What AI Consulting for Businesses Includes

What is AI consulting for businesses?#

AI consulting for businesses is a process in which a provider works with the client to identify where artificial intelligence makes sense within the business, what data and systems are available for it, and how such a solution fits into the existing environment without disrupting operations. It covers process analysis, choosing a technical approach — AI agents, RAG for semantic search across documents, or automated data processing — and a plan for deployment, security and post-launch support.

For a company with more than one million euros in annual revenue, consulting rarely means a single meeting or a tool demo. It is work that touches several departments at once — IT, finance, production or HR — because an AI solution almost always reaches into data and processes used by many people. Consulting therefore has to answer questions about data ownership, accountability for decisions the system proposes, and who ultimately signs off before the solution goes into production.

At Epix, AI consulting draws on the areas the company covers: building AI agents, RAG systems for semantic search, document processing, and automating business processes and reporting. We can take on a project in full, from analysis through deployment and support, or as a single work package, if the client already has part of the solution and needs an upgrade or a connection to a new system.

Where AI consulting starts#

AI consulting starts with an analysis of existing processes and data, before any technology is chosen. The provider and the client review together which procedures consume the most time, where manual work produces errors, and which data already exists in a structured form suitable for processing by an AI system.

Data quality often decides whether a project is feasible within the planned scope. Data scattered across several systems, kept in different formats, or without a clear ownership structure, requires an extra cleanup step before it can be used for an AI solution. The analysis therefore also looks at where the data originates, who maintains it, and how it moves between departments.

The output of the analysis is a list of priority areas, ranked by business impact and implementation complexity. This review typically covers the following areas.

• existing business processes and their owners
• availability and quality of the underlying data
• links to existing systems such as ERP, CRM or document systems
• security and regulatory requirements for data processing
• expectations of the departments that will use the solution

What decisions need to be made before deploying an AI solution?#

Before deploying an AI solution, a company has to define the scope of tasks the system takes on, how much autonomy it has in making decisions, and how its operation is monitored. These are not technical details — they determine how AI fits into employees' responsibilities and existing processes.

One key decision is whether the AI system only proposes an outcome — classifying a document or drafting a reply to a customer, for example — or carries out the action automatically. In regulated industries and the public sector, the more common choice is a model where AI prepares a proposal and a person confirms it, which keeps accountability with the employee and preserves an audit trail.

Another decision concerns data access: which part of the database the AI system may read, whether it can reach personal or commercially sensitive data, and how that access is limited by the user's role. This stage also settles the following points.

• the scope of tasks AI handles on its own
• the approval process for decisions with significant impact
• data access rights by user role
• how the system's decisions are logged and tracked
• who is responsible for monitoring operation after launch

Integrating an AI solution with existing systems#

An AI solution rarely works on its own, since to deliver real value it has to reach data inside the company's existing systems — ERP, CRM, document systems or reporting tools. Integration happens through APIs, which let the AI system and other applications exchange data without duplicating it or moving it by hand.

Companies commonly run one of the established business systems for finance, inventory or customer relationships, and each has its own limits on how far it opens its data outward. AI consulting therefore checks which data is reachable through the vendor's API, which needs an extra export step, and where it makes sense to consolidate data into a single base before an AI system uses it.

Epix builds and connects ERP, CRM, DMS and ECM systems and integrates them through APIs, which is a precondition for an AI solution reaching the right data at the right time. When taking over an existing system, we first review the source code, architecture and data structure, and only then propose where AI fits in.

Data security and compliance in AI consulting#

Data security matters as much in AI consulting as the choice of technology, since an AI system often processes data that is commercially sensitive or personal. Consulting has to define where the data is stored, who can access it, and how it is protected while moving between systems.

In the public sector and regulated industries, beyond general security, companies also have to account for personal data protection and an audit trail, since every decision an AI system makes must later be verifiable. Development, test and production environments stay separate, and real personal data is never used in the test environment.

Within Epix and its specialist partner network there are people certified in cloud security, identity management and incident response, and standards such as ISO 27001 are part of the network's delivery structure. This means security requirements are addressed throughout an AI project, not only at launch.

How AI consulting affects employees#

AI consulting affects employees mainly where it changes how routine tasks get done, so part of consulting is preparing staff for a new division of work, not just installing technology.

When AI takes over part of a procedure — sorting documents or drafting a first version of a report, for example — the employee's role shifts toward reviewing and confirming results. That shift needs clear communication about what is changing and why; otherwise employees experience the solution as oversight of their work rather than as a tool.

It therefore makes sense to involve representatives from the affected departments as early as the analysis stage, not only during testing. That way, edge cases the AI system missed surface in time, and someone in the department is assigned to confirm results after deployment.

How does an AI consulting project unfold?#

An AI consulting project unfolds in steps that run from analysis to post-launch support, with the result of each step agreed with the client before the next one begins.

On larger projects, a project manager is named, along with team responsibilities, deadlines and a reporting method, since the work usually involves the client's IT department, business users and the provider together. Changes are introduced gradually, across several shorter cycles, rather than as one large launch of the whole solution.

The steps typically include the following phases.

Epix can take on an entire project or just one work package — for example only the analysis, or only the integration with an existing system — depending on what the client already has in place.

• analysis of processes and data
• choosing the technical approach and solution architecture
• planning integration with existing systems
• development and incremental testing
• acceptance testing together with the client
• deployment into the production environment
• support and further development after launch

Testing and accepting an AI solution#

Testing an AI solution before deployment checks whether the system behaves correctly on real, but safely prepared, data, and whether the results match what the client expects. It is the same quality process that applies to other information systems, with the addition that AI testing also covers cases where the system does not know what to do.

Test scenarios include manual and automated testing, API testing and regression testing after every change, and on larger projects an independent QA team separate from development. Acceptance testing follows criteria agreed in advance and confirmed by the client before the solution moves into the production environment.

Success criteria are set during the analysis stage, since that is the only way to later check whether the AI solution actually solves the problem the project set out to address. Criteria are specific to each project and differ depending on whether it automates a procedure, searches documents, or supports decision-making.

Support after an AI solution goes live#

Support after an AI solution goes live covers monitoring operation, fixing issues and further development, since the need for adjustment continues after launch, once the system starts working on real data. Epix can take over operational monitoring, SLA support, and technical and security updates after deployment.

Issues and support requests are classified by their impact on the business.

Response times for each class are set out in the support agreement according to how critical the system is for the client, since they differ between a public institution and a manufacturing company running continuous operations. Changes after launch go through the same review and testing as the initial development before they are released into production.

• outage – the system or a key part of it is down, handled with top priority
• disruption – the system runs but a specific function does not work correctly
• defect – a minor issue with no impact on operations, scheduled for the next release
• request – a change or upgrade, for which scope is assessed and a timeline agreed

How to choose a provider for AI consulting?#

Choosing a provider for AI consulting means checking whether the team understands both the AI technology and the client's existing systems, since integration is often the harder part of the project, not the AI component itself.

A good sign is whether a provider can take over an unfinished or existing system after reviewing the source code and data, not just build from scratch, since most companies with over a million euros in revenue already run systems that need connecting, not replacing. How a provider handles ownership of data and documentation matters just as much.

At Epix, source code and data always remain the client's property, and documentation, access and passwords are part of the handover at project close. That leaves the client free of dependence on a single provider for any further changes to the system.

It is also worth checking who actually does the work inside the provider's organization: for larger AI projects, Epix assembles a project team from its own specialists and its specialist partner network, including senior engineers, solution architects and AI/ML specialists, depending on the scope of the project.