External firm or in-house team? How to implement AI without building an IT department from scratch

Author: Karol Jurewicz (Strategic Advisor & Business Analyst) · Last updated:

A company is considering an AI implementation. Document automation, predictive analytics, an intelligent assistant for the team — there's no shortage of ideas. But before the question "which tool?" is even asked, another one comes up: who is going to do it?

Hire a programmer? A Data Scientist? An AI Engineer? Outsource it to an external firm? Or will "someone from IT figure it out"?

The question looks like a simple choice: full-time hire vs outsourcing. In practice it's complex — because it's not only about cost, but about competencies, data security, the pace of implementation and what happens once the system goes live.

Below we break the topic down into its parts — without promoting one option. There are situations where a full-time hire makes sense. There are ones where an external firm is the only realistic option. And there are ones where a combination of both works best.

1. Why "someone from IT" usually isn't enough

⚡ In one sentence

Implementing AI requires different competencies than maintaining IT infrastructure — a network administrator won't replace a Data Scientist, just as an electrician won't replace an architect.

💡 In plain terms

In many companies, the reaction to the topic of AI is: "we have an IT department, let them handle it." The problem is that maintaining infrastructure (servers, network, licenses, helpdesk) and building AI solutions are two completely different specializations.

The person maintaining IT systems makes sure the existing tools keep working. The person implementing AI has to understand the business process, design the solution, prepare the data, train the model, integrate it with the company's systems and monitor it after deployment. This isn't an "extra task" — it's a separate profession.

An analogy: a company has electricians who maintain the wiring in a production hall. Now it wants to build a new hall. It won't ask the electricians to design the building — it will hire an architect. The electrician is still needed (they'll wire up the new building), but designing it requires someone with different competencies.

It's the same with AI: the IT department will be needed (integration, infrastructure, permissions), but designing and implementing an AI solution requires someone who combines technical knowledge (Python, ML, Cloud) with the ability to understand a business problem.

There's one more problem that's rarely talked about: the AI market is full of people who've taken courses and bootcamps but have no real implementations behind them. You can't become a Data Scientist or an AI Engineer from theory alone — it takes practice on real data, in real processes, with real constraints. A company recruiting for a full-time role risks hiring someone who interviewed well but can't handle the first non-standard problem — and the person doing the recruiting often doesn't have the competencies to assess this before hiring. An external firm solves this differently: it's accountable for the outcome, not for the CV. If the implementation doesn't work — that's the external firm's problem, not the client's.

🔧 Deep dive

An AI implementation requires a set of competencies spread across several roles:

  • Data Engineer — prepares the data: builds pipelines, cleans, transforms, combines sources. No data, no model.
  • Data Scientist / ML Engineer — designs and trains the models, selects algorithms, evaluates quality (precision, recall, F1), iterates.
  • AI Engineer — builds the production infrastructure: API, monitoring, retraining, scaling. Responsible for making the model work not in a notebook but in the company's system.
  • Process architect — understands which process makes sense to automate, how to measure the effect, how to manage the change within the team.

For most companies — especially those just starting out with AI — building these competencies through full-time hires is difficult. An experienced person can cover several roles at once, but finding such a candidate on the market is a challenge — and when they leave, the company loses everything at once (see What is process optimization? — the section on dependence on a single person). That's why a common model in the EU is the hybrid approach: strategic competencies in-house, implementation competencies outside.

2. Full-time hire vs external firm — a comparison

⚡ In one sentence

A full-time hire gives you constant presence and exclusivity; an external firm gives you speed and breadth of competencies — the key is matching the model to the stage the company is at.

💡 In plain terms

A full-time hire works when the company already has a clearly defined scope of AI work and needs someone to carry it out day to day. For example: the company has a Computer Vision system deployed on the production line and needs an engineer to maintain it, train models on current data and respond to problems.

An external firm works when the organization doesn't yet know where to start, needs competencies from several fields at once (processes + data + ML + integration) and wants to see results in weeks, not months. For example: the company wants to automate document processing but has no experience with OCR, NLP or systems integration — it needs someone to guide it through the whole process.

The key difference: a full-time hire is a fixed cost regardless of workload. An external firm is a cost proportional to the scope of work. For a company that needs 3 months of intensive work and then 2 hours of support a week, it's worth calculating whether a full-time hire pays off.

Criterion In-house team External partner
Availability On site, every day According to an agreed collaboration model
Objectivity and independence An employee is part of the company structure — which makes it harder to speak plainly about problems involving superiors or colleagues An external partner isn't entangled in internal relationships — it can assess the situation objectively and say plainly what needs to change
Way of working Carries out tasks assigned by a superior — the company has to know what to assign and how to assess the result Agrees the goal with the company and organizes the path to the result itself — accountable for the agreed scope, can cover multiple roles within the project and is measured on outcomes
Project start Requires recruiting and onboarding an employee Can start within days to weeks
Cost model Fixed — regardless of current workload Tied to the scope of work
Continuity of knowledge Knowledge stays in the company but leaves with the person Knowledge lives in documentation and code, independent of turnover
Control over data Direct Depends on architecture and contract (DPA, NDA)

🔧 Deep dive

In the European market (PL/DE/EU), finding an experienced AI/ML specialist is a challenge — and once you succeed, the total cost of employment (salary, employer contributions, equipment, licenses, training, cloud, recruitment) can be a surprise. It's worth calculating the full TCO of a full-time hire and comparing it with the cost of a fixed-scope implementation project — including the time during which a full-time employee is "waiting for tasks."

For comparison: an implementation project with an external firm (diagnosis + PoC + implementation of one process) has a fixed scope and schedule — once it's done, the company has a working solution and costs drop to the level of post-implementation care.

EY's 2025 report confirms this pattern: 51% of companies see benefits from AI, but the other half are disappointed. The main cause isn't technology — it's the "last mile" of implementation: integration with processes, training the team, change management. An external firm with experience in these areas solves a problem that a lone programmer on the payroll can't — because they lack the process competencies and the experience in managing change.

3. Data security — am I giving up control?

⚡ In one sentence

Data security when working with an external firm is a matter of architecture and contract — not of trust.

💡 In plain terms

This is the most common concern: "how can I hand my data to an external firm?". The answer is: you don't have to hand over the data. A well-designed implementation works so that the external firm builds the system but the data stays with the client.

What this looks like in practice:

  • Infrastructure in the client's cloud — the system runs on the client's AWS/Azure/GCP account. The external firm has access to the code and configuration, but the data is under the client's control. Once the project ends, access can be revoked with a single click.
  • NDA and DPA — a non-disclosure agreement (NDA) and a data processing agreement (DPA, required by GDPR) regulate what the external firm can and cannot do with the data.
  • Audit trail — every action in the system is logged. The client can see who had access, when and to what.

A comparison with a full-time hire: a full-time employee has full access to the data — and it's daily access, not project-based. When they leave, you have to revoke permissions and verify that all the data stayed in the company. The risk of a data leak is different, but no smaller.

The key question isn't "full-time hire or external firm?". It's: does the system's architecture protect the data regardless of who has access to it? (see What is RAG and an AI agent? — the section on security).

🔧 Deep dive

In a regulatory context (GDPR, EU AI Act), working with an external firm requires:

  • A DPA (Data Processing Agreement) — defines the purpose and scope of processing, the processor's obligations, the right to audit. Required under Article 28 of the GDPR.
  • Data localization — the data has to stay in a region compliant with the regulations (for EU companies: servers in the EU). Cloud infrastructure lets you control this precisely.
  • The least-privilege principle — the external firm is given access only to what's necessary to deliver the project. Not to the entire customer database, not to the HR system.
  • Offboarding procedures — once the project ends: removing access, handing over documentation, verifying the logs.

In a well-designed collaboration model, an external firm has less access to the data than the average internal employee — because its access is limited to a specific scope and to the duration of the project (see What is RAG and an AI agent? — the section on data security).

4. Four collaboration models — from consulting to ongoing care

⚡ In one sentence

Working with an advisory-and-implementation partner doesn't have to mean a long-term contract — there are four models, from a one-off consultation to ongoing care.

💡 In plain terms

Model 1: Consultation / Discovery — a one-off meeting or a short workshop (1–3 days). The goal: to understand where to start, which processes have automation potential, what data is available. The output: a process map, recommendations, an implementation plan — not code. This is "diagnosis before treatment" (see What is process optimization?).

Model 2: Project (PoC → implementation) — a fixed-scope project with a clear goal, budget and schedule. We start with a Proof of Concept on a limited scope (e.g. one type of document, one process), verify the results, and if they work — we roll it out at full scale. The output: a working solution in production.

Model 3: Managed service (ongoing care) — after implementation, the external firm monitors the system, responds to problems, retrains the models and develops the solution as needs change. A fixed, predictable cost — like a subscription. The company doesn't have to build maintenance competencies in-house.

Model 4: Co-development (building competencies) — the external firm works side by side with the client's team. It builds the solution but at the same time trains the internal team — so that over time the company can take over maintenance and development. This is the model for companies that ultimately want to have AI competencies in-house.

Most companies start with model 1 or 2 and then move to 3 or 4. It's a natural path — from "I don't know where to start" to "we have a working system and we know how to develop it."

🔧 Deep dive

The choice of model depends on four factors:

  • The company's process maturity — are the processes mapped and measured? If not — start with model 1 (see What is process optimization? — the maturity model).
  • Data availability — does the company have data in a form fit for analysis? If the data is scattered across spreadsheets and emails — model 2 has to start by organizing the sources (see What is systems integration?).
  • The scale of the problem — a one-off automation of a single process is model 2. Continuous improvement of many processes is model 3 or 4.
  • The company's ambition — does it want to have AI competencies in-house (model 4), or would it rather focus on its core business and delegate AI externally (model 3)?

In practice the boundaries between the models blur. A project (model 2) flows smoothly into care (model 3), and during that care the company builds competencies (model 4). A good partner doesn't lock the client into one model — it adapts to how the company grows.

"We work across all four models — from one-day discovery workshops to many months of post-implementation care. We most often start with model 1 (diagnosis) or 2 (a PoC on one process). The key is that we combine process and technical competencies in a single team — the client doesn't have to coordinate a process consultant and an AI firm separately. We work with companies in Poland, Germany and across the EU, in the cloud, while maintaining the security standards required by the GDPR and the EU AI Act."

— The cm-opti perspective

5. When a full-time hire makes sense — and when it doesn't

⚡ In one sentence

A full-time hire makes sense when the company has a steady, daily scope of work for an AI specialist — not when it needs a one-off implementation.

💡 In plain terms

A full-time hire DOES make sense when:

  • The company has AI solutions deployed and needs someone to maintain and develop them day to day.
  • The scope of work is enough for a full-time role — not 2 hours a week.
  • The company is ready for the risk that its only "AI person" leaves — it has a plan B (documentation, a second person on the team, a contract with an external firm as backup).
  • The company's industry requires deep subject knowledge (e.g. medical regulations, the specifics of chemical manufacturing) — and it's easier to teach AI to someone who knows the industry than to teach the industry to someone who knows AI.

A full-time hire does NOT make sense when:

  • The company is just starting out with AI and doesn't yet know exactly what it needs.
  • It needs competencies from several fields (processes + data + ML + Computer Vision + NLP + integration) — one person won't cover that.
  • It needs an implementation once and then 2–3 hours of care a week — a full-time role is excessive.
  • The labor market is tough — recruiting an experienced AI/ML Engineer can take months, and turnover in the field is high.

The hybrid model — most often the best: the external firm implements, while an internal person (even a non-technical one) is the "project owner" — they understand the process, define the requirements, verify the results and are the point of contact. Over time that person can develop technical competencies (model 4 from the section above).

🔧 Deep dive

The "full-time hire vs external firm" decision is worth calculating as TCO (Total Cost of Ownership). For a full-time hire, the cost components reach far beyond the salary — you have to add employer contributions, equipment, tool and cloud licenses, training, conferences and the cost of recruitment itself. Often it's only the full picture that shows how much maintaining a single specialist really costs — and whether the scope of work justifies that cost across a whole year.

For an external firm, the cost is tied to the scope — once the project ends, it drops to the level of post-implementation care.

It's also worth factoring in time — how long it takes to recruit and onboard an employee vs starting a project with an external partner.

6. What to look for when choosing a firm to work with on AI

⚡ In one sentence

A good advisory-and-implementation partner starts with questions about your process — not with a presentation of its tool.

💡 In plain terms

Warning signs:

  • "We have a ready-made solution that fits every company" — there's no single AI solution for everyone. Every company has different processes, data and problems.
  • They start with technology, not diagnosis — "we'll deploy a chatbot for you" before anyone has asked whether a chatbot is what you need.
  • They don't ask about data — an AI model is only as good as the data it works on. A firm that doesn't ask about data quality and availability doesn't know what it's doing.
  • They don't talk about post-implementation care — an AI implementation isn't an "installation"; the system needs monitoring, retraining and development.

Positive signs:

  • They start with a conversation about the process and the problem, not a demo of a tool.
  • They propose a PoC or a pilot — before you invest a lot, they want to prove the value at small scale.
  • They talk about data — they ask what you have, in what form, how much of it, how it's structured.
  • They combine technical and process competencies — they understand not only AI but also how the company works.
  • They have a plan for "after the implementation" — monitoring, care, development.
  • They work in the cloud while maintaining security — DPA, NDA, infrastructure in the EU region.

🔧 Deep dive

When assessing an advisory-and-implementation partner, it's worth asking specific questions:

  • Architecture: Where will the data be stored? Who owns the infrastructure? Can I move the solution to another provider (vendor lock-in)?
  • Code ownership: Does the source code belong to me? Do I receive documentation?
  • Security: Does the firm have experience with the GDPR? Does it sign a DPA? What security certifications does the infrastructure hold?
  • References: Can it show implementations in a similar industry/scale?
  • Care: What does support look like after production go-live? What's the SLA? What happens when the model starts to lose quality?
  • Competencies: Does the team combine technical competencies (ML, Cloud) with process ones (optimization, change management)? Do I have to coordinate two separate entities?

Frequently asked questions (FAQ)

Is an external firm more expensive than a full-time employee?

It depends on three things: the scope of work, the duration of the project and the competencies that need to be covered. An external partner's hourly rate is higher than an employee's cost per hour. But the hourly rate isn't the full picture. On the employee side you have to add the cost of recruitment, onboarding, the risk of turnover, training, equipment, licenses — and the risk that the competencies turn out not to be sufficient. These costs are hidden and spread over time, but they are real. A full-time hire makes sense when the scope of work is steady and daily — and when the company can use that person effectively. An external partner makes sense when you need a specific result within a set timeframe, and then support on a smaller scale.

Does my team need to understand AI to work with an external firm?

No — but it does need to understand its own process. A good advisory-and-implementation partner doesn't expect the client to talk about models and algorithms. It expects the client to be able to describe the problem: what takes too long, what generates errors, what costs too much. The rest is on the partner's side.

What happens if the external firm disappears from the market?

That's a real risk — and it has to be addressed before the collaboration starts, not after the fact. If the solution is well designed, the departure of an external partner doesn't mean losing the system. The key is to take care of three things from the start: (1) the source code belongs to the client, (2) the infrastructure is on the client's account (not the provider's), (3) the documentation is complete. With these conditions met, another firm can take over maintenance of the solution.

Am I handing control of my data to an external firm?

No, if the architecture is well designed. The data can stay on the client's infrastructure (e.g. the client's cloud). The external firm works on it but does not own it. A DPA and an NDA regulate this legally.

How long does a typical AI implementation with an external firm take?

It depends on the complexity of the process, the quality of the data and the organization's readiness. As a rough guide: diagnosis and a PoC take weeks, a full implementation of a complex process — from a few weeks to a few months. The timeline is driven not only by the technical side but also by data availability, internal alignment and the team's readiness for change.

Can I start with an external firm and then build my own team?

Yes — and it's a proven model. The external firm implements the first solutions while you build internal competencies in parallel. Over time the internal team takes over maintenance and development, and the external firm shifts into a consultant role for more complex challenges.

Summary

The question "external firm or full-time hire?" is a false alternative. Most companies need both — but at different moments and in different roles.

At the start of the AI journey — when the company doesn't yet know where to start, what data it has, what's worth automating — an external partner lets you see the first results faster and reduce the risk of a bad hire.

Over time, once the company has solutions deployed and a daily scope of work — it's worth building internal competencies. But even then an external firm can remain a partner: from strategic consulting, through model monitoring, to developing solutions that go beyond the internal team's competencies.

The most important thing is not to lock yourself into one model — and not to build an IT department from scratch to solve a problem that can be solved another way.

Want to check which collaboration model fits your company? Let's talk — we'll start by understanding what you want to achieve.

Get in touch

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