Posted by USEReady Co-Founder and CEO Uday Hegde on what enterprises must change before trusting AI with real workflows
For most large organisations, artificial intelligence (AI) has so far remained in an advisory role. Up until now, AI systems have been summarising documents, offering up insights and supporting decision-making. But the final step, that of acting on those insights, has been the job of users. In 2026, as AI moves from advice to action, that is starting to change in a big way.
And as enterprises experiment with agentic AI systems that can initiate workflows, move data, and trigger transactions, several new questions seem to emerge. Questions like whether organisations are ready to let “software” trigger decisions inside business processes that were initially designed around human judgement.
According to USEReady Co-Founder and CEO Uday Hegde, this shift is more disruptive than most people realise. “Agentic AI is a critical step as we go driverless in several human-led processes that we are accustomed to today,” he says noting that most enterprise systems were never designed for this kind of autonomy.
Legacy systems weren’t built for AI Agents
One of the biggest bottlenecks in deploying agentic AI is not the AI itself. It is the way enterprise systems have been designed. Most legacy platforms assume that a human will always be in the loop. There are screens for data entry, manual checks, and hand-offs across teams. Agentic systems break that assumption.
“With agentic applications, these interfaces are not necessary,” Hegde says. “A human can be looped simply for approval.” In practice, this means entire workflows may need to be rethought, not just automated.
In areas like finance operations, this becomes especially visible. Invoice processing systems, for example, were designed around manual reviews and rule-based checks. Moving to reasoning-based automation often means rebuilding parts of the workflow, not just adding an AI layer on top.
This shift also brings new operational requirements. Governance, observability, platform resilience, security controls, and testing processes become more central when software is allowed to act across systems.
Many organisations underestimate this work. They assume autonomy can be “added on” to existing platforms. In reality, the operating model often needs to change.
RPA is not the same as Autonomy
There is also confusion in the market about what “agentic” actually means. Many offerings being sold as agentic AI look like older automation tools with a new label. The difference, Hegde argues, is fundamental. “RPA systems are rule-based, whereas AI is reasoning-based.”
Rule-based automation follows pre-defined paths. Agentic systems interpret context and decide what to do next. This makes them more flexible, but also harder to govern.
Some vendors that built their businesses on RPA are now reworking their products for this shift. The pressure is real. Enterprises are increasingly unwilling to invest in brittle automation that breaks when conditions change.
In some deployments, organisations are already replacing rule-based bots with reasoning-driven workflows, particularly in high-volume processes like invoicing.
Guardrails matter when there’s no interface
Traditional enterprise applications and business processes assume that a human user is present. There are forms to fill, screens to review, and approvals to click. Once AI agents begin to operate without those human interfaces, the old safety nets weaken.
“Modern systems open holes for fraudsters as we replace interfaces designed for humans with approval systems or no user interface at all,” Hegde notes. In practice, this means organisations need to rethink how permissions, identity, and approvals work when actions are triggered by software rather than people.
Some of the basic guardrails, he says, are not new ideas, but they become more important in an agentic context. These include detailed logging of agent activity, strong access controls, and human validation at key stages. There is also the practical issue of ensuring agents do not hallucinate or exaggerate information within business processes.
Another mistake enterprises make is trying to create a single, all-powerful agent. “Avoid creating a single agent with a complex task,” Hegde says. Breaking responsibilities across agents with clearer scopes reduces both risk and failure impact.
Just as importantly, employees need to be trained to work with what he calls “digital workers.” Since autonomy without organisational readiness can create more confusion than efficiency.
Where Agentic AI is working (and where it’s not)
Today, agentic AI appears to be finding safer ground in back-office functions. “Most of the back-office applications are already semi-digital in nature and lend themselves to automation,” Hegde says. Finance, procurement, and contract-heavy operations are easier places to experiment with autonomy because the workflows are structured and the risk surface is narrower.
In contract management, for instance, enterprises are beginning to rely on AI-powered solutions that can monitor obligations and trigger reviews or escalations when thresholds are breached, rather than waiting for manual discovery. This changes how legal and commercial teams work, shifting effort from searching for information to acting on it.
A similar shift is seen in B2B product discovery. Buyers increasingly rely on AI-driven search and recommendation systems to interpret their needs, rather than navigating static catalogs. This changes how revenue teams think about discoverability and demand generation, because buying journeys are now shaped inside AI-driven discovery flows.
Customer-facing use cases, however, remain more sensitive. Identity fraud, bias, and misuse risks are higher. “Additional guardrails are needed,” Hegde says, and many organisations are still cautious about allowing autonomous systems to act directly in front of customers.
Who owns mistakes by AI Agents?
One of the hardest questions facing agentic AI use inside business workflows is of accountability. If an autonomous agent makes a costly mistake in procurement or compliance, where does responsibility sit?
Hegde argues that ownership becomes less clear-cut. “The ownership to the outcomes could become subjective depending on the process and policy governing the process,” he says. Well-designed systems will include checks and balances, with humans in the loop at critical points.
There are parallels with earlier technology shifts. The internet created new fraud risks and regulatory questions, yet organisations gradually built controls around them. AI agents are no different, Hegde says. The risk is not new, but the scale and speed of impact are. The difference this time is operational.
Operational readiness is the real challenge
The move towards agentic AI is not waiting for enterprises to catch up. Systems that can act across workflows already exist. The challenge is organisational readiness.
Enterprises that treat agentic AI as a simple technology upgrade are likely to struggle. The real work lies in redesigning workflows, updating governance models, and deciding where autonomy is acceptable and where it is not.
As AI moves from advice to action – from being a source of insight to becoming an operational actor – the cost of getting these decisions wrong increases.
The question for 2026 is not whether agentic AI will enter enterprise workflows. It is whether enterprises are ready for what changes when it does.