Aliasgar Dohadwala, CEO of Visiontech Systems, is a visionary and serial entrepreneur with a knack for turning challenges into opportunities
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Over the past few years, businesses have invested heavily in AI systems, such as dashboards, co-pilots and automation. Yet, despite all this investment, a fundamental problem remains: From what I've seen, most organizations still aren't significantly faster, more efficient or more agile, as most AI tools still depend on humans to interpret, make decisions and execute.
This is exactly where agentic AI changes the equation.
These days, organizations aren’t just adopting AI; they’re under pressure to operate differently. This brings higher customer expectations, always-on digital environments and increasing operational complexity.
And the critical issue is that decision latency is becoming a business risk. The longer it takes to move from insight to action, the higher the cost, whether in downtime, lost revenue or poor customer experiences. Agentic AI compresses that cycle.
A major reason AI hasn’t delivered anticipated ROI is because it stops at insight. According to a 2025 report by Gartner researchers, a significant number of AI initiatives struggle "due to escalating costs, unclear business value or inadequate risk controls," with over 40% of agentic AI projects expected to be canceled before 2028.
From what I’ve seen, the issue isn’t the technology. It’s the gap between knowing and doing. Agentic AI can bridge that gap.
Agentic AI's impact is already emerging in key areas:
• Customer service is expected to be transformed, with up to 80% of common issues resolved autonomously by 2029 and reducing costs by 30%.
• Enterprise environments are rapidly integrating agents, with nearly half of applications expected to embed them in the near future.
• Early deployments are showing tangible productivity gains, with teams saving up to 12 hours each week through agent-driven automation by 2029.
For almost a decade, AI has been framed as a tool for efficiency, something that helps organizations do more with less. But that framing is now outdated. What agentic AI introduces isn't just another layer of automation; it introduces a new way of working. We're beginning to see the formation of what can only be described as a digital workforce.
In this model, humans are no longer buried in execution; they define direction, set priorities and provide oversight. AI agents take on the responsibility of carrying out workflows, making decisions within defined boundaries, responding to context and improving outcomes over time. Sitting above this interaction are systems that continuously learn and optimize, often without explicit intervention.
For years, scaling a business meant scaling people. More demand required more hiring, more onboarding and more management layers. Agentic AI begins to decouple growth from headcount.
Although the opportunity is massive, so is the risk. Per a Gartner 2025 report, only 15% of organizations are currently deploying or piloting fully autonomous agents. As shared above, a large portion of projects are expected to fail due to unclear ROI, poor governance and misaligned use cases.
Many organizations approach agentic AI as a tool to be layered onto existing workflows. But those workflows were never designed for autonomy. They rely on manual checkpoints, fragmented ownership and legacy decision-making processes. Introducing autonomous agents into that environment doesn’t fix the inefficiencies—it accelerates them.
There’s also the question of accountability. When decisions are made by AI agents, who owns the outcome? Without clearly defined governance models, organizations risk creating systems that operate efficiently but lack transparency and control.
In my perspective, the challenge isn’t deploying agentic AI but in integrating it into the fabric of how work gets done.
The organizations seeing real traction start with workflows that are high in volume and repetition. They prioritize areas where speed directly influences business outcomes, such as cybersecurity response, customer support resolution or financial operations. They focus on functions where success can be clearly measured.
The most critical thing is to redesign workflows before introducing agents. Businesses need to rethink how work flows across teams, where decisions should sit and how humans and AI interact at each stage. Agentic AI isn't a shortcut to transformation but an accelerator. It will amplify whatever system it's placed into. If that system is well-designed, the results can be exponential. If it isn’t, the inefficiencies scale just as fast.
What makes agentic AI different from previous waves of technology is that it doesn’t just change tools—it changes operating models. This requires a leadership discussion about how the organization structures work, defines roles and creates value. Companies that understand how to integrate it meaningfully will have a distinct advantage.
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