Artificial Intelligence for businesses
Practical, managed automation integrated into business processes
Artificial intelligence (AI) helps businesses interpret and analyze documents and data, automate operational tasks, and coordinate complex processes with greater speed and control.
DEFINITION
What is artificial intelligence (AI) for businesses?
AI encompasses technologies that can analyze information, interpret documents, identify anomalies, and perform tasks within business processes.
Unlike traditional automation, which relies on rigid instructions, AI can handle variable and unstructured information, such as emails, PDFs, orders, invoices, and shipping documents.
However, value does not come from the technology alone. Clear processes, reliable data, integration with business systems, and controls proportionate to the tasks assigned to the AI are all necessary.
BENEFITS
What advantages does artificial intelligence offer businesses?
AI creates value when applied to repetitive, document-based processes or those involving large amounts of information. AI speeds up work, improves data quality, streamlines processes, and helps companies manage growing volumes.
Higher productivity
AI can capture and analyze information, classify documents, verify data, and initiate follow-up tasks.
This reduces manual steps, allowing people to focus on checks, exceptions, and higher-value decisions.
Faster processes
Emails, PDFs, orders, invoices, and transport documents can be processed without waiting for each step to be completed manually.
In compliant cases, the process continues automatically. Anomalies, on the other hand, are routed to an operator.
More complete and reliable data
Automated checks identify missing fields, duplicates, and inconsistencies before information is recorded in business systems.
Unstructured data can be transformed into organized, searchable information for use in workflows.
Greater operational capacity
Automating recurring tasks allows companies to handle higher volumes without proportionally increasing their workload.
This is particularly useful during peak periods or in processes involving large volumes of documents.
Greater control over processes
AI should not operate without limits.
Permissions, rules, blocking conditions, logs, and escalation procedures allow you to define the system's capabilities and the circumstances under which human intervention is necessary.
The goal is not to automate everything, but rather to identify tasks in which AI can produce measurable results while maintaining control over sensitive steps.
TECHNOLOGIES
Machine learning, generative AI and AI agents
There is no single technology that works for every process. Businesses can combine traditional automation with machine learning, generative AI, and AI agents based on data variability, task performance, and the level of autonomy required.
Traditional automation
This is ideal for stable processes based on predictable conditions and precise rules. It delivers repeatable results, but it struggles with documents or situations that change frequently.
Machine learning
Machine learning uses data and past examples to recognize patterns, classify information, and make predictions. It can be used to identify a document type, assign a category, or detect an anomaly.
Generative AI
Generative AI can interpret complex texts, emails, and documents, even when their structure and wording vary. It supports contextual extraction, multilingual processing, and understanding the relationships between pieces of information.
Agentic AI
AI agents can use authorized tools, consult data, apply controls, and perform a sequence of tasks within a workflow.
In practice, these technologies are often combined. AI interprets information, rules verify conditions, and workflows determine whether to proceed automatically or involve an operator.
APPLICATIONS
Where to use Artificial Intelligence in business
AI is effective in many departments, particularly those involving repetitive tasks, variable documents, and information scattered across emails, archives, and management systems.
Administration and accounting
AI can capture invoices, extract data, compare it with orders and delivery documents, suggest cost centers, and route documents to the correct approval workflow.
Sales and accounts receivable
Orders received via email or as attachments can be interpreted, verified, and prepared for entry into the ERP system. Incomplete or inconsistent information is flagged before processing.
Logistics
AI can read transport documents, verify deliveries, and reconcile information with orders and invoices in logistics workflows. This reduces manual entries and delays.
Procurement
Comparing orders, deliveries, and invoices enables the detection of discrepancies, verification of supplier documents, and triggering of approvals required by company policies.
Document Management
PDFs, scans, emails, and attachments can be classified and converted into structured data. This makes the information available for searching, archiving, and initiating follow-up tasks.
Compliance and control
AI can support the enforcement of corporate policies, activity logging, exception management, and oversight of sensitive processes.
Other business areas
AI can also be applied to customer service, marketing, human resources, data analysis, IT, and knowledge management.
Specifically, Comply Platform focuses on automating document, administrative, and operational processes integrated with management systems.
USE CASES
AI applied to business processes
Customer orders
Learn how to digitize the process of entering orders from email into your ERP system to eliminate operational delays and speed up deliveries.
Supplier invoices
Use agentic AI to verify and record supplier invoices, reducing processing delays.
Transport documents
See how AI agents automate the reconciliation and recording of transport documents, thereby eliminating workflow inefficiencies.
CHOOSING A PROJECT
Which project should you choose to experiment with AI?
It shouldn't be based on which uses the most advanced technology, but rather, which solves a concrete, measurable problem.
A good use case involves repetitive tasks, sufficient volume, identifiable rules, and readily available data. It must also be possible to distinguish standard cases from exceptions and verify the results produced by the AI.
For instance, a process that takes many hours, generates frequent errors, and uses digital documents may be a better candidate than a rare, non-standardized activity based on difficult-to-control decisions.
A process is a good candidate if it:
- requires many manual operations,
- involves recurring delays or errors,
- uses accessible data and documents,
- follows rules that are at least partially definable,
- has a designated business owner,
- allows for the measurement of time, costs, and quality, and
- can be tested within a limited scope.
Once these elements are in place, you can launch a pilot project and compare the results with those of the previous process.
IMPLEMENTATION
How to introduce Artificial Intelligence into your company
An effective AI project begins with the process, not the technology. Before automating, define your objectives, available data, integrations, and the desired level of human oversight.
- Analyse the process: identify tasks, systems, timelines, errors, and exceptions.
- Define an objective: establish measurable KPIs, such as timeline, cost, error rate, and percentage of automated cases.
- Verify the data and integrations: ensure the quality of the information, as well as access to the ERP systems, APIs, archives, and documents.
- Establish autonomy and controls: define what the AI can do, when it should stop, and when to involve a human operator.
- Launch a pilot project: test the workflow using real-world cases that include anomalous documents and exceptions.
- Measure and scale: based on the results, improve the process and gradually roll it out into production.
SECURITY AND GOVERNANCE
Maintaining control over AI
Enterprise AI agents must operate within defined authorizations, rules, and shutdown conditions. Organizations must be able to determine which systems their agents can use, what actions they can perform, and when human intervention is required.
Comply Platform supports these controls through human-in-the-loop supervision, permission and policy management, operational traceability, and structured exception handling.
The platform provides technical tools to support governance and audit activities. Applicable requirements also depend on the use case, the company’s role, and the organizational procedures adopted.
Security and Governance of AI Agents on the Comply Platform >
RESULT
How to measure the value of an AI project
The value of an AI project is not determined by the number of features used, but rather by its measurable impact on the process.
Therefore, before launching a pilot project, you must assess current performance, define improvement metrics, and establish clear success thresholds. Only then can you distinguish between an "interesting" project and one that generates real ROI.
Operational KPIs
Measure speed and scalability
- Average processing time per document: measures how much the operational cycle is reduced.
- Volume of documents processed per unit of time: measures production capacity.
- Percentage of cases handled end-to-end autonomously: indicates the level of automation.
- Exception rate: shows how much human intervention the process requires.
- Approval time: measures its impact on the decision-making workflow.
Qualitative KPIs
Measure the process's accuracy and robustness
- Errors detected: include a reduction in rejects and non-conformities.
- Completeness of extracted data: indicates the quality of the data inputted into ERP and accounting systems.
- Number of manual corrections: indicates the extent to which AI reduces repetitive tasks.
- Quality of entries: measures the consistency and reliability of journal entries.
- Accessibility of information: measures the ease of auditing and traceability.
Financial KPIs
Measure the impact on the income statement
- Cost per file or document: direct comparison before and after automation;
- Manual labor hours eliminated: tangible operational savings;
- Additional operational capacity: how much productivity increases without hiring new staff;
- Integration and management costs: the project’s long-term sustainability;
- Payback period: speed of ROI.
Why there are no "standard" percentages
Results depend on process structure, volume handled, data quality, integration complexity, and number of exceptions. For this reason, it is preferable to measure a specific use case with real data rather than applying generic benchmarks. A well-designed pilot project allows for the objective quantification of value and enables accurate decision-making regarding the scaling, optimization, or redesign of the process.
LIMITATIONS
When Artificial Intelligence isn’t the best choice
Not every process requires AI. In some cases, traditional automation is simpler, more cost-effective, and more predictable.
AI may be ill-suited for processes involving low volumes of tasks, frequent changes, missing data, or unreliable results.
Processes lacking rules, accountability, or measurable objectives should be structured before being automated.
Therefore, rather than using AI for every process, it is best to combine technology, rules, and human intervention based on the characteristics of the process.
From analysis to process automation
Comply Platform uses machine learning, generative AI, and AI agents to automate administrative, document management, and operational workflows.
The agents can:
- Interpret documents and communications
- Verify data against rules and systems
- Perform authorized tasks
- Escalate anomalies to operators
- Log completed operations.
- Interact with SAP and other ERP systems.
Our goal is to automate recurring cases while maintaining control over exceptions and sensitive steps.
Comply’s process
- First, we analyse the process and operational challenges.
- We identify tasks, data, and controls.
- We define integrations and authorizations.
- Then, we design the workflow and escalation procedures.
- Thereafter, we test the process using real-world cases.
- We measure the results.
- We gradually expand automation.
F.A.Q.
Artificial Intelligence for enterprises
What is artificial intelligence for enterprises?
It involves applying AI technologies to data, documents, and business processes to analyze information, automate tasks, and support workflow execution.
What business processes can be automated?
The most suitable ones are repetitive, measurable, and based on available documents or data. Examples include customer orders, supplier invoices, shipping documents, document classification, and approval workflows.
How does AI differ from traditional automation?
Traditional automation follows defined conditions and rules. AI, on the other hand, can interpret variable and unstructured information. In business projects, the two are often used together.
What is the difference between generative AI and AI agents?
Generative AI interprets or generates content. AI agents use authorized tools to perform a sequence of tasks within a process.
Can AI integrate with SAP and other ERPs?
Yes, through integrations and APIs configured to enable the necessary processes. However, permissions and controls must restrict access to authorized data and functions.
See how Comply Platform integrates with enterprise ERP systems >
How are errors handled?
Through completeness checks, business rules, data comparisons, blocking conditions, and operator escalation.
How much does it cost to implement artificial intelligence?
The cost depends on the complexity of the process, the volume of data, the number of integrations, the quality of the data, the required controls, and the level of customization.
How is the ROI of an AI project measured?
It is measured by comparing the investment to metrics such as time saved, error reduction, cost per case, operational capacity, and the percentage of cases processed automatically.
Is artificial intelligence suitable for SMEs?
Yes, if the process is repetitive enough and the benefits are measurable. It’s best to start with a limited use case rather than a broad project.
Does AI replace human oversight?
Not necessarily. In sensitive processes, an operator can approve actions, intervene in exceptions, or perform spot checks.
Where’s the best place to start?
With a process that has a concrete operational problem, available data, a person in charge, and measurable results.
When is AI not a good fit?
When the process has very low volumes, changes constantly, lacks adequate data, or produces difficult-to-verify results.