AI development is more popular than ever now that businesses understand just how powerful business process automation through AI can be. While a quality AI developer can get tools live relatively quickly, in-house projects can be a bit more complex. To help businesses understand how to do the AI development process the right way, we’ve developed a 7-step process for businesses to improve the odds of a successful AI development project:
7T’s AI Development Process
| 1 | Business Requirements | Define objectives, assess the feasibility of using AI models, and outline success criteria |
|---|---|---|
| 2 | Data Requirements | Identify data sources, audit data quality, and explore data characteristics |
| 3 | Data Preparation | Data cleaning, wrangling, labeling, and feature engineering |
| 4 | AI Prototyping | Model Selection, experimentation, comparison |
| 5 | AI Model Development | Select algorithms, experiment, train models, and tune parameters |
| 6 | AI App Implementation | Core development of UI/UX, business logic, ML Model integration(s), including enterprise data chunking, embedding and vectorizing, back-end integration(s), orchestration and workflow management, logic for error handling and resilience |
| 7 | Evaluation, Testing, & Monitoring of AI | Test for performance, fairness (Weights and Biases), reliability, compare against business goals, assess with new/holdout samples |
| 8 | AI Operationalization | Deploy AI model to production, monitor drift, build feedback loop, retrain as needed |
| 9 | Project Review | Gather feedback, measure impact, document lessons learned, close or iterate |
| 10 | AI Application Versioning | Covers ongoing support and updates of prompts, code (including Python, Jupiter, etc), data, models, experimentation, prompts, and pipelines |
Let’s break down this process and how each step improves your final product. We’ll also explain how the steps change when you hire a custom AI software developer, so you know what to expect from a quality third party.
Step 1: Business Requirements
To begin the development process, your team must conduct comprehensive stakeholder interviews across all departments that will interact with the AI system. This involves mapping current workflows, identifying pain points, and defining measurable success criteria.
Your business analysts will also need to assess AI feasibility from the outset. This is done by evaluating whether your problem can realistically be solved with current AI capabilities. If it can, your team must then establish clear project boundaries, budget constraints, and timeline expectations to lay out a clear project framework.
With an AI Development Partner
An experienced AI development partner brings proven frameworks for requirements gathering, often having worked across multiple industries and use cases. These processes and frameworks can expose hidden requirements you might miss internally. They can also identify opportunities for AI implementation that your internal team might overlook, ensuring you maximize the potential return on investment. Some providers even offer at-home tools to identify potential opportunities in minutes, like our AI Genie.
Step 2: Data Requirements
Once the initial business requirements are outlined, your internal team must conduct a comprehensive data audit across all systems, databases, and data sources within your organization. This includes cataloging data types, volumes, quality levels, and accessibility. The goal here is to identify gaps in your data collection and determine what additional data sources or storage infrastructure may be required.
The team must also evaluate data privacy and security requirements, especially when dealing with sensitive business information. You’ll need to establish data governance protocols and ensure compliance with relevant regulations like GDPR or HIPAA so that these can be implemented within your AI project.
With an AI Development Partner
An experienced AI development partner brings systematic data assessment methodologies that have been proven across multiple projects. These are used to quickly identify data quality issues, recommend strategies, and establish connections to external data sources when needed. Partners also often have established relationships with data providers, allowing them to streamline the process of acquiring third-party data storage. Due to the amount of time and resources these initial steps take, providers sometimes offer a la carte assistance with just these beginning steps with apps like our AI Transformation Studio.
Step 3: Data Preparation
Data preparation is often the most time-consuming phase of any AI project, typically consuming 60-80% of the total development timeline. Your team must clean up inconsistent data formats, handle missing values, and standardize data across multiple sources so a unified AI platform can uniformly access it.
This involves:
- Writing custom scripts for data extraction, transformation, and loading (ETL)
- Implementing data labeling processes, which may require subject matter experts to manually tag thousands of data points
- Feature engineering as your team identifies which data attributes will most effectively train your AI models
As a result, this step is essentially three steps in one, which is why it is such a time-consuming part of the development process.
With an AI Development Partner
Professional AI developers have automated tools and proven workflows for data preparation that can reduce time spent in this phase from months to weeks. They bring experience with various data formats and can quickly identify the most efficient cleaning and transformation approaches. Partners often have access to advanced data labeling tools and can leverage techniques like active learning to minimize manual labeling requirements. Their feature engineering expertise also helps identify the most predictive data attributes faster than internal teams typically can. This is also included in our AI Transformation Studio.
Step 4: AI Prototyping
The prototyping process involves iterative testing, comparing model performance across different metrics, and fine-tuning hyperparameters to optimize results. During the prototyping phase, your team will experiment with different AI models and algorithms to find the best solutions for your objective. This requires significant technical expertise in machine learning frameworks, and your team must understand the strengths and limitations of various model types.
Experience in both traditional machine learning algorithms and deep learning neural networks will be necessary to make some of the decisions involved in this step.
With an AI Development Partner
AI development partners bring extensive experience with model selection and can quickly identify the most promising approaches based on your specific use case and data characteristics. Because their primary workflow is selecting and implementing AI tools for a wide variety of use cases, they have established testing frameworks that accelerate the prototyping process and can leverage pre-trained models where available. We can even take businesses from step 1 all the way to this prototyping step in our comprehensive AI Launchpad program to give them the tools to get the rest done themselves.
Step 5: AI Model Development
After prototyping and selection, model development requires deep technical expertise in algorithm selection, training methodologies, and parameter optimization. Your team must understand concepts like overfitting, underfitting, and bias-variance tradeoffs– something that can be tough to achieve without hiring AI expert staff.
This phase involves extensive experimentation with different architectures, training techniques, and validation strategies. It also requires significant computational resources for model training, which may require cloud infrastructure setup and management if you don’t have the necessary hardware within your existing network.
With an AI Development Partner
Professional AI developers bring both the necessary model development methodologies and access to advanced computational resources. They also come in with an innate understanding of which algorithms work best for specific problems and can optimize training processes for both performance and cost efficiency. Partners typically have established MLOps practices that ensure reproducible and scalable model development as well, ensuring better long-term results and a tool that consistently improves over time.
Generally, if your partner is handling this step, you’ve likely entered a full-service custom software development contract that will cover all the way through step 10, such as 7T’s AI Development Process. The best developers can offer this on a fixed-price model to ensure you aren’t subject to extra costs if this process gets more time consuming or resource-intensive than originally predicted.
Step 6: AI App Implementation
The implementation phase requires full-stack development capabilities, which include both AI/ML expertise and traditional software development skills.
Your team must:
- Design and build user interfaces
- Implement business logic
- Integrate AI models
- Develop APIs for model serving
- Implement data pipelines for real-time processing
- Build monitoring systems for system accountability and reliability
Enterprise businesses have their own specific requirements in addition to those above, like single sign-on integration, role-based access controls, and system scalability considerations. Developers on the project need expertise in cloud platforms, containerization technologies like Docker, and orchestration tools for managing complex AI workflows.
With an AI Development Partner
AI development partners have profound experience bridging the gap between experimental models and production-ready applications. Established architectures for AI application development and proven frameworks for model serving/integration sped up this step exponentially. Partners with enterprise experience also understand enterprise-specific requirements and can build robust, scalable applications that meet your industry’s security and performance standards.
Step 7: Evaluation, Testing, & Monitoring of AI
Testing AI systems is far more complex than traditional software QA. It requires specialized knowledge of machine learning validation techniques beyond traditional software testing, and your team must establish testing protocols for model performance, bias detection, and fairness evaluation using frameworks like Weights and Biases.
This involves creating holdout datasets, implementing A/B testing frameworks, and establishing performance benchmarks against business objectives. Testing for edge cases, data drift, and model degradation over time is also essential to ensure that your model improves over time instead of eventually becoming confused and unusable. The team must also implement monitoring dashboards to track KPIs and establish alert systems for when models fall below benchmark.
With an AI Development Partner
Experienced AI developers bring comprehensive testing methodologies, with established protocols for bias detection, fairness evaluation, and performance monitoring that ensure your AI system meets both technical standards, regulatory requirements, and core business KPIs. Partners top this off by implementing advanced monitoring systems that provide real-time insights into model performance and can proactively identify issues before they impact business operations, drastically improving QoL as the tool is used over longer time periods.
Step 8: AI Operationalization
Deploying AI models to production requires expertise in MLOps practices, as your team must:
- Establish monitoring systems to detect model drift
- Implement feedback loops for continuous learning
- Create processes for model retraining when performance degrades
- Set up cloud infrastructure
- Manage computational resources
- Ensure availability and scalability over time
You’ll also need to establish governance processes for model updates and rollbacks when issues arise, as the goal of ML-powered platforms is constant improvement as they are fed more context through regular business use.
With an AI Development Partner
Professional AI developers have proven MLOps practices that ensure smooth deployment and ongoing operation of AI systems. They bring expertise in cloud infrastructure management, automated deployment pipelines, and comprehensive monitoring systems that keep your AI system performing at its best in perpetuity.
Step 9: Project Review
The goal of the project review phase is to evaluate your platform’s outcomes against your original objectives. Your team must gather feedback from all stakeholders through processes like analyzing user adoption rates, measuring business process improvements, and calculating actual cost savings or revenue generation. Then, they should measure the real impact against projected KPIs and document lessons learned for future AI initiatives and platform updates.
With an AI Development Partner
AI development partners bring structured project review criteria informed by countless historical project successes. They know exactly how to measure both technical achievements and business impact, providing detailed reports that demonstrate ROI and identify opportunities for optimization. Additionally, they can provide excellent strategic recommendations for future AI initiatives based on their experience in similar industries.
Step 10: AI Application Versioning
Ongoing maintenance of AI applications requires continuous monitoring of multiple components, including prompts, code, data, models, and processing pipelines. Dev teams must establish version control systems for all AI components, implement regular update schedules for models and data, and maintain compatibility across different system versions.
This includes managing Python environments, Jupyter notebooks, experimental code branches, and production releases. They also must track model performance over time, schedule regular retraining cycles, and maintain documentation for all system changes. This gets exponentially more challenging when managing multiple AI applications or models simultaneously, as this requires sophisticated orchestration and dependency management to avoid a technological hydra, wherein one problem is solved and three others pop up in its place.
With an AI Development Partner
Professional AI developers’ aforementioned DevOps and MLOps practices ensure seamless ongoing maintenance for AI applications. They have established processes for version control, automated testing, and deployment of updates across all system components, and they typically provide ongoing support contracts that include regular model updates, performance monitoring, and proactive maintenance.
Their experience managing multiple client systems means they can efficiently handle updates, security patches, and feature enhancements while minimizing disruption to your business operations.
Get Help With the AI Development Process From 7T
If you’re unsure whether or not you want to take on such a big project in-house or simply prefer the partnership experience outlined for each step of the process, you may want to consider working with a third party for your AI development process.
At 7T, we use a “Business First, Technology Follows” approach to implement AI/ML solutions for our clients across countless industries. Our custom-built platforms leverage machine learning and AI technology to deliver significant operational advantages with a robust ROI. Our team will audit your organization’s challenges, often showing up on location to embed ourselves in your business for a time to understand your needs from a first-person perspective. Then, we’ll architect a value-generating solution to transform your vital processes and meet your goals.
7T is based in Dallas, Houston, and Charlotte, NC, but our clientele spans the globe. If you’re ready to learn more about the AI development process, contact 7T today.
