For a while, it felt like every product was becoming an “AI product,” with tools promising to replace entire workflows and radically transform how people work.
That phase is gradually fading, and what is emerging in its place is less visible but significantly more practical. The market is becoming more grounded, with less focus on broad promises and more attention to systems that solve specific problems inside real workflows.
Ihor Shovkoplias is a New York–based video producer and founder of IS Creative. Over the past several years, he has worked closely with AI across content production and marketing systems, focusing not on experimentation, but on how these tools behave in real workflows, under real deadlines, and with real business constraints.
In this piece for The Business Matters, he shares a grounded perspective on where the AI market is actually heading, based on hands-on experience rather than industry hype, and why the shift toward more focused, practical tools is already changing how teams build, create, and operate.
One of the clearest shifts is the declining relevance of all-in-one AI platforms. The idea that a single tool can handle everything from research to execution sounds appealing, but it rarely aligns with how people actually operate. Instead, products are evolving into more focused layers that plug into existing workflows rather than trying to replace them. These layers typically include:
search and retrieval systems that prioritize speed and relevance
data extraction tools that structure unorganized input
model routing systems that choose the right model for a specific task
This approach reduces friction. If a product requires users to change how they work, migrate systems, or rebuild processes, adoption slows down. Tools that integrate into what already exists are far more likely to become part of daily operations.
Another important shift is how value is perceived. Despite the common narrative, people are not paying for “AI” or intelligence itself; they are paying for speed and efficiency. Teams are not looking for an assistant that does everything, but for solutions that remove specific bottlenecks and reduce the number of steps required to reach a result.
In practice, valuable tools tend to share a few consistent characteristics:
they shorten the path from input to output
they reduce the number of manual steps
they require minimal setup or onboarding
they fit into existing workflows without disruption
Products that fail usually do the opposite. They introduce complexity, require behavioral change, or promise transformation while slowing execution.
At the same time, software is increasingly being designed not only for human users but also for agents. APIs and systems are structured in a way that allows automated processes to call tools, interpret responses, and determine next actions. This represents a deeper structural change, where software begins to interact with other software more autonomously, reducing the need for constant manual input and reshaping how workflows are built.
Adoption patterns are also becoming more predictable. Products that succeed are rarely those that feel disruptive or require users to rethink their behavior. Instead, they are the ones that integrate naturally into familiar environments, such as development tools, terminals, or existing platforms. In most cases, adoption accelerates when a product is:
embedded inside tools people already use
consistent with existing habits and interfaces
easy to test without long-term commitment
The less a user needs to adjust their behavior, the faster a product becomes part of their routine, which is why familiarity often outperforms novelty in practical settings.
Finally, the role of data has shifted. The challenge is no longer access to information, as there is already more data available than can be effectively processed. The real value lies in transformation — in the ability to convert raw input into clear, actionable outcomes. Users are not looking for more data, but for clarity and direction, and the most effective tools are those that reduce ambiguity and provide immediate, usable results.
Overall, the market is becoming more pragmatic and selective. Large, generalized promises are being replaced by focused solutions that address specific needs quickly and efficiently. This shift does not reduce the importance of AI; rather, it reflects a more mature understanding of how it should be applied in practice, with emphasis on precision, integration, and real-world utility.
Read more:
The “AI for Everything” Era Is Ending — And That’s a Good Thing
