How to Implement AI in Your Digital Product — Steps and Strategies (Expert Talks)

Time and again, we gather with our expert friends to discuss how things around us work. Whether it's the latest software marvels, cutting-edge marketing trends, or the newest blockbuster, there's always a buzz of excitement when minds open up to discuss.

Just recently, we had a great chat with Eugene Plokhoy, Head of Product at Readdle, Co-founder of KOLO, and AI enthusiast.

Chances are, you've dabbled with AI tools yourself, thinking about ways to infuse that magic into your own projects. If not, no worries. You're in the right place, and we're thrilled to have you along for the ride. After all, sharing is caring, right?

Before we go through the highlights of our chat with Eugene, let's make sure we're all on the same page with a quick rundown of the key terms — AI and LLM:

‍Artificial Intelligence

Artificial Intelligence (AI) is the simulation of human intelligence in machines programmed to think and act like humans. Learning, reasoning, problem-solving, perception, and language comprehension are all examples of their cognitive abilities. IT companies are at the forefront of developing and implementing AI technologies across various sectors, revolutionizing industries with their innovative applications.

AI is also a method of making a computer, a computer-controlled robot, or a software think intelligently like the human mind. AI is accomplished by studying the patterns of the human brain and by analyzing the cognitive process. The outcome of these studies develops intelligent software and systems.To be fair, AI can also encompass non-human-like approaches and methods that solve complex problems in ways that are different from human cognition, e.g. data-driven.

Large Language Models

Large language models (LLMs) are advanced artificial intelligence models that use deep learning techniques, including a subset of neural networks known as transformers. LLMs use transformers to perform natural language processing tasks like language translation, text classification, sentiment analysis, text generation, and question-answering.

Whether it's whipping up text, translating languages, summarizing content, or even generating images from mere words, LLMs are the Swiss Army knives of the AI world.

There you go. Now we're all set to follow Eugene!

3 Steps of Implementing AI in Your Digital Products

Getting started with AI in your products involves three key steps. The first step is all about exploration. Experiment with different AI solutions alongside your team. Hands-on experience not only helps you understand the technology but also sparks creativity and innovation.

Once you've familiarized yourself with the technology, move on to the second step — implementation. Tailor the features of your AI solution to meet the specific needs of your clients. Ensure a seamless integration by enhancing existing use cases and refining your product's functionalities.

The third step invites you to think outside the box. Go beyond the expected and explore innovative features that might surprise and delight your clients. Introduce new use cases that showcase the full potential of your AI-driven features, positioning your products at the forefront of technological advancement.

Step 1. Exploration

Start by researching various AI technologies, algorithms, and applications. Understand the basics of machine learning, deep learning, natural language processing, computer vision, etc.

Conduct hands-on experiments with AI tools and platforms. Explore pre-built models, libraries, and frameworks such as TensorFlow, PyTorch, or scikit-learn. For instance, you could experiment with image recognition using pre-trained models or try building a simple chatbot.

Invest in training programs or workshops for your team to enhance their AI skills. This could include attending online courses, workshops, or bringing in experts for in-house training sessions.

Step 2. Implementation

Once you have a solid understanding of AI, it's time to implement it into your products. Here's how to tailor AI solutions to meet your clients' needs:

‍Work closely with your clients to understand their pain points and requirements. Determine how the technology can address these needs and add value to their experiences. For example, if you're developing a healthcare app, AI could be used for predictive analytics to identify potential health issues before they arise.

‍Tailor AI features to fit the specific requirements of your clients. This could involve fine-tuning algorithms, adjusting parameters, or integrating with existing systems. For instance, if you're developing a recommendation engine for an ecommerce platform, personalize recommendations based on user preferences and behavior patterns.

‍Continuously gather feedback from clients during the implementation phase. Iterate on the AI solution based on their feedback to ensure it aligns with their evolving needs and expectations.

Step 3. Innovation

To truly differentiate your products, you need to think innovatively about AI. Here are some ways to go beyond the expected:

‍Organize brainstorming sessions with your team to generate innovative ideas for AI-powered features. Encourage thinking outside the box and exploring unconventional use cases.

‍Rapidly prototype new AI features or use cases to test feasibility and gather feedback. This could involve building minimum viable products (MVPs) or conducting pilot studies with a select group of users.

‍Stay updated on emerging AI technologies and trends. Experiment with cutting-edge techniques such as generative adversarial networks (GANs), reinforcement learning, or federated learning to push the boundaries of what's possible with AI.

How to Choose a Strategy for Using AI in Your Product

It's a good idea to start with selecting the most suitable strategy based on a comprehensive analysis of your product's ecosystem and target audience. 

Examine the needs and pain points of your users to figure out the areas where AI can deliver the most significant impact. Next, look at the overarching goals of your product and organization:

‍Are you aiming to streamline processes, enhance user engagement, or get new revenue streams?

Also, you can assess the feasibility of implementing AI within your existing infrastructure and resources. Think whether you have the necessary expertise in-house or if partnerships with AI specialists are required. 

Once these factors are evaluated, you're ready to devise a roadmap outlining the implementation process, including milestones, timelines, and resource allocation.

Does Every Product Need AI?

Although AI has become increasingly pervasive in modern products, we should understand that not every product necessarily requires AI at its core. This technology can undoubtedly enhance functionality and user experiences, but its integration should be purposeful and aligned with the product's objectives.

Some products thrive without AI, especially those with straightforward functionalities or niche markets where it may not add significant value. However, as the technology continues to evolve, we may witness a spectrum of products ranging from those with AI as a supplementary feature to those where AI serves as the central driving force. 

The future may hold a delicate balance between leveraging AI for its transformative capabilities while ensuring that human ingenuity and intuition remain at the forefront. And as for the notion of artificial general intelligence (AGI) heralding the end of the world, well, let's leave that to the realm of science fiction and jest.

‍Note! AGI is not yet real – it’s a hypothetical form of AI where a machine learns and thinks exactly like a human does.

How AI Makes Life Easier for Marketers

AI has ushered in a new era of efficiency and cost-effectiveness in marketing processes. First and foremost, it helps marketers streamline customer acquisition efforts and optimize customer lifetime value (LTV) with unprecedented precision. 

AI may recognize that customers who have previously bought a certain product category and engage frequently with your campaigns are more likely to make repeat purchases and have a high LTV. Armed with this insight, you can target similar customers with personalized marketing messages, special offers, and recommendations, increasing the likelihood of conversion and retention.

Automatization and Product Quality Improvement

One of the most significant impacts of AI in marketing is the automation of repetitive tasks and the enhancement of product quality. AI-driven automation tools can handle tasks such as data analysis, campaign optimization, and personalized content creation, freeing up marketers' time to focus on strategic initiatives. 

In addition, AI algorithms can continuously monitor and optimize marketing campaigns in real time, ensuring that products and services are always presented in the best possible light to consumers. Like if you're using AI in real estate marketing, it cananalyze market trends and buyer preferences to deliver more targeted andeffective property listings.

AI for Copywriting

AI-powered tools have emerged as robust assets for copywriters to craft compelling, personalized content at scale. AI-powered platforms can generate engaging copy tailored to specific audience segments. 

This not only saves time and effort but also ensures that content resonates more strongly with target audiences, leading to higher engagement and conversion rates. Additionally, AI can analyze consumer data and trends to provide insights into the most effective messaging strategies, further enhancing the effectiveness of copywriting efforts.

Large Language Models — Getting Rid of Routine Tasks

You know those repetitive tasks that often bog us down? Well, LLMs are here to shake things up by handling the mundane stuff, leaving us free to focus on the exciting aspects of our job. Think of them as incredibly smart assistants that can analyze vast amounts of text data, understand natural language, and even generate human-like responses.

LLM takes away the scope of routine work for humans to do creative tasks. You spend less time on tedious data entry or repetitive research and more time brainstorming innovative ideas or crafting compelling campaigns. LLMs make this possible by automating the grunt work, allowing you to focus on the big picture. For example, instead of manually compiling customer feedback surveys, LLMs can analyze the data and highlight key insights, freeing up our time to develop new marketing strategies based on those insights.

LLM can provide unconventional solutions/inspiration for tasks that go nowhere. Here's where it gets really interesting — LLMs don't just stop at mundane tasks; they can also provide unconventional solutions and inspiration when you hit a creative roadblock. For instance, if you are struggling to come up with a catchy tagline for a campaign, LLMs can generate a range of options based on our input, sparking new ideas and directions.

The potential use cases for LLMs are as diverse as our imagination. Whether you're a data-driven strategist, a wordsmith with a flair for storytelling, or a visionary with big ideas, LLMs have something to offer everyone.

How to Choose an LLM for Your Project?

When selecting an LLM for your tasks, you need to consider several criteria to ensure it meets your needs effectively. First off, consider whether to opt for an open-source or commercial model.

Open Source Models

Open-source models are typically chosen when there's a need to keep the code within your own environment. If you have sufficient available memory and seek to reduce costs, open-source models can be a suitable option. 

Opting for open-source models also grants you access to management and optimization, providing greater flexibility in customization and fine-tuning to meet your specific requirements.

Commercial Models

On the other hand, commercial models are preferred when a simple and reliable configuration is desired. If your priority is to have a model that seamlessly integrates without requiring extensive configuration or adjustments, commercial models can be a more straightforward choice. 

Commercial models are suitable when there are no constraints regarding the use of proprietary code within your environment.

What the Future Holds for AI-driven Digital Products

With the integration of LLMs, we're on the brink of a revolution in AI-driven products. Instead of the same old chat interfaces, imagine visiting an ecommerce website where the interface adapts to your every search query. It's like having a personal shopper catered to your needs. With LLMs in action, these platforms will understand what you're looking for and adjust the browsing experience accordingly — each visitor gets their own journey. 

Investing in AI, especially LLMs, for your digital product is a smart decision. It's an investment that pays off, whether by saving you time and resources or by boosting your revenue and authority in your industry. 

Meanwhile, AI continues to evolve, so get ready for more innovations that redefine how we interact with technology.