Companies are under pressure to move faster and operate leaner. A growing number are turning to AI-powered systems to replace manual, time-consuming workflows. According to a recent McKinsey report, 92% of companies expect to increase their AI investments over the next three years. Despite that, only 1% say their current AI systems are fully mature, highlighting the disconnect between ambition and implementation.
Enterprise data pipelines have long been labor-intensive and costly. Processing data typically requires teams of engineers, complex integrations, and manual updates. By the time insights were produced, the data was often outdated. Yet, data infrastructure remains a top priority. Companies across various industries continue to invest billions in efforts to extract value from internal and external data sources.
To address the lag between data collection and action, newer AI systems are emerging that aim to automate these processes in real time.
The Rise of AI Agents: Traditional Data Workflows Fall Short
One of the more recent developments in this space is the use of AI agents—intelligent systems that can independently execute workflows, adapt to new information, and, in some cases, generate new agents to handle additional tasks.
Platforms built around these agents allow users to input simple, natural-language instructions, such as:
“Analyze customer behavior to identify churn risks and notify the retention team in real time.”
These systems then translate the instruction into a full workflow, eliminating the need for manual coding or configuration.
Satya Nitta, co-founder and CEO of Emergence AI, describes these tools as enabling “outcomes, not templates,” noting the potential to reduce technical bottlenecks and free up engineering resources. While specific platforms vary in their capabilities, the trend indicates a broader adoption of intelligent agents that learn, adapt, and scale without requiring constant human input.
Going Beyond Automation
Unlike earlier automation tools that focused on repeating static tasks, modern AI agents are designed to work more dynamically. Some platforms are experimenting with what’s known as recursive agent architecture—systems that can plan, test, and deploy other agents based on evolving needs. This enables companies to manage complex tasks, such as cleaning datasets, migrating data between platforms, validating machine learning outputs, and adjusting models, without adding staff. The underlying goal is to enable scale through intelligent automation rather than headcount growth.
Emergence AI is one of several companies working in this area, but it’s not alone. Hebbia, for instance, has developed an AI platform tailored to industries like finance and law. Its tools help smaller teams quickly process large volumes of documents and data, although the technology relies on retrieval-augmented generation (RAG) rather than autonomous agents.
Another firm, Stack AI, has recently secured funding to expand its no-code platform, which enables businesses to build workflows powered by large language models (LLMs). While it focuses on practical applications such as CRM integration and data entry, its approach is closer to traditional automation than to the emerging agentic frameworks.
Measuring the Impact
Although agent-based AI platforms are still in their early stages, some are already showing measurable results.
In various sectors, organizations using these tools have reported:
- Weekly cost savings in the millions through more responsive analytics
- Up to 70% reductions in time spent on data governance and discovery
- Real-time moderation of millions of pieces of content per month in online forums
These early use cases suggest the potential for AI agents to become foundational technology for businesses looking to streamline operations.
Implications for the Future of Work
Companies adopting these tools are finding that they can make faster, more accurate decisions while relying less on traditional engineering resources. That shift could signal a broader change in how teams are structured and how work gets done.
Industry observers note that the move toward agent-based systems could also reshape IT services and staff augmentation. In response, some firms are beginning to train workers in these technologies, anticipating growing demand for implementation expertise. One such example is a recent partnership between an AI platform company and Andela, a global talent network for engineers. The goal is to prepare technical talent for working with multi-agent systems that are likely to become standard across enterprise environments.
As AI agents evolve from experimental tools into reliable infrastructure, they may redefine how organizations operate, reducing overhead, improving decision speed, and increasing adaptability.
For now, companies that can effectively implement and scale these systems may gain a significant advantage over competitors still bound by manual processes.
Featured Image Credit: Photo by Google DeepMind; Pexels
