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There is no secret that AI is gradually taking over certain jobs. It can write text, generate code, create content, and many other things. In such a case, the question arises: can AI completely replace employers, or at least certain departments?
A strategic mindset or framework in which artificial intelligence is placed at the core of a product, service, or business model is called an AI-first approach. These are the main traits that characterize such an approach:
1. AI is foundational, not supplemental – In theory, for companies adopting an AI-first approach, AI isn’t merely a feature — it’s the core of their entire business model. In practice, however, the AI-first approach has not completely removed people from companies. Instead, most organizations are combining AI with human employees to enhance productivity, decision-making, and personalization.
2. Continuous learning and automation – AI-first systems continuously collect data, learn from it, and adapt.
3. Decisions are driven by data models – Strategic decisions, product development, and user experiences are guided by AI insights
4. User experience is reimagined with AI – Instead of just optimizing UX, AI-first products often create entirely new ways to interact, like voice assistants, AI-driven search, or adaptive interfaces (think: ChatGPT vs. traditional search).
An AI-first approach has not been achieved yet, but the companies are slowly shifting to it. Here are the benefits of AI AI-first approach and the examples of how companies are already using it.
Creating hyper-personalized user experiences has largely become a task handled by AI. Content, interfaces, and recommendations are uniquely made for each user based on behavior, preferences, and context, often in real time.
Spotify uses AI to analyze listening history, time of day, mood, and even song structure to generate personalized playlists like Discover Weekly or Daily Mix. No two users have the same experience.
In certain fields, AI models make decisions faster and more accurately than humans, often in areas like finance, logistics.
Google was one of the first who implement such an approach. Google Ads uses machine learning to automatically optimize bids, placements, and targeting for advertisers. Instead of manually adjusting campaigns, AI continuously finds the best-performing combinations across thousands of parameters.
AI systems improve with more usage/data. Over time, they get better at predicting outcomes, recommending actions, or adapting to trends. Thanks to AI, the processes are getting improved faster and more flexibly.
Tesla’s self-driving AI collects data from every vehicle on the road. This network effect allows it to train its models faster and more effectively than competitors, improving safety and autonomy with each mile driven.
Like many technologies, the AI-first approach has certain disadvantages. At the moment, it’s far from a flawless process.
Creating AI AI-first approach is not cheap. Developing AI-first solutions requires significant upfront costs, including hiring AI talent, purchasing infrastructure (like GPUs), collecting/cleaning data, and running experiments.
A startup building an AI-powered legal assistant may need to invest millions to collect legal texts, annotate them, train NLP models, and ensure compliance before even launching.
This problem derives from the first one. AI systems need large volumes of accurate, diverse, and well-labeled data to train effectively. Poor-quality or biased data leads to poor results, flawed predictions, or harmful decisions. And high-quality data is not available to every company.
As an example, facial recognition systems trained on mostly white faces have historically struggled with accurately identifying people of color, leading to false arrests and discrimination. This is due to underrepresented or skewed training datasets.
AI can unintentionally reinforce societal biases, violate user privacy, or make decisions that are hard to explain or audit. Governments and users are increasingly demanding ethical, transparent AI systems.
Amazon’s AI recruiting tool was found to downgrade resumes that included the word “women’s” (as in “women’s chess club”)—because it had learned from historical hiring data biased against women. The project was shut down.
Many AI models, especially deep learning models, are black boxes. That means that they give results, but humans don’t fully understand how they arrived at those results. This limits trust, especially in critical fields like healthcare, finance, or law.
In healthcare, an AI might recommend a specific cancer treatment plan, but doctors may be unable to understand or explain the model’s reasoning, raising concerns about accountability if something goes wrong.
Very often, when the company declares using an AI-first approach instead of a human workforce, it raises negative reactions among the pubic.
Recently, Duolingo announced its own shift to an AI-first model, aiming to automate roles previously handled by contractors. This sparked a wave of backlash on social media, particularly among younger users on TikTok, who accused the company of dehumanizing education and harming employees. Duolingo responded by clarifying that AI is used under expert supervision to support, rather than replace, human educators.
The AI-first approach is not perfect yet. However, already today, we see how companies are implementing more and more of this approach. The trend is toward human-AI collaboration, where AI handles the heavy lifting and humans focus on high-value, nuanced work, especially in fields like marketing, where emotional intelligence and brand storytelling will critical role in the near future and beyond.