Agentic artificial intelligence (AI) has landed in that familiar enterprise danger zone: everybody can see the upside, but very few organisations feel ready to bet real workflows on it.

That tension comes through clearly in Agentic AI: Expectations, Readiness, Results from Harvard Business Review Analytic Services. Respondents largely believe agentic AI will reshape business, yet only 26% say their organisation is very effective at leveraging any type of AI to achieve positive business outcomes. The ambition is there. The execution engine often isn’t.

And this is where most discussions start to drift into the same predictable advice. Put guardrails in place. Strengthen governance. Upskill people. Define metrics. All true, but also not specific enough to help someone who’s responsible for putting agentic AI into production without breaking trust, compliance, or the business.

The Agentic AI Readiness Gap Is an Execution Problem, Not an Imagination Problem

It’s tempting to treat agentic AI as a technology problem. Get the right model. Pick the right tools. Plug it in.

The report suggests the real challenge sits elsewhere. The market agrees agentic AI matters, yet effectiveness remains uneven. If only a quarter of organisations feel very effective at leveraging AI outcomes today, then throwing more autonomy into the mix doesn’t magically fix that. It amplifies whatever weaknesses already exist.

This is why so many early AI pilots stall. They prove something interesting in a sandbox, then hit the wall when they meet the real world:

• messy, inconsistent inputs
• hand-offs between teams
• compliance requirements
• approval chains
• brittle processes that only work because people patch the gaps

Agentic AI tends to get positioned as the solution to complexity. But if the underlying process is unclear, unowned, or undocumented, autonomy becomes a liability. You don’t get speed. You get uncertainty, rework, and endless “let’s test it a bit more” cycles.

The report also flags a measurement problem, which is tightly tied to execution. Only 5% of respondents say their organisation has well-defined success metrics for agentic AI implementation. That’s not a small oversight. It’s a signal that many organisations are still treating agentic AI as experimentation rather than operational change.

If you want to move from belief to outcomes, the question isn’t “how intelligent is the agent?” It’s “what environment are we placing it in?”

Deterministic Automation Is the Missing Middle Layer in Agentic AI Adoption

Deterministic automation sounds boring, which is usually a compliment in enterprise operations.

“Deterministic” means the system behaves predictably. When X happens, the workflow does Y. When the rule is met, the decision is made. It doesn’t improvise. It doesn’t interpret. It executes.

This matters because enterprises don’t only care about whether something works. They care about whether it works the same way tomorrow, under pressure, with a regulator asking questions, and with a customer waiting for an answer.

Deterministic automation provides three things agentic AI often lacks on its own:

• Predictable execution: A workflow engine or RPA bot follows steps in a defined order. That makes outcomes more stable and easier to debug.
• Auditable decision paths: Rule-based logic can be inspected. You can explain why the system did what it did, which is essential for risk, compliance, and internal accountability.
• Known failure modes: If a deterministic flow breaks, it usually breaks in expected ways. A missing field. A failed login. A timeout. You can build monitoring and exception handling around that.

This is why deterministic systems are already trusted to run payroll processes, invoice matching, HR onboarding steps, customer service triage, and countless “unsexy” operations that keep organisations functioning.

Agentic AI doesn’t replace that layer. It benefits from it.

If the enterprise wants autonomy at scale, it helps to start with the parts of the process where the business already insists on predictability.

Why RPA Creates Natural Guardrails for Agentic AI

“Guardrails” has become one of those words people say when they don’t want to admit they haven’t decided what control looks like.

RPA and rule-based automation offer a practical definition: the business has already encoded what must be true, what must be checked, and what must not happen. Those rules are not theoretical. They’re production logic.

That’s why the RPA-to-agentic bridge is so powerful.

As John Santaferraro explains in the report:

“The beauty of RPA is that there’s a lot of logic that’s already been created. RPA is 100% deterministic and rule-based, so why not add insight in the form of agentic AI on top of that? … In addition, the business rules that are already embedded in RPA turn out to be fantastic guardrails for the agent.”

~ John Santaferraro, Founder, Ferraro Consulting

Business rules constrain behaviour without constant human oversight

If a workflow says “do not approve a refund above X without manager approval”, that rule remains. The agent can assist with context, summarisation, and next steps, but it cannot bypass the condition if the automation layer enforces it.

Deterministic workflows define what must never change

In many processes, there are “non-negotiables” that exist for regulatory, contractual, or governance reasons. RPA and workflow automation are already built around those fixed points.

Agents introduce judgement only where variability exists

This is the sweet spot. The agent can interpret unstructured data, classify requests, pull relevant policy language, or draft communications. The deterministic layer then ensures the right action happens in the right order.

Put simply, agentic AI is most valuable where humans currently slow down a process because they’re dealing with ambiguity. Deterministic automation is most valuable where the business needs certainty.

Combined, you get a system that can handle real-world messiness without becoming unpredictable.

That combination also changes how risk is managed. Instead of asking, “Can we trust the agent?”, the question becomes, “Which parts of this workflow require autonomy, and which parts must stay rule-bound?” That’s a much more solvable problem.

From Automation to Agency: A Practical Adoption Path for Enterprises

If you want agentic AI to move beyond demos, the adoption path needs to look like enterprise change. Incremental. Measurable. Owned.

A practical roadmap starts with what you already have.

1. Identify stable, rule-driven processes that are already automated

Look for workflows that are already partly handled by RPA, workflow engines, or decision rules. These are often the processes where the organisation has done the hard work of defining “what good looks like”.

Strong starting points tend to be:

• onboarding steps with defined checks
• finance operations like invoice validation
• customer service triage with clear routing logic
• compliance workflows where approvals are mandatory
  
   

1. Expose the unstructured inputs where humans add judgement

This is where time disappears. People read emails. Interpret requests. Check policy documents. Compare context across systems. Decide whether something qualifies.

This is also where agentic AI can contribute without taking over the entire workflow.

1. Introduce agentic AI to interpret context, not override rules

This is the key design choice. Use the agent to:

• extract intent and key fields from unstructured text
• summarise context across documents
• propose a recommended action and justification
• draft a response that a human can approve

Then let the deterministic system decide whether the action is permitted, which steps must happen next, and what approvals are required.

1. Define escalation points and human accountability

Autonomy should not be a vibe. It should be explicit.

• Escalation points often include:
• financial thresholds
• policy exceptions
• anything tied to customer harm or legal exposure
• missing or conflicting data
• low confidence classifications

Humans should not be there to babysit every step. They should be there when the workflow hits a decision that is meaningfully risky or ambiguous.

This roadmap also aligns with what the report shows about uneven readiness. If organisations are still developing governance structures, workforce preparedness, and success metrics, this approach gives them a way to progress without pretending they’re ready for full autonomy across high-impact workflows.

It’s not slower. It’s safer acceleration.

Why This Approach Makes Measurement and ROI Possible

A lot of agentic AI initiatives die in the “we think it’s helping” phase.

That’s why the report’s measurement finding matters: if only 5% have well-defined success metrics for agentic AI, then most organisations are not set up to prove value consistently. But this isn’t just a planning problem. It’s an architecture problem.

When an agent operates without clear boundaries, it becomes harder to attribute outcomes:

• Was the improvement due to the agent, or due to process changes around it?
• Did the agent reduce cycle time, or did it just shift work to a different team?
• Did the agent improve accuracy, or did people stop checking because they assumed it was right?

A deterministic foundation changes that.

Bounded systems are easier to measure because:

• the workflow still defines start and end states
• decisions still pass through rules and approvals
• exceptions are logged in consistent ways
• hand-offs are visible

That means you can measure outcomes that matter to the business, not just model-level metrics.

What should enterprises measure first?

Process performance metrics

• cycle time from request to resolution
• percentage of cases handled without escalation
• exception rates and reasons
• rework rates

Quality and risk indicators

• policy compliance rate
• audit findings related to the workflow
• customer complaint rate for the affected process
• error rates in extracted fields or classifications

Operational impact

• time saved in manual triage
• backlog reduction
• consistency of service levels

These are the metrics your finance and operations stakeholders already understand. They also map neatly to the reality that many organisations are still building confidence in AI effectiveness. Start with measurable operational wins, anchored in workflows you already trust, and the business case becomes easier to defend.

What Enterprise Leaders Should Reconsider Before Scaling Agentic AI

The RPA-to-agentic bridge is not only a technical approach. It’s a leadership decision about how change happens.

Skipping automation foundations increases risk

Leaders often want “transformational” outcomes, but transformation without scaffolding becomes chaos. If the organisation cannot consistently execute and measure existing workflows, giving those workflows autonomy won’t fix the fundamentals. It will magnify the gaps.

“AI-first” thinking often creates brittle systems

When agentic AI is treated like a replacement layer, teams end up designing around the model rather than around the business outcome. That usually leads to fragile systems, unclear ownership, and never-ending debates about safety.

Reframing agentic AI as an extension of automation changes how it gets funded and owned

This is a subtle but important shift. If agentic AI sits inside automation and operations, it’s easier to treat it as part of process improvement and digital transformation. That often unlocks:

• clearer executive sponsorship
• more realistic rollout plans
• better alignment with governance and risk teams
• a stronger operating model for ongoing change

It also changes how workforce readiness is approached. Instead of trying to make everyone “AI literate” overnight, organisations can focus on the people who own and run workflows: operations leaders, process owners, automation teams, and risk stakeholders. Those are the roles that shape whether autonomy becomes an asset or a mess.

Agentic AI doesn’t need the business to become a research lab. It needs the business to become operationally ready for autonomy.

Final Thoughts: Agentic AI Scales When Autonomy Is Built on Rules

The report’s message is clear: belief in agentic AI is widespread, but readiness is uneven, and measurement is still rare. The takeaway isn’t that organisations should slow down. It’s that they should stop treating agentic AI like a clean-slate shift.

Agentic AI scales faster when it’s built on deterministic automation because the business already trusts rules, workflows, and repeatable logic. That foundation turns autonomy into something you can govern, measure, and improve, instead of something you simply hope behaves.

The organisations that get this right won’t just deploy agents. They’ll design operating models where autonomy is earned, bounded, and tied to outcomes people can defend in a boardroom.

As organisations move from experimentation to execution, the real challenge is not deciding whether to use agentic AI, but deciding how to operationalise it responsibly. EM360Tech continues to work with analysts and practitioners to bring you the patterns that separate scalable adoption from stalled ambition.