Artificial intelligence brings automation to industries, streamlining workflows, improving operations, and increasing revenue.
The first step in leveraging the power of artificial intelligence is identifying what operations it could enhance in your business. Next, you need to determine whether off-the-shelf products could work for you or whether a custom AI solution is needed or desired, a case in which you would have to decide if you want to build it in-house or outsource the development.
As AI solutions can be obtained in many ways, each offering varying degrees of customization, determining how your business can benefit from AI can be challenging.
This article breaks down the various approaches how to adopt AI in business, from off-the-shelf products to fully custom solutions.
1. Readymade AI-powered products (zero or low customization)
Out-of-the-box AI solutions are easier to adopt since they don't require training and are ready for use. These solutions target a wider audience and solve generic problems related to the industry. They are generally offered in a plug-and-play fashion but without the flexibility of modifications.
You can think of these as stand-alone products that you buy and use right away in your business. They require no additional development or implementation effort.
One such example is Rossum.ai, an Intelligent Document Processing (IDP) solution for invoice processing that handles text data from receipts and other documents.
2. API-based AI-as-a-service solutions (semi-customization)
API-based AI-as-a-service solutions are another type of ready-to-use AI solution.
APIs allow you to communicate with other products and services without requiring knowledge regarding their implementation. Product vendors and service providers expose their documented APIs that can be used and expanded. While the AI-related development heavy lifting is ensured by the provider, using the solution requires interacting with it through API calls in a certain programming language.
API-based solutions are ideal for integration into your systems
So, as opposed to ready-made products, these are generally meant to be integrated into working systems and, thus, require some more technical expertise. Additionally, they are usually offered on a pay-as-you-go basis. Similarly, however, the users are bound to the functionality provided by the product and have little or no control over the inner workings of the systems.
Similarly to the IDP example above, butlerlabs.ai provides a suite of APIs for developers to automate data capturing and processing from documents. They offer clients the ability to create, train and use models for any specific type of document as part of their applications.
These actions need to be integrated through code in your system, enabling it to use the AI solution through requests and response processing only. While this is just an example, it showcases what an API-based solution entails: interaction through code.
An example of a workflow in order to benefit from Butler’s capabilities would be:
1. Triggering the creation of a model via a POST request; the request should contain specific information, as per the documentation, like the types of fields that need to be recognized.
2. The training of the model, via a request, on your specific data.
3. Using the model.
3.1. First, the developer has to send a request specifying the model to be used and submit the files for uploading, followed by a request for the processing itself.
3.2. The response received in a specific computer-friendly needs to be processed according to the needs. This can mean, for example, displaying it in a certain way or storing it in a specific location.
3. Open-source AI models (semi/full-customization)
Open-source AI models are specialized pieces of software, designed for a specific purpose, freely available online. These models can be used as they are, but can (and often need to) also be retrained or even modified to benefit business use cases further. This approach allows for more customization than the previous options, but also requires more technical expertise.
Search for models through GitHub or Kaggle
Examples of reliable sources are GitHub and Kaggle, where one can find models that display excellent results for a wide range of specific tasks, from predicting house prices to identifying objects in images and generating text or audio.
The first step is to define what task you need to perform and search for models with appropriate capabilities. Then, you should test if their performance is satisfactory for your use cases. While open-source models are generally developed by skilled professionals and even tested and improved by communities, they are not always one-size-fits-all.
Use the AI model as-is, retrain it, or even adjust
Sometimes, simply using the pre-trained model can lead to satisfactory results. In some others, training the model with your data might be needed but sufficient. Sometimes, going through the code and exploring and tweaking the model’s parameters can be necessary for good results. This requires at least some understanding of AI concepts.
Finally, you might want to integrate the model into your system, which usually requires some coding and MLOps skills to maintain the model up-to-date and reliable.
4. Models built from scratch (fully custom AI solutions)
The next option is to build AI models from scratch. This approach takes developers through the entire AI development life cycle, which we will discuss later.
Users can define the project functionality by narrowing AI to specific use cases following their business needs. Taking OCR (Optical Character Recognition) as an example, after extracting details from the text, a fully customized solution can be trained to capture handwriting and identify text in movies or video clips or similar variations. On top of that, it has complete freedom over inputs and outputs, not restricted to the predefined limitations of any APIs. The possibilities are limitless. Additionally, customized models might offer better scalability potential as they are purpose-built for your business.
Building a custom AI model is a gradual process accompanying several steps. Let's look at those below.
Machine Learning Development Cycle
A machine learning development cycle is a step-by-step procedure. Each step is carefully planned to optimize development. These steps are as follows:
- Identify AI/ML opportunities: Companies must first identify areas of their business where AI could be put to work. They then need to prioritize those areas based on their ability to solve the problem (e.g., availability of data), the potential economic value of the outcome, and potential ethical implications.
- Data Gathering and Exploration: Every AI project needs data for building models. Publicly available datasets can be used for generic tasks, such as people detection. However, when targeting niche problems, generic open-source datasets may not be suitable. Often, data engineers first develop and implement data mining procedures to collect sufficient and appropriate data for model training. Besides that, companies sometimes must undergo lengthy data modification and cleansing procedures.
- Feature extraction: Data scientists also need to define, what are the important attributes of the data that will be used within the AI model and are assumed to have a significant impact on its results.
- Model selection and customization: There are multiple ways to solve similar problems with AI, each with its pros and cons. Often choosing the right model is a decision about making tradeoffs between performance, versatility, or computing power requirements.
- Model Training: Training AI models requires cutting-edge computing resources. While businesses can install in-house machines, one-time investments are often prohibitive of this path. Instead, many opt for using online services in the form of PaaS or IaaS to rent computing power. Either way, training models require companies to employ AI engineers and data scientists with the relevant knowledge.
- valuation of model performance and deployment: When the outputs of the model meet the business requirements, it then often needs to be integrated into existing systems.
- Monitoring: Engineers must continuously monitor AI models once deployed. The model's performance dictates how useful it is against the targeted problem. If the performance is unsatisfactory, troubleshooting is required.
- Fixing mistakes / Re-training: AI models are rarely 100% accurate, which is why re-training is essential to the development pipeline. Every re-training process includes newer data and targets issues faced by previous model versions.
Organizations, that decide to build their own models also have to weight the pros and cons of on-premise vs. cloud security of the solution.
5. Use Low-Code/No-Code platforms
The entire ML development cycle can be overwhelming, especially considering the time and costs involved. However, organizations can also build no-code/low-code custom AI solutions with the help of platforms such as botx.cloud. Low-code solutions require little to no programming expertise, while still offering flexibility, allowing customization in selecting appropriate models, custom training, and rich integration possibilities.
They also have a fully developed backend which includes all the functionality to carry out AI operations. Developers can link the solution with their existing system via REST APIs, which makes integrations simple and fast.
Additionally, similar to AI-as-a-service solutions, these platforms are cloud based and are secured by the providers, taking even more work off the developers' hands.
Businesses that want to benefit from adopting AI can do so in different ways, depending on factors like the desired degree of flexibility or the technical expertise available. While out-of-the-box solutions can be easier to embrace, they come with limitations. On the other hand, custom AI solutions allow businesses to automate entire workflows with the added flexibility of customized development and deployment.
An experienced AI partner will know what the most efficient way to build a custom AI solution for a particular use case is and will balance the need for customization with the economic viability of the model.
Low-code or No-code AI platforms allow professionals without AI backgrounds to build AI solutions without worrying about AI infrastructure or problems with scaling the solution.
Interested but need help figuring out where to start? Reach out to us. We will help you understand your business needs and their appropriate solutions. Contact us today.