How much does it cost to create a custom artificial intelligence system? The honest answer would be “it depends”, as the price of developing, implementing, and maintaining custom artificial intelligence systems is driven by a number of factors and can only be evaluated on a case-by-case basis. In this article, however, we’ll figure out what these factors are and provide ballpark estimates of several AI-based solutions from our portfolio. Additionally, we’ll give you several tips on how to approach your first artificial intelligence project and get the most value out of your AI investments.
These are the top 5 factors behind AI cost
**1. The type of software you’re eyeing to build. **Artificial intelligence is an umbrella term that refers to any device or application that makes decisions based on the information it consumes, thus mimicking human intelligence. Voice assistants that understand questions uttered in natural language, security cameras recognizing people in live video footage, and expert systems that spot cancerous tumors in CT scans can all be described as artificial intelligence. However, their complexity, performance requirements, and, subsequently, costs vary greatly
**2. The level of intelligence you’re aiming for. **When talking about AI, people tend to envision Boston Dynamics robots and holographic avatars from Blade Runner 2049. In reality, most business AI solutions can be described as narrow artificial intelligence, meaning they’re only programmed to perform a particular task — for example, recognize text in PDF files and convert them into editable documents. To classify as truly intelligent, AI algorithms should be able to uncover patterns in data with little to no human intervention, assess the probability or improbability of an event, justify their assumptions, continuously process new data, and learn from it.
**3. The amount and quality of data you’re going to feed your system. **Artificial intelligence is only as good as the data it’s been trained on, and the more data algorithms consume, the better they get.
AI can ingest both structured data, which is properly organized and stored in relational database management systems (RDBMs), and unstructured data like emails, images, and videos, which is typically bulk-uploaded to data lakes.
As far as AI cost is concerned, it is cheaper to work with structured data — especially if there is a substantial quantity of information to boost your algorithms’ accuracy. With unstructured data, AI experts have to go the extra mile to organize and label it, while software engineers need to set up the complete infrastructure ensuring continuous data flow between the components of your system. In some cases, such as training AI-powered medical imaging solutions, data can be hard to obtain due to privacy or security reasons.
To overcome this hurdle, AI engineers may artificially expand the size of a limited dataset or reuse existing classification algorithms. Operations like these are bound to eventually increase the cost of building an AI program.
4. The algorithm accuracy you’re hoping to achieve. The accuracy of your AI solution and its predictions depends directly on the type of application and the requirements you impose on it. A customer support chatbot, for example, is only expected to handle up to 60% of routine user queries; for complex issues, there’s always a human specialist waiting on the other end of the line. A pilotless delivery drone that transports blood and human organs, on the other hand, should be able to maneuver around objects with immaculate precision. Higher accuracy and reliability of artificial intelligence predictions directly affect your project’s lifespan and increases AI development costs. Also, it should be noted that AI algorithms will continue to absorb new data as they work alongside human specialists, which may carry additional training and maintenance expenses.
5. The complexity of an AI solution you’re working on. Artificial intelligence is the brain of a technology system that pushes data in and out of your business app and presents insights to users — including those who don’t have a technical background. When we discuss the cost of artificial intelligence, we should be talking about the price of creating proper software, with a cloud-driven back end, ETL/streaming tools, APIs supporting integration with internal and external systems, and some kind of interface, be it a cloud dashboard, mobile app, or voice assistant.
Lightweight AI, like the customer support chatbots mentioned in the previous section, may live inside a corporate messenger and does not require a complex infrastructure to function. AI-powered data ecosystems providing a 360-degree view into your company’s operations are a whole different story. Additional AI implementation challenges will arise when you start scaling your intelligent system from one or several use cases (think predicting customer churn rate or analyzing sales data in a particular brick-and-mortar store) to a company-wide deployment. In fact, that’s the reason why only 53% of enterprise AI projects make it from prototypes to production.
Speaking of failures, it should be noted that only a tiny fraction of AI projects (Gartner believes it’s 20%; VentureBeat is even less optimistic) eventually deliver on their promise. A staggering failure rate can be attributed to several factors, including a lack of collaboration between data scientists and software engineers, limited or low-quality training data, and the absence of a company-wide data strategy. Most often, however, failed AI projects are characterized as “moon shots” — i.e., over-ambitious endeavors led by starry-eyed data scientists and CIOs seeking to “completely change the way our company has been operating for decades.” Such projects may take forever to complete, and it’s only natural that, at some point, a company’s C-Suite stops pouring money into the bottomless pit without seeing a glimpse of real value.
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