A New Direction For The AI Journal Newsletter & 3 Examples of Businesses Using AI To Increase Users

Hey, what’s going on, it’s Tom, the Founder and CEO of The AI Journal.

I promise this is the only time you’ll get a somewhat lengthy introduction like this as I want to explain a new direction we’re taking the newsletter which has grown to over 80k+ subscribers across here (Beehiiv) and our LinkedIn with some months having more than 4 million views across all our platforms.

When I see those numbers it is a real ‘pinch-me moment’ and makes me extremely grateful every single day to each of you who click follow, read this newsletter, write an article, or simply drop a lovely message of encouragement or much-needed feedback on areas of improvement.

Anyway, you’ll see the new format below and please skip ahead if you just want to jump to the juicy bit of this email - product with a case study on Spotify.

But first I wanted to share briefly about why we’re making the shift without just landing it in your inbox without any explanation.

I’ve come to realize, after speaking to many friends I’ve made thanks to this business, readers, contributors, and newsletter operators that doing regular news updates in the newsletter doesn’t match with who we are and why I started this business. There are plenty of good newsletters to go to for daily news such as the Rundown AI, AI Valley, Mindstream, and various others in more general niches such as Bay Area Times, Morning Brew, and The Hustle.

I started The AI Journal in 2020 because I wanted to make an independent media platform, that thrived off opinions, thought leadership, and raw advice from some of the biggest, baddest, innovators, thinkers, and action-takers in the technology domain. While also giving newbies, startups, and people from outside the domain a space to challenge the thinking and narrative we can find ourselves trapped in.

And I want the newsletter to reflect that.

For that reason, we will now be sending one email a week on a Wednesday (please excuse me for this one being a bit late as I added, deleted, then added, then deleted sections of this introduction).

Each week I’ll be sharing stories, examples, case studies, frameworks, interviews, and exclusive interviews relating to the new schedule below.

Week 1: Growth: scaling your business, ideas for increasing revenue, and effective marketing and sales tactics.

Week 2: Product: the process of building AI-powered products, strategies for ideation, development, deployment, and integration.

Week 3: Career: high-demand skills, salary trends, interview preparation tips, and pathways for roles like engineers, data scientists, and product managers.

Week 4: Disruption: Uncover the industries and sectors where AI is driving the most significant transformation. Find the next big opportunity for startups to disrupt a market.

Each edition will have Ask Me Anything (AMA), topics we’re looking for articles from you as a contributor, and curated resources for that week. As you might expect, each edition will include numerous AI tools, technologies, vendors, customer examples, and frameworks for all the above topics.

That doesn’t mean we won’t be moving back to 5 days a week at some point. We will decide based on the feedback you give us through direct messages, a comment, or a response to the survey. If that’s what you decide you want, that’s what we will do as we are here to serve and help you!

Happy reading and thanks again for being a subscriber of this adventure!

Tom Allen
LinkedIn Pro
CEO & Founder

IN PARTNERSHIP WITH MORNING BREW

Your job called—it wants better business news

Welcome to Morning Brew—the world’s most engaging business newsletter. Seriously, we mean it.

Morning Brew’s daily email keeps professionals informed on the business news that matters, but with a twist—think jokes, pop culture, quick writeups, and anything that makes traditionally dull news actually enjoyable.

It’s 100% free—so why not give it a shot? And if you decide you’d rather stick with dry, long-winded business news, you can always unsubscribe.

Check it out

GROWTH

In today’s fast-paced digital landscape, businesses and startups are constantly searching for ways to stand out, grow their user base, and deliver exceptional value. AI is game-changing for achieving scale and sales at a faster rate than ever before—but the key to success isn’t simply adopting AI for the sake of it. Instead, it’s about strategically integrating AI features that solve real problems your customers or a user group has, enhance user experiences, and align with your business’s core mission, vision, or reason for existing.

For many companies, the challenge lies in identifying which AI features to build and how to ensure they deliver tangible results. Should you focus on personalization to improve engagement? Automate workflows to save time? Or perhaps use generative AI to create entirely new experiences?

The one thing that annoys me personally about people with their predictions about AGI, AI, jobs being affected, and all the other stories whether positive or doom and gloom, we don’t know and no one can see into the future. And for that reason it baffles me people try and have a right or wrong approach to a situation that is changing every day. And that means there is no set answer for each one of you reading this.

The answer will be different. It depends on your users, your industry, and your long-term vision.

The most successful businesses approach AI integration with a clear strategy and usually have pillars that align or are similar to the following:

1. User-Centric Design: They start by deeply understanding their users’ needs and pain points. What frustrates your users? What excites them? Where do they spend the most time?

2. Alignment with Core Value: They ensure that any AI feature enhances their brand’s core value proposition. For example, Spotify’s mission is music discovery, so its AI focuses on personalized recommendations. Adobe empowers creatives, so its Firefly feature helps users generate content faster while staying true to their vision.

3. Scalability and Impact: They prioritize features that can scale effectively and deliver measurable results—whether it’s increased engagement, higher retention rates, or new revenue streams.

Adding helpful AI features isn’t just about technology; it’s about creating meaningful experiences that resonate with your audience. When done right, these features can transform how users interact with your product or service, driving loyalty and growth in ways that were previously unimaginable.

Why Strategic Thinking Matters in AI Integration

AI is not a one-size-fits-all solution. For every success story of a company leveraging AI to unlock growth, there are countless others that fail due to poor implementation or a lack of strategic focus. Here are some key reasons why strategic thinking is critical when adding AI features. Because, remember, not every feature is needed or wanted by a user. Just look at countless hardware flops such as the recent Rabbit R1, Humane AI Pin, and Apple Vision Pro:

1. Avoiding Feature Overload: Adding too many features can overwhelm users and dilute your product’s core value. By focusing on high-impact areas where AI can make a real difference—such as personalization or automation—you can avoid this pitfall.

2. Building Trust Through Transparency: Users are increasingly concerned about how their data is used by AI systems. Businesses must ensure their AI features are ethical, transparent, and aligned with user expectations to build trust and loyalty.

3. Maximizing ROI: Developing AI features requires significant investment in time, talent, and resources. A strategic approach ensures that these investments pay off by targeting areas with the highest potential for impact and scalability.

How to Identify the Right AI Features for Your Business

To determine which AI features will be most effective for your business, consider the following steps. Asking these will help you determine areas such as what tech vendor to use, who to hire, what questions to ask existing user group or customers, and what expertise you need in the company.

And remember, what people say and what they do can be two very different things. Marc Benioff explains in his book Trailblazer this exact problem with the adoption of the Salesforce platform in a large USA bank.

1. Understand Your Users’ Needs

   • Conduct user research to identify pain points that could be solved with AI (e.g., choice overload, inefficiencies).

   • Analyze user behavior data to uncover trends (e.g., common drop-off points in your app or website).

2. Define Clear Goals

   • What do you want this feature to achieve? Increased engagement? Higher retention? New revenue streams?

   • Define measurable KPIs before you begin development.

3. Start Small and Iterate

   • Begin with a minimum viable feature (MVF) that solves one specific problem well. Test it with a small group of users before scaling up based on feedback and performance metrics.

4. Leverage Existing Tools

   • You don’t need to build everything from scratch—many platforms offer pre-built AI tools that can be customized for your needs (e.g., AWS SageMaker for machine learning models or OpenAI APIs for generative content).

5. Ensure Ethical Use of Data

   • Be transparent about how user data is collected and used by your AI systems. Implement safeguards against bias and misuse.

The Benefits of Adding Helpful AI Features

When thoughtfully integrated, AI features can deliver transformative benefits for businesses:

1. Enhanced User Experience: Personalized recommendations (like Spotify’s Discover Weekly) or automated workflows (like Adobe Firefly) make products more intuitive and enjoyable to use. More on those case studies below.

2. Increased Engagement: Features like dynamic content generation or real-time analytics keep users coming back by offering fresh value every time they interact with your product.

3. Operational Efficiency: Automating repetitive tasks frees up resources for higher-value activities like innovation and customer support.

4. New Revenue Streams: Generative tools or predictive analytics can open up entirely new ways to monetize your platform.

Case Study: Spotify

Spotify’s success isn’t luck—it’s a masterclass in leveraging artificial intelligence to turn passive listeners into loyal superfans. By combining machine learning, natural language processing (NLP), and behavioral psychology, Spotify created a hyper-personalized experience that keeps users engaged, reduces churn, and drives revenue. Let’s explore how they did it—and what your business can learn from their playbook.

The Science Behind Spotify’s AI Personalization

Spotify’s AI models analyze half a trillion daily data points—songs played, skipped, liked, or added to playlists—to identify patterns among users with similar tastes. This technique, called collaborative filtering, powers features like Discover Weekly and Daily Mix.

This method drives 40% of all user discoveries on Spotify, keeping listeners engaged with fresh content tailored to their preferences6.

Understanding Music’s ‘Vibe’ With NLP

Spotify’s Natural Language Programming (NLP) algorithms scan lyrics, blogs, news articles, and social media to understand the cultural context of songs. This allows the platform to categorize tracks by mood, genre, or activity (e.g., “workout beats” or “rainy day jazz”).

In 2025, Spotify introduced Daylists—playlists that adapt to users’ real-time routines. For example, a “Tuesday Morning Focus” playlist might blend lo-fi beats and classical piano based on your historical productivity patterns.

Audio Analysis & Decoding the DNA of Music

Spotify’s convolutional neural networks (CNNs) analyze raw audio waveforms to extract features like tempo, key, and energy levels. This “audio fingerprinting” identifies similarities between tracks that metadata alone can’t capture. For example, it might link an obscure indie rock song to a popular pop track because both share a 120 BPM rhythm and major key tonality.

Reinforcement Learning: Optimizing for Addiction

Spotify uses reinforcement learning to fine-tune recommendations. The AI models prioritize metrics like:

• Session length (longer listening = better retention)

• Likes/saves (indicates user satisfaction)

• Skip rates (flags mismatched recommendations)

By rewarding algorithms that boost these metrics, Spotify increased user engagement by 35% between 2021 and 2025.

Spotify Wrapped—AI as a Growth Engine

Each December, Spotify Wrapped dominates social media, with users sharing personalized summaries of their yearly listening habits. This viral campaign isn’t just fun—it’s a growth machine.

How It Works

• Data Collection: Tracks 500+ data points per user, including genres, artists, and listening times.

• AI Storytelling: Algorithms transform raw data into shareable narratives (e.g., “You’re a Vampire Weekend Fanatic”).

• Dynamic Visuals: Generative AI creates custom graphics and videos for each user.

Results:

• Wrapped drove a 21% surge in December subscriptions in 2024.

• User-generated Wrapped posts generated 2.3 billion social impressions in 2024, equivalent to $200M in free advertising.

The Human-AI Symbiosis

Despite its algorithmic prowess, Spotify relies on human editors to curate playlists like RapCaviar and Rock This. These editors use AI-generated insights to identify emerging trends but retain creative control to maintain cultural relevance.

Challenges and Ethical Dilemmas

Royalty Disputes

Indie artists argue Spotify’s algorithm favors major labels. In 2024, 87% of streams came from just 0.4% of artists, intensifying calls for equitable payout models.

Filter Bubbles

Users often find themselves trapped in “musical echo chambers.” Spotify’s Blend feature—which merges two users’ tastes—attempts to combat this by introducing diversity through social connections1.

AI-Generated Music

Spotify recently removed 7% of its catalog (over 7 million tracks) due to AI-generated spam songs flooding the platform. The company now uses audio fingerprinting to detect synthetic content.

Lessons For Your Business

1. Adopt Collaborative Filtering:
   Use tools like Amazon Personalize to group customers by behavior and predict preferences (e.g., e-commerce product recommendations).

2. Leverage NLP for Context:
   Analyze reviews, social posts, or support tickets to understand customer sentiment. Tools like MonkeyLearn can automate this process.

3. Optimize for Engagement:
   Implement reinforcement learning models that prioritize metrics like time spent in-app. Platforms like TensorFlow and PyTorch offer open-source frameworks.

4. Balance AI with Human Insight:
   Follow Spotify’s hybrid model: Use AI for scalability but retain human oversight for creativity and ethical guardrails.

Closing Thoughts: Building the Future with Strategic AI

Adding helpful AI features isn’t just about keeping up with trends—it’s about staying ahead of them by delivering meaningful innovation that resonates with your users’ needs and aspirations. Whether you’re a startup looking for rapid growth or an established business aiming to stay competitive, the right AI features can be the catalyst for transformative change.

IN PARTNERSHIP WITH ARTISAN

10x Your Outbound With Our AI BDR

Your BDR team is wasting time on things AI can automate. Artisan’s AI BDR Ava automates lead research, multi-channel outreach and follow-ups on behalf of your team.

Ava operates within the Artisan platform, which consolidates every tool you need for outbound:

• 300M+ High-Quality B2B Prospects, including E-Commerce and Local Business Leads

• Automated Lead Enrichment With 10+ Data Sources

• Full Email Deliverability Management

• Multi-Channel Outreach Across Email & LinkedIn

• Human-Level Personalization

Book a demo to see how Ava can 10x your outbound.

RECOMMENDATIONS

Book: 
Antifragile: Things that Gain from Disorder.

Podcast:
Naval Ravikant on Modern Wisdom.

Newsletter:
Lenny’s Newsletter, is all about product.

Platform 
Beehiiv is the platform we build these newsletters on.

ASK ME ANYTHING!

We want to hear from you! Each week, we’ll answer your burning questions about AI, business, technology, my life building The AI Journal, or anything else whether you’re curious about tools, trends, building a business, AI strategies, this is your chance to ask each week.

Here are ideas to get you started:

• What’s one AI tool you’d recommend for startups looking to scale quickly?

• What are the things you wish you knew about starting a media company from scratch before you started?

• How can AI help me improve my team’s productivity?

• What are the biggest challenges businesses face when adopting AI?

• How do I transition into an AI-focused career without a technical background?

• What industries will see the most disruption from AI in the next 5 years?

• How do I know if my business is ready for AI implementation?

• What’s the best way to learn machine learning as a beginner?

We’ll feature questions with answers in our newsletter next week! I’ll also try and answer as many as I can directly.

Enjoying this newsletter? Please forward it to a friend to have them sign up.

See ya next Wednesday!

Yours truly, Tom Allen.

Join 150k+ AI and Data enthusiasts by subscribing to our LinkedIn page.