Modern artificial intelligence solutions are extremely popular due to the numerous useful features and services they offer. However, apart from being simply convenient tools, modern AI-driven software also suggests innumerable ways to reduce expenses, which is highly important for any business.
Cost-efficiency is the backbone of any company that aims to succeed. Yet, efficient budget and resource management are even more critical for startups and small or mid-sized businesses.
Ultimately, modern reality compels companies across all fields to enhance their online presence. Offering software solutions has already become a must-have option for everyone, regardless of their field or niche. One of the most popular and efficient ways for such product companies to meet this demand is the minimum viable product development approach.
The core idea behind MVP projects is to reduce expenses and development time by minimizing the software functionality of an app to the bare minimum. Yet, is it possible to reduce costs even more, using AI automation services for MVP creation? To answer it, let’s discuss some basics first.
Traditional MVP Development in Detail
Every MVP project is unique. Nevertheless, each of them should start by finding a reliable full-cycle software development company capable of delivering the expected results. Additionally, all of them share the same algorithm and core stages, being:
- Ideation and market research.
- Defining core features and services.
- Software prototyping and design.
- MVP development process.
- Software testing and quality assurance.
- Software application launch.
- Feedback gathering and iteration.
The first stage helps shape the concept for a future software product, learn about current market offerings, and understand the target audience, its needs, and expectations.
The second step is to narrow down the functionality and define the most essential features that must be included in the Minimum Viable Product (MVP) release. This selection is possible only if your team has conducted proper market and audience research.
After defining the core functionality, but before initiating software development, you also need to create a prototype of the future app, which will serve as a visualization of the future product. Apart from showing the expected results, prototyping helps your development team understand the upcoming challenges and tasks more clearly.
The traditional MVP development process is similar to that of other software projects. Yet, most development teams prefer more flexible and agile project development and approaches that allow specialists to address unplanned or urgent challenges immediately. Frankly, the same is true for the rest of the stages, starting with testing or QA, and ending with feedback gathering.
So, what is the potential role of artificial intelligence in such software development projects, and how can it boost these stages?
How AI Transforms MVP Development?
Long story short, artificial intelligence solutions can be easily integrated into every MVP development stage, helping to reduce expenses rapidly and the time required for each of them. The only nuance is that it is better to choose separate AI assistants for each stage. Despite the variety of modern market solutions, each such AI has its pros and cons, making them more suitable for some instances while not being the best choice for others.
For a better illustration, we created a list of the most efficient AI tools for each stage:
- GWI Spark or Simporter for ideation and market research.
- Hotjar for choosing core features.
- Uizard or Figma AI plugins for prototyping.
- GitHub Copilot or Tabnine for development.
- Testim or Keploy for testing and QA.
- Taboola or Copy.ai for product launch.
- Qualtrics or Hotjar for feedback and iteration.
Simply put, all the foregoing AI-powered instruments help streamline specific workflows within the core processes of each stage, also providing assigned specialists with more detailed reports or suggestions for more informed decision-making.
For instance, GWI Spark analyzes global consumer data to help build more accurate real-time market insights, enabling faster and more precise analysis. Hotjar AI can be used for two stages simultaneously because its core feature is advanced clickstream and user behavior analysis, based on heatmaps. It can also help with identifying core UX friction points to define what can be improved in existing software.
Uizard and Figma AI plugins are basically generative solutions that can be used to accelerate prototyping. Yet, both models have unique features: Uizard can generate interactive UI interfaces, while Figma AI can suggest potential UI improvements, and is also a much better collaborative platform for designers and developers.
GitHub Copilot can auto-generate code, while Tabnine offers a privacy-first code completion solution. Testim facilitates flexible self-healing tests that adapt to UI changes, and Keploy aids in the auto-generation of test cases.
Taboola suggests more personalized and cost-efficient advertisements; Copy.ai helps to streamline content creation for go-to-market campaigns. Finally, Qualtrics provides companies with new-era tools for customer surveying, allowing them to create sophisticated and personalized surveys that can change in real-time based on users’ previous responses.
How AI Solutions Impact MVP Development Costs?
To be honest, the initial budget for MVP development with artificial intelligence is higher, as most AI solutions are subscription-based and require ongoing fees. Additionally, apart from estimating the expenses for traditional MVP creation, companies must also include the costs for additional AI services.
However, the actual development cost will be lower, thanks to the faster implementation of the product. The primary impact of using AI is the potential to accelerate the concept-to-market process significantly. For illustration, traditional minimum viable product development projects usually take 4-9 months for completion, while AI-powered MVP creation can require just 3-6 months.
Such speed-up not only helps reduce labor costs (for instance, the hourly rates of developers) but also enables software monetization and testing of the user experience faster.
Usually, AI-powered development of MVP solutions is 30% faster. Yet, the actual time saving varies and is unique for each project, depending on the chosen tools and processes to be streamlined. Using AI-driven solutions also enables businesses to hire smaller development teams, as streamlined processes require less human involvement.
Usually, the lower cost bar for AI-based and traditional MVP projects is the same, being around $10,000 for simple products and $40,000-$50,000 for more complex and custom solutions. Yet, the price ceiling differs significantly, being $50,000 against $20,000 for basic, and $200,000+ vs. $150,000+ for more advanced MVPs.
Note: the price ceiling is defined by a higher number of must-have features and services to enable, yet it can expand with basically no limitations. Even though more scalable software development projects are barely classified as MVPs at this moment, they are rather traditional projects.
To read more content like this, explore The Brand Hopper
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