How much does it cost to develop an AI app?
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For this reason, more and more companies are asking themselves how much it costs to develop an AI app and what steps they should take to achieve it successfully.
In this article, we explain the factors that influence the price, the stages of development, and the critical points you should consider if you are a leader or manager in a company that wants to invest in innovation.
Why invest in an AI app?
Before discussing costs and development, it's important to understand the strategic value of an AI app. These solutions make it possible to automate processes, predict behaviors, personalize services, and analyze large volumes of data quickly and accurately.
Sectors such as banking, retail, healthcare, logistics, and telecommunications are already using AI to optimize operations and generate new revenue streams. For business leaders, developing an AI app can be a decisive competitive advantage.
“Overall, the use of AI continues to gain momentum: more than three-quarters of respondents now say their organizations use AI in at least one business function,” McKinsey commented in a report.
“AI-powered decision-making is faster and smarter, providing the data business leaders need in real time. AI can also indicate current market fluctuations and other factors that could affect campaign success. Marketing leaders can access this information quickly and make quick, confident decisions based on accurate data,” they added in an article published by the University of Cincinnati.
Factors that influence the cost of developing an AI App
There is no single or standard price. The cost of developing an AI app varies considerably depending on several factors:
Complexity of the problem to be solved
An AI that detects banking fraud or diagnoses complex diseases does not have the same level of difficulty as one that automates customer service responses.
Quantity and quality of available data
AI needs data to learn. If your company already has a clean and well-structured database, the process will be more agile and cost-effective. Otherwise, you will need to invest in data collection, cleaning, and preparation.
Type of AI to implement
An AI app based on supervised machine learning is different from one based on deep learning, natural language processing (NLP), or computer vision. The more advanced the technology, the greater the investment.
Technological Infrastructure
Using cloud services (such as AWS, Azure, or Google Cloud) reduces initial costs, but if on-premises infrastructure or highly customized environments are required, the investment increases.
Development Team
Having data science experts, machine learning engineers, backend/frontend developers, and project leaders is essential. You can hire an internal team, work with freelancers, or partner with a company specializing in developing AI app.
Steps to develop a successful AI App
To ensure the project's success, it's not enough to have an innovative idea. It's essential to follow a clear methodology that integrates business objectives, data, and technology. Here are the key steps:
1. Define the Use Case: Identify a clear and measurable business problem that can be solved with AI. Are you looking to reduce operational times? Improve customer service? Forecast product demand?
2. Assess data availability: Check if you have enough data to train AI models. If not, define how you will obtain it.
3. Design the solution: Together with a technical team, select the appropriate algorithms, define the architecture and technology stack for the AI app.
4. Training and validation: Train models with the available data, tune hyperparameters, and validate results to avoid bias or errors.
5. Development and integration: Build the user interface, connect the AI with other systems, and test the solution in controlled environments.
6. Deployment and Monitoring: Launch the AI app into production and perform continuous monitoring to ensure its performance and adaptability over time.
What equipment do you need to develop an AI app?
Developing AI-based solutions isn't a solo endeavor. It requires a multidisciplinary team with expertise in technology, business, and data science. These are the key roles:
- Data Scientist: designs and trains the AI models.
- Machine Learning Engineer: deploys the models into production.
- Backend Developer: builds the server-side logic.
- Frontend Developer: develops the user interface if necessary.
- Project Manager: ensures the project meets schedules and objectives.
- Solutions Architect: defines the technological infrastructure.
In some cases, it's advisable to work with a specialized company that already has experience developing AI app in your sector.
Recommendations for reducing costs without compromising quality
Investing in AI is strategic, but it's also important to be efficient. Here are some recommendations to optimize your investment:
- Start with an MVP (Minimum Viable Product): this way you validate the value of AI without large initial investments.
- Reuse pre-trained models: many platforms offer ready-to-adapt AI models.
- Use scalable cloud services: You only pay for what you use.
- Define success metrics from the start: This prevents deviations and ensures focus.
Developing an AI app is a strategic investment
Developing an AI app represents a unique opportunity to transform processes, differentiate yourself, and scale business models. However, it requires planning, talent, and a clear vision of the expected return.
With a business-centric approach and robust technical execution, your company can take full advantage of the potential of AI and remain competitive in an increasingly intelligent market.