The successor
to RPA

Not rip-and-replace. A practical path from bot estates to governed agentic operations.

Traditional RPA executes prescribed clicks. Agentic AI works inside enterprise context, reasons across workflow data, uses policy-bound tools, and can close work with evidence. In most enterprise estates, that makes it the next operating model, not merely the next tool. ServiceNow provides the workflow fabric enterprises already trust, making it the natural operating layer for governed, observable, policy‑bound agentic AI.

 

Start a Conversation
The successor to RPA

Reduce automation estate cost

Simplify tooling, improve resilience

Govern autonomy

Customer Story

Markerstudy: RPA estate assessment and modernisation

An assessment identified 457 automations across RPA platforms including Automation Anywhere, Blue Prism, Kofax, Nintex and UiPath. Findings included tool sprawl, tactical workaround bots, and contracts running September 2025 to April 2028, driving a consolidation and redesign path around native workflow, APIs and AI.

Markerstudy: RPA estate assessment and modernisation
Solution
Why ServiceNow Agentic AI is the next enterprise operating model
The distinction matters commercially as much as technically. RPA mainly reduces task labour through automation of key processes. Agentic AI reduces coordination cost, decision latency and exception handling. It can move work across teams, systems and approvals because it sits on top of the workflow fabric the enterprise already trusts, aligning system of record, workflow engine, agent orchestration, data fabric and control tower.

From scripted tasks to governed decision execution

ServiceNow Agentic AI changes the centre of gravity. Instead of automations that mimic clicks, agents operate inside workflows, use enterprise context, coordinate across systems, and act under policy with audit, telemetry and approval thresholds. Hitachi Digital Services' managed services model reinforces that shift with governance, observability, drift control, reliability SLOs and continuous optimisation through HARC for AI and R2O2.ai.

From scripted tasks to governed decision execution
  • Works from an enterprise context
    Uses workflow context, CMDB, knowledge and approvals to act with the enterprise’s memory and policies.
  • Orchestrates work, not clicks
    Plans and acts through agents and orchestrators to move work across teams, systems and approvals.
  • Acts through APIs and tools
    Uses APIs, tools and enterprise systems already in place instead of relying on UI stability.
  • Built-in governance signals
    Creates auditable telemetry, cost and outcome signals by design, with human approval thresholds for higher-risk actions.

Shrink cost before scaling ambition

Retire low-value, duplicated, file-based or workaround bots. Reduce tool sprawl and overhead by eliminating automations that compensate for process weaknesses rather than fixing them at source.

Shrink cost before scaling ambition
  • What to retire
    Low-value, duplicated, file-based or workaround
    bots.
  • Why retire
    Too many tools, rising licence/support costs, duplicated capabilities.
  • What replaces them
    Eliminate or replace with native workflow, APIs, or lightweight scripts embedded in the system that owns the work.
  • What it unlocks
    Consolidation and rationalisation rather than another bot refresh.

Move automation closer to the system that owns the work

Rebuild reporting, batch and application logic that belongs in the platform. Shift from UI-driven scripts to APIs, workflow, data products and engineering patterns, reducing fragility and improving maintainability and control.

Move automation closer to the system that owns the work
  • Typical candidates
    Reporting, batch and application logic that belongs in the platform.
  • Target state
    Use APIs, workflow, data products and engineering patterns.
  • Pressure addressed
    Reduces duplicated capabilities and fragile UI-driven automations.
  • Commercial impact
    Shifts spend from licence/support overhead toward redesigned capability.

Upgrade from task execution to governed decision execution

Agentify high-volume service work, decision support, case handling and multi-step exceptions. Deploy ServiceNow agents with orchestration, approvals and telemetry, so autonomy is policy-bound, observable and safe at enterprise scale.

Upgrade from task execution to governed decision execution
  • Typical candidates
    High-volume service work, decision support, case handling and multi-step exceptions.
  • How it works
    Plans and acts through agents and orchestrators inside workflows.
  • Governance controls
    Human approval thresholds for higher-risk actions.
  • Operational proof
    Auditable telemetry, cost and outcome signals by design.

Govern, observe, and optimise continuously

Differentiate with managed outcomes: strategy, transition, controls, observability, cost management and continuous optimisation. Add governance, observability, drift control and reliability SLOs, reinforced through HARC for AI and R2O2.ai.

Govern, observe, and optimise continuously
  • Strategy and transition
    Make the shift safely, commercially and at enterprise scale.
  • Controls by design
    Policy, audit, telemetry and approval thresholds.
  • Reliability discipline
    Reliability SLOs and drift control for operational stability.
  • Continuous optimisation
    Continuous optimisation through HARC for AI and R2O2.ai.

Additional Solutions

Consolidate the tool estate

Reduce duplicated capabilities across multiple RPA tools by consolidating, rationalising and redesigning around native workflow, APIs and AI.

Replace fragile UI automations

Shift away from UI stability dependency by using APIs, tools and enterprise systems already in place.

Make autonomy governable

Run agents inside workflows with policy, audit, approvals, observability and outcome telemetry.

Challenge & Opportunity

The Markerstudy signal: RPA estates are starting to collapse under their own weight

RPA helped remove manual repetition, bridge legacy gaps and create quick wins where systems could not easily talk to one another.

In practice, many RPA estates have become operational overhead: too many tools, rising licence and support costs, duplicated capabilities, fragile UI‑driven automations, limited observability, and bots compensating for broken processes rather than fixing them at source.

The opportunity is to consolidate, rationalise and redesign around native workflow, APIs and AI, retaining automation only where legacy interfaces make it unavoidable, and introducing AI disposition where autonomy adds value.

How We Work

A practical transition path from bot estates to managed agentic operations

 

Assess

We assess use cases by automation
type

Decide

We decide where native platform capability or APIs should replace bots

Retain

We retain automation only where legacy interfaces make it unavoidable

Introduce

We introduce AI disposition where autonomy adds value

Agentify + Govern

We Agentify + Govern with ServiceNow agents (orchestration, approvals, telemetry) and managed assurance (governance, observability, drift control, reliability SLOs, continuous optimisation through HARC for AI and R2O2.ai)

Managed outcomes for the shift to agentic operations
Why Hitachi Digital Services

Managed outcomes for the shift to agentic operations

Hitachi helps clients make the shift safely, commercially and at enterprise scale, reinforced by a managed services model that adds governance, observability, drift control, reliability SLOs and continuous optimisation through HARC for AI and R2O2.ai.

Our Experts Our Experts
Our Experts
Vitor Domingos
Vitor Domingos
Head of AI, EMEA
linkedin
Start a Conversation

Related Content

Hitachi Digital Services Launches HARC Agents to Power Enterprise-Grade Agentic AI
Hitachi Digital Services Launches HARC Agents to Power Enterprise-Grade Agentic AI
Read Press Release
Operationalizing GenAI and AI at Scale for Enterprises eBook
Operationalizing GenAI and AI at Scale for Enterprises eBook
Read eBook
Enhancing Due Diligence with GenAI or a Leading US Financial Services Firm
Enhancing Due Diligence with GenAI or a Leading US Financial Services Firm
Read Customer Story

FAQ

Agentic AI works inside enterprise context, reasons across workflow data, uses policy-bound tools, and can close work with evidence, making it the next operating model, not merely the next tool.

Instead of automations that mimic clicks, agents operate inside workflows, use enterprise context, coordinate across systems, and act under policy with audit, telemetry and approval thresholds.

No. This is not a “rip‑and‑replace” message. RPA remains useful for stable, low‑change tasks and legacy interfaces. What’s changing is the operating model around automation. Enterprises now decide which automations should be retired, which should move to native workflows or APIs, and which should become agents operating within ServiceNow.

They are assessed, not discarded. Many organisations have large RPA estates created to compensate for fragmented systems. Some automations are retired because they are low‑value or unnecessary, some are rebuilt natively using workflows or APIs, and some evolve into agents where autonomy adds value.

Assessments such as the Markerstudy case show a familiar pattern: tool sprawl, duplicated capabilities, fragile UI‑driven automations, rising licence and support costs, and limited observability. In some estates, around 20% of use cases are unnecessary or easily replaced with simpler patterns.

Traditional RPA executes predefined steps and depends heavily on UI stability. Agentic automation works from workflow context, CMDB, knowledge and approvals; plans and acts through agents and orchestrators; uses APIs and enterprise systems already in place; and generates auditable telemetry by design.

ServiceNow becomes the enterprise operating layer for agents. It already contains the workflow fabric enterprises trust, workflows, approvals, CMDB, knowledge bases, integrations, and orchestration, making it the natural environment for governed, observable, policy‑bound agents.

Workflow‑native execution means automation logic lives in the platform that owns the work using workflows, APIs, and data products, rather than being layered on top as UI‑driven scripts. This reduces fragility, improves observability, and simplifies governance.

Agentic AI is designed with governance by default. Higher‑risk actions can require human approval thresholds. Every action produces audit trails, telemetry, and outcome signals, allowing organisations to observe, control, and continuously improve how work is executed.

Hitachi helps clients make the shift safely, commercially, and at enterprise scale. Beyond strategy and transition, Hitachi provides managed services that add governance, observability, drift control, reliability SLOs, and continuous optimisation, so agentic operations remain stable and valuable over time.

HARC for AI underpins operational assurance for agentic systems. It applies SRE‑led disciplines to AI and agents—covering reliability targets, observability, drift control, and continuous optimisation, so agentic automation performs predictably in production, not just in pilots.

R2O2.ai provides a governance framework focused on responsible, reliable, observable, and optimised AI. It helps ensure agents operate within defined policies, produce explainable outcomes, and deliver sustained value rather than uncontrolled automation sprawl.

Success is no longer measured by bot count. It is measured by decisions executed safely inside enterprise workflows using outcome metrics, cost signals, reliability SLOs, and operational telemetry rather than the number of automations deployed.

Start by assessing the automation estate. Identify which automations are unnecessary, which belong natively in workflows or APIs, and which should become agents. From there, introduce agentic capabilities inside ServiceNow with governance and managed assurance built in from day one.

RPA automated tasks. Agentic AI manages work. The next phase of automation is not about more bots, but about governed decision execution, enabled by an integrated agent fabric, supported by ServiceNow, and sustained through managed operations.