of a third-party AI solution is that the management of the programs will be outsourced to an agency, freelancer or contractor. While in-house AI management brings the benefits of a team that knows the ins-and-outs of the business, outsourcing often costs less than retaining a dedicated AI team. Moreover, it connects businesses with more experienced data scientists than they might normally be able to hire or afford.

Avoiding a 'blanket' approach

As with any form of technology, implementing AI cannot be done with a one-size-fits-all approach, so business leaders should be aware of some of the pitfalls that firms can be subject to.

Indeed, off-the-shelf AI can be limited in its adaptability and compatibility if decision-makers do not choose the right tools. For example, firms with pre-existing software might struggle to find compatible AI solutions that integrate well with their wider resources and internal systems.

Likewise, if businesses are to automate a great deal of unique and company specific processes, an off-the-shelf AI program may not be appropriate. It is not always possible to adapt the algorithms from these products, without purchasing the source code off the third-party vendor. So, a solution that might work when the business initially invests in it might become obsolete as the company grows.

Unlocking the benefits of APIs

Despite this, many businesses are already benefiting from off-the-shelf AI.

For instance, AI can be incredibly useful when automating some of the more time-consuming and cumbersome functions of running a business. Low-code platforms like Appian enable businesses to coordinate all their staff, data and systems on a single interface, automating anything from data input to fraud detection. The end-result is that employees can focus on bigger picture tasks, allowing AI algorithms to look after the minutia.

Machine learning (ML) and natural language processing (NLP) AI solutions which 'read' and analyze pieces of text, provide another interesting use-case, allowing firms to boost their knowledge management abilities. For instance, data analysis could become a lot easier with introduction of AI assistant solutions (think Alexa or Siri), which can be used to find key figures in large data sets, simply by inputting a command by voice or text. As such, the future of AI in the world of business looks rather like the present, as AI’s competency to automate tasks increases in efficacy.

Final thoughts

As with any nascent technology, investments into AI should not be made on a whim and companies must ensure that they will be able to embed AI into their day-to-day processes effectively. Those that do so successfully, can look forward to a more innovative and efficient way of carrying out their operations.

Photo Credit: Sergey Nivens/Shutterstock

Nikolas Kairinos is the chief executive officer and founder of Soffos, Inc., which is on a mission to democratize Artificial Intelligence (AI). Soffos offers its APIs and advanced Natural Language Processing (NLP) PaaS (Platform as a Service) solutions to businesses of all kinds that are looking to build cutting-edge apps, without the cost, Research and Development (R+D) commitments and complications that come with creating AI in-house. Based in Austin, TX and founded in 2018, Soffos is unique because it will auto-assimilate and ‘understand’ knowledge without human oversight, allowing it to deliver value immediately without weeks of configuration.

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