AI Application Development Guide for Business Owners 

• By Diksha Sharma
• 13-06-2022
• Artificial Intelligence

Artificial intelligence has become a buzzword; the truth is that the concept has been around since the 50s.

Technical advancements in the field of artificial neural networks, statistical algorithms, and data analysis have acted as a catalyst in the growth of AI. Substantial qualitative change can be delivered by technology that can help business organizations to grow and create new opportunities. Corporate giants have already started to invest in AI development services.

What is Artificial Intelligence?
AI is a field of computer science with the objective of creating intelligent machines; and these machines solve problems like humans but in a more efficient manner. Machines are fed with abundant information about the surrounding world so that they can make intelligent and informed decisions. The term “Artificial Intelligence” was first used in 1956, and the development in the field really picked up pace in the 2000s.

Examples of AI include computer chess games, voice-activated personal assistants, autonomous vehicles, etc. Various industries use artificial intelligence solutions.

Why does AI matter?
Artificial intelligence is here; it’s no longer futuristic or unreal. We use AI in our real lives! As per TechJury, 77% of the world population uses AI-powered devices or services, but only 33% are aware of it. Voice assistants such as Alexa, Siri, chatbots on eCommerce websites, and personalized recommendations on Netflix or Amazon are all examples of AI.

AI-Powered Apps
The mobile app market is thriving, with over 5.19 billion mobile users. Therefore, one of the main areas of expansion of AI is mobile apps. By incorporating AI in mobile app development, there can be endless opportunities for user personalization and tailoring. With AI, businesses can gather crucial information about customer preferences, and they can be offered highly contextualized and unique brand interactions.

Mobile Apps Bots
We are all quite familiar with chatbots; they ensure 24/7 service and instantly resolve customer issues. According to Gartner, 85% of customer interactions will start with self-service in 2022, up from 48% in 2019.

Virtual Mobile Assistants
When it comes to virtual mobile assistants, there are many options. They involve highly complex technologies that involve collecting heaps of data, which is fed to AI solutions. Virtual assistants merge image and speech recognition, Machine Learning, Natural Language Processing, and other components of artificial intelligence to improve user experience.

AI in Web Development
In web development, AI covers AI-infused user experience, voice-based search, chatbots, etc. These technologies can be used to facilitate buyers’ journeys and improve access to products and services. AI and data science provide analysis of user preferences, user behavior, and feedback so that brands can curate solutions accordingly.

How to Build an AI App from scratch for Your Business?
The development of AI solutions is research-based. If the business wishes to come up with an innovative and complex solution, the AI features of the app are the riskiest part of the project.

Integration of AI Components into an Existing Solution
Integration into an existing solution is different from building a new app. The main issue is that the existing app is built without considering any AI features, hence the architecture of the app is different. Integrating AI components requires changes in the app architecture.

With respect to AI, existing apps may be classified as follows:
DB-based projects:
Recommendation systems
Chatbots
Text processing
Time series forecasting
Non-DB based projects:
Voice / sound processing
Image / video processing

Business owners who want to develop AI apps need to understand the main Stages of the AI App Development Process
Business Analysis Stage

The very first stage is the business analysis stage in the development process. At this stage, business problems are considered, and the business problems are addressed to app development companies. For instance, a supermarket wants to achieve a balance through the analysis of purchases and sales. AI engineers would do a relational analysis task to find a solution that predicts specific numbers.

ML Problem Determination Stage
This stage takes into account the technological capabilities of subfields of AI like Natural Language Processing, computer vision, speech recognition, generative AI, forecasting, etc.

Data Collection
The data collection stage is of utmost importance as data is the fuel that drives AI/ML. Here, one has to narrow down the target audience, demographic, age, gender, and other important factors. For data, six sources can be used:

Open-source public datasets
Scraping
Data annotation or unsupervised
Collection of data with a simpler product
Domain-specific data
Synthetic data

PoC Development Stage
The step outlines business and technical metrics, and they differ significantly from project to project. PoC is beneficial in depicting the minimal accuracy possible. PoC is based on a hypothesis and solves a specific task. After the input preparation, working on PoC is started and the team measures the results..

Iteration and the Production Stage
After the first PoC, new iterations or further improvements can be made. Making a new PoC means the processing of cases, error analysis, data edition, etc. Iterations depend upon the project requirement and are conditional.

Wrapping Up
AI projects are linked to risks. Risks and challenges can be faced with the help of data suitability. It’s wise to start with product development when the accuracy of AI components meets the business expectations and goals. Initial steps towards embracing AI can seem uncertain, but with top ai development companies, these risks can be reduced drastically.