AI is everywhere these days, with businesses scrambling to develop and integrate AI software to enhance operations. However, creating AI applications and AI app development can be challenging. For instance, let's say you want to build an AI solution to improve customer service. It can be tough to know where to start. This blog will show you how to build AI software that drives innovation, enhances business processes, and seamlessly fits into existing systems to deliver long-term value.
Lamatic's solution, a generative AI tech stack, can help you achieve your goals. It enables users to create and integrate AI applications quickly and easily, ensuring you can build AI software that delivers value and meets your business needs.
Why is AI Software Worth Investing In?
Artificial intelligence is crucial across industries today, and it’s only gaining more importance over time. If you want to wow the investors or customers, you should shift your gaze to AI software.
AI-powered software can automate your business processes, improve decision-making, and help you achieve your business goals faster. AI apps are already used to analyze customer behavior, predict sales trends, and automate marketing campaigns.
Why Build AI Software? Here Are the Top Three Reasons
Let’s highlight the top three reasons why building AI software is a great opportunity for tech startup founders and software product companies.
1. Build Solutions That Create More Value for Users
AI can help create business solutions that reduce costs, save time, and increase revenue. These solutions can analyze large amounts of data to identify trends and patterns. For example, AI software can help personalize the customer experience to increase satisfaction and boost sales.
2. Stay Competitive in the Market
AI is becoming a standard business operation, so companies that don’t utilize AI software will fall behind their competitors. By creating your AI solution, you can gain a competitive advantage, whether improving your business operations or offering your customers a more valuable product or service.
3. Higher Profit Margins
AI software can significantly reduce operational costs and boost business revenue. Therefore, investing in AI will pay off in the long run. For instance, according to McKinsey, over 60% of companies that invest in AI have reported a moderate to substantial increase in their cash flow. In addition, AI solutions can create new business opportunities and revenue streams.
Promising Statistics About AI Software
Let’s look at the stats because it shows very promising numbers for those who want to make AI software.According to a report by Accenture, AI has the potential to double the economic growth rate of some developed countries by 2035. The global AI software market is expected to reach $126 billion by 2025.
AI Software: Benefits and Use Cases
AI algorithms can process large amounts of data faster than humans and provide more accurate results. For example, AI-powered chatbots can handle customer inquiries 24/7, freeing human resources to work on more complex tasks. AI software is already used to:
- Analyze customer behavior
- Predict sales trends
- Automate marketing campaigns
With so many potential benefits, investing in AI software and partnering with AI development companies is a smart move for any startup looking to gain a competitive advantage.
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How to Build AI Software from Scratch
1. Planning and Strategy
The first step to building AI software is to clarify your business objectives. Don’t just chase the shiny new trend. Analyze how emerging AI technologies can truly bring value to your business.
- Do they solve your pain points that cannot be solved otherwise?
- What are their genuine contributions?
Many businesses fail in their AI undertakings because they let FOMO (fear of missing out) squander away their resources on costly but poorly fitted and futile AI projects. When it seems everyone else is doing it, think critically about why you want your own AI software. The above section may help.
Evaluate Available Data
Once you have nailed down the justifiable business objectives, consider your available data. Is your data siloed or inconsistent? The current state of your data will affect your choice of the tech stack and the skills you will need.
Your business objectives and the available data significantly impact the scope of your project and the investment amount. Ensure the first two steps are properly done - they justify the “why” of your project.
Choose The Right Tech Stack
The right tech stack should satisfy your project requirements. This includes:
- Programming languages
- Development tools
- Testing tools
- Cloud services
- Big data solutions
You can only narrow your options when you know your project requirements. Your AI team (see below) should be able to advise on these AI solutions.
Invest in IT Infrastructure
Training machine learning models eat up a lot of computational resources. You may need to invest in:
- extra servers
- Storage
- Network
This is to ensure the AI operation does not disrupt your current operation. If your business already has updated technologies such as cloud computing and data analytics infrastructure, scaling them up to make space for AI capabilities is a reasonable expectation.
Those with legacy systems will need to modernize before they can get started with AI projects. Having an AI-ready infrastructure makes attracting and building an AI team easier.
Build an AI Team
Three types of roles comprise a comprehensive AI team:
- Business
- Data analytics
- Software engineering
The business and engineering people may be familiar with working together on previous non-AI projects, but the data specialists are likely new to the team.
The AI team needs to figure out how best to work together by answering questions such as:
- How should AI projects be managed? Consider this within the context of your company.
- If your company favors the Waterfall project management methodology, will it be sustainable for high-risk AI projects?
- To manage the risks of AI project failure, how big is the expected learning curve for your organization?
- How experienced are my team members in working with AI?
AI is still a new technology requiring a major shift in mindset. Adding to this is a global AI talent shortage. It is a big ask to expect that your company can compete against Big Tech for an entire team of AI experts. It is normal to have team members who will be working on an enterprise-level AI project for the first time. Ensure you have some experienced AI team members working alongside others willing to start their own AI journey.
Key Roles in an AI Development Team
Whether you intend to develop AI software using your in-house developers or hiring external resources to cover tasks, a typical AI team or department often consists of three key roles: business, data, and engineering. Data literacy is the leading quality that any AI team member needs, regardless of position. It is easy to get confused about new AI algorithms.
Training programs should improve the team’s competencies in working with big data so that they share the same understanding and can communicate data as meaningful information to external stakeholders. In terms of soft skills, the most important ones are communication and teamwork because AI software projects demand team members to work with highly collaborative frameworks to be successful.
Business Roles
These are the project managers, product managers, product owners, and business analysts. They are responsible for:
- Keep the AI team focused on the project requirements
- Ensure the AI project satisfies the business objectives
- Stays within time and budget constraints.
Work with stakeholders such as:
- Legal
- Accounting
- Marketing
- Investors
While preventing the AI team from getting isolated from the rest of the company. Represent the interests of the end-users whose feedback and education will be crucial to the business success of the AI project.
Data Specialists
As mentioned above, depending on the state of your data, your AI software will need several data specialists:
- Data engineers
- Data scientists
- Data analysts
- Machine learning engineers
This list is not exhaustive. Other roles may include database architect and data architect. They should have technical skills in:
- Maths
- Statistics
- Programming tools
- Cloud computing
- Database management
- Other data-related skills.
AI Software Engineers
You may be familiar with these software engineering roles:
- Front-end engineers
- Back-end engineers
- Testers
- QA engineers
- UI/UX designers, etc.
More importantly, software engineers for AI projects should have experience with or are willing to adopt DevOps and MLOps.
2. Development and Implementation
To illustrate the steps of data analytics that your data goes through before it can be deployed into AI software development, let’s consider the Data Science Hierarchy of Needs. Your company’s data goes through each pyramid level to reach the top AI software development.
Depending on the state of your data, you will need to set up the following framework, with several data specialists moving your data from raw to AI deployment. A data engineer collects structured and unstructured data. A data scientist cleans and prepares the data for analysis. A data analyst interprets and visualizes data for insights and decision-making. A machine learning (ML) engineer trains ML models with data to make predictions.
Deploy ML Models into AI Software Development
The software engineers deploy ML models to create artificial intelligence software during this phase. Since the risks in AI projects are high, adopting Agile into the software development cycle is better to manage risks at every phase. The AI team should follow MLOps precisely.
MLOps provides the framework for a software team to incorporate ML models into development. MLOps is an extension of DevOps, a practice that improves software quality with continuous feedback between operations and development. MLOps facilitates the collaboration between the AI software team and operations, enabling a comprehensive workflow that traverses software, hardware, and IT support. Its ability to break down silos between departments is a quality that will benefit AI software development.
3. Testing and Deployment
Testing is crucial to ensure the AI software performs reliably and meets the defined objectives. When testing your AI system, consider these key areas:
- Evaluate Accuracy: Measure performance using metrics like precision, recall, and F1 scores. Test the software in various scenarios to validate its robustness.
- Address Bias and Ethics: Ensure your model doesn’t favor or exclude specific groups due to biased training data. Ethical AI practices build trust and transparency.
- Simulate Real-World Scenarios: Deploy the software in a controlled environment that mimics user interactions to identify potential issues. Transitioning to deployment requires a well-tested solution that easily delivers consistent performance.
4. Deployment and Maintenance
The final step is operationalizing your AI software and ensuring it remains effective over time. When deploying and maintaining your AI solution, consider the following:
- Deploy Strategically: Use tools like Docker for containerization or Kubernetes to manage large-scale deployments.
- Monitor Performance: Track metrics like response time, accuracy, and user feedback to identify areas for improvement.
- Update Regularly: AI models need retraining and updates to stay relevant as data patterns evolve or user needs change. Transitioning from deployment to continuous improvement ensures your AI software remains effective and valuable.
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Best Practices for AI Software Development
Artificial intelligence can write code. But it doesn’t understand it like a human does. That means relying on AI to help you develop software without checking its work can lead to bugs and security issues. AI isn’t great at thinking about the future. Scalability and adaptability might not be their strong suits.
Data Compliance Challenges: A New World of Rules
Using AI in software development can bring a new world of data compliance rules. Depending on your industry, regulations like GDPR and HIPAA can come into play. Get ready to deal with issues related to data quality, accessibility, governance, and security. Your data should be highly quality and follow compliance regulations to avoid hefty fines and damaging your reputation.
Handle Ethical and Legal Matters: A New Level of Responsibility
Suppose you integrate generative AI into your banking, healthcare app, or any product that works with users' personal information (almost every product). You enter the ethical and legal landscape. When implementing AI systems, consider:
- Privacy
- Bias
- Transparency
- Accountability
Slip-ups Happen: Be Prepared to Step In
Even the most innovative AI can have a “duh” moment. It might cough up information that's not entirely accurate. That's when we, the human experts, come to the rescue.
Attract and Keep Top Talent: The Great AI Worker Shortage
Last but not least, finding and retaining top-notch AI professionals is a big challenge. Given the high demand for AI expertise, it can be challenging for businesses to find professionals dealing with AI tech.
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Start Building GenAI Apps for Free Today with Our Managed Generative AI Tech Stack
Lamatic offers a comprehensive managed generative AI tech stack that takes the guesswork of building AI software. Their platform automates workflows for a smooth user experience, reducing the likelihood of tech debt that comes with custom software development.
With Lamatic, teams can easily build, test, and deploy AI applications that integrate seamlessly into existing software.
The best part?
You can start building AI applications for free with Lamatic’s managed generative AI tech stack.
Managed GenAI Middleware: Generative AI for Everyone
Building AI software that leverages generative AI capabilities doesn’t have to be complicated. Lamatic’s managed middleware handles the heavy lifting to help users get started quickly. The plug-and-play solution allows teams to focus on building applications instead of worrying about the underlying technology and infrastructure.
Custom GenAI API (GraphQL): Tailored to Your Needs
Lamatic offers a customized GraphQL API that helps to optimize performance for your specific generative AI application. Instead of a generic REST API, the GraphQL option allows your application to request only the data it needs, which can reduce load times, improve performance, and create a better user experience.
Low Code Agent Builder: Build Custom AI Agents
With the low code agent builder, users can create bespoke AI agents that cater to unique business needs or user demands. The intuitive interface allows users to build custom agents without extensive programming knowledge. Teams can leverage existing templates instead of starting from scratch to accelerate development.
Automated GenAI Workflow (CI/CD): Streamline Development Processes
Building and implementing a new software application can be arduous, especially if the goal is to integrate generative AI capabilities into an existing product. Lamatic’s automated workflows simplify development processes by streamlining AI applications' continuous integration and delivery (CI/CD). The computerized processes help teams reduce errors, accelerate development, and get applications into production faster so users can benefit from the new technology.
GenOps (DevOps for GenAI): Optimize for Success
With Lamatic, you won’t just build AI applications. You’ll optimize them for success. GenOps is the next evolution of DevOps, specifically for generative AI applications. The practice focuses on the unique challenges of AI software, including:
- Model performance
- Drift
- Continuous training
By implementing GenOps, organizations can ensure their applications remain efficient and effective over time. Lamatic provides integrated tools to help teams monitor, evaluate, and retrain AI models as needed to keep applications running smoothly.
Edge Deployment via Cloudflare Workers: Enhance Speed and Performance
Lamatic’s platform enables organizations to deploy generative AI applications at the edge via Cloudflare workers. Edge deployment enhances application performance by reducing latency and load times.
It also improves the overall user experience by allowing quick, on-site data processing instead of relying on distant cloud servers. Quickly deploying GenAI applications on the edge can have a huge impact on business operations by speeding up processes and improving task automation.
Integrated Vector Database (Weaviate): Simplify Data Management
Generative AI applications can require hefty datasets to function correctly. Lamatic features a built-in Weaviate vector database to help users quickly manage data. The integrated solution allows for quick, efficient data retrieval to optimize the performance of AI applications. The database is also highly scalable, accommodating growing business operations and evolving AI applications.
