What are the top 5 factors behind AI cost?
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
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.
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.
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, relying on advanced computer vision algorithms. Higher accuracy and reliability of artificial intelligence predictions directly affect your project’s lifespan and increases AI development cost. 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.
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 “moonshots” — 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.
How much does AI cost? These examples from the ITRex portfolio might give you a hint
Project 1: AI-powered telemedicine solution
A healthcare technology company approached ITRex to upgrade a telehealth system, which is implemented in multiple hospitals across the USA, with video recording capabilities.
The new version of the system would allow healthcare providers to apply facial recognition and natural language processing technologies to analyze videos filmed during consultations and potentially improve doctor-patient interactions.
A basic version of a video/speech analysis AI platform may cost $36-56 thousand.
Project 2: An intelligent recommendation engine
A technopreneur was looking to add AI capabilities to a B2C platform connecting users with local service providers. Our client’s idea revolved around replacing cumbersome search filters with advanced machine learning algorithms that would analyze input text and come up with a list of service providers that match a user’s query.
The development, testing, and deployment of a similar artificial intelligence platform (MVP) would cost you anything between $20 thousand and $35 thousand.
Project 3: An AI-driven art generator
A renowned visual artist turned to ITRex to create a generative AI solution that would produce new paintings based on his works and the works of other authors who inspire him. The client was looking to build a minimum viable product (MVP) version of the system over the course of several weeks to present it at an exhibition.
Depending on the type of training data (e.g., abstract vs. figurative art), image resolution (HD vs. low-resolution output images), and deployment approach, the cost of building an MVP version of an artificial intelligence system like this could reach $19-34 thousand.
How to reduce AI costs — and start benefiting from artificial intelligence ASAP
A recent article published by the Forbes Technology Council suggests that building and deploying an AI solution will ultimately cost your company 15 times more than you planned originally —unless you already have an efficiently built data ecosystem in place.
Larger AI development costs typically stem from significant infrastructure optimization, data integration, security, and artificial intelligence management and control efforts. However, you can minimize these expenses by thoroughly planning your project and starting small while having a bigger picture in the corner of your mind. And you can always use pre-trained foundation AI models to streamline your project or just test the artificial intelligence waters.
Here’s how you should handle your pilot project:
Collect stakeholder feedback. Before you start building an AI system, we recommend you talk to internal and external stakeholders to determine the key process and decision flows that can be augmented or automated with AI’s help.
Identify priority use cases. In this step, you should use a product prioritization framework (e.g., MoSCoW, RICE, or Kano) to select business cases that will drive the most value during the interim period and serve as a basis for further AI implementations.
Select the optimum technology stack. Utilize a combination of custom-made, open-source, and off-the-shelf components (e.g., plug-and-play facial recognition engines, API-driven voice assistants, and cloud-based services supporting the creation and training of AI algorithms) to build a vendor-agnostic solution and reduce overall AI development cost. Special attention should be paid to UI/UX design: your future AI system should incorporate a user-friendly interface that enables stakeholders to ask artificial intelligence questions, get instant insights, or automate tasks without seeking assistance from your IT department
Prepare data subject to AI-driven analysis. To help algorithms make sense of your business data, it is essential to gather information, assess its quantity and quality, and bring it into a unified format. For this, several data collection, preparation, and normalization techniques can be applied. You can learn more about this in our blog post on data preparation for machine learning.
Create an MVP version of your AI system. Building a minimum viable product supporting the essential use cases is one of AI development best practices. With an MVP at your hands, you’ll be able to check the feasibility of your concept, pinpoint areas for algorithm improvement, and start scaling the system across different use cases and departments. You should not confuse an MVP with an AI proof of concept (PoC) though; the latter serves as a validation of your idea and is meant for internal use only.
Treat AI implementation as a work in progress. Once you put artificial intelligence to work, you may not get perfect results right from the onset; as your AI system consumes new information under the supervision of human specialists, it will deliver more accurate predictions and become more autonomous. It is therefore important to continue gathering feedback from your company’s stakeholders, making the necessary changes to the system, and repeating the steps enumerated above when introducing new features and use cases.
In the end, how much does artificial intelligence cost?
Although it’s hard to estimate the cost of creating and implementing an artificial intelligence application without diving into your project’s details, you may easily spend $50 thousand on a very basic version of the custom system you’re looking to build. But you can still get the ball rolling on a smaller budget — especially if you’re eyeing a PoC or thinking of using pre-trained ML models or plug-and-play services.
Is the game worth the candle?
By 2030, artificial intelligence could contribute up to $15.7 trillion to the global economy, with increased productivity and automation driving the lion’s share of this sum.
Currently, the AI revolution is still in its early stages. While some countries, industries, and companies might be better prepared for the disruption (meaning they do have the necessary data and IT infrastructure in place to create and deploy custom AI solutions at scale), the competitive advantage is elusive, since there is an opportunity for every business to transform the way they work and lead the AI race. And your company is no exception.