Why the path to autonomous AI agents will be slower, smarter, and more sustainable
AI has already transformed how we search, create, and augment. But the next leap – implementing fully autonomous software agents – is proving more complex to land than headlines suggest. The promise is bold and sweeping, yet the reality inside most enterprises is far more measured. The road to agentic AI at scale will be steady and deliberate, not instantaneous.
Agentic AI imagines a future where software agents can independently execute multistep tasks, make decisions, and manage complex workflows across systems with minimal human input. It’s a compelling vision, but still largely aspirational. Most current implementations operate within tight parameters and require significant human oversight. The gap between ambition and reality reflects both the state of the technology and the readiness of enterprise environments to support it.
We’ve seen this pattern before. Cloud computing’s advantages were undeniable, yet it took over a decade for enterprises to migrate the bulk of workloads. Legacy infrastructure, regulatory hurdles, and the need for workforce upskilling all slowed progress. Agentic AI is now following a similar trajectory.
Gartner projects more than 40% of agentic AI projects will be canceled by the end of 2027. Not because of a lack of innovation, but because organizations simply are not ready. A recent Deloitte report identifies outdated systems and compliance barriers as the biggest roadblocks. The limiting factor isn’t enthusiasm – it’s readiness, a reminder that scaling autonomy demands just as much operational change as technical innovation.
Yet the trajectory is unmistakable: the same forecasts from Gartner predict that by 2028, 15% of day-to-day work decisions will be made autonomously through agentic AI, up from virtually 0% in 2024. Adoption may be uneven, but the direction is clear.
The rise of agentic AI won’t be a binary leap from human to machine control. Instead, it will be a gradual evolution.
The Reality Behind the Revolution
Agentic AI is designed to be a dynamic collaborator; a system that doesn’t just respond to requests, but can proactively execute tasks across systems based on broader objectives. The concept is powerful, but the implementation is hard. To succeed, enterprises need deep integration with internal systems, strong guardrails, clear objectives, and, most of all, trust.
That level of readiness doesn’t happen overnight.
Many organizations are still managing fragmented data pipelines, siloed information, and legacy tech stacks that simply weren’t built for intelligent automation. Expecting AI agents to seamlessly orchestrate mission-critical workflows overlooks the entrenched complexity of modern enterprise systems.
For now, the real opportunity lies in augmentation, not automation. Agentic AI will enhance, not replace, existing workflows. Early success stories will come from thoughtful integration into current systems, not wholesale reinvention.
Even incremental progress requires major cultural and operational change. Organizations must reimagine everything from team structures and approval chains to data governance and compliance. Agentic AI won’t simply plug into existing processes – it requires redesigning them.
Bridging Potential and Readiness
Agentic AI is already delivering value – in CRM assistants, task orchestration, and automated insights. Yet most organizations remain far from enterprise-wide deployment. The challenge isn’t vision; it’s infrastructure.
True agentic AI depends on clean, connected, and accessible data; interoperable systems; well-defined processes; and clear oversight mechanisms. Without that foundation, pilots stay pilots. Ambition outpaces execution.
Executives must resist the urge to scale prematurely. Instead, they should focus on building strong data and process foundations, experimenting within low-risk areas, and expanding deliberately. Projects that skip these steps are the ones most likely to fail – not because of the technology, but because of the organization.
Humans in the Loop, by Design
Despite the progress, human oversight remains indispensable. AI can analyze, recommend, and execute, but accountability still rests with human users. Especially in regulated industries or customer-facing roles, the margin for error is simply too small to hand over control entirely.
The winning approach is to build human-in-the-loop systems that blend AI’s speed and scale with human expertise and accountability. These hybrid models aren’t just safer, they are smarter. Collaboration between humans and machine intelligence drives trust, accuracy, and long-term adoption.
The Strategic Path Forward
Agentic AI’s potential is enormous, but meaningful progress will only come through thoughtful execution. For business leaders, the message is clear: build the right data and foundations, embed human oversight and clear governance, and start with targeted, high-value use cases. Then, scale from there.
As we’ve seen time and again, the winners in technology revolutions won’t be the first movers, they will be the smart builders who prioritize resilience over speed.
About The Author Of This Article
Thor Olof Philogène is CEO and Founder at Stravito
Also Read: The AI-Powered Digital Front Door: Creating Personalized and Proactive Access to Healthcare
[To share your insights with us, please write to psen@itechseries.com ]
Comments are closed.