Enterprise AI: How to Go from Business Need to Industrialization?

Artificial intelligence is creating new opportunities to automate processes, leverage data, support employees and develop new services. But between identifying a business need and deploying an AI solution at enterprise scale, the journey can be complex.

Assessment of the existing environment, needs analysis, evaluation of data and information systems, technology choices, experimentation, architecture, integration, security, industrialization… An AI project does not necessarily start with a POC.

From the earliest stages, organizations need to identify the conditions required for the project to succeed and define a path that fits their existing environment.

So how can a business need be transformed into an enterprise AI solution that is reliable, secure, integrated into the information system and able to evolve over time?

What is enterprise AI?

Building enterprise AI does not necessarily mean developing your own artificial intelligence model.

Depending on the need, an organization can rely on existing models, proprietary or open-source solutions, cloud services, RAG (Retrieval-Augmented Generation) architectures, AI agents, specialized models or custom developments.

Several of these technologies can also be combined within the same solution.

The challenge lies less in selecting a single tool than in making different components work together within a consistent environment: models, data, applications, infrastructure, security and business processes.

Enterprise AI needs to leverage the right data, interact with the information system, comply with the organization’s governance and security requirements, and deliver a level of performance suitable for real-world use.

Building enterprise AI therefore means creating an environment tailored to your business needs, data and information system.

Before building: assess your environment and analyze the need

An AI project can begin well before the first line of code is written.

When a need emerges, an assessment and analysis phase helps evaluate the environment in which the future solution will operate.

Which applications are involved? What infrastructure is already available? Where is the data stored? Is it sufficiently accessible and usable? Which data flows need to be created? What security, performance or hosting constraints need to be considered?

This analysis also helps assess the technical feasibility of the project and identify the different approaches available.

Depending on the context, the solution may involve using an existing model through an API, deploying a model within a controlled environment, implementing a RAG architecture, creating a multi-agent system or developing a custom solution.

The objective is not to choose the technology before understanding the need.

This assessment helps align the project’s ambition with the reality of the organization’s data, information system and constraints, in order to define a target architecture and a realistic implementation path.

Data: the foundation of an AI solution

Whatever technology is selected, the quality of an AI solution largely depends on the data it can leverage.

Databases, CRM systems, ERP platforms, internal documents, business applications, Data Lakes, Data Warehouses, APIs… relevant information may be distributed across many different environments.

Organizations therefore need to determine which data is required and how it can be accessed, prepared, updated and governed.

For a generative AI application, for example, a RAG architecture can allow a model to leverage company-specific knowledge to provide contextualized responses.

But not every business challenge requires RAG.

A use case may involve a predictive model, recommendation engine, classification system, computer vision, an agent capable of performing specific actions or a combination of several technologies.

The Data and AI architecture should therefore be built around the need, rather than around a technology selected by default.

Choosing the right models and technologies

The most powerful model is not necessarily the most relevant one.

Output quality, response time, cost, confidentiality, data location, hosting requirements, integration capabilities and industry specialization are all factors that need to be considered.

An organization may choose models from different providers, use open-source models or combine several technologies depending on the tasks to be performed.

The architecture can also include an orchestration layer capable of selecting different models or services according to the context.

This approach can provide greater flexibility in a rapidly evolving market and, where possible, reduce excessive dependency on a single technology.

Building enterprise AI is therefore not about finding “the best model”, but about selecting the right combination of technologies for the business need.

From POC to AI solution

Once the architecture has been defined, an experimentation phase can be used to test the selected approach in real-world conditions.

A Proof of Concept (POC) helps determine whether a technological approach can effectively address the need, whether the required data can be leveraged and whether the results are sufficiently relevant.

But a POC remains an experiment.

It is generally built within a limited scope, using controlled volumes of data and a small number of users.

A production-ready solution needs to meet additional requirements, including availability, access management, performance, security, monitoring, resilience, cost control and the ability to support increasing levels of usage.

This is why a successful POC is not yet an industrialized AI solution.

Integrating AI into the information system

An AI solution rarely delivers its full value when operating in isolation.

To support real-world use cases, it often needs to interact with existing applications: CRM and ERP platforms, collaborative environments, business applications, Data platforms or web applications.

This integration can rely on APIs, connectors or custom development.

An AI agent designed to support a customer service team, for example, may need to search an internal knowledge base, access authorized information from the CRM and interact with a business application in order to provide meaningful assistance to employees.

The objective is to ensure that AI does not simply become another standalone tool, but a component integrated into the organization’s processes and information system.

Securing AI by design

The more an AI solution is connected to company data and information systems, the more important security becomes.

Which users can access the solution? Which data can they view? What information can be sent to an external model? How is confidential information protected? How can interactions be traced?

Identity and access management, encryption, logging, sensitive data protection and data flow controls should all be considered as part of the architecture.

Regulatory compliance also needs to be anticipated. The European AI Act introduces different requirements depending on the nature and risk level of the AI systems concerned.

Security, governance and compliance should therefore be built into the solution from the outset, rather than added when the project reaches production.

Industrializing AI to scale

Industrialization means transforming a functional solution into a system that can operate reliably and consistently within a production environment.

MLOps and LLMOps practices can help manage the lifecycle of AI models and applications, including deployment, testing, version management, updates, monitoring and performance control.

Infrastructure also needs to evolve as usage grows.

An application tested by a handful of users does not face the same constraints as a solution used daily by hundreds or thousands of people.

Scaling therefore requires organizations to anticipate scalability, availability, resilience and performance across the entire architecture.

Monitoring performance and controlling costs

An industrialized AI solution needs to be measured and managed over time.

Response quality, error rates, availability, latency and model performance are among the indicators that may need to be monitored.

But scaling also introduces another consideration: the actual operating cost of AI.

API calls, token consumption, Cloud resources, computing power, storage and specialized infrastructure can generate significant costs as usage increases.

The model with the highest theoretical performance is therefore not necessarily the one offering the best balance between quality, speed and cost for every use case.

Industrialization should make it possible to achieve the right level of performance while maintaining control over the economics of the solution.

Planning for Run from the start

Going live is not the final stage of an AI project.

Models evolve, data changes, providers update their services and business needs continue to develop.

An AI solution therefore needs to be maintained, monitored and improved over time.

This means detecting anomalies, monitoring performance, managing incidents, maintaining integrations and updating components when required.

Planning for Run and operations from the design stage helps prevent a solution from performing well at launch but becoming difficult to maintain or evolve a few months later.

Building an AI capability, not a succession of POCs

Ultimately, industrializing AI is not about multiplying independent projects.

The foundations created for an initial solution — model access, infrastructure, Data architecture, APIs, security, observability, governance and deployment mechanisms — can gradually be shared and reused.

New use cases no longer require the entire technical environment to be rebuilt from scratch.

The organization progressively moves from a POC-based approach to a genuine enterprise AI capability, able to support, industrialize and evolve multiple solutions within a controlled framework.

This is what allows AI to become more than a succession of experiments and instead become a sustainable component of the information system.

Lùkla supports your AI journey from business need to industrialization

With expertise across Apps & Data, Cloud & Cyber and Managed Services, Lùkla can get involved as soon as a need emerges and support every stage of an AI project.

Our teams conduct the assessments and analyses required to evaluate the existing environment, data, information system and technical feasibility, before supporting architecture definition, technology selection, development and experimentation, integration and deployment into production.

Data, AI models and agents, APIs, Cloud infrastructure, security, MLOps/LLMOps, observability and Run: our expertise covers the different components required to build robust, secure AI solutions designed to scale.

Because there is no universal AI architecture, each path needs to be defined according to the business need, available data, existing information system and the organization’s specific constraints.