A C-Suite Guide to Building AI-Powered Mobile Apps

Implementation

19 September, 2024

Ifra Amin

Introduction

This article will serve as a strategic guide to building AI-powered mobile apps, emphasizing the challenges faced by C-level executives and the costs associated with their development. With careful planning and intelligent use of AI, your organization may survive and flourish in an AI-driven environment, transforming obstacles into chances for expansion and achievement.

The limitless possibilities brought in by artificial intelligence open doors for newer and smaller businesses to innovate and disrupt industries. According to a McKinsey report from 2017, only 20% of firms were willing to adopt AI for their products and offerings. This year, a McKinsey survey has reported that 72% of businesses around the globe are adopting AI for their products. Moreover, PwC predicts that AI will add an astounding $15.7 trillion to the global economy by 2030, influencing businesses worldwide. As a leader responsible for directing the business, you must keep up with this dynamic environment and find ways to use AI to your benefit.

Incorporating AI into your systems and building something exceptional in today’s market requires time, financial investment, and energy. To improve consumer experiences, increase efficiency, and fuel development with an AI-powered mobile app, you need more than simply technology—you need to identify the problem, set clear goals, invest in technology, and form a strategic partnership with a good IT provider to support you in this journey.

Most Common Challenges in Building AI-Powered Mobile App Development

Data privacy, job displacement, and societal ethics are just a few of the obstacles that AI research and development must overcome.

1. Integrating AI with Existing Business Processes:

Most decision-makers face the challenge of effectively integrating AI into existing workflows and systems.

Effectively navigating the space between AI innovations and existing processes necessitates careful planning and precise execution. Here are a few ways to optimize the integration process:

Utilizing APIs
Implementing modular architectures
Embracing agile strategies

2. Effective Management of Data Quality and Quantity:

Data is crucial for AI algorithms, providing the foundation for their learning and predictive abilities. Nevertheless, a lot of complexity is involved in controlling the amount and quality of data.

Strong data integrity can only be achieved by using varied and representative datasets and by strictly enforcing data privacy and security protocols.

3. Understanding Complex AI Algorithms:

One of the major hurdles in AI-powered mobile app development is the complicated nature of AI models. It’s important to remember that simple solutions are often more refined than complex ones.

Another issue with complex models is their lack of interpretability. They function as “black boxes,” with their internal functions hidden from the user’s view.

4. Addressing Ethical and Bias Concerns:

AI models often reflect the biases present in the data used to train them. AI models trained on skewed data raise significant ethical concerns. Consider AI facial recognition systems, for instance.

In a study conducted by the National Institute of Standards and Technology (NIST) in 2019, it was found that numerous commercial facial recognition products exhibited bias. Asian and African American individuals were consistently misidentified at a significantly higher rate compared to their white American counterparts, with the likelihood of misidentification being up to 100 times greater.

To avoid these biases, you must ensure the accuracy and quality of data provided to the AI model.

5. Talent Acquisition and Retention:

Businesses that need skills to create and manage AI systems face challenges due to the increased demand for experienced AI workers. It is essential to cultivate an atmosphere encouraging creativity and innovation to attract great talent.

It’s also important to provide your staff with ongoing training and education opportunities so they may advance their AI abilities.

Further reading: Guide to four actionable steps for business leaders to implement AI.

Building AI-Powered Mobile Apps: A Step-By-Step Guide

Leaders may take different approaches when it comes to creating AI software. This can be influenced by factors such as resources and skills at their disposal. Let’s talk about the most significant steps during this process that will help create an app with exceptional AI capabilities.

Step 1. Define the Problem and Goals:

Understanding the problem, you aim to solve with AI and establishing clear objectives is crucial for any software development project. This first stage is quite important because it directs the entire project.

It is essential to conduct meetings with project stakeholders to ensure the successful execution of objectives and outcome planning. These meetings serve as a platform to gather requirements, gain valuable insights, and align expectations. Perform a thorough analysis to determine if AI can address the issue at hand and define measurable benchmarks to gauge the project’s achievements.

Think about the app’s operations and features before deciding how to use the AI technology stack. The following questions need answers:

What is the problem that we are attempting to solve?

Consider Grammarly as an example. It aims to enhance clarity, accuracy, and professionalism by identifying and rectifying grammar errors in written communication.

How can AI contribute to resolving or enhancing this issue?

Grammarly uses advanced AI to thoroughly analyze text, detecting and offering valuable suggestions for enhancing grammar, punctuation, and style in real-time.

What kind of outcome should you anticipate?

Grammarly ultimately expects greater writing quality, customer satisfaction, and an increasing user base due to communication efficiency.

How would this help you?

In this case, Grammarly increased its subscriptions and market share as a business.

A leader must establish specific, quantifiable goals for the AI system. Consider KPIs that follow the SMART (Specific, Measurable, Achievable, Relevant, Time-framed) framework for tracking the progress of your app after your main goal has been determined. Here are some key performance indicators (KPIs) that are specific to AI:

Accuracy of the model
Precision of the model
Recall of the model
F1-score
Fairness of the model

These metrics are precious when it comes to assessing the effectiveness of your AI framework. Furthermore, it is essential to establish explicit development and deployment milestones.

FREE EBOOK A Comprehensive Guide to Ensure Your Business is Ready for AI Implementation Download a free checklist and eBook to ensure you team is ready to adopt AI.

Step 2: Start Collecting and Preparing Data

The foundation of building AI-powered mobile apps is rooted in data collection and preparation. High-quality training data is critical to the performance of even the most advanced AI models. You must avoid sacrificing data quality for quantity to avoid the hefty expenses of incorrect data. According to a Gartner survey, organizations suffer significant financial losses, averaging $15 million yearly, because of poor data quality.

The first step is choosing the appropriate datasets to train your AI model. Platforms such as Kaggle and AWS Data Exchange offer a wide range of specialized datasets that can be utilized without any cost. After selecting your datasets, the next crucial steps revolve around meticulous data preparation. This includes cleaning, preliminary processing, and wrangling to ensure the data is in optimal condition.

With the help of data cleansing services like OpenRefine and Tibco Clarity, errors like duplicate entries can be removed. Preprocessing goes beyond just cleaning your data. It involves various steps such as transformation, integration, standardization, smoothing, and reduction. Each of these steps is aimed at improving the quality of your data.

After the data has been preprocessed, it undergoes data wrangling to convert it into a format appropriate for training AI models. Once these stages are complete, your data is prepared for AI model training.

Bring Your AI-Powered App Vision to Life with AlphaBOLD!

Are you prepared to transform your revolutionary concepts into an effective mobile application powered by artificial intelligence? Explore how AlphaBOLD can assist you in reaching your objectives through our tailored AI solutions. Contact us today to schedule a personalized consultation.

Request a Consultation

Step 3: Choose the Right AI development Model and Tools

Depending on your project’s type and scale, you can choose a pre-trained, cloud-based AI/ML model accessible through an API or opt for custom AI development that utilizes a framework.

Custom AI Application Development:

This approach is most suitable for projects that require model architectures or when you intend to train models from scratch using your company’s data. This option is ideal for those who deeply understand AI app development and desire comprehensive control over every aspect of the process, from design and training to data preparation and evaluation.

Cloud-based AI/ML model:

This option is ideal for projects requiring fast and effortless integration of AI features, including chatbots, image/ speech study, and language translation services. This approach enables you to effortlessly incorporate AI capabilities into your applications without needing in-depth knowledge of intricate model structures or the burden of managing the computational resources required for AI model training. All you need is a basic understanding of web programming and the ability to utilize external APIs.

Consider choosing a cloud model from reliable vendors such as OpenAI, Google Cloud Vision, or IBM Watson. Utilizing their API allows you to integrate artificial intelligence capabilities seamlessly into your application.

Numerous cloud-based AI platforms offer extensive environments for developing, training, and deploying AI models.

Google Cloud AI
Amazon SageMaker
Azure Machine Learning

Step 4: Design and Train the AI Model

This is one of the most crucial steps in this guide to building AI-powered apps. Here’s what you need to know about designing and training the AI model.

For a pre-trained cloud-based model:

Cloud-based AI models are equipped with extensive pre-training, allowing them to handle various tasks from the get-go effortlessly. Nevertheless, it is crucial to personalize and fine-tune them to meet the specific requirements of each project. There are two primary strategies for this:

The complete model fine-tuning method involves updating the model’s settings throughout the training phase. It provides great flexibility but may require additional data and processing power.
The adapter module approach entails incorporating and fine-tuning a limited number of additional parameters while keeping the original model weights intact. It is a more efficient use of resources, although it may have limitations in terms of versatility.

Now, establish a meticulous fine-tuning process utilizing the AI framework of your preference.

First, we need to load the pre-trained model.
Preparing the dataset by splitting it into sets for training and validation.
Setting up the training parameters, such as the learning rate, number of epochs, and batch size. Starting the training phase, your particular data is used to teach the model.
Tracking the training procedure to avoid overfitting and making necessary hyperparameter adjustments.

Once the training is complete, assessing the model’s performance on a distinct test set is important to ensure its effectiveness on new and unseen data.

For a custom model:

If you want to build a customized AI model from scratch, choose a model architecture that fits your needs. Develop your model using deep learning. This requires defining model layers, functions for activation, and an output layer. Next, you can start training your model on the available dataset and continue following the process below:

Input data into the model, create predictions and fine-tune weights depending on performance.
Validate hyperparameters and prevent overfitting by considering batch size, epochs, and learning rate.
Run a test to evaluate the model’s general capabilities. Use problem-specific measures like accuracy, precision, recall, or F1 score as KPIs to evaluate the performance.

If the results don’t meet your expectations, you may need to acquire more data, try an alternative model design, or adjust the hyperparameters.

Step 5: Deploy the AI solution

Integrating an AI model into an existing app or creating a new one is an important skill when deploying an AI model.

Begin by verifying that the model is easily accessible and can be effectively utilized within the selected software environment. This step may involve encapsulating the model to improve its adaptability while creating the required infrastructure in the selected environment.

As a C-level executive and the driving force behind the AI deployment, ensuring that your development team has considered everything associated with the infrastructure setup is important. Ensure that your team has developed APIs, implemented microservices architecture, and has necessary resources such as computing capacity and storage at their disposal.

Furthermore, application-level networking and security settings frequently require adjustment. Once the model is deployed, establish a monitoring system to closely observe its performance and ensure it consistently satisfies user requirements.

Step 6: Maintain and Evaluate the AI Model

As time passes, the effectiveness of AI models diminishes as data and real-world conditions evolve. It is crucial for leaders to closely monitor the performance of the model and any changes in data patterns. This will enable you to take proactive steps, such as retraining the model with fresh information or making necessary modifications to ensure its precision.

In addition, the dynamic nature of AI and ML can modify existing legal and ethical norms. You should stay well-informed and adhere to data privacy regulations such as GDPR and CCPA. Additionally, it is important to actively work towards minimizing bias and promoting openness throughout AI decision-making processes.

Many businesses partner with a reliable IT services company to maintain and support their artificial intelligence-powered applications while meeting business objectives.

Cost Considerations in AI App Development

Even though the initial costs of developing an AI app might appear high, it’s important to consider the long-term rewards that AI-powered solutions can provide. This guide to building AI-powered apps will give you a basic overview of what counts when it comes to costing for an initiative such as this.

How Much Does It Cost to Build an AI-Powered Mobile App Solution?

We can break down the price of creating an AI solution into its parts by looking at the following:

Development Team Compensation: The expertise and size of the development team can significantly impact costs.
Data Preparation and Collection: Gathering and preparing high-quality data is essential for training AI models and can be resource-intensive.
AI Model Training: Depending on the complexity of the AI models, training can require significant computational resources and specialized expertise.
Development Tools and Platforms: The choice of development tools, platforms, and frameworks can influence initial and ongoing costs.
Infrastructure and Hosting: The solution’s infrastructure, whether on-premises or cloud-based, must support scalability, high availability, and reliability. This includes considerations for platform/cloud infrastructure, security, and compliance.
User Experience (UX) Design: Investing in a robust UI/UX architecture ensures the solution is user-friendly and engaging.
Maintenance and Updates: Ongoing maintenance, updates, and scaling efforts are necessary to keep the application performing optimally.
Security and Compliance: Ensuring compliance with industry standards and regulations adds another layer of complexity and cost.

For a deeper understanding of how changes in requirements change the cost structure, refer to this table below:

AI-Powered Application	Cost Details
Basic	A basic AI app usually entails the implementation of pre-trained models or the integration of existing AI services. It allows for limited customization and training on information. Developing these apps can be cost-effective for businesses seeking to integrate AI features into their offerings.
Standard	Mid-range AI applications typically require custom modifications to existing AI models or the development of new models using specialized training data. Therefore, they require more investment, time, and energy than basic AI models.
Advanced	Advanced AI applications necessitate the development of custom algorithms from the ground up and thorough training using substantial, top-notch datasets. Due to the considerable complexity and specialized expertise required, these AI functionalities can cost a lot.

Why Choose AlphaBOLD for Your Custom AI-Powered Mobile App?

At AlphaBOLD, our team of experts provides comprehensive support throughout every step of the journey. We will help you define your AI strategy, choose the most suitable technologies, and handle everything related to developing your AI-powered solutions.

When you choose AlphaBOLD to build AI software, you get a team with unparalleled industry knowledge and competence across various platforms. Our expertise extends beyond app development. We provide custom solutions that are designed to work in conjunction with your current infrastructure.

To fulfill your unique requirements, we do thorough research and development to guarantee that you will obtain innovative, cost-effective solutions. If you are looking for an AI partner who can help you build a custom app that achieves commercial success, your search ends here.

Here’s what another CEO has to say about partnering with AlphaBOLD for custom software development:

Conclusion

Developing a mobile app powered by AI can be a smart decision for your business, helping you improve operational efficiency, boost customer satisfaction, and gain a competitive edge. AlphaBOLD specializes in converting complex AI technologies into customized, user-friendly applications catering to your business requirements. From integrating AI into current processes to managing data quality, we have a deep understanding of C-level executives and the difficulties they face.

Collaborate with Our AI Experts and Find the Support You Need!

Mastering the intricate details of AI implementation can prove daunting. AlphaBOLD is committed to providing comprehensive support throughout your journey. Schedule a consultation to discover how our extensive knowledge can assist you in creating a standout mobile app powered by AI.