For a while, it seemed like every product would become an “AI product,” with tools that promised to replace entire workflows and radically change the way people work.
This phase gradually fades, and what emerges in its place is less visible but much more practical. The market is becoming more down-to-earth, with less focus on general promises and more on systems that solve specific problems within real-world workflows.
Ihor Shovkoplias is a New York-based video producer and founder of IS Creative. Over the past few years, he has worked closely with AI across content production and marketing systems, focusing not on experiments but on how these tools perform in real workflows, under real deadlines, and with real business constraints.
In this piece for Business is importanthe shares an informed perspective on where the AI market is actually headed, based on practical experience rather than industry hype, and why the shift toward more focused, practical tools is already changing the way teams build, create and work.
One of the most noticeable changes is the decreasing importance 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 work. Instead, products are evolving into more focused layers that integrate 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 disorganized input
- Model routing systems that select the right model for a specific task
This approach reduces friction. If a product requires users to change the way they work, migrate systems, or rebuild processes, adoption slows. Tools that integrate with what already exists are more likely to become part of daily operations.
Another important change concerns the way value is perceived. Contrary to the common narrative, people are not paying for “AI” or intelligence itself; You pay for speed and efficiency. Teams are not looking for an assistant to do everything, but rather for solutions that eliminate specific bottlenecks and reduce the number of steps required to achieve an outcome.
In practice, valuable tools tend to share some common characteristics:
- They shorten the path from input to output
- You reduce the number of manual steps
- They require minimal setup or onboarding
- They fit into existing workflows without interruption
Products that fail usually have the opposite effect. They introduce complexity, require behavioral changes, or promise transformation but slow implementation.
At the same time, software is increasingly being developed not only for human users but also for agents. APIs and systems are structured so that automated processes can invoke tools, interpret responses, and determine next actions. This represents a deeper structural shift where software begins to interact more autonomously with other software, reducing the need for constant manual input and redesigning the way workflows are built.
Adoption patterns are also becoming more predictable. Successful products are rarely those that are disruptive or require users to rethink things. 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:
- embedded in tools people already use
- in line with existing habits and interfaces
- easy to test and without long-term commitment
The less a user has to adapt their behavior, the faster a product becomes part of their routine, which is why familiarity often takes precedence over novelty in practice.
Finally, the role of data has changed. The challenge is no longer access to information, as there is already more data available than can be effectively processed. The true value lies in transformation – the ability to transform raw data into clear, actionable results. Users aren’t looking for more data, they’re looking for clarity and guidance. The most effective tools are those that reduce ambiguity and deliver immediate, actionable results.
Overall, the market is becoming more pragmatic and selective. Big, general promises are being replaced by targeted solutions that quickly and efficiently address specific needs. This shift does not diminish the importance of AI; Rather, it reflects a more sophisticated understanding of how it should be applied in practice, with an emphasis on precision, integration and practical utility.
Read more:
The era of “AI for everything” is coming to an end – and that’s a good thing
