Agentic AI is fast emerging as the new operating layer for enterprise intelligence, marking a clear shift from static automation toward systems that can reason, act, and adapt autonomously. As organizations move beyond pilots, the focus is now on deploying multi-agent systems that deliver measurable business outcomes. With ecosystem momentum growing and large partnerships — such as Accenture’s collaborations with HCLTech and Google Cloud — helping enterprises scale these capabilities, Agentic AI is increasingly shaping boardroom conversations and long-term enterprise planning. Raunak Gulshan, Head of Insights at Tredence Inc., highlights how this transition reflects a deeper rethink of how intelligence is operationalized across businesses.
The growing interest comes alongside a peak in the hype cycle, prompting enterprises to adopt a more disciplined and pragmatic approach. Leaders are no longer satisfied with demonstrations or proof-of-concepts; instead, they are demanding systems that are reliable, explainable, and governed by clear accountability frameworks. Concerns around governance, transparency, and consistency remain central, particularly as AI systems take on more autonomous roles in decision-making. This cautious optimism is reshaping expectations, pushing organizations to define value more clearly before committing to scale.
As a result, the success metric is evolving. Rather than evaluating initiatives purely through traditional ROI, enterprises are beginning to focus on what can be described as Return on Agentic Intelligence. This broader lens captures outcomes such as autonomous execution, shorter decision cycles, improved responsiveness, and the ability of systems to continuously learn and adapt over time. The emphasis is shifting from efficiency alone to sustained intelligence that compounds value as it operates.
Within this context, Tredence is positioning Agentic AI as a catalyst for last-mile adoption. Through Tredence Studio and MilkyWay — its multi-agent decision intelligence system — the company aims to help enterprises move from experimentation to production at scale. These systems are designed to accelerate productivity, reduce operational costs, and embed intelligence directly into business workflows.
From enhancing customer experience to optimizing promotions, pricing, and on-shelf availability, Agentic AI is increasingly being framed as a practical enabler of real-world outcomes. The narrative is becoming clearer across enterprises: this is no longer about future promise, but about deploying adaptive intelligence that translates insights into consistent, measurable business impact.
