Custom AI Solutions or Ready-To-Use Products? How to Approach AI Software Development?

Custom AI Solutions or Ready-To-Use Products? How to Approach AI Software Development?

Dorota Owczarek - November 29, 2021

Building a custom artificial intelligence (AI) solution is often considered the best way to get started with AI and machine learning and turn it into business value. However, many ready-to-use AI products can easily be implemented and integrated into your operations and will solve your problem just as well.

As a business owner or entrepreneur, there are two main ways in which you can approach AI development: through custom AI software development or with ready-to-use AI products. For some businesses, it may be better to focus on custom artificial intelligence solutions. In contrast, others might find that one of the prebuilt solutions available today could meet their needs.

What are the possibilities of ready-to-use software vs. custom AI development services? Let’s take a closer look at both options!

Typical Applications of Machine Learning Technologies

Let’s consider the most common applications of artificial intelligence solutions in businesses nowadays:

  • natural language processing (e.g., chatbots, machine translation, document processing, text mining),
  • image and video analysis (e.g., facial, text or object detection, visual inspection on production lines, automated video content moderation),
  • predictive analytics (e.g., sales forecasting, planning production, optimizing processes and decisions),
  • recommender systems (e.g., personalized offer and marketing, recommendations for products, content, and services),
  • speech recognition (e.g., voice search assistants, speech to text for dictation),
  • automated decision-making (e.g., credit scoring, fraud detection, risk management, and compliance),

and other similar applications that use machine learning and deep learning algorithms to analyze data, provide insights into business operations or customer behavior, or enable automation to business processes. Many of these applications can already be met with ready-to-use AI products, such as:

  • IBM Watson,
  • Google Cloud Platform AI & Machine Learning Solutions,
  • Amazon Rekognition and tools for Natural Language Solutions,
  • Microsoft Azure AI.

However, do keep in mind that most of these AI products are not free! They require you to pay a fee per request sent to the service by your software application. To connect them into your business processes, you’ll probably need some level of custom integration development works, which also might be a significant investment.

How to Decide Between Custom Solutions and Off-The-Shelf Products?

Deciding between custom solutions and off-the-shelf products is not easy. There are many factors involved in the process.

At nexocode, to help you choose the best approach for your project, we follow the iterative agile approach. Our AI Design Sprint workshops are the starting point that allows for quick validation of a company’s AI needs. We sit down together with the client to identify the potential AI use-cases for their business and explore the opportunities and available software development options.

Below you will find several considerations to decide between bespoke artificial intelligence software and off-the-shelf AI technology products.

Advantages of Developing Custom AI Solutions

Developing your own AI tool brings many benefits but involves building custom algorithms and proprietary APIs. Whichever path you follow, it’s essential to understand and properly weigh the pros and cons. Let’s now look at the advantages of custom artificial intelligence development.

Elimination of Redundancy

The scope of features offered by ready-to-use products is often confusing. With the number of AI products available on the market today, choosing one that best meets your needs is a difficult decision, even for a seasoned engineer, not to mention a business decision-maker. As a result, identifying and choosing the right AI tool becomes a daunting and time-consuming task. At the end of the day, when picking a ready-made AI tool, you will end up with several features which you don’t need but still have to pay for.

Custom-made artificial intelligence products offer you just what you need – nothing more, nothing less. This is a way to eliminate the overhead of features you don’t need or wouldn’t like to pay for.

While many tools offer unique features, it is doubtful you will benefit from them all. And there is usually no way to pick only those functionalities that you need and drop those you don’t or already own. Such an overlap in functionalities across the stack may generate unnecessary costs.

Competitive Edge

Many innovative companies simply cannot afford to stick with a ready tool. How can you stand a chance against your competitors if you’re using the same tools? By identifying emerging problems and trends in the industry, innovative companies look for new ways to solve problems - ahead of the competition and offer cutting-edge solutions that are different from most other players. By using a ready-to-use product, you just lose the edge.

Also, while many AI products are marketed as innovative and unique, they are anything but. By using ready-to-use products, you put yourself at the whim of the company behind the product. There is little control over the features it offers and the development roadmap.

Intellectual Property

This might seem somewhat obvious, but by developing a custom artificial intelligence solution, you own the software forever. This opens many possibilities which are not available to you when using a ready-made third-party solution. For one, you can potentially sell the technology to third parties. That’s especially important if this is part of your core business.

Integration With Existing Tools and Platforms

Custom artificial intelligence solutions are always the better choice when you’re considering integrations with existing software. With ready-to-use products, it’s mostly hit or miss – support for specific integrations can be missing or fail to cover the scope you require. Again, this stems from the lack of control over the functionalities and the roadmap of the product. Integration with further customized applications and developing dedicated visual interfaces might bring the most significant benefits for the business.

Benefits of custom AI software development and further integrations
Benefits of custom AI software development and further integrations

Elimination of Fees

Since custom-developed software will always belong to you, you won’t have to pay subscription fees or extra data processing and are not at the whim of the vendor’s changing pricing.

High Quality of Predictions for Specialized Data

If you need AI and machine learning to solve a common problem that many vendors specialize in and have a ready solution for, creating your tool from scratch may not be the most efficient approach. Many use cases have already been solved, and there are high-quality off-the-shelf products on the market which are very likely the most cost-efficient solution for you. Popular examples include natural language processing and chatbot software or computer vision-related products that have all been built and well-tested at this point. It usually makes the most sense to buy instead of build.

When processing specialized sets of data, there’s less likely a ready-to-use solution that can do that really well. And even if there is one on the market, it’s much less likely that it’s able to produce great results. A custom artificial intelligence solution can offer output well-suited to your specific business problem.

In custom AI development, testing is also adjusted to the specific data sets to ensure outstanding performance. This is an advantage you lose when deciding on a ready-made solution.

More Control Over the Product and Feature Roadmap

Once the development is over, you own the product. This means you’re no longer dependent on a third-party provider. As you advance, how you update or scale the product next is entirely up to you. Business leaders and project stakeholders can direct the project development based on end users’ requirements, feedback, and business strategy.

Disadvantages of Custom AI Software Development

With all the benefits of custom development, there are also some critical use cases where it’s not necessarily the most efficient approach. Let’s now consider the specific disadvantages of custom AI development when choosing it over an off-the-shelf AI product.

High Entry Cost

The relatively high entry cost of developing their own AI and machine learning technology usually makes many companies shy away from pursuing this avenue. With all the benefits, creating your own software is connected with a steep learning curve and calls for the support of domain-specific know-how. This is where domain experts like nexocode come in handy, offering you the expertise derived from years of custom AI software development.

On the plus side, the high cost of development that you take at the beginning is usually offset because there are fewer recurring costs and license fees associated with using an off-the-shelf AI product.

Lack of Talent and Domain Expertise

Among the key factors, businesses should consider when deciding to develop a custom solution is the availability of skilled software developers. You need to be realistic and understand the strengths and weaknesses of your team. The long-term maintenance of supporting your own AI tools is expensive both from a time and cost perspective.

Do you have the available talent to handle an AI project? Would it be the best use of your resources for the goal you’re trying to achieve? Do you have relevant domain expertise and software developers to build it? This is why a partnership with a proven track record of successful AI and machine learning implementations might be needed to get your product off the ground.

Regulatory Considerations

Some regulated industries like finance and banking need to consider developing bespoke AI tools to meet the specific regulatory requirements of their industry. There might be specific regulations in place that require organizations to keep sensitive data on-premises rather than having them processed by a third party. This is where building a solution in-house may be the only reasonable way forward.

However, for most use cases, buying cloud-based, off-the-shelf software will still be a more affordable option.

Hosting Costs

Hosting costs are often overlooked when considering developing a custom-built AI product. Because AI tools require a significant processing capacity, necessitating investment in either physical hardware or cloud-based services capable of handling AI workloads. It is not easy to get precise estimates on the costs before the project kickoff, but as you move ahead with the first model deployment, accurate estimates start to arise.

Advantages of Ready-To-Use Artificial Intelligence Tools

AI and machine learning are no longer niche concepts, and an increasing number of ready-to-use products are being developed today. It’s worth considering a ready solution. To help you decide, we’ve collected the key advantages of this approach.

Custom development, as discussed above, makes sense for many scenarios. Still, specific use cases and factors speak in favor of choosing a ready-made, off-the-shelf solution. Let’s now have a look at these considerations.

Time to Market

The development of AI products and feeding the machine learning methods with training data takes much time, so the traditional software development lifecycle takes longer than using a solution already developed by a third party.

This being said, custom development is not recommended for businesses on tight schedules or need the specific AI functionality ready fast. This is something to consider in project scoping. In the case of a ready product, all configuration and onboarding is only a matter of days or weeks.

Low Development Cost

The initial cost of buying ready-made AI software is going to be significantly lower than building your product from scratch. Before engaging in custom development in artificial intelligence, it always makes sense to do thorough research and find out if relevant software already exists on the market.

Better for Generic Use Cases

The ready-made AI solutions available on the market today offer excellent capabilities for many generic use cases. For example, for recognition of handwriting, forms or images, or NLP (natural language processing), an off-the-shelf AI-based solution will do just fine, and there is no need for custom development.

Microsoft Azure AI consists of Azure Cognitive Services and Bot Service. It offers prebuilt models, Azure Cognitive Search and Form Recognizer, as well as Azure Databricks, Azure Machine Learning, and Azure AI Infrastructure. Azure Cognitive Services powers the artificial intelligence capabilities like natural language processing in many Microsoft products and services, from XBOX to Bing.

On the other hand, AWS offers Amazon Personalize, Amazon Comprehend, Amazon Rekognition, and other pre-trained AI Services. AWS AI services provide the AI to streamline processes and solve different business problems.

Ready-made artificial intelligence solutions are widely used by companies such as Toyota, Tetra Pak, and ASUS. AWS AI solutions power the products and services of companies such as Netflix, Siemens, and PwC.

Importantly, using ready-made solutions is still usually connected with some level of customization ( NLP solutions is a good example here).

Hands-off Management

With a ready-to-use product, there is no need to get involved in the maintenance of the software. This responsibility is on the vendor’s side while you conveniently take the client’s seat, who requires that the provisions of your SLA are adequately enforced, and the product works as expected.

Cons of Ready-To-Use AI Software

Naturally, ready-to-use products come with their share of cons. We will discuss them briefly below.

Vendor Lock-in

The use of commercial AI solutions usually entails some degree of vendor lock-in. This can take many forms and shapes but should be considered when deciding as it may influence your future. Specific vendors may implement tactics to make their customers dependent on their products and services, making switching to another vendor difficult or excessively expensive due to the need for large-scale refactoring.

Because so much is changing in artificial intelligence technologies today, you may not afford to lose your wiggle space - especially if the service doesn’t meet your needs or the cooperation goes sideways.

Lower-Quality Predictions for Specific Data Cases

To make the machine learning platform work to your advantage, you still need to feed it with your own data sets. This will ensure that the output is helpful to your specific use case. Also, making sure that the platform’s predictions are good takes a lot of effort before the product can launch commercially. This is why many off-the-shelf AI solutions offer lower-quality predictions for specific data cases but excel in areas like natural language processing.

Lack of Control Over Cost

While many machine learning platforms are offered in a subscription model, the actual cost often depends on the data processed. This is something worth considering to make sure your monthly fee doesn’t get out of hand. Business leaders need to understand how complex systems work and how to predict and calculate the ROI of AI to boost their ability for strategic decision-making.

Wrapping It All Up

As you can see, there is no silver bullet to solve all your AI problems. When deciding between custom development and a ready-made solution, many factors are considered, and multiple trade-offs are made. The decision often boils down to your specific use case and the available budget.

A custom AI system offers you complete control over cost, flexibility in terms of future changes, additional features, or even switching to another product. But it comes at a price - higher development costs and longer delivery timeframes. Ready-made solutions are usually faster but don’t offer the flexibility and accuracy of tailored-made solutions.

Not sure which way to go? If you’re looking to develop your own AI product or trying to find a provider of AI software development services, send us an email. Our experts will be happy to discuss your project. At nexocode, we approach each project with a standardized, iterative strategy for AI systems implementation based on Design Thinking and Agile methodologies.

A standardized, iterative development process for AI systems implementation based on Design Thinking and Agile methodologies
A standardized, iterative development process for AI systems implementation based on Design Thinking and Agile methodologies

We start small with low-investment workshops where we apply a set of tools for each step of the design-thinking process to help our clients leverage the power of AI and machine learning algorithms and turn these technologies into a tangible competitive advantage. Over the two days of our AI Design Sprint, our machine learning engineers, data scientists, and design facilitators will help your team understand the benefits of custom software development to help you unleash the power of AI, identify opportunities, create new ideas and propose a roadmap of the software development process.

The next step is the Proof of AI development (based on custom models or ready-made products), here our data engineers evaluate available data, suggest data collection strategy, and conduct data preparation part to build an initial model and test it against set benchmarks. This is a critical stage, where we work with our data scientists to build custom machine learning models or decide on a vendor for the one-off solution.

If you need further assistance in building AI products, our development team will help in creating custom AI-based systems or we can help you implement and integrate software solutions based on AI technologies that are already available publicly.

Designing better software products based on big data and emerging technologies with AI Design Sprint workshops
Designing better software products based on big data and emerging technologies with AI Design Sprint workshops

About the author

Dorota Owczarek

Dorota Owczarek

AI Product Lead & Design Thinking Facilitator

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With over ten years of professional experience in designing and developing software, Dorota is quick to recognize the best ways to serve users and stakeholders by shaping strategies and ensuring their execution by working closely with engineering and design teams.
She acts as a Product Leader, covering the ongoing AI agile development processes and operationalizing AI throughout the business.

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