
Hiring an AI Specialist, Consulting, or Team Training: A Founder's Guide
TL;DR
- •Hiring an AI specialist** is expensive and risky due to talent shortages and the founder's lack of internal expertise to audit their work.
- •Consulting** delivers quick results at a specific point but leaves the company dependent on external hands and high invoices for every minor change.
- •Team training** is the most sustainable path, where automations remain company property and employees maintain the systems themselves.
A company owner usually finds themselves at a crossroads: you want to implement AI yesterday, but it is unclear whether to buy ready-made brains from the market, grow your own, or simply pay consultants for the result. The question isn't about technology; it's about where you will lose less money and gain more control over your processes.
Option 1: Hiring an In-House AI Specialist
This solution seems logical: hire someone who will "set everything up." In practice, the founder faces a hyper-competitive market where verifying a candidate's qualifications is nearly impossible unless you are a developer yourself. Often, such a specialist becomes a "lone wolf" writing complex code that no one else in the company can read or fix.
Who it's for: Large enterprises (100+ employees) with a budget for a full R&D department. For small and medium businesses, this usually becomes an expensive toy that leaves the company along with the passwords and logic of the configured systems.
Option 2: AI Consulting
You buy the time of experts who perform audits, draw diagrams, and promise "turnkey" automation. The upside is that you don't need to understand the technical details. The downside is that you are buying a "black box." As soon as the consultant leaves, any change in your business process requires a new invoice because the knowledge did not stay within the team.
We discussed the structure of these costs in detail in our article on /uk/blog/ai-consulting-vs-corporate-training-cost-comparison.
Option 3: Training the Team and the Founder
This is our approach. You don't look for an external "wizard"; instead, you give tools to your people who already know your processes. When a Head of Sales builds an AI agent to analyze calls, they know exactly how to tweak it tomorrow. The company of the future is one where every key employee manages 10–20 of their own automations. Ownership of the technology remains internal, not with a contractor.
Definition: An AI Agent is a configured program or script that uses artificial intelligence to perform specific business tasks (e.g., writing client replies or processing reports) based on defined logic.
Comparison Table for Founders
| Criterion | Hiring a Specialist | Consulting | Team Training |
|---|---|---|---|
| Speed to Start | Low (2-4 months search) | High | Medium (2-4 weeks) |
| Cost | High (Salary + Taxes) | High (Project-based) | Fixed and predictable |
| Knowledge Retention | Only in the specialist's head | Stays with contractor | Stays within the company |
| Flexibility | Depends on workload | Every change = new invoice | Employees adjust it themselves |
Where Should a Founder Start?
The implementation order should be as follows: first, the founder or key decision-makers learn to build automations manually, and only then do line employees join. Don't try to teach everyone at once—start with those who are genuinely "on fire" and want to offload their routine tasks.
The first step is a top-down view of the company. For this, we use the free company org chart. It helps identify which departments have the most manual routine that can be handed over to AI agents right now.
Read more about the sequential steps in our /uk/blog/ai-implementation-roadmap-guide.
Definition: No-code automation is a method of creating work tools and integrations between services without writing programming code, where logic is described in plain words or through visual blocks.
How this works on our side: Our corporate intensive for key personnel includes 4 live 2-hour sessions over 2 weeks. The company selects 3 priority tasks, and by the end of the program, you get at least 3 working automations built on your data with a money-back guarantee. No programming is required: participants describe the logic in words, and AI writes the code. https://course.aiadvisoryboard.me/corporate?utm_source=blog&utm_medium=article_body&utm_campaign=corporate
FAQ
Isn't it easier to just buy Copilot for every employee? Buying a license is like buying a gym membership. Without knowing the methodology or understanding how to connect AI to your CRM or spreadsheets, employees will simply play with the chat without creating systemic value for the business.
Who should be responsible for AI in the company? Initially—the founder or the COO. AI implementation isn't an IT project; it's a change in business processes. If you delegate this to "IT guys," you will get complex technical solutions that the business cannot use daily.
How can we ensure company data doesn't become public? For sensitive processes, use API connections or enterprise tiers where data is not used for model training. We teach teams to work with test or anonymized data during the development phase.
Conclusion
Hiring a specialist is an investment in a person, consulting is an investment in someone else's experience, and team training is an investment in your own infrastructure. For a company of 10–100 people, the highest ROI comes from the path where your people build solutions for your specific tasks. You can start small tomorrow: audit the tasks eating up your time and join a free diagnostic session to turn one of them into a working system.
Frequently Asked Questions
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Implements AI agents in companies and teaches founders and their teams to work with them — through courses and corporate programs.
This article was prepared with AI assistance, based on Yaroslav Maxymovych's methodology and materials. Spotted an inaccuracy — let us know via the form below.
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