How To Build Custom AI Solutions for Specific Business Problems?

Custom artificial intelligence (AI) is a game changer for businesses in every industry because it enables them to directly address critical issues by creating specific solutions to fix those problems. The power and potential of modern AI tools coupled with machine learning models is unlimited, as they can process large amounts of data quickly and process valuable insights. This can help solve complex problems efficiently, drive innovation and improve a business’s operational efficiency.

However, it is essential to note that building the right custom AI solutions is a process that needs an expert opinion, detailed research, a thoughtful approach and a complete strategy.

This guide will explore the concept of custom AI in more detail, looking into what it is, its possible applications and the processes organisations use to build such solutions into their businesses. We will also be looking at how machine learning and custom AI can help to improve business operations.

What is custom AI?

Custom AI refers to identifying, planning and developing the right artificial intelligence solutions to help businesses operate better by catering to their specific issues and requirements. There is no doubt that a range of capable off-the-shelf AI solutions are pre-built to optimise different operations for an organisation. But with custom AI solutions, businesses get a model adjusted from an existing one or built entirely from scratch, providing a personalised touch to the final execution.

This is particularly useful for businesses with more sensitive security requirements and the budget to build tools that satisfy their expectations and keep them one step ahead of the competition. Some expected benefits of these solutions are cost reductions, task automation, improved supply chain management and better customer experience.

Developing the solutions in-house requires a team of data scientists hired by the business. Another option is outsourcing the creation to a third-party firm specialising in developing AI solutions.

What are some examples of custom AI solutions?

Some common examples of custom AI solutions are fraud detection systems, image recognition, chatbots and predictive maintenance. While these are some general uses of these tools, a firm’s solution will depend on multiple factors, including the available data and resources, the industry and the business’s problems. Here is a detailed look into some common uses:

Fraud detection

An everyday use of custom AI solutions for financial institutions is detecting fraudulent transactions and alerting the business’s security team. The tool can do this by employing analytic tools that look at the patterns in data, authentication measures, and the question between multiple data points to identify any suspicious activity.

For instance, banks can implement machine learning algorithms to study consumer behaviour and pause the ability of customers to operate their bank cards if there are any suspicious behaviours like substantial purchases or transactions made from irregular locations. The system could be designed to resume after the user passes through specific authentication measures, and it will usually remain locked until this process has been completed.

Personalised recommendations

Another widespread use of custom AI solutions used by multiple online businesses is to analyse consumer behaviour and use the insights to provide personalised recommendations to them. Some data sources for this approach are analysis of customers’ past purchases, their interaction with chatbots, or their browsing history. Custom AI software must be trained meticulously to look at the given data and pick out products to recommend that a user might be interested in based on their past decisions. E-commerce giants like Shopify and Amazon are great examples of this in play. They create tailored recommendations based on their collected data to provide a more personalised customer experience.

Predictive maintenance

With predictive maintenance, custom AI and machine learning models can anticipate at what point the machinery employed by a business is likely to fail. This makes scheduling maintenance and implementation of other measures possible ahead of time, ensuring that operations can flow seamlessly.

Several data sources are analysed as part of these tools, including machine metrics and sensor data, which enables the predictive analytics algorithms to provide the best results and keep the equipment “healthy”. Ultimately the employment of this approach can save money and reduce downtime.

Chatbots and virtual assistants

Another instance of custom AI tools used is virtual assistants and chatbots that can perform basic tasks and answer customer queries 24/7 through online sites or social media. Based on machine learning, natural language processing (NLP) and conversational AI, these custom AI solutions can be trained to perform in different environments and learn the answers to frequently asked questions as their training data.

It is possible to have customised AI chatbots that understand industry-specific language and even learn from consumer interaction to perform better when interacting with that customer in the future. Furthermore, with the rise of online businesses, digital entrepreneurs commonly use AI-powered chatbots, and by personalising these solutions, companies stand to gain a distinct competitive advantage.

How to build custom AI solutions for specific business problems?

Before developing a custom AI solution, you must identify and understand the problem you want to address. Once you have integrated the custom AI solution to address this problem, it is essential to remember that the job of implementing the solution will continue. Therefore, you must continuously monitor the solution and ensure it is based on a solid infrastructure while targeting the critical issue it is designed for. It is also essential for the developers working on the solution to have a strong understanding of data science, AI and machine learning.

Here is a look at the critical steps used while developing customised AI solutions:

Understanding the business problem

Since a customised AI solution can initially be a substantial financial investment, it is essential to be clear on what business problem you are trying to solve so that the solution can be developed with that end goal in mind. Naturally, this will involve carefully analysing the problem, why it is happening, and what the outcome would mean if it is solved.

After the problem has been identified, the next step is to define the exact requirements for the target solution, including a proper understanding of which algorithms will be used, the overall performance matrices and the primary data sources.

Data collection and AI development

AI solutions are only helpful if solid machine learning algorithms and quality data back them. Therefore, it is essential to consistently gather and prepare data from suitable sources relevant to business problems to provide reliable and accurate results.

After this step, the focus should be on developing and training the AI model with the proper techniques and algorithms to produce a final prototype capable of solving the business problem. Testing this prototype further in different scenarios is also essential to ensure it performs up to standards and results in minimal errors.

Implementation and monitoring

After the AI solution has been developed and tested, the next step is implementing it within the business and integrating it into the regular workflow. Even after the solution is implemented, it is essential to monitor it diligently to ensure it performs up to standard. If it does not, it will need to be adjusted accordingly.

With time, ensuring that your AI solutions are updated with the correct data, and are functioning as required, is crucial. This will need continuous monitoring, adjusting of parameters and potentially adding more data sources as necessary.

Can custom AI and machine learning improve business operations?

Custom AI and machine learning can improve business operations, helping businesses reach their goals faster and operate more efficiently. This can be done by streamlining workflows, optimising business processes, and even automating specific tasks. While we have already looked at the benefits of some custom AI applications, like predictive maintenance to avoid equipment and fraud detection to ensure ethical operations, there are also other benefits to a business using custom AI solutions.

It is important to remember that custom AI can be “customised” to provide any possible result with machine learning and artificial intelligence models. For instance, businesses can use these solutions to forecast future sales, improve customer service and provide better recommendations. To learn more about the differences between machine learning and AI, check out our detailed guide on this topic!

The target of implementing these solutions is the same as at any business – optimising operations, improving customer satisfaction and reducing costs. Of course, companies that invest in these tools will need to pay a financial price, but with the right strategy, these tools can provide unmatched benefits.

Conclusion

There is no denying the fact that building custom AI solutions that target specific business problems can provide several competitive and strategic advantages to a business. As long as they follow the right strategy to develop and implement these tools, companies can quickly identify the most critical challenges and opportunities to address. After that, they can collect the relevant data and implement targeted solutions that address those needs.

Despite their advantages, however, the solutions can fall short of fulfilling their goals if they are not handled and developed with the right resources and expertise, which is why it is crucial to have a team of data scientists and AI experts that utilise their knowledge along with the right tools and techniques to provide the desired results.

To learn more about machine learning, data science, and AI working together to shape modern businesses, check out the Institute of Data’s course on data science and AI. To learn how to approach a career in these fields, schedule a career consultation with one of our experts now to develop a plan to move you towards that goal!