A few decades ago artificial intelligence software was exclusively the realm of science fiction, but now it has turned into a daily routine for many. For instance, the one that doesn’t need introduction - ChatGPT, a tool that has become a game-changer for all business industry sectors all over the world. With such pervasive and fast implementation of AI in our lives, along with augmented software that enhances our user experiences through every possible gadget, we are witnessing a major technological shift that is yet to unfold.
According to Global Market Insights, the wearable AI market is forecasted to reach USD 180 billion by next year. The global wearable neural network market value includes successful products such as Apple Watches and Fitbits. As we go into the details about AI in software development, the more obvious it becomes that this technology is not a trend but a structural change in how software is envisioned, built, and deployed.
AI implementation: what it means for business
Our world is data-driven in many areas. According to consulting firm McKinsey & Company, there has been a doubling in the use of artificial intelligence in business operations since 2017. One of the reasons AI technology is in demand is because it can be customized to meet the unique needs of each organization. That said, 63% of McKinsey respondents expect their investment in AI technologies to increase over the next three years. Experts note: for AI to benefit and add value to business strategy, an organization must clearly understand:
its business functions;
how AI works;
the aspects of the business that can be improved by implementing AI.
AI implementation is all about turning raw data into useful insights using advanced algorithms, and carefully customizing data and processes according to business objectives. This helps the business in decision-making, improves efficiency and promotes innovation. However, creating sophisticated machine learning algorithms or neural networks is a complex game of data collection, data processing and analysis. These algorithms are designed and created to identify patterns, make predictions, and automate tasks to optimize various processes.
A good example of AI implementation in business operations is shown by Amazon. The Internet service, the largest by turnover in the world, successfully applies user recommendation systems, thanks to which it makes 35% of sales. There is evidence that three quarters of consumers are more likely to make a purchase after receiving personalized recommendations.
It's not just e-commerce and retail that is demonstrating best practices. Many industries such as streaming companies, tourism, media and entertainment, finance, healthcare, insurance, real estate and others are successfully interacting with AI.
The healthcare industry is also being boosted by the adoption of artificial intelligence software solutions and machine learning algorithms. In medicine, it is important to provide accurate image processing, analysis and interpretation for diagnosis. Personalization of prevention and individualized treatment plans improve prognosis as they take into account important factors: genetic information, patient's lifestyle, medical history and other data.
In other business areas, such as artificial intelligence operations analytics (AIOps), AI is being applied to IT operations, speeding up the way teams look at large data sets. This identifies anomalies, optimizes system performance by automating searches, and troubleshoots problems.
Artificial intelligence models must learn to become more sophisticated over time. When we try to describe the uniqueness of artificial intelligence in technology, our team often thinks of it as a perpetual motion machine that goes through training and feedback and can self-optimize to become more accurate and productive. Once trained, this machine goes to work to make predictions and inference about future trends, customer behavior, or market conditions.
Continuous learning and adaptation is the way to implement AI. Machine learning models are constantly updated and retrained due to data processing and business requirements. In fact, AI-powered chatbots can support customers 24/7 and handle simple requests on their own, freeing up employees to handle more complex tasks. Generative AI tools like ChatGPT help content teams come up with ideas and create unique content.
Importantly, AI improves companies’ cybersecurity and helps prevent data leaks. A deep learning model analyzes network flows for similar threats to help organizations protect confidential and sensitive information.
Benefits of AI in software development
It is evident that AI technologies have already been successfully integrated into personal and professional aspects of humanity. Businesses are being helped by such solutions to be more efficient, faster, accurate and profitable. And in the future, the role of AI-enhanced software will grow. Especially since AI coding assistants have been developed to create other AIs. This rapid evolution of artificial intelligence allows engineers to create better versions with improved abilities.
AI interacts with software on two levels:
As an assistant to developers, AI in software development performs code generation, requirements gathering, planning, evaluation and design, code quality control, and feedback management. The technology improves product quality and speeds time to market. For example, tools such as GitHub Copilot use natural language processing to suggest code snippets, simplifying the coding process and allowing software development teams to focus on solving complex problems.
Integrating existing AI into a solution under development or creating AI powered tools for a specific purpose. This means that AI assisted development model can take over rapid task completion without human influence (human control may or may not remain). And empower the human and the company by providing accurate planning, bug detection, reduced workload, predictable workflow, better safety and quality control. Through accurate estimation, decision making is improved.
So... What are the benefits of implementing AI?
Automation of repetitive tasks
Automating time-consuming tasks saves employees time and allows them to focus on strategic and creative work. It also reduces errors associated with manual data entry and repetitive actions. This can be done using various software tools (e.g. Selenium or Eggplant Digital AI) and methods to optimize manual work. AI can also predict bottlenecks or potential problems, allowing teams to solve them in advance.
Scalability and flexibility
Scalability is extremely important in modern software. When workloads grow or the user base increases, artificial intelligence is able to predict and manage those needs. A good example: the Google Cloud platform with artificial intelligence can automatically scale based on demand to provide better performance and increase customer satisfaction.
Flexibility in software means rapid change according to current needs. AI helps with data analysis and issues recommendations for the latest features and updates. This way, your software will always be up to date with the latest market requirements.
Improved user experience and personalized service
The interface becomes more responsive and personalized for the user. Any contact is intuitive, including the use of inclusive options (e.g. voice commands).
A personalized interaction between a company with AI powered software tools and the user will always be more accurate, efficient, tailored to specific needs, and therefore lead to benefits such as:
increase in time spent on the website;
increased conversion rates;
increase in AOV (average order value);
incentivizing the number of sales;
user retention and repeat sales - those who use recommendations are 37% more likely to return, whereas without the use of recommendations, the probability of return is at 19%;
optimizing operational efficiency - the right product finds the right customers at the right time.
Innovation in products and services
AI is important in driving innovation by enabling faster iterations and more intelligent solutions in software products. While AI is not the sole driver of technological advancement, it enhances engineers' ability to implement advanced features and respond quickly to changing user demands. Using AI, software engineers can accelerate product development cycles, improve data-driven decision-making, and create highly adaptive and scalable solutions. As a result, AI-enabled products and services evolve faster, delivering innovation and value to customers.
Data-driven decision making
AI-based decision making has a number of advantages:
objectivity;
is based on numerical data;
is displayed in statistics.
It's easy to verify how a decision affects metrics, and it allows you to evaluate performance. Speed of response to opportunities and challenges, transparency and fact-based accountability are the benefits that drive any business to success.
Improved risk management
Compared to human intelligence, the AI solutions being developed detect risks and potential problems long before they occur. This allows companies to be proactive. Anomalies are detected in real time, thus identifying suspicious patterns in security breaches or other forms of operational risk.
In business, artificial intelligence identifies financial, operational, legal, reputational risks and also identifies cybersecurity threats. An example of an AI service for risk management in finance is IBM Watson for Risk & Compliance. It analyzes a large volume of regulatory documents and suggests how changes will affect the company, predicts potential financial losses, automatically assesses risks and identifies fraudulent transactions.
The benefits of AI in risk management boil down to:
automation;
speed;
accuracy;
predictability;
adaptability;
cost reduction;
integrated analysis.
Cost reduction
In the long run, software leaders can realize a higher return on investment by shifting their focus from cost reduction to value creation. These are the benefits that AI can unlock for software developers. Here's one use case: AI-powered cloud platforms like Amazon SageMaker can automatically increase or decrease resources based on demand, reducing infrastructure costs.
Expanding the capabilities of existing AI systems
AI has a profound transformative impact on software development by enhancing existing AI systems. This iterative process, called AI augmentation, utilizes AI to:
doing the “dirty work” of analyzing large data sets for patterns and uncertainties to fine-tune models;
automate routine activities such as data cleansing and model deployment, allowing developers to focus on key projects.
Embedding it in an application allows user data to be collected so that the system can learn and continuously improve performance.
Risks of using artificial intelligence in software development
With all the opportunities that the use of AI brings to businesses, there are still certain risks. Companies need to be aware of these risks and minimize their impact, test AI solutions thoroughly, adhere to ethical and safety standards, and maintain a balance between automation and human control.
Here are 5 risks of using AI in software development and how our team combats each of these risks:
1. Biases, inaccuracies, and errors
Algorithms can produce errors due to biased data, biases in the model, and incorrect settings.
The solution to the problem:
Provide AI systems with diverse datasets when training and audit the data regularly to identify biases.
Verify that the decisions made are consistent with the actual patterns and correlations in that data.
Utilize tools to detect bias.
2. Security and privacy vulnerabilities
Hackers can target massive data sets in large language models that are processed by AI. In addition to targeted cyberattacks, data can be poisoned accidentally by training the system on unreliable or unverified sources. Both types of poisoning can occur either within an active AI chatbot application or from the LLM supply chain. These utilize pre-trained models, crowdsourced data, and insecure plugin extensions that can lead to biased data output, security breaches, or system failures.
If not handled properly, the use of sensitive data through AI can jeopardize user privacy. Without adhering to strict protocols, there is the potential for data leakage or unauthorized access.
Problem Solving:
Your company should make sure it uses secure protocols, such as SSL encryption, and follow data protection legislation (GDPR).
Implementing strict access controls will prevent those who are not authorized to handle information from accessing it.
Address liability for legal and ethical risks in the event of AI errors.
3. Dependence on technology and loss of human control
Over-automating processes in AI coding can upset the balance of AI and human influence. When a system fails or malfunctions, the dependent business can become paralyzed. Also, when AI lacks detail, it makes decisions based on little data, resulting in poor generation of required results.
The solution to the problem:
Organizations should adopt a human-in-the-loop approach.
Avoid a situation of lack of human context or oversight.
Work with large datasets during the training process.
4. Unpredictable behavior of AI
Sometimes the introduction of AI in software development leads to unexpected results. Because algorithm-based decisions in data don't always match the problem statement or its context. This is another reason why a team of professionals should always conduct a thorough review after the AI accomplishes a task.
To reduce unpredictability, you need to:
train the AI-generated results under different conditions;
use explainable AI techniques that make decisions understandable.
Importance of AI in software development in different industries
As you can see, there are some drawbacks, but we want to show you that the importance of AI in software development outweighs its possible risks. The connection between enterprise AI adoption and software development has become a two-way beneficial partnership with two major levels of engagement with software engineering. Here are a few examples:
AI, coupled with its immense processing power and storage capabilities, easily surpasses human abilities, helping developers solve more complex problems.
Software creators are often tasked with implementing AI for small businesses or implementing AI in the industry to get fast, cost-effective solutions with minimal risks, and hence outperforming competitors.
AI in software development process
What is the impact of AI-based software on the development lifecycle? Positive: code becomes cleaner, more efficient, testing and debugging are less time-consuming, and predictive maintenance becomes possible.
Artificial intelligence will help software companies and technology startups to get an innovative solution for end-to-end automation of tasks, optimize resource allocation, and scale for use in different industries.
AI in healthcare software development
AI in healthcare software development is making a fundamental shift through its application in medical imaging, drug discovery, patient diagnosis, and individualization of treatment plans.
We've been able to see this change by partnering with Embryonics, an innovative healthcare company that is expanding IVF capabilities through artificial intelligence. We collaborated to develop a HIPAA-compliant web application that supports an innovative algorithm for predicting implantation success. Another of our goals was to create an interactive interface for physicians and patients and integrate the platform with AWS to improve performance.
AI will ensure that tons of medical data analysis will improve patient outcomes and speed up research.
Banking and financial services
Fintech is a field that is building efficiency and improving customer experience with the help of artificial intelligence. The opportunities for AI in finance are numerous:
credit scoring - access to finance is enhanced when creditworthiness is assessed against an expanded data set;
legal compliance - analyzing regulatory requirements and minimizing violations;
automation of processes and reduction of time for financial analytics, processing of loan applications and payments;
personalization of services - analysis of client behavior and individual offers;
chatbots and voice assistants for 24/7 service;
trading and investing - real-time market analysis to help traders make decisions;
anti-fraud systems - algorithms can detect and prevent suspicious transactions that look like fraudulent transactions.
Example: The UK's Financial Conduct Authority (FCA) used synthetic payment data to enhance an artificial intelligence model to accurately detect fraud, avoiding the disclosure of real customer data.
Travel/Leisure
Tourism is a fertile area for AI technologies and machine learning software development. Using the example of Voyagi, an online travel planner we developed, we are rethinking the entire travel planning process. The new-generation AI augmented service combines many functions into a single platform: itinerary planning, booking restaurants and museums, searching and booking airline tickets and hotels, communicating with friends, and travel templates. With the introduction of AI, travelers can generate personalized trips based on preferences, budget, and destination.
Insurance
AI helps insurers process claims, detect fraud, assess risk, and automate customer service. It reduces costs and increases efficiency by analyzing data for intelligent predictions. For example, Allstate uses AI to estimate a driver's future risk based on their driving behavior and a number of other factors.
Retail & eCommerce
AI transforms retail data into intelligent predictions that improve customer service, increase revenue, and optimize operations. It transforms businesses by delivering more innovative shopping experiences and new ways to solve problems.
AI-powered recommendation engines are changing the game and leaving traditional competitors behind. The customer interacts with recommendation engines that personalize their journey. This is reflected in:
tailored homepages;
product recommendations;
relevant search;
emails and notifications;
dynamic cart recommendations for cross-selling and upselling.
The efficiency and profitability of retail businesses now directly depend on the implementation of AI. The recommendation engine market is projected to grow 7.5 times, from $2.12 billion in 2020 to $15.13 billion in 2026.
Manufacturing
Did you know that the AI Manufacturing Market was valued at around $2.3 billion in 2022? It is expected to grow to $20.8 billion by 2028. AI is improving the manufacturing industry, mainly through increased efficiency, reduced costs, improved product quality, inventory optimization, and predictive maintenance. AI can help companies make data-driven decisions to reduce defects and ensure high standards through data analytics and predictive maintenance.
FoodTech
Al in the food industry uses data analytics and machine learning to improve food production, precision farming, quality control, supply chain management, and customer service. They also offer many benefits, including improved customer service through chatbots and virtual assistants for efficient order processing and personalized recommendations.
We also had a chance to implement AI in the food tech industry once. We developed an app that uses AI to create recipes for products in grocery stores. The idea was to train a neural network on a large number of recipes so that it could suggest unique combinations of ingredients that affect the taste, nutritional value, and cost of the product. Once the AI has suggested a recipe, you can provide feedback to help the algorithm refine it and create a recipe that meets consumer preferences and business goals.
LawTech/Legal
AI tools for lawyers can help them automate manual processes to increase efficiency. However, as AI technology advances, legal professionals need to understand how to balance the benefits with potential risks, as well as the ethical issues associated with the use of AI in law and legal practice. For lawyers, AI is the best solution because it allows for faster research with less risk of inaccuracies or missing documents.
By the way, we have developed an app for lawyers or judges where they can enter detailed data about the defendant, such as criminal history, age, type of crime, sentencing method, etc. The AI would then provide a predicted verdict based on the analyzed data. It will be pre-trained on thousands of court cases and their verdicts, allowing it to make accurate predictions.
Transportation and Logistics
AI driven tools improve logistics through route planning, inventory management, and demand forecasting. This saves money, increases efficiency, and helps the company reduce its environmental impact and improve customer satisfaction.
Professional Services
Professional AI services improve productivity by automating project management, bug tracking, and code review. This allows the development team to focus on strategic aspects of their work, such as product innovation, improving workflow efficiency, and customer satisfaction. In our SignifAI example, we built server management and monitoring services, but let’s talk about it in more detail.
SignifAI uses AI and machine learning algorithms to monitor server performance and gain operational insights into impending issues. It is a solution for proactive problem solving by correlating events of interest with performance optimization by DevOps and SRE teams.
Telecom
By analyzing data to predict traffic to avoid congestion, AI based tools improve network efficiency. It ensures good connectivity and allows networks to handle more traffic without sacrificing performance.
Education
AI enriches the learning experience with personalized instructions and automated assessments. Incidentally, this makes the educational experience richer and more interactive for students, enabling them to learn better in the future.
At Overcode, we can help you with custom AI implementation, i.e. providing you with ready-made AI solutions that you can integrate into your business. Whether you want to improve the interfaces in your product or integrate AI into specific software use cases, our team is ready to help.
Check out our case studies and learn how deep learning or machine learning can help your business.
How to Incorporate AI into Your Business
Implementing AI is a process that requires planning. When a company is considering how to implement AI, these questions can help guide them.
Find the problems
Have your team identify pain points and tasks that are time-consuming, error-prone, or boring. Anything from data entry and scheduling to creating social media content.
Identify the goals of your AI implementation
Finding a way to solve problems requires identifying the goals.
This could be a competitive advantage, increased capabilities, scalability, risk management, cost reduction, greater efficiency, or personalized customer service.
Choosing an AI method
Work with the experts at Overcode to find the right tool for your business. There is a wide variety of AI tools and models that cover planning, forecasting, automation, resource management, reporting, and cybersecurity. Different models are adapted to different types of data and learning tasks: supervised and unsupervised ML, reinforcement learning, deep neural networks.
Measuring success
Define clear and actionable KPIs that align with your business goals. They should reflect the impact you expect from each AI integration.
Achieving AI Excellence
You now have a quick overview of how to build AI software that aligns with your vision. Use a step-by-step approach because it facilitates employee training, accelerates ROI, and drives broader AI adoption to drive continuous innovation and business transformation. And don’t forget about the synergies with human intelligence.
How to Develop Artificial Intelligence Software
The process of developing AI software is different from traditional software. One of the key steps is integrating AI and adapting it to the needs and requirements of your AI driven software.
Once you have defined your tasks and goals, we will determine what AI technology your project deserves: machine learning algorithms, advanced natural language processing, computer vision, humanoid chatbot, content or code generator. We will help with data collection, preparation and cleaning, as well as training. The ongoing process of AI training and AI programming is one of the main aspects of AI implementation.
In the comparison table, you will find several ready-made AI models/services that offer interesting features. We recommend that you familiarize yourself with their capabilities.
| Model/Service | Best For | Unique Task | Strength | Industry Application |
|---|---|---|---|---|
| GPT-4/4o | Programming, Learning, Planning | Writing/debugging code, project planning | Versatile; excels in complex tasks requiring deep understanding | SaaS, Development Tools, Education |
| Claude 3 | Natural-Sounding Conversations | Handling customer inquiries, virtual assistance | Natural conversation flow, low-latency interaction | Customer Service, Virtual Assistants |
| Gemini 1.5 | Working with Large Texts | Processing large documents, analyzing extensive text corpora | Handles up to 1 million tokens, ideal for large documents | Research, Legal, Content Creation |
| Perplexity | Deep-Dive Research | Offering insights from academic papers, market analysis | Synthesizes information, provides in-depth research | Academia, Market Research, Analytics |
| Cohere | Text Generation & Understanding | Generating high-quality text, classifying data | Strong in NLP, organizes unstructured data efficiently | NLP, Chatbots, Search Engines |
| LLaMA | Efficient NLP Tasks | Completing NLP tasks with low computational power | Low computational requirements, robust text capabilities | Lightweight NLP applications |
| Mistral | High-speed NLP with Low Latency | Real-time text understanding, fast response | Ultra-low latency, top-tier performance | Live Chat, Voice Assistance, Real-time Systems |
Deploying your AI solution
The final step in our short description of how to build an AI program is deployment. First, integrate your AI model into a production environment, where it should process real data and produce accurate, scalable, and reliable results. The most important aspect of the AI implementation process is choosing the right hardware and software infrastructure. After that, it is necessary to develop effective monitoring and maintenance procedures, as well as address various ethical and legal issues.
At Overcode, we can implement ready-made AI solutions into your business and develop an interface for products. Intelligent solutions are suitable for any business.
Here is how we can help with AI implementation at the consultation stage:
select the best tech stack and creative solutions;
select AI integrations for your MVP;
structure the team for your project;
announce market prices for the relevant expertise.
Contact us to have Overcode experts strengthen your business.
