The real subject is no longer to use AI, but to organize it

Most businesses are already using artificial intelligence tools. Content generation, document analysis, customer support assistance: AI has entered the teams. However, in a large majority of cases, these uses remain isolated, manual and dependent on individuals, which greatly limits their operational impact.

This discrepancy is clearly highlighted in the report. The State of AI in 2024 posted by McKinsey. The firm shows that while AI adoption is growing rapidly with 72% of respondents having integrated AI into at least one service in 2024, enterprise-wide value creation remains limited. The main reason is not the lack of efficient models, but the lack of integration of AI into structured and repeatable processes.

In other words, the challenge is no longer to access AI, but to Put into production in the processes.

This is precisely what the concept of AI workflow : an approach that makes it possible to transform dispersed uses into business processes, capable of producing reliable, measurable and scalable results.

What is an AI workflow?

One AI workflow Is a structured sequence of tasks, automated from end to end, in which one or more artificial intelligences intervene to analyze, decide or produce, within a clearly defined business process.

The key difference with the simple use of AI lies in the sequencing logic. An isolated prompt produces a response. An AI workflow, on the other hand, Orient the AI towards an action : trigger a next step, apply a business rule, or feed another system.

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AI Workflow, Classic Automation, and Scripting: What's Really Changing

At first glance, an AI workflow may look like classic automation or an advanced script. In reality, the difference is Essential and lies in the very nature of the decision.

One classical automation is based on fixed rules: If A, then B. It works well as long as the cases are predictable. As soon as the situation goes out of the box, it either fails or requires human intervention.

One script goes further technically, but remains rigid. It executes logic defined in advance, with no real capacity for interpretation. He doesn't understand the context, he's applying.

The AI workflow, on the other hand, introduces a break: it allows the machine to analyze a non-standardized situation, to produce a probabilistic output, and then to automatically proceed to the next step in the process.

So the key difference is not automation, but the ability to adapt.

Where traditional automation deals with known cases, the AI workflow deals with:

• ambiguous texts,
  
  
• imprecise requests,
  
  
• heterogeneous documents,
  
  
• weak signals.
  
  

Example: a script can route an email according to a specific keyword. An AI workflow can understand the real intent of a message, assess its urgency, and then decide on the right treatment, even if the wording is new.

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What is an AI workflow in business made of?

An AI workflow is a Assembly of clearly identified bricks, each with a specific role in the process.

The essential building blocks

An AI workflow in business is systematically based on five elements:

1. The entrances
   Raw data from existing systems: emails, documents, CRM, ERP, forms, API.
   Their variety is precisely what makes AI necessary.
   
   
2. AI models
   They analyze, classify, summarize, or generate. Their role is not to “decide”, but to produce a usable interpretation.
   
   
3. Business logic
   It's the core of the workflow. Rules, thresholds, priorities, safeguards.
   It turns the output of AI into operational decision.
   
   
4. The actions
   Update a tool, send a message, create a ticket, trigger another workflow.
   Without action, there is no value.
   
   
5. Human supervision
   Optional but strategic. It intervenes in sensitive, ambiguous or high-impact cases.
   
   

Example: an incoming document is analyzed by AI, classified according to its type, and then automatically stored, routed, or flagged for validation.

Why orchestration is more important than model

In an AI workflow, performance does not come from the “best model”, but from the coordination between several models and business rules.

The same process can involve:

• a model to understand,
  
  
• another to generate,
  
  
• business logic to arbitrate,
  
  
• a system for acting.
  
  

It is this orchestration that makes it possible to adapt AI to the real constraints of the company: reliability, traceability, compliance.

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AI workflow, AI agent, agency: clarifying the concepts

These terms are often used interchangeably. In practice, they do not cover not the same level of structuring, nor the same challenges for the company.

AI workflow: the framework

The AI workflow Is the overall structure. It defines the sequence of tasks, decision points, business rules, and moments of human supervision. It is he who guarantees the consistency, traceability and governance of the process.

AI agent: the executor

One AI agent is a software entity capable of performing a given task: analyze a text, write a response, search for information, trigger an action.

An agent is asset, sometimes autonomous, but always embedded in a frame.
In business, an agent without a workflow quickly becomes unpredictable or unusable at scale.

To deepen your understanding of the concept of an AI agent, we invite you to consult our article entitled “What is an AI agent and how to use it? ”.

AI agency: the approach

THEAI agentic refers to an approach in which several agents collaborate, respond to, or correct each other to achieve an objective.

This logic is powerful, but risky if it is not orchestrated :

• infinite loops,
  
  
• opaque decisions,
  
  
• difficulty in control.

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Why every business should structure their processes with AI workflows

After defining the AI workflow and clarified the difference with scripts, automations and agents, the question becomes simple: why formalize it at the company level?
Answer: to get measurable gains, a stable quality And a risk management.

1) Really measurable productivity

An AI workflow eliminates repetitive micro-tasks (sorting, reformulation, extraction, routing).
Result : less time lost, less back and forth, more volume processed without operational overload.

2) Standardization at scale

Without a workflow, everyone uses AI “in their own way”: variable outputs, unequal quality, impossible to maintain a standard.
With a workflow, you impose the same framework: criteria, formats, control steps, escalation levels.

3) Governance and risk management

One AI workflow Above all, bring mastery.
It makes decisions trackable (what, when, on what basis), explainable and auditable, where an AI used in isolation remains opaque.

It also allows you to integrate operational safeguards (confidence thresholds, targeted human validations, automatic blocking on sensitive cases). We don't “trust AI” by default, we frame its action.

As explained IBM in his analysis AI workflow, the value of AI in business comes when AI capabilities are integrated into defined workflows, combining automation, business rules and human supervision. The AI then becomes a Governable component of the process, and not a standalone tool that is difficult to control.

Conclusion: The AI workflow as a company's operational base

AI has become mature enough to be used everywhere. The real differentiator, now, is the ability to Put into production In AI workflows : reliable, controllable task chains, and connected to everyday tools.

Clearly, winning companies don't plus of AI. They make an AI better integrated to real work (documents, research, writing, writing, reports, follow-up, support), with business rules and supervision when necessary.

In this logic, Delos orchestrate several models (ChatGPT, Claude, Claude, Mistral, Llama...) to build business assistants, co-pilots, RAG and AI agents, connected to your tools and supervised by your business rules. Delos brings together several applications and allows create your own AI agents according to use cases, while orchestrating different models in an environment designed for the company.

To take action, identify a priority process (support, back office, marketing) and build a first end-to-end AI workflow and see how Delos can orchestrate it with your tools and business rules.