ManageEngine CEO Rajesh Ganesan says ‘partners have the card to play’ in autonomy-led enterprise operations

According to Ganesan, partners building AI agents, orchestrating workflows and delivering operational outcomes are expected to gain larger strategic roles inside enterprise IT environments.

Speaking to CRN India around the rollout of Zia Agents across the ManageEngine digital enterprise management suite, the company’s CEO, Rajesh Ganesan, described AI agents less as another automation capability and more as the beginning of a structural shift in how enterprise IT operations are managed, governed, and operationalised.

The rollout extends autonomous AI capabilities across areas, including IT service management, observability, endpoint management, and security operations, while allowing users to deploy prebuilt agents or create their own through the Zia Agent Studio.

According to Ganesan, the larger opportunity for channel partners is no longer limited to software deployment, infrastructure management, or operational support. Instead, AI-driven autonomy is beginning to create demand for workflow orchestration, infrastructure governance, operational optimisation, and outcome-led enterprise engagements.

“Partners have the card to play,” Ganesan said, arguing that AI agents expand how partners can build vertical-specific operational workflows, create reusable intelligence layers, and deliver measurable business outcomes across customer environments.

He also positioned the industry’s current transition as a movement beyond traditional automation models toward operational autonomy, where enterprise systems interpret context, correlate activity, and execute decisions with reduced human intervention.

The previous generations of enterprise automation still required significant scripting, integration work and manual oversight, Ganesan said. With AI agents, however, partners can “build workflows using natural language instructions” instead of traditional programming-heavy approaches.

He said partners can now describe customer requirements, infrastructure behaviour, attack vectors, or operational workflows in plain language and generate autonomous agents tailored for specific enterprise environments.

The shift becomes relevant across industry-specific environments where operational requirements differ significantly.

Ganesan said partners familiar with sector-specific operational complexity are likely to play a “larger role” in building and operationalising AI-driven workflows.

According to him, the real value of AI agents lies not in automation, but in enabling systems to correlate activities, interpret operational context, and respond autonomously across infrastructure, applications, and security environments.

Marketplace economics and partner monetisation

ManageEngine sees partners building commercial opportunities around agent development itself. Ganesan said partners could “either build AI agents reactively based on customer requirements or proactively develop reusable workflows” and publish them into a broader marketplace model consumable across multiple customers.

“We envision this becoming a revenue stream for partners,” he said.

At the same time, Ganesan repeatedly distanced ManageEngine from the idea of positioning AI as a standalone monetisation layer.

According to him, the company does not currently view generative AI or AI agents as separate chargeable product categories. Instead, AI capabilities are being integrated directly into the broader ManageEngine platform portfolio across service management, endpoint management, and operational environments. The industry itself is still trying to determine sustainable AI monetisation models.

Ganesan pointed to token-based pricing structures emerging across the broader AI ecosystem and suggested that many of those approaches are still disconnected from actual operational outcomes.

“The world of AI is still figuring out the right way of monetisation,” he said.

For partners, the monetisation opportunity lies around faster solution delivery, workflow orchestration, infrastructure optimisation, integrations, and operational outcomes.

According to him, AI agents allow partners to build customer capabilities “in double quick time”, increasing both deployment velocity and operational productivity.

Customers pushing channel ecosystems toward outcomes

Another major shift, according to Ganesan, is the customer expectation around measurable operational outcomes rather than infrastructure management alone.

He argued that the rise of AI-driven autonomy naturally changes how enterprise customers evaluate technology investments and partner relationships.

“The customers of our partners will push them,” Ganesan said while discussing outcome-led engagements.

According to him, earlier generations of enterprise automation lacked the intelligence layer necessary to support truly autonomous operational models. AI agents, however, are changing that equation by combining automation with contextual decision-making.

As a result, customers expect systems not only to automate workflows but also to reduce incidents, improve operational stability, strengthen security posture, and minimise manual intervention.

This, according to Ganesan, will gradually “push partners toward more consulting-oriented” and operationally accountable engagements.

At the same time, he rejected the idea that AI agents diminish partner relevance. “I don’t expect partners to disappear because of agents,” Ganesan said. “The way they do their work will change, but their significance and relevance will not change.”

Infrastructure reliability becomes foundational

Beyond AI capabilities themselves, Ganesan emphasised the importance of infrastructure readiness, governance, and operational reliability.

According to him, autonomous systems can only function effectively when the underlying technology infrastructure is reliable, observable, and operationally mature.

“You only give autonomy when you truly trust something,” he said.

This positioning places infrastructure management itself at the centre of enterprise AI readiness discussions.

Organisations looking to operationalise AI at scale must first strengthen areas of infrastructure visibility, data quality, operational consistency, and security posture before autonomous workflows can be trusted inside production environments, said Ganesan.

Autonomy raises partner accountability

There is a growing industry recognition that AI agents still require substantial human supervision despite advances in autonomous capabilities.

Ganesan pointed to how AI companies themselves are increasingly building consulting, deployment, and operational engineering functions to help customers productionise AI systems safely.

According to him, building the first version of an AI agent may eventually become relatively simple, but maintaining, debugging, fine-tuning, and governing autonomous systems will remain operationally complex.

This becomes important because AI agents can dynamically evolve behaviour inside live enterprise environments.

“How do you control such a scenario?” Ganesan asked while discussing long-term governance requirements around autonomous systems.

The operational responsibilities around AI agents, according to Ganesan, will remain distributed across platform providers, partners, and customers.

Ownership of customer data remains entirely with the customer. Accountability around incidents, agent behaviour, deployment configurations, and operational outcomes will increasingly function as a shared responsibility framework between platform providers, customers, and channel partners, Ganesan said.

He added that responsibilities across all parties would need to remain “clearly defined” as autonomous systems become deeply integrated into enterprise operations.

Ganesan argued that AI agents may fundamentally change how enterprise operations are executed, but they are increasing the importance of partners capable of operationalising autonomy reliably inside customer environments.

According to him, that shift is expected to expand the role of channel ecosystems rather than reduce them.

“We are continuing to expand our channel and partner ecosystem,” Ganesan said, adding that ManageEngine “expects more partners to join the ecosystem” as enterprise customers seek operational guidance around AI-driven infrastructure, governance, and autonomy.