Operational Reality

Repetitive work is still eating up people's time

40–60%

Half or more of office working hours go to repetitive tasks

Data entry, uploads and downloads, consolidation and report writing eat into core working time.

1px

Legacy RPA stops when the screen changes even slightly

When exceptions occur, someone has to step in manually, which makes fully unattended operation hard.

Linear growth

More work has meant more headcount

When the workload doubles, headcount and operating burden grow with it.

Architecture

The three core components of an AI agent

Agent Brain

Situational understanding · reasoning · priority decisions

Natural language understanding
Context analysis
Self Decision Engine

Orchestration

Task breakdown · execution sequencing · access control

Task Decomposition
Guardrails / permission checks
Exception boundaries

Action Tools

System actions · recovery verification · automated reporting

Triage → RCA → Action
Automatic p95 / error-rate checks
Auto-generated report drafts

RPA vs Agentic AI

Rule-based automation vs. goal-based agentic AI

LEGACY

RPA (rule-based)

Runs only the script it was given. Stops when a screen or rule changes.

SHIFTONE

Agentic AI (goal-based)

Give it a goal and it plans, executes, handles exceptions and improves on its own.

01Planning Intelligence
People design fixed rules by hand
Enter a goal and the agent builds the execution plan dynamically
02Exception Handling
The process stops when an exception occurs
Handles exceptions itself, weighing context and priority
03Adaptation Speed
Scripts must be redeveloped for every change
Reconfigures orchestration immediately as conditions change
04Ops Continuity
Manual monitoring leaves gaps in operations
Runs 24/7 without interruption and reports results automatically

What Changes

How it differs from conventional automation

01

Goal-driven autonomous execution

Instead of people writing step-by-step scripts, you give a goal and the agent plans and carries it through.

Gather information
Build a plan
Execute in systems
Verify results and write the report

02

Self-handled exceptions

Rather than stopping at an error, the agent handles exceptions by weighing priority and context.

Alert triage
Root-cause candidates
Runbook execution
Recovery verification

03

Parallel scaling as work grows

As workload grows, the same agents run in parallel, raising throughput without adding headcount.

Absorbs repetitive work
Keeps quality consistent
Automates collaboration tickets
Drafts operations reports

Autonomous Ops Cycle

Intake → Understand → Execute → Validate → Improve

01

Request curation

Classifies requests from multiple channels by meaning and curates priorities

02

Context and planning

Analyzes the context of each request and builds the execution plan automatically

03

Cross-system execution

Connects ERP, CRM, messengers and document tools to carry out the actual work

04

Validation and exceptions

Verifies result quality and handles exceptions within guardrails

05

Reporting and improvement

Reports results, organizes logs and proposes improvements automatically

People decide. Agents finish.

Agents take over repetitive execution.
People move into managing,
planning and deciding.

Use Cases

Use cases by business function

IT Operations

Automates monitoring, incident response and operations reporting routines

Faster response and standardized operations

Sales & Marketing

Automates lead capture, CRM entry and follow-ups

Shorter sales cycles and tighter conversion management

HR & General Affairs

Automates attendance tallies, document handling and internal requests

Less repetitive admin time and faster responses

Finance & Accounting

Automates the repetitive steps in billing, settlement and verification

Lower error rates and less closing risk

Customer Service

Automates inquiry classification, reply drafts and routing to owners

Shorter response times and more consistent service

Procurement

Automates purchase requests, supplier communication and documentation

Shorter lead times and fewer missed items

Legal & Compliance

Policy checks, document review support and audit response materials

More accurate checks and less response time

Production & Quality

Automates quality data consolidation, issue classification and report writing

Shorter quality analysis cycles and faster improvement

SCM & Logistics

Automates repetitive checks on orders, shipments and inventory

Fewer operational bottlenecks and higher accuracy

Management Support & Strategy

Automates management data consolidation, report drafts and meeting prep

Less time preparing for decisions

R&D

Organizes experiment and development logs, searches knowledge and summarizes

Less search time and higher research productivity

PMO & Project Management

Automates progress updates, issue tracking and action items

Better visibility into project operations

Adoption Roadmap

Three stages of AI agent adoption

01

STEP 01

Pilot (1–2 months)

Pick one or two highly repetitive, clearly rule-based tasks, apply at small scale and secure quantified ROI.

02

STEP 02

Expand (3–6 months)

Build on the pilot to extend into cross-department work, and put guardrails and permission structures in order.

03

STEP 03

Company-wide (6 months on)

Integrate agents into company-wide processes and redesign people's roles around oversight, supervision and decision-making.

Get in touch

From adoption design to live operations, we'll connect you right away.

Ask about Agentic SHIFT

Taking repetitive work off people's handsAI agents as digital employees