AI & Automation

AI Automation: Spot the right use cases in your organization

Understand what AI automation is, how it differs from classic automation, and how to identify use cases that create real value.

What is AI automation?

AI automation differs from traditional automation, which is defined by processes with clear inputs and outputs. Systems like RPA already solve rule-based tasks effectively. The value of AI appears where fixed rules fall short, for example when workflows involve unstructured data or complex assessments.

Why is it hard to get started with AI?

The potential of AI is clear to most companies. The challenge is turning it into action. There are many ideas, but it is often unclear which problems AI can actually solve and how to approach them.

That makes it hard to prioritize, build a business case, and move beyond the drawing board. We use a framework that makes it easier to break down your challenges and assess which type of solution fits which type of process.

Three forms of AI automation

This framework gives you a shared language for prioritizing AI initiatives and assessing which type of automation you are facing.

01

Background Agents

AI that works independently in the background. The agent monitors systems, handles routine tasks, and prepares the groundwork.

How to spot it

  • You have large volumes of unstructured data, such as documents, emails, or images, that require manual entry or assessment
  • Data exists in CRM or ERP systems, but manual judgment is needed to decide what to act on
  • Incoming cases are delayed because someone has to manually read and assess them

Industry examples

Life Science

Automatic extraction of data from clinical deviations to generate first drafts of CAPA reports in the QMS

Banking

Continuous monitoring to collect, read, and validate loan documents and credit reports in the background

02

Co-worker

An AI assistant that works alongside employees in the tools they already use, such as Excel, email, or document management systems.

How to spot it

  • Employees constantly switch between systems to gather the information needed for a task
  • Preparing a case or document requires manual data handling
  • Knowledge is trapped in long instructions, SOPs, or with a few key people

Industry examples

Life Science

An AI agent that helps regulatory affairs access QMS, compare SOPs with new regulations, and prepare a findings report in Word

Banking

An AI agent that helps an advisor pull customer data, cross-check credit policies, and draft a credit memo

03

Conversational Analytics

A chat-based solution connected directly to your database or systems. Instead of building reports or navigating dashboards, users ask questions in natural language and get answers grounded in your own data.

How to spot it

  • You have structured data in databases, warehouses, or BI tools, but the business struggles to access it independently
  • Leaders often wait for someone with access to pull a specific number or report
  • Data questions stop at what happened because investigating why is too resource-intensive in the current setup

Industry examples

Life Science

How many critical deviations did we have on line 3 last quarter, and what was the average resolution time?

Banking

Which commercial real estate loans have the highest default risk based on the latest market data?

Frequently asked questions about AI automation

What is the difference between AI automation and classic RPA?
Classic RPA follows fixed if-then rules and is well suited for predictable, repetitive tasks. AI automation goes further by using language models to understand context, handle unstructured data, and adapt to new inputs.
How do we know if a task is a good AI use case?
A good AI use case is frequent enough to justify the investment and involves unstructured data, variable inputs, or judgment-heavy work.
What is the difference between the three categories of AI solutions?
Background Agents run independently without human involvement and handle routine tasks automatically. Co-workers are interactive AI assistants where the employee takes the initiative and asks for help. Conversational Analytics lets you ask questions about your data in natural language. The three types can be combined in one solution to solve several problems.
Do we need to start with one category?
In most cases, yes. Companies usually get the most value by starting with one well-defined use case, building governance and trust before scaling.
What does it require from our data?
It depends on the use case. Background Agents typically require access to the systems that already contain data, such as email, CRM, or domain-specific systems. Co-workers require access to the programs and tools employees use day to day. Conversational Analytics requires access to a database or system with structured data that the solution can query directly.
Can you help with implementation?
Yes. We support the full path from discovery and prioritization to implementation, operations, and governance.
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