# What’s happening to AI startups after the 2024 boom?

Just a year ago, it seemed like the world of artificial intelligence was experiencing a golden age. Dozens of new AI startups were emerging every week: some promised to forever change office work, others — to rewrite the rules of business, and some — to simplify life for every person on the planet. It felt like simply adding the word "AI" to a project could instantly attract millions of dollars in investment.\
But the beginning of 2025 painted a very different picture. Somewhere between the headlines about new models and polished presentations, reality started to falter. The flow of money slowed. Startups began shutting down. Investors became more cautious.\
What happened? And what lies ahead for AI startups?

## It all started with a gold rush

Understanding the situation begins with what happened in 2024.\
It truly was a year of madness. Huge language models became available to everyone. Companies raced to integrate neural networks into their products. Ambitious plans were announced at the governmental level: from national AI development programs to integrating the technology into courts, hospitals, and schools.\
This wave gave rise to hundreds of new projects. Often they were driven by talented people. Sometimes — by those who simply knew how to catch the moment.\
But alongside this excitement, another reality emerged: many startups hastily built solutions without checking whether the market actually needed their technology. Some created yet another tool for writing emails; others — assistants for resume building. There were too many such products, and users quickly grew tired.\
When the novelty wore off, it turned out that not every AI application was truly necessary.

![Image from kitrum.com](img/failure-rate-1024x762.jpg "Image from kitrum.com")

Image from [kitrum.com](https://kitrum.com/blog/why-do-ai-startups-fail-5-lessons-learned-from-startup-failures/)

## When technology outpaced reality

In 2025, what was bound to happen sooner or later finally did: the market started cooling down. Investors who, just half a year earlier, were signing checks without fully reading pitch decks, began asking uncomfortable questions: How many real customers do you have? How are you planning to make money? What is the cost of implementation?\
It turned out that creating a prototype based on ready-made models was one thing, but building a sustainable business model was quite another. AI products demanded constant updates, investments in infrastructure, and solutions to legal issues related to data.\
Some startups folded without even reaching a second product version. Others quietly sold off for modest sums or went underground, trying to find a new niche.

But to say that the market is dead would be a huge mistake.\
Amid the chaos, another trend began to emerge: those who had been building technologies to solve real problems — not just to ride the hype — started to survive.\
Companies like Anthropic, which focused on system safety, became especially in demand among financial and healthcare clients.\
Perplexity AI proved that high-quality information search is far from a solved problem — it's a vast field for innovation.\
Young teams working on open models, such as Mistral AI, attracted a whole community of developers eager to grow new products together.\
The surviving AI startups became quieter. Less buzz, more real-world cases. They focused on narrow tasks: automating specific processes, smart data handling, and building efficient personal assistants for specific industries.

### How the rules changed for investors

Investors evolved too.\
They no longer want stories about how a startup will "change everything forever." They are looking for projects that can demonstrate tangible results — even if it's small but steady growth.\
It became fashionable not to promise revolutions, but to calmly say, "We automate accounting data processing for small businesses and earned $500,000 in six months."\
Those who can deal with reality became easier to spot. These are the startups now getting funded, hiring people, and growing — perhaps not at the dizzying speeds of last year, but much more confidently.

### New trends are emerging on the horizon

Universal AI products are giving way to niche solutions: for healthcare, legal services, construction. There is growing demand for systems that can act independently — not just respond to questions but truly perform tasks from start to finish.\
Finally, the issue of regulation is becoming more pressing. Businesses now understand that solutions must be built with data protection, ethics, and algorithm transparency in mind. Those who ignore this quickly lose client trust.

## What awaits AI in 2025: New trends

While some startups are shutting down, others are adapting to the new market expectations. And in 2025, clear trends are already visible that will shape the future of AI.

1. **The Era of AI Agents**\
   The main trend of 2025 is the shift from "smart assistants" to autonomous AI agents.\
   These are no longer just chatbots or text generators. Agents can plan tasks, make decisions, and execute them from start to finish without human intervention. And not just in experimental settings, but in real business operations — like marketing, CRM management, or internal process automation.\
   (You can read more about AI agents' potential in the latest review by The Cymes: *[What Are AI Agents and How They Are Changing Business Processes](https://thecymes.com/article/smart-fast-and-indispensable-why-ai-agents-are-the-future)*.)
2. **A Shift from "Text and Images" to Actions**\
   It's no longer enough just to generate text, images, or code. In 2025, there’s a growing demand for AI solutions that can act: manage workflows, take initiative, and interact with systems without constant user prompts.
3. **Specialization Over Universality**\
   Universal models are moving to the background. Startups offering narrow but deep solutions for specific industries — healthcare, finance, logistics, law — are winning.\
   This allows them to quickly demonstrate real product value — and makes it easier to attract and retain customers.
4. **The Growing Importance of Trust and Ethics**\
   Issues of privacy, data reliability, and algorithm transparency are no longer just about reputation — they are becoming critical business factors.\
   Companies are increasingly choosing solutions that proactively address ethical standards and offer data security guarantees.

![Image by Pexels](img/pexels-goumbik-577210-3-.jpg "Image by Pexels")

Image by Pexels

## What’s next?

The AI market is not disappearing.\
The early years’ naivety — the belief that neural networks would solve all humanity’s problems — is fading. A new philosophy is taking hold: AI is just a tool. A powerful, promising tool — but one that requires conscious, careful use.\
For startups, this means one thing: it’s no longer enough to win with hype alone. Those who can identify real problems and build simple, reliable solutions for them will succeed.\
And perhaps, that’s the best thing that could have happened to the industry after the 2024 boom.

After the 2024 AI startup boom, the market is undergoing a major shift. Hype is fading, investors are demanding real results, and only startups solving real-world problems are thriving. Discover why the era of AI agents, industry-specific solutions, and ethical AI is reshaping the future — and what it means for startups and investors in 2025.

What’s Happening to AI Startups After the 2024 Boom?

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A leading AI, Blockchain and Gaming consulting giant driving AI and Web3 solutions over the past 12 years under the flagship events that are globally renowned under the brand of Global AI Show, Global Games Show and Global Blockchain Show. With a strong UAE, UK, India and Hongkong footprint, our expert team of 170+ professionals ensures our clients remain at the forefront of innovation. We drive innovation through Strategic PR and Marketing, Bounty Campaigns, and Global Events that showcase the brightest minds in the transformative fields of Web3, AI and Gaming. We also offer services in Advertising & Media and Staffing.

Discover more about Global AI Show at Abu Dhabi in our latest article!

VAP Group in association with Abu Dhabi Convention & Exhibition Bureau is set to host all AI futurists at The Global AI Show at Abu Dhabi, on 8-9th December 2025

The Global State Of Responsible AI In Enterprise 

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# Why Data Science Is the Secret Weapon Behind Today’s Smartest Business Decisions

While gut instinct might have guided executives in the past, modern business leaders are turning to something far more powerful: data science.

From retail giants to scrappy startups, data science is reshaping how companies make decisions, optimize operations, and find opportunities before their competitors do. It’s no longer just a “tech thing”—it’s the backbone of strategic thinking.

## What Is Data Science?

Let’s strip away the buzzwords. Data science is the art (and science) of extracting insights from raw data using a combination of statistics, machine learning, and domain expertise. It’s not just about crunching numbers—it’s about turning those numbers into actionable strategy.

Think of it as the business world’s GPS: collecting data from various sources, analyzing the terrain, and charting the best course forward.

## Why Every Business—Big or Small—Should Care

Harvard Online puts it simply: companies that leverage data science make better decisions. But what does that really mean?

In practical terms, it means:

* Knowing your customers better than they know themselves
* Anticipating market trends before they happen
* Maximizing efficiency across operations
* Saving money—and time—on trial-and-error approaches

In short: data science turns guesswork into a competitive edge.

## 5 Real Ways Data Science Is Shaping Smarter Decisions

### 1. Customer Insights That Actually Matter

Understanding customer behavior is gold. But data science goes beyond surface-level demographics.

Modern algorithms can analyze everything from website clicks and purchase history to social media sentiment. The result? A deep, real-time understanding of what your customers want, when they want it, and why.

Take Netflix. It doesn’t just recommend shows—it uses behavioral data to decide what new series to produce. That’s customer insight turned into product strategy.

### 2. Forecasting Like a Crystal Ball (But Better)

Imagine knowing which products will sell next quarter—or which suppliers are likely to delay shipments.

Data science helps businesses build predictive models based on past data. This enables more accurate sales forecasting, demand planning, and inventory management. No more scrambling to catch up—just smart, proactive planning.

According to In Time Tec, companies using data science for forecasting report reduced waste, better resource allocation, and fewer nasty surprises in the supply chain.

### 3. Marketing That’s Not Just Guesswork

Say goodbye to throwing ads at the wall to see what sticks.

With data science, marketing teams can segment audiences with laser precision, run A/B tests in real time, and measure ROI down to the dollar. Algorithms help determine not just what message to send, but when and to whom.

Companies like Amazon, Spotify, and even small e-commerce brands are optimizing campaigns with machine learning tools—automating the grind while getting better results.

### 4. Operational Efficiency That Cuts the Fat

Want to cut costs without cutting corners?

Data science pinpoints inefficiencies across operations—from factory floor bottlenecks to lagging delivery times. By mapping out these friction points, companies can streamline processes and reduce overhead.

A classic example: UPS famously used data analytics to reroute deliveries and avoid left-hand turns, saving millions in fuel costs annually. That’s data in motion, literally.

### 5. Smarter Hiring and Talent Retention

Even HR is going data-driven.

Companies are using predictive analytics to identify the best candidates, forecast employee churn, and even design better career development plans. When hiring becomes data-informed, bias goes down and fit goes up.

Harvard Online emphasizes how data-driven HR practices can improve both team performance and employee satisfaction. It’s not about replacing humans—it’s about helping them thrive.

## From Data to Dollars: Measuring the ROI

Businesses investing in data science are seeing tangible returns:

* Higher revenue through targeted marketing
* Lower costs via efficient operations
* Faster decision-making with real-time dashboards
* More innovation as teams test new ideas backed by data

In fact, McKinsey estimates that data-driven organizations are 23 times more likely to acquire customers and 19 times more likely to be profitable. That’s not hype—it’s hard math.

## But Let’s Be Honest: It’s Not Plug-and-Play

While the benefits are clear, data science isn’t magic, and it’s not a one-size-fits-all solution.

### Common Challenges:

* Data quality issues: Garbage in, garbage out.
* Siloed teams: Without collaboration, insights die in spreadsheets.
* Lack of skilled talent: Data scientists are in high demand, and they’re not cheap.
* Ethical concerns: How you collect and use data matters more than ever.

To succeed, companies need a solid foundation: clean data, clear goals, and cross-functional teamwork.

## The Good News? You Don’t Have to Start Big

Small wins are the gateway to long-term success.

Not every business needs a full-fledged data science department. Many start with a single analyst or consultant and scale from there. Tools like Power BI, Google Looker, and open-source libraries (hello, Python) make entry more accessible than ever.

Want to test the waters? Here’s how:

1. Choose a clear use case (like improving customer retention)
2. Collect relevant data (purchase history, survey responses, etc.)
3. Analyze patterns (what do loyal vs. lost customers have in common?)
4. Act on insights (offer targeted loyalty rewards or improve service touchpoints)

Rinse, refine, and repeat.

## Real Companies, Real Impact

Here are just a few stories from the field:

* Zara uses real-time sales data to adapt designs weekly, keeping stock fresh and on-trend.
* Starbucks analyzes location-based data to choose new store sites with the highest ROI.
* Spotify uses listener data not only to personalize playlists but to inform partnerships and artist promotion.

These aren’t side projects—they’re core business strategies, powered by data science.

## Data Is the New Decision Currency

In 2025 and beyond, success won’t go to the company with the loudest ads or the flashiest branding—it will go to the company with the clearest insights.

Whether it’s predicting what customers want, refining your operations, or deciding where to expand next, data science gives leaders the clarity they need in an increasingly chaotic world.

So if your business isn’t already thinking about data science, the question isn’t “should we do it?”

It’s “How soon can we start?”

How can data science help your business grow? Explore real examples of how companies use data to make smarter decisions, boost efficiency, and stay ahead of the competition.

How Data Science helps businesses make decisions based on facts, not intuition

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