Today, it is impossible to be in the tech industry without getting caught up in the artificial intelligence (AI) implementation hype. AI is everywhere, from fueling post suggestions on social media to working on early detection of cancer. Tech media is full of articles on AI benefits, talking about how integrating AI into your products can boost business.
Despite being a modern trend, artificial intelligence is hardly new. Academically, AI dates back to as long as the 1950s, when Alan Turing detailed his groundbreaking procedure known as the Turing Test, which is a test to see if a computer can behave intelligently in a way that is comparable to or identical to that of a human. Since then, AI has made a long way from research to the mass market. The most known examples of its widespread use include Apple’s Siri and Amazon’s Echo.
However, it was the arrival of ChatGPT in 2022 that propelled AI to its mainstream popularity. For bad or for worse, AI development exploded, and companies started hastily adding AI services to their offerings; if they hadn’t offered them before. Developers are researching how to use AI in an app to provide a more interesting and tailored user experience. Suddenly, everybody wanted a ‘ChatGPT-like’ function in their products.
But here is the question: does your company really need AI-powered functions in your app? Is it the only way to solve challenges and optimize user experience?
In this blog article, Bamboo Agile tries to separate the real benefits of AI integration from the hype and suggest valuable ideas for such integrations.
Feeling the FOMO? Distinguish Real Advantages from Hype
Staying ahead in the highly competitive technology industry often means adopting the latest technology trends as soon as they emerge. Integrating AI is no different. It offers a huge potential for various domains and tremendous advantages for applications. Yet, what does it take to distinguish real advantage from mere hype? And do you need AI here and now or is it the fear of missing out (FOMO) talking?
To make a thoughtful decision, think about the main goals to consider implementing artificial intelligence. These are:
- Increased efficiency
- Process automation
- Improved decision-making
- Enhanced user experience (UX)
- Innovation and new products or services
- Competitive advantage.
AI can be a powerful tool to boost your business processes, but it’s not a universal solution. If you have many repetitive tasks or struggle with data analysis, then AI may be what you need. But if you run a small, data-light business, think twice. Maybe a simpler, more cost-effective solution is the right choice for you.
AI Has Different Shades. Which Benefits You Most?
As we mentioned before, when it comes to integrating AI, many businesses want a ‘ChatGPT-like’ function in their apps. In reality, implementing artificial intelligence is much more than just generative AI integration (this is what makes ChatGPT work).
Let us take a look at different types of AI and their benefits for businesses. Which serves your objectives best?
Machine Learning (and Deep Learning As a Subset)
Advancements in AI are often driven by machine learning (ML) and deep learning (DL). They enable apps to learn from data and continuously improve.
Machine learning is often used to create personalized recommendations. Think Netflix – the streaming platform uses ML to evaluate its users’ viewing habits and create customized suggestions for them.
As a section of machine learning, deep learning goes further and employs neural networks to analyze data patterns. DL is vital in autonomous driving, object recognition, fraud detection, predictive analytics, and more. A prime example of this is Tesla: its autopilot technology uses deep learning to recognize different driving scenarios and react to them.
Generative AI (GenAI) is another branch of machine learning. This is exactly what stands behind the famous GPT-4 tool by OpenAI. GenAI models train on large amounts of data or learn by analyzing image patterns, and then produce new content. In GenAI, unsupervised learning plays a major role – it can identify patterns on its own without human assistance. Generative AI generates cohesive, human-like text, creates digital images, and can aid its users in customer support.
Machine learning approaches are generally split into three categories:
- Supervised learning, where humans teach computers by presenting example inputs and desired outputs
- Unsupervised learning, where the algorithm itself finds structure in inputs
- Reinforcement learning, where the program completes a goal and receives feedback that is comparable to rewards, which it tries to increase.
Natural Language Processing (NLP)
Natural language processing provides apps with an understanding of human language and how to interact with it. NLP supports tools like virtual assistants and chatbots as it excels at comprehending and responding to questions. NLP also performs sentiment analysis. This function enables companies to monitor customer feedback and social media, and discover how the general public feels about their goods or services.
Apple’s Siri uses NLP for interactions with its users, as well as simpler tasks, e.g. setting reminders, searching the web, and sending messages. Another popular example of natural language processing in practice is Zendesk – AI-powered chatbots enhance the company’s customer service by reducing response times.
Computer Vision
Tasks that involve image and video analysis often rely on computer vision. This AI technology is focused on allowing objects and people in pictures and videos to be recognized and understood by computers.
Computer vision is used across many industries. For example, in retail, computer vision powers cashier-less shopping experiences, like Amazon Go. To make it work, cameras and sensors track the items that customers pick up and automatically charge their accounts when they exit the store. In healthcare, computer vision helps doctors make faster and more accurate diagnoses. Google’s DeepMind analyzes medical images and finds abnormalities. In the automotive industry, computer vision is being used in the development of advanced driver-assistance systems (ADAS). These systems improve road safety by recognizing pedestrians, road signs, and other vehicles in real time.
Robotics
Robotics changes industries with repetitive or hazardous tasks. For instance, in manufacturing, robotic arms do assembly, welding, and packaging – BMW uses high-precision AI-powered robots to assemble its cars. In logistics, robots work in Amazon warehouses, doing sorting, packing, and goods transportation. In healthcare, surgeons collaborate with the da Vinci Surgical System in carrying out minimally invasive procedures.
Depending on the industry, specific circumstances, and values, artificial intelligence implementation offers multiple benefits for businesses. If you think integrating AI can enhance your business processes, find a reliable service provider to bring your idea to life, preferably with AI API integration experience.
Key Examples of AI Integration in Software Products
A smartly used artificial intelligence integration can transform your software into a more logical and intelligent tool. We look deeper into some of the most well-known AI integrations and highlight what they do and the advantages they provide.
Analyze Data for Predictions or Recommendations
Artificial intelligence is very good at identifying patterns in large datasets. This allows for employing AI integrations to improve forecasts and suggestions.
Data analysis: Salesforce Einstein
Salesforce Einstein can be compared to a sidekick to salespeople. Integrated into CRM software, Einstein sifts through large amounts of data and uncovers hidden trends and preferences. This provides salespeople with information to help them understand and predict customer behavior and customize their approach. Einstein might even encourage the salesperson to reach out to a customer who is likely to renew their contact.
Predictions and recommendations: IBM Watson for Oncology, Spotify Recommendation Engine
IBM Watson for Oncology (WFO) is a clinical decision-support AI tool. WFO provides oncologists with evidence-based therapy recommendations. These recommendations are based on genetic information, research data, and a patient’s medical history, as well as successful treatment options for similar patients.
Spotify is another company that uses AI for its product. Whenever you stream music, you see suggestions generated by Spotify’s recommendation system. To create personalized playlists, Spotify combines two types of filtering, namely collaborative and content-based. The platform analyzes listening habits and song characteristics (meta data, tempo, genre, mood, instrumentation, and more) and recommends music that shares similar characteristics.
Automate Tasks, Decision-Making, and Reporting
Task automation frees up employees for strategic work, in addition to reducing errors and costs. It also leads to better, more informed choices based on data, ensuring that everyone has the latest information.
IBM Watson Orchestrate is a platform for task automation. It provides multiple methods for creating automations, including importing pre-existing ones and generating new ones with the use of APIs and robotic process automation (RPA). These automations are referred to as skills, and Watson gains new abilities as it acquires them. The platform can be integrated with an existing product and support processes in various departments, from talent acquisition to supply management.
Google Workspace integrated an AI grammar checker into its set of tools. It allows users to utilize advanced grammar and writing assistance in their communication and, as a result, write more accurately. The tool also provides suggestions for writing style and clarity improvements.
The grammar checker relies on NLP, ML, and statistical analysis to fuel its functionalities.
Process Texts and Speech
Compared to humans, artificial intelligence processes speech and text far more quickly, accurately, and affordably. In this sense, it makes information more accessible and searchable.
Microsoft Teams applies AI for real-time captioning. This function makes virtual meetings more accessible, instantly transcribing speech into text. Thus, participants who are hard of hearing or deaf can follow the discussion. Driven by AI, real-time captioning promotes inclusivity and comprehension in virtual conversations.
Notion, a popular app for note-taking, integrated an AI search assistant. It operates on the NLP technology and saves users’ time and effort by understanding queries and bringing up relevant information.
Integrate Chatbots and Assistants
NLP is the mastermind behind chatbots, making them work. Chatbots learn and adapt over time, becoming more helpful and precise and personalizing their users’ experience.
Slackbot, a chatbot integrated within Slack, can automate simple tasks, answer common questions, and set reminders. It acts as a virtual assistant, allowing humans to concentrate on more complex tasks. The same applies to Zendesk, which we have mentioned already in this article.
Generate Textual or Media Content
Lately, content creation has been heavily influenced by AI tools. Generative AI breathes life into digital image creation through a technique called diffusion models. These models are trained on texts and images, building connections between them. Apps like Midjourney, DALL-E, and Stable Diffusion generate visual content in response to text prompts.
Adobe Creative Cloud developed Firefly, its own GenAI product. Firefly-powered functions are now included in other Adobe products. For example, in Photoshop, it enables users to add, extend, and remove content from images using Generative Fill and Generative Expand.
Canva, another popular graphic design tool, also employs AI tools for editing. Magic Media, Canva’s proprietary GenAI instrument, creates images, graphics, and videos in various styles with a text prompt.
Recognize Objects or Patterns on Images and Videos
Many tools and technologies can recognize objects or patterns in images and videos. Here are some examples.
Photoshop neural filters. These AI-powered filters can analyze photos and apply effects according to the content. For instance, they can be used to improve landscapes, add artistic styles, or even alter the weather in a picture.
Lightroom’s masking tool. Using this tool, you can pick out particular areas of an image depending on their brightness, color, or other attributes. This helps add effects or make precise modifications to particular areas of a picture.
Speaker Coach by Microsoft. This software analyzes your speech patterns using artificial intelligence (AI) to provide feedback on your pacing, delivery, and other speaking-related factors. It can boost your confidence and help you get better at public speaking.
Feasibility Assessment for AI Implementation
So you have looked through ideas for integrating AI and found the one that looks promising for your business. What comes next?
We recommend starting with a feasibility assessment. Imagine this as preparation for a big journey where integration is your destination. Then, proceed to follow these steps:
- Clearly define your business goals.
- Examine your current equipment – existing technology, data, and skills – and determine what needs upgrading or training.
- Think about the upfront and recurring costs, and compare them with the possible benefits.
- Analyze the risks. Make sure you follow the rules and standards in privacy and data security.
- Start with a brief test run to see how things proceed and make the required tweaks.
- Plan the course of your journey with a detailed roadmap, allocate resources, and handle changes.
- Maintain a close eye on developments and make adjustments while consulting with important parties to make sure everyone is on board and the journey is successful.
Finally, How to Integrate AI into an App?
To integrate AI into your company’s app, you need to identify your business needs, choose appropriate technologies and tools, gather and prepare data, train and refine AI models, integrate these models into your app’s code, and then monitor and optimize its performance.
Integrating AI can involve a large number of technologies, such as ML libraries (TensorFlow, PyTorch), NLP frameworks (spaCy, NLTK), computer vision tools (OpenCV, TensorFlow Object Detection API), and AI service APIs (OpenAI API, Google Cloud AI, IBM Watson).
Sounds like a complex process, does it not? Ask yourself if your in-house team has the necessary expertise to deliver this kind of project. In some cases, AI integrations require attracting a skilled team of data scientists, AI experts, Python developers, and other software engineers to ensure a full development cycle, from requirements gathering and research, to implementation.
Improving AI Performance and Mitigating Bias
AI APIs are changing the software development industry, making it easier to add integrations like chatbots and virtual assistants. But AI is a technology that needs continuous improvement. The team behind the model must check if it is learning correctly and, if necessary, make adjustments.
To understand how the AI integration performs, you should follow such metrics as accuracy (does it get things right?) and precision (how relevant are the answers?).
There is an important aspect of this, which is fairness. Involuntarily, AI can be biased against certain groups. There are a couple of examples that come to mind.
In 2015, it was revealed that Google’s advertising system displayed ads of high-paying positions to male candidates more often to females.
If you think of GenAI image generators, Midjourney used to portray men and women differently, with women always being younger and men ‘allowing’ to have wrinkles. The program also tended to exclusively feature light-skinned people.
To prevent this, your team should use diverse data while training AI models and check AI’s decisions regularly to catch and fix any bias.
A Few Words About Information Privacy and Security
Last but not least, there is a question of data privacy and security. As an illustration of this, AI can generate realistic voice or photo renditions – and it can be hard to distinguish between the real and the artificial.
One of the examples of this is deepfakes. It replaces one person’s likeness with that of another and can be a real problem for celebrities and non-famous people. This year, pop superstar Taylor Swift found herself a victim of deepfake images, which instigated a legislation change in the European Union.
AI is a revolutionary technology, yet, it is not perfect. In the UK, people reported being misidentified as shoplifters or wanted by police by a facial recognition system.
Data breaches should also be a concern as AI works with a massive amount of data, often personal. Whenever you work with AI systems, you must ensure compliance with data protection regulations, as well as implement strong security protocols to shield private information.
Wrapping Up
Implementing artificial intelligence into an app can offer multiple benefits across domains. Enhancing your app with AI has many faces, from generative AI integration for content generation to robotics for automation of repetitive processes, and more.
However, while it can enhance productivity and offer a richer user experience, AI integration is a complex project that requires a thorough feasibility study and an experienced tech team by your side to help you navigate issues like training AI models, performance improvement, involuntary bias, and data privacy.
Here at Bamboo Agile, we hope you find these integration ideas insightful and inspiring. If you have decided in favor of integrating AI into your app, our team will be happy to discuss your ideas.
