4 Reasons for Custom AI Development
Any company that considers deploying an AI-powered solution usually faces a choice whether to use an out-of-the-box one or to build an entirely new one from scratch.
Even though readymade solutions have their advantages, sometimes it’s just not enough for a business. So when does a company should design its own AI-powered product and how to know if the time is right for it? Let’s have a closer look.
The benefits of Artificial Intelligence
It is impossible to discuss the creation of an AI solution without realizing what kind of benefits it brings to a business. Though the list of these benefits is really vast, we will focus on the core advantages.
Robotic Process Automation
Robotic Process Automation is an immense benefit that has proved to be of great advantage for companies within different industries. The main idea behind RPA is to replace the mundane manual performance of certain tasks with specifically programmed machines. Such an approach significantly speeds up the task performance, eliminates the need to involve human employees with routine and minor tasks, and leads to higher accuracy and productivity.
Better accuracy and fewer risks
Whether it is a smart chatbot, an RPA solution, or an advanced data processing, an Artificial Intelligence product significantly increases the accuracy of the performed operations and minimizes the chances of an error. Due to the ability of AI to process and manage complex data sets, it handles massive amounts of data much more efficiently than human employees do and it does not make such common mistakes as typos, missing entries, etc.
Detailed data analysis
Artificial Intelligence is capable of processing huge data sets, identifying hidden patterns and dependencies, and overall delivering stellar results when it comes to working with Big Data. With an AI-driven tool, companies can take their data management to a brand-new level and gain more meaningful insights from it.
What is a custom AI solution?
Now that we are clear on the benefits that AI brings to a company, let’s move on to our initial question - the creation of a custom AI solution. But what is it exactly?
A custom AI solution is a company-specific solution powered by the Artificial Intelligence technology. The main idea of such a solution is to solve a specific problem within the company and to provide the company with a unique set of tools.
There are two types of custom AI solutions:
- An existing (or open-source) solution that is configured as per your needs,
- A solution created entirely from scratch.
The choice will depend on your needs and budget so keep that in mind before starting to work on the solution. And now is a perfect time to ask: do I really need to create a solution from scratch? The answer will be: it depends.
Below are four main reasons why you might want to develop AI-based products from scratch. Note that these reasons do not apply to all companies as each company has an individual set of requirements.
Reason #1: AI as a critical competitive advantage
One of the reasons why a company decides to develop a unique AI solution is because the company’s processes are built around it. As an example, think of Netflix with its smart recommendation system or Uber with autonomous vehicles.
In these examples, companies rely on AI heavily and without AI the company will not have a distinct competitive advantage. Hence, if your business revolves around a unique idea and AI can become your core competitive advantage, it will be a good idea to create a custom solution from scratch.
Reason #2: Long-run profit from AI implementation
When choosing between a readymade or a custom solution, one of the questions to answer is whether you want AI to help you resolve certain issues right now or to impact your business in the future.
If you need immediate results and do not expect AI to heavily impact your business in the long run, then a readymade solution is perfect for you. However, if you plan to seriously adjust your processes and constantly derive value from the AI technology, it would be better to develop a custom solution and tweak it in such a way that it brings value to your specific business for a long period of time. In this way, you can be sure of getting a return on investment and steadily growing your business in accordance with the set strategy.
Reason #3: Lack of needed configuration
The main issue with all readymade solutions (whether AI-based or not) is that they have a preset configuration that can be really tricky to change. Since such solutions are aimed to solve certain problems in general, they cope excellently with a limited scope of tasks but may not be suitable for your specific company.
Therefore, if you have unique business requirements and readymade solutions do not resolve them, a custom-made AI product is the option that you need. Otherwise, you might spend more time and money on trying to configure the existing solution and to make it work together with the systems that you are currently using.
Reason #4: Availability of resources
Another valid argument in favor of building your own custom AI solution is actually the availability of resources needed (i.e. data engineers, a proper infrastructure, a big database, etc.).
For years, the biggest bottleneck for the companies willing to adopt AI was the lack of the needed resources. As some experts described the situation, “a few were serving many”, meaning there were a few big industry giants that offered their AI products to other companies. But now AI is becoming more and more available so there is a good chance of building your own custom solution on the condition that you have all the resources.
Building an AI product: challenges to know about
Being a rather sophisticated technology, Artificial Intelligence brings certain challenges that can turn into project stoppers in the future. Thus, it is important to learn about these challenges in advance so they do not impact the development process.
Requirements for specialized hardware
Deep learning is an AI subset that implies the process of “learning” something by a machine. And in order for the machine to efficiently learn and produce accurate results, it needs massive data sets. This, in turn, demands a lot of computational power.
If we take an average computer, it will probably be able to process about 100,000 parameters within a few minutes or hours. But if we talk about deep learning, it requires billions of parameters to process - and no average computer is able to handle that.
This is why companies that work with AI deploy GPUs - graphics processing units that are designed for efficient AI model training. And the use of a GPU comes with certain challenges: you need to perfectly understand its architecture, have needed engineering skills to manage and configure it, and it should also be integrated with the rest of your system. All this should be kept in mind before you start planning the AI product development.
The requirement for big amount of data
As said above, AI models require massive data sets to use for learning. So if you consider building an AI solution, one of the first things to take care of is to check whether you have enough data to “feed” to your AI model.
Note though that you cannot just feed raw unprocessed data to the model. In order for the AI model to use it, the data needs to be processed, brought to a single format, and properly arranged. And this also requires quite a lot of time and resources.
Black box problem
One more common challenge associated with AI is the black box problem. In simple words, it means the following: you cannot really explain how exactly a machine came to a certain conclusion because its “thinking process” is hidden.
While it may not be a big deal for some companies, the lack of transparency can become a real issue for others. For example, if there is an AI-powered lending solution that can independently evaluate the borrowers and make decisions on whether to approve a credit or not, the borrowers can ask the company to justify its decision. And don’t forget about the GDPR regulation that demands companies to explain how exactly the customers’ data is used.
Hence, the black box problem is a serious issue that can even cause major financial losses if not handled properly.
Artificial Intelligence is a highly beneficial technology that can bring a competitive edge to your company and take it to the next level. At the same time, it requires significant investment in terms of time, resources, and finances.
If you decide to build a custom AI solution, it is obligatory to do thorough research and determine the main goals, list available resources, and identify the missing ones. In this way, you will outline the future AI implementation and will be able to see all the milestones and possible pitfalls as well as the ways to avoid them.
Irina is a professional copywriter with over 7 years of experience in this domain. She loves creating compelling and informative copy that provides readers with all the needed information. Irina is also a frequent contributor to different blogs and websites across different domains.View all articles by this author.
Great article! Black box will surely become a major issue and a driver for more data privacy regulations.
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