AI solutions architect: what they do and when your company needs this profile

Implementing artificial intelligence in a company can start with a relatively limited project: an internal assistant, a generative functionality integrated into an application, or an automation that uses a language model. The architecture becomes more demanding when these projects begin to connect with corporate data, applications, APIs, cloud infrastructure, and critical processes.

In this scenario, the AI Solutions Architect appears, a professional specialized in transforming business needs into architectures capable of integrating artificial intelligence models with an organization's technological ecosystem.

Their responsibility goes beyond choosing a model or a platform. They must determine how AI will access the data, where the components will run, which systems need to be integrated, how permissions will be controlled, what observability mechanisms the solution needs, and how it can evolve without creating a difficult-to-maintain architecture.

Microsoft currently recognizes this specialization within the role of Agentic AI Business Solutions Architect, whose responsibilities include defining architectural strategies to integrate AI and agents, interpreting technical and business requirements, and directing secure and scalable implementations.

For a company, understanding when it needs this profile can make the difference between deploying isolated initiatives and building an AI capability ready to grow.

What is an AI solutions architect

An AI Solutions Architect designs the technical architecture that allows turning an artificial intelligence use case into a viable business solution.

They work between technological strategy and implementation. They receive needs from the business, analyze existing constraints, and define how models, applications, data, infrastructure, and controls should relate to each other. The result usually materializes in architectural patterns, technological decisions, information flows, and criteria that later guide the engineering team.

This requires knowledge of artificial intelligence, but also software architecture. An AI-based solution still needs authentication, APIs, storage, monitoring, networks, integration, and mechanisms to manage errors.

The architect's job is precisely to ensure that all these elements work as a coherent system. Business projects that require this level of design can rely on artificial intelligence specialists with experience in integration and development of production-oriented solutions.

What does an AI solutions architect really do

Responsibilities change depending on the size of the project and the technological maturity of the organization. In one company, they may focus on designing an internal AI platform; in another, on integrating generative models into existing products or building an architecture that allows deploying multiple agents.

Among their usual responsibilities are:

• Translating business requirements into technical decisions, identifying what components each use case needs.

• Designing the AI architecture, including models, applications, data, APIs, storage, and infrastructure.

• Evaluating models and providers, considering quality, cost, latency, privacy, and integration capability.

• Defining patterns for RAG and agents when applications need corporate knowledge or the ability to execute actions.

• Establishing security and governance requirements, including identity, permissions, traceability, and data access.

• Designing scalability and observability criteria to operate and maintain the solution in production.

• Coordinating decisions between engineering and business, preventing each initiative from evolving independently.

Translating business requirements into technical decisions, identifying what components each use case needs.

Designing the AI architecture, including models, applications, data, APIs, storage, and infrastructure.

Evaluating models and providers, considering quality, cost, latency, privacy, and integration capability.

Defining patterns for RAG and agents when applications need corporate knowledge or the ability to execute actions.

Establishing security and governance requirements, including identity, permissions, traceability, and data access.

Designing scalability and observability criteria so that the solution can operate and be maintained in production.

Coordinating decisions between engineering and business, preventing each AI initiative from evolving independently.

The architect does not necessarily have to implement each component personally. Their role is to define how they should relate and establish a technical direction that allows different specialists to work on a common architecture.

What an enterprise AI architecture looks like

A modern AI architecture contains many more pieces than just a model. AWS currently structures its enterprise architectures of Agentic AI around different layers that include applications, agents, and core services for access to models, tools, and knowledge bases. Security and observability run through these layers as common capabilities.

In the application layer, there are the interfaces and systems used by employees or customers. These components can be applications specifically developed for AI or existing corporate systems that incorporate new capabilities.

Next are the artificial intelligence services. Here, different models can coexist depending on the needs for quality, speed, cost, or privacy. An organization can use external models via API, managed cloud services, and proprietary models for specific cases.

Data constitutes another critical layer. RAG systems, agents, and intelligent applications need to retrieve corporate information in a controlled manner. The architecture must determine which sources are accessible, how they are indexed, what permissions are respected, and how the information is kept up to date.

Finally, there are the transversal capabilities: security, observability, evaluation, governance, and cost control. When the organization incorporates numerous use cases, these capabilities prevent each team from building their own solution from scratch.

The role of data in an AI architecture

A generative application can function with general knowledge of the model, but many business solutions need to use specific information from the organization. Documentation, catalogs, policies, contracts, historical data, operational information, or customer records can become context for AI.

RAG allows retrieving relevant information from external sources and providing it to the model before generating a response. This introduces decisions about ingestion, document fragmentation, embeddings, storage, retrieval, and permissions.

AWS emphasizes that enterprise knowledge bases can rely on vector stores or graphs and must incorporate access controls to maintain principles of least privilege.

Therefore, the AI Solutions Architect works closely with Data Engineers. The quality of an AI application largely depends on the availability, structure, governance, and accessibility of the data it uses.

Choosing the right model is also an architectural decision

The most powerful model available is not automatically the best choice for all tasks. A business application must balance accuracy, speed, cost, privacy, available context, and operational requirements.

Some tasks may require advanced models with high reasoning capacity. Others may be executed with smaller, more economical models. There may also be regulatory or privacy reasons that affect where the information is processed.

The AI Solutions Architect defines criteria for making these decisions and prevents each team from selecting providers solely based on individual preference. When multiple models exist, a common layer can also be proposed to manage authentication, routing, policies, and cost tracking.

AWS warns that when generative applications proliferate without a common control layer, security, cost, and credential management issues can arise. A centralized architecture can provide uniform mechanisms for access and governance.

RAG, agents, and multi-agent systems

The evolution towards more autonomous applications increases the importance of architectural design. An agent can interpret a goal, consult knowledge, use tools, and execute actions on other systems.

When these capabilities go into production, the architect must decide what tools each agent can use, how they authenticate, what data they can query, and what operations require human approval.

Multi-agent architectures add another dimension. Different agents can take on specialized responsibilities and coordinate to complete more complex processes. Microsoft currently includes the design of agentic-first solutions and multi-agent orchestration among the competencies associated with its AI solutions architecture role.

The AI Solutions Architect must assess when this complexity provides a real advantage. A simple process may be better resolved through a conventional application or a single agent. The architecture must respond to the business problem, not to the sophistication of the available technology.

Security and governance must be part of the design

Security takes on an additional dimension when an AI application can query corporate information or execute actions. The system needs to correctly identify users, control permissions, and limit what information can reach each model or agent.

This is especially important in RAG architectures. AWS recommends applying access controls at different layers and principles of least privilege to protect the data used by generative applications.

It is also necessary to maintain traceability. The organization must be able to identify which model processed a request, what information it received, what tools an agent used, and what result it produced.

In regulated sectors, these capabilities can extend to audit logs, continuous monitoring, and specific compliance controls. AWS shows, for example, generative architectures for healthcare environments where security, isolation, RAG, governance, and auditing are implemented as distinct layers.

The architect incorporates these requirements from the design stage to avoid later reconstructing a solution that has already begun to use sensitive data or processes.

AI solutions architect vs AI engineer vs software architect vs cloud architect

The proximity between these profiles can create confusion during hiring. All can participate in an artificial intelligence project, although their main responsibilities are different.

In small organizations, one person may take on several of these responsibilities. As complexity increases, it becomes more common to separate architecture from implementation.

The AI Solutions Architect adds particular value when decisions about AI simultaneously affect software, data, infrastructure, and security. In that scenario, they can work alongside specialized profiles in software architecture to ensure that new capabilities are properly integrated with existing systems.

Cost and scalability: two decisions that start in architecture

A proof of concept may work correctly with a few users and generate a low cost. Behavior can change significantly when thousands of requests use models, vector searches, agents, and external tools.

Cost must be analyzed per operation and not just as a monthly infrastructure bill. Tokens, storage, inference, databases, processing, external APIs, and observability can contribute to the final cost of each task.

The architecture also affects latency. A workflow that makes multiple sequential calls to models and tools may be too slow for an interactive application even if it produces a correct response.

The AI Solutions Architect must consider these variables before scaling. In projects with significant infrastructure, collaboration with Cloud Engineers allows designing capacity, availability, security, and monitoring tailored to the actual behavior of the solution.

7 signs that your company needs an AI solutions architect

There is no specific number of projects at which this profile becomes mandatory. The need arises when decisions stop affecting a single application and begin to have consequences on different parts of the organization.

These signs usually indicate that the architecture already needs a cross-sectional vision:

• There are several AI projects running independently, and each team is making different technological decisions.

• AI needs to connect with corporate applications, databases, CRM, ERP, or other internal systems.

• Agents capable of executing actions are being developed, which requires controlling tools, identities, and permissions.

• Different models or providers are beginning to coexist, and it is necessary to establish common selection and access criteria.

• The organization handles sensitive information, so privacy, security, and traceability must be part of the architecture.

• AI costs are starting to grow, and it is necessary to control consumption, routing, and infrastructure utilization.

• Prototypes need to become production products, with requirements for availability, monitoring, and maintenance.

There are several AI projects running independently, and each team is making different technological decisions.

AI needs to connect with corporate applications, databases, CRM, ERP, or other internal systems.

Agents capable of executing actions are being developed, which requires controlling tools, identities, and permissions.

Different models or providers are beginning to coexist, and it is necessary to establish common selection and access criteria.

The organization handles sensitive information, so privacy, security, and traceability must be part of the architecture.

AI costs are starting to grow, and it is necessary to control consumption, routing, and infrastructure utilization.

Prototypes need to become production products, with requirements for availability, monitoring, and maintenance.

One of these situations does not necessarily imply the immediate hiring of a dedicated architect. When several appear simultaneously, keeping decisions distributed among different teams can generate technical debt and hinder future growth.

When you still don’t need this profile

A company validating its first use case may not need a dedicated AI Solutions Architect. If the project has a limited scope, uses few data, and does not interact with critical processes, an experienced AI Engineer can design the first version and take it to production.

AWS recommends that organizations in early stages do not attempt to build an entire enterprise architecture of Agentic AI from the beginning. For exploratory projects, it suggests initially focusing on one or two use cases and incorporating additional capabilities as maturity increases.

This approach avoids over-dimensioning the solution. Enterprise architecture does not mean incorporating all possible layers but designing the necessary ones to solve the current problem without blocking future evolution.

The moment to specialize the function arises when decisions begin to repeat across projects, and the organization needs shared standards.

What skills does an AI solutions architect need

The profile requires a broad foundation because they must make decisions that span different technological domains. They do not need to be the top specialist in each one, but they should understand their implications sufficiently to design a coherent solution.

Experience in software architecture and distributed systems provides an important foundation. On top of that, knowledge of language models, machine learning, RAG, agents, data, APIs, and cloud platforms is incorporated.

They also need to understand security, identity, permissions, observability, and costs. When a system goes into production, these elements can be as important as the model's accuracy.

Finally, there is a less visible competence: translating business objectives into technical decisions. Microsoft includes among the responsibilities of the profile analyzing business and technology requirements, defining architectural strategies, and guiding end-to-end implementations.

Hiring an internal AI solutions architect or through IT outsourcing

The decision depends on the strategic importance of artificial intelligence within the organization and the expected continuity of the projects.

A company building its own AI platform and expecting to maintain a permanent volume of initiatives may justify an internal architect. This professional accumulates knowledge about systems, constraints, and corporate standards in the long term.

Other companies need specialization during specific stages: initial design, platform definition, migration of prototypes to production, or review of an existing architecture. In these scenarios, incorporating external expertise can accelerate decisions without creating a permanent position before there is sufficient workload.

Outsourcing IT models allow incorporating specialized capacity according to the project phase, working alongside internal development, data, infrastructure, and security teams.

A solid architecture allows AI to grow without multiplying complexity

The first artificial intelligence application of a company can be built as an independent project. The real challenge arises when the second, fifth, or tenth arrives, and all need models, data, infrastructure, permissions, and control mechanisms.

The AI Solutions Architect provides a common vision for those decisions. Their role is to convert business needs into an architecture where artificial intelligence, software, data, and cloud can evolve together.

Their incorporation makes particular sense when the organization is moving beyond experimentation and needs to turn different initiatives into a stable technological capability. At that moment, architecture, security, observability, and governance cease to be secondary issues and begin to determine the viability of the AI strategy.

If your company is in that phase and needs to define the architecture or the right profiles to bring its AI solutions to production, you can schedule a call with lateam to discuss the technical needs of the project.