AI pricing is not a one-size-fits-all answer, as the cost of implementing an AI strategy depends on a number of factors. The complexity of automated systems, the data management environment, and the type of solution all play a role in the cost of artificial intelligence. Companies must take these elements into account when budgeting for an AI project. The entire development and maintenance of a custom-made intelligence system is calculated on an individual basis.
Ready-to-use solutions are less expensive than custom AI, although they may not meet all of a company's needs. To create a tailor-made solution, programmers and software experts must build the entire system from scratch. Data and intelligent functionality go hand in hand. To construct a reliable training algorithm model, the development team must input large amounts of data into the system.
The larger the data set, the more accurate the algorithm will be. Additionally, data-loaded algorithms can uncover hidden relationships between inputs and outputs more quickly. Your internal data resources may not be sufficient to generate all the data needed to train the model. Therefore, your technology provider will have to search for third-party data sources, which translates into more time and manual entry.
ETL (extraction, transformation and loading) is used to extract and add data from multiple sources to a unified data repository. The structure of the data is another cost factor in implementing AI. Structured information isn't difficult to manage, but unstructured data is more challenging to handle. Therefore, your technical provider must sort, manage and organize the data before including it in the model.
On the other hand, you can get away with unstructured or semi-structured inputs. In this case, advanced machine learning algorithms are applied that focus on this specific type of data and also increase the costs of AI. You can't implement an AI solution without designing a data-driven algorithm. That's why algorithms are another critical factor affecting the overall development of AI. In particular, the accuracy rate of the algorithm is where the technology budget increases or decreases.
For example, if you're looking to create a facial recognition system, this software should have nearly perfect accuracy. Being a complex technical solution, any intelligent system must be built gradually. This means that the implementation process is divided into multiple stages - each phase of development consumes manual and financial resources. Therefore, it's important to know how to price an AI project during each stage of development. In general, it falls into the category of project complexity - realizing an AI strategy that aligns with current business objectives goes hand in hand with some challenges. Companies need to implement a data management environment that ensures supreme security against vulnerabilities and easy access to data for AI projects. The cost of implementing AI is heavily influenced by the complexity of automated systems - this list covers only part of all the benefits that automation brings to your business.
That said, you can pay for AI at a cheaper price, although these are usually pre-designed services that have customization limits. In many cases, AI projects require certain R&D activities to identify the best delivery approach - it is generally valid for custom software development. A feasibility study helps the supplier to ensure that the customer has all the necessary data and also helps them identify an appropriate technological solution to address the business problem. The main objective of this stage is to ensure that the project objectives and the technical capabilities of machine learning engineers align - applications for intelligent systems usually revolve around achievable objectives that amplify the company and help its end users. Once the data is present, the actual implementation is likely to become the easiest part - with a clear objective in mind, developers can match business requirements with most optimal technical solutions. The result is detailed functional and non-functional requirements for AI - they include accuracy rate of solution, explanability, fairness, privacy and response time. Overall, solution architecture bridges the gap between business problems and technological solutions - it involves identifying an appropriate technological solution to address the business problem.