Smaller companies in the U.S. are not using AI because of high AI development costs.
According to the Business Trends and Outlook Survey, AI adoption has been highly uneven in the U.S., with fewer than 3.8% of companies using AI to produce goods and services. 50 to 60% of the companies that employ AI are large companies with 5000 to 10000+ employees.
This means there isn’t a grain of truth in the widespread AI adoption claims.
Umpteen articles singing hallelujah about the rise of AI—be it in healthcare, aviation, fashion, or education— are just half the truth.
So, What’s Stopping 50% of Companies from Adopting AI?
Well, partly inertia. Partly, high AI development cost.
It’s a known fact that people dislike change, and so it goes with AI adoption. Plus, the cost factor is not working in favor of it. Businesses don’t want to shoot themselves in the foot, considering the high costs associated with AI.
A recent Business Trends and Outlook survey suggests that businesses use human workers for specific tasks rather than automate them with AI because of the cost involved.
McKinsey reports that developing a single generative AI model costs $200 million, and tailoring an existing model with internal data costs about $10 million.
Approx. AI Development cost, according to risingmax.com. AI Developers charge $25 - $49 per hour. Training and maintenance cost ranges from $8,999 - $14,999.
Even the size of the AI development company directly affects the cost of AI development. It’s been widely understood that the bigger the AI development company you hire, the higher the price. While small-sized AI development companies with 50-200 employees charge around $20,000 to $45,000, large-sized companies with staff strength of 1000-10000 quote extraordinarily high prices, i.e., in the range of $1,00,000 to $15,00,000.
Plus, the complexity of the AI application drives up the cost.
Given the escalating cost, most businesses are not open to AI adoption.
The artificial intelligence cost estimation should consider the complexity of the AI solution and the expertise and experience of the AI development company you hire to develop custom AI development services.
Wondering what factors contribute to such a high AI app development cost?
Several factors contribute to such high AI development costs besides the company size.
Factors that Drive Up the AI App Development Cost
Many factors directly impact AI Development costs. Harnessing open-source software could be the most economical option, while a customized AI app could burn a hole in your pockets. A customized AI app's success depends entirely on the type of AI solution, data quality, hardware, and software requirements, among many other things.
Without further ado, let’s learn more about the most influential AI pricing factors:
1. Types of AI solutions
Several AI solutions have inundated the market, even the free ones. So, the cost largely depends on the kind of solution, in other words, the complexity of the solution you choose.
Free AI tools
Free AI software tools like ChatGPT and Gemini have truly transformed the tech space. With the aid of this super-talented tool, writing scripts for presentations, essays, videos, and more is a walk in the park. But despite the hype and hoopla attached to this AI tool, one massive shortcoming repeatedly pointed out is its robot cadence, in other words, the lack of human touch. However, the drawback has pushed the chatbot to improvise, helping it relay information more humanely.
Free AI tools will help you better understand the use cases that resonate with your business.
Virtual Assistant Cost
Virtual assistants have become ubiquitous. Think of Siri, Alexa, and Google Assistant, which we use daily to make our lives easier and more comfortable. Using speech recognition and NLP, such AIs process user commands and queries and respond using speech synthesis.
Considering its many uses, companies have been integrating AI-powered voice recognition tools in their call centers, performing internet searches, translating languages, and even sending messages. More than anything, these voice assistants have now become an integral part of the AI-powered customer support tools suite.
How much does a virtual assistant cost?
The average cost of developing a Virtual assistant AI App could range between $40,000 and $1,00,000. However, with the integration of advanced AI features and functionalities, the cost could reach $500,000 or more. Source: appinventiv.com
Chatbot Development Cost
The online space is flooded with shops of all shapes and sizes, from small-time mom-and-pop boutique shops to multinational retailers. Despite the size differences, one common element that binds these shops is their AI customer service. That is to say, these companies don’t hire customer service staff but rather use AI chatbots trained to answer common customer inquiries. And not just that. A custom or off-the-shelf chatbot can be used for product recommendations, order updates, and even direct shoppers to FAQ pages. More importantly, you can run such bots on social media platforms like Facebook and engage with the shoppers there.
How much does an AI chatbot cost?
If you are looking for custom chatbot development, the price is $10,000 monthly to $500,000 per project.
Outsourcing chatbot development could be cost-effective as the cost per project would be $1,000 to $5,000+ per project.
For small businesses, chatbots are priced between $0 to $500 monthly.
Developing and deploying enterprise chatbots could cost you between $1,000 to $10,000 monthly. Source: helpcrunch.com
Predictive Analysis System Costs
Predictive Analysis systems (decision tree or cluster analysis) use machine learning and, to some extent, deep learning to solve data analytics problems, make predictions, and support data analytics and data science.
Businesses can use it to analyze and take action from large quantities of data, specifically for sales and marketing initiatives, including paid advertising and search engine optimization. Adopting a data-driven approach can help improve advertising and marketing spend and increase ROI.
How much does it cost to build a predictive model?
A bare-bones ML model can be developed, deployed, and maintained for around $60k in the first five years. However, given its skeletal structure, scaling the model would be impossible.
A scalable model will cost $95k over the first five years, but it’s worth it because it can support future additions and deletions. Source: phdata.io
Bottomline: The AI app development cost is directly proportional to the level of intelligence you aim to achieve with your AI.
- The amount, quality, and type of data (good or bad) you will feed to the AI
- The kind of accuracy (high, medium) you wish to achieve with the algorithm
- The complexity of the AI infrastructure. Simpler AI chatbots require simpler infrastructure and are less costly than intricate AI software.
All these factors help determine the price, process, and technology needed to create an AI app.
Ask Yourself: Does open-source software meet all your AI app development needs, or do you need to build on it through customization? If yes, how much more customization is required, and what could be the cost?
1. Hiring AI Experts with Knowledge of Deep Learning Algorithms and NLP
To develop and deploy an effective AI app, you need AI experts and consultants with a deep understanding of AI tools and processes. Such expertise is not cheap, let alone readily available.
Complex AI projects require specialized AI developers and data scientists, which could escalate the costs. Businesses look for developers familiar with AI tools and have expertise in integrating with existing systems.
In other words, it means developers with domain expertise, business processes and workflow knowledge, and specific regulatory or compliance requirements to develop a pertinent AI solution that works for your industry.
However, the issue is that it’s not easy to find experienced AI experts in specific industries, which further drives up the cost.
More to the point, specific technologies or platforms, such as deep learning algorithms, natural language processing tools, and even cloud computing platforms, require specialized skills to develop and implement. Hiring and training staff with these necessary skills can be expensive, adding up to the cost.
Ask Yourself: Are you working on a complex project requiring deep customization? If so, hiring in-house AI experts makes sense, but outsourcing is the best bet as the costs would be relatively lower, and you will have a variety of AI experts at your beck and call.
2. Garbage-in, Garbage Out - Data Availability & Quality
As the name implies, if the input data to an AI model is of poor quality, the system output will be equally poor and irrelevant.
The point is that data quality matters in AI projects. What do you mean by quality data? It simply means data that is clean, complete, credible, relevant, dated, and, more importantly, pertinent to your application. Owning poor-quality data would mean cleaning it up, which would mean hiring a data scraper or leveraging internal resources to filter data, further accelerating the costs.
Pro tip: Leveraging a tool likeDeCubewill help you gauge and monitor your data sets during ML algorithm implementation and maintenance. The tool is known to increase the productivity of data engineers and reduce debugging time by 45%.
3. Hardware Requirements
A few AI projects call for specialized hardware, including GPUs, TPUs, and other processors that help perform computational tasks more efficiently. These hardware components offer performance advantages over mundane CPUs but are expensive and require significant investments.
Ask yourself: Do you require specialized hardware for the project? Will renting the hardware be more beneficial?
5. Software Requirements
AI projects call for development frameworks and libraries. These frameworks and libraries offer the requisite tools and functionality for developing and even training AI models. The choice of software directly impacts the development process, the outcome, and the cost.
Ask yourself: What libraries are required for your AI projects? Will the off-the-shelf ones work, or do you need to customize them?
6. Data Storage and Processing Requirements
AI projects require a lot of data storage and processing power. Storing and processing large datasets is time-consuming and, more than anything requires top-tier infrastructure to support them. Moreover, you must ensure that data is stored securely and accessible to those who need it, which adds to the project's complexity.
Ask Yourself: Will you require additional storage or processing power than currently available?
7. Inhouse AI Team Vs. Outsourced AI Team
The exact cost of an AI team, whether in-house or outsourced, depends on several factors. Here’s a rough range to give you an idea, though:
In-House AI Team:
Total Cost: $400,000 - $1,000,000+ per year (the cost covers salaries, benefits, hardware, software, and overhead)
Salary Range (US):
AI Specialists/Data Scientists: $100,000 - $200,000+ per year
Software Engineers: $80,000 - $150,000+ per year
Project Manager: $70,000 - $120,000+ per year.
Outsourced AI Team:
Project Cost: $6,000 - $300,000+ (This depends on project complexity and scope)
Hourly Rate Range (US): $200 - $350+ per hour (for AI consultants).
Get this: Having an outsourced AI team costs less upfront investment and overhead costs than an in-house AI team.
Ask Yourself: Are you a small business running on tight budgets, or are you struggling to find top-tier AI talent? Then outsourcing should be your go-to option.
8. Project Duration
Like everything else in the tech world, AI pricing directly relates to the project duration. If you are developing a basic or MVP version, the project's price and duration will be less than that of a full-fledged AI app development project.
Ask Yourself: Will the AI developer deliver the project within the stipulated time, or is there a possibility of extending the deadline, thereby increasing the costs?
Additional AI Development Mistakes that Contribute to Project Complexity and Cost
80% of AI projects fail, almost double the rate of IT project failures, according to an HBR article, “ Keep your AI Projects on Track.
1. Over-estimation of AI capabilities:
Most AI project failures can be attributed to overconfidence in AI capabilities. However, businesses can’t be entirely blamed for them, given how the market has been equally culpable for overestimating its strength, given that the tech is still in the experimental stages, as in the case of many companies.
According to IBM's Global AI Adoption Index, 40% of companies are still exploring or experimenting with AI models, which means they have not been deployed yet.
Sometimes, AI is a trade-off for a dysfunctional business model or headcount reductions.
2. Treating AI Projects like App Dev Projects
One big mistake that AI experts have identified leading to AI project failures is treating AI projects as app development projects. According to a Forbes article,” The one practice that separates AI success from failures,” that’s the biggest blunder AI development companies are committing. App development projects are code-centric, while AI projects are data-centric; the two are opposite. AI projects bank on actual data, so insights gleaned from data are crucial; however, app projects are based on ideas, and functionalities built on those ideas matter.
In short, AI projects are data projects, and most AI development companies seem to understand this; however, given the failure rate, they need to understand this better.
3. Businesses Defining ‘Customer Success’ Metrics Wrongly
When businesses decide what’s good for customers, there’s a strategic misalignment and failure. This means more money needs to be pumped into righting the wrong. The best approach is to rope in the customers to determine what’s good for them and accordingly design the ‘AI success metrics for every project,’ which is then agreed upon by the AI development company and the entrepreneur.
4. AI Solutions that Aren’t Customized as per Your Business Needs
AI-generated job descriptions are fine. However, randomly using AI solutions not specifically tailored to your industry is not a good strategy because, eventually, it would be a gloomy situation, and repairing it would add extra cost. Hiring an AI development company that understands your industry and can draw up benchmarks and data pertinent to your industry requirements is ideal.
5. High-tolerance of AI Inaccuracies
Human tolerance for AI inaccuracies varies on a case-to-case basis. An AI offering personalized recommendations with 75% accuracy on an ecommerce site may be considered good enough, but an AI used in a hospital with the same accuracy would come under scrutiny and might as well be stopped from being. CNET and Sports Illustrated are paying the price for using AI content without a care in the world. On the other hand, the launch of autonomous vehicles is still in the pipeline phase, given the associated loss of life. If your business has adopted a high tolerance approach for AI inaccuracies, fixing and tying up loose ends could prove costly in the long run.
6. Not Tracking Metrics in Real-time
AI developers have successfully built co-pilots but have failed to create real-time, AI-driven dashboards that could track projects in real time and flag issues.
Tips to Reduce AI App Development Cost
Now you know what factors lead to high AI development costs. Exoneration from such high development costs is not impossible, provided that you adopt a satisficing approach. The word satisficing combines “satisfy” and “suffice.”
Adopting a ‘Satisficing’ approach simply means “ take a good enough approach.”
In the bestseller‘The Anatomy of Breakthrough,’ the authorAdam Alterdiscusses cognitive scientist and economist Herb Simon's two-way approach to decision-making: maximizing and satisficing. According to Alter, “maximizing requires an exhaustive search of the environment for the best outcome, whereas in satisficing, you search until you land on an option that’s just good enough.”
Maximizing is filled with anxiety, is a proxy to perfectionism, and is paralyzing. How can you confirm that you’ve finally found the best choice in a world filled with millions of options? On the other hand, satisficing is liberating and helps you unstick when you cannot arrive at a decision/conclusion. “Whereas maximizing is inherently stubborn, satisfying is flexible, nimble, accommodating—and most importantly, forward-focused,” Alter adds.
The point is: How do you adopt a satisficing mindset in the AI app development process to ensure it stays cost-effective?
1. Develop a Minimum Viable Product: Develop a Minimum Viable Product using only satisficing or good enough features. These features fall within the threshold of acceptability and fully meet your AI app requirement standards.
Once the MVP has been deployed, you can iterate depending on market requirements and fix issues wherever required, thus cutting costs during future reworks. According to AI developers, projects broken into smaller increments bring benefits such as approximate cost estimations and flexibility for future adjustments.
2. Leverage AI app builders: Using AI app builders like Softr and QuickBase will reduce the cost of software development and help you launch a good enough app. In addition to AI app builders, you can check open-source tools and frameworks with community support if you don’t intend to build custom solutions from scratch.
3. Hire experienced AI developers: Experienced AI developers can help you winnow bad data from good and, more importantly, will help you develop a cost-effective checklist to ensure your app is developed and deployed within the given budget.
4. Outsource Selective Development Tasks: Non-core tasks, such as data labeling or ongoing maintenance tasks, can be outsourced, dramatically lowering the AI development cost in return. Getting the same job done by a dedicated in-house team member would mean higher costs.
5. Maximize Reusable Components and Microservice
Reuse reusable components and microservices to reduce the cost of AI software development. This way, you don’t have to build everything from scratch for every development project.
6. Implement Continuous Integration and Deployment (CI/CD) Pipeline
A CI/CD pipeline saves costs. It automates the testing and deploying process, which means bugs can be caught faster, the product's testing time can be cut down, and the product can be launched faster in the market.
CI/CD pipelines adopt a modular approach to software development, implying that developers can work independently on different parts of the AI system. The integrated code could be deployed faster, yielding an agile and cost-effective development process.
7. Optimize Cloud Resource Usage
AI development is a resource-intensive process. Cloud services help you optimize resources; in other words, you pay according to your requirements.
AI is Not Expensive for Businesses if it's Done in A Satisficing Fashion
AI development need not be expensive if you debunk the maximizing mindset and adopt a satisficing mindset instead. Careful consideration of the cost factors mentioned above, such as the type of AI required, hardware and software requirements, data storage requirements, and data quality, could help you cap the cost-effectively. Plus, taking advantage of the tips to curtail costs, such as deploying MVP over the finished product, using open source software, outsourcing non-crucial tasks, reusing components and microservices, and so on, can shave off a considerable chunk of your AI development costs.
So, are you ready to dive into an AI app project right away? The top AI Development Companies in India are just a click away.
