Small AI Consultancy or Big Consulting Firm: Which Is Better for a 30 to 100 Person Company?

A small, hands-on AI consultancy is usually the better choice for a 30 to 100 person company. The people who scope the work also build it and train your staff, and a first project can be running within weeks. A big consulting firm earns its higher fee when regulators, a board that wants a known name, or work across several countries is involved.

Why does a small AI consultancy usually fit a 30 to 100 person company better than a big consulting firm?

A small AI consultancy fits a company of 30 to 100 people because the size of the team matches the size of the problem. Large consulting firms staff projects as a pyramid: a partner sells and reviews, a manager runs the project, and consultants early in their careers deliver most of the hours. That structure is built for programmes where dozens of people work in parallel. On a contained job, such as putting an AI assistant on your quoting process and teaching ten people to use it, the pyramid turns into overhead you pay for. Big firms also tend to scope in phases (discovery, current state, target model, roadmap, then build), so for a company your size the first working system can arrive months after kickoff. A small firm usually starts with one workflow and one working version.

Many small companies prefer to start with one small project. A 30-person company wants to see one thing work before it pays for the next.

How do a small consultancy, a big firm, a freelancer and an in-house hire compare?

Four options come up when a 30 to 100 person company looks for AI help, and each is built for a different job. The table below compares them on the points that decide the outcome at this size: who does the work, how fast something runs, who teaches your people, and what is left when the main person is away.

The in-house hire deserves a closer look, because small companies choose it often. Many owners would rather do it themselves, and sometimes that is the right answer. The trap is hiring one person and expecting them to build the systems and train 60 colleagues in the same job. We cover that trade-off in our piece on hiring an AI person or a partner.

What drives the cost of each option?

The cost of AI help follows the team structure more than the hourly rate. With a big firm you pay for several people at once, plus the steering meetings and status reports that keep a large team coordinated; on a small project that coordination can eat a large share of the budget. With a small consultancy you pay for one or two senior people, and the bill rises mainly with the number of systems to connect and the state of your data. A freelancer usually costs less per hour than a firm, but you spend your own time managing the work and you carry the risk if they move on. An in-house hire costs a salary plus the months it takes to recruit. For more on fair pricing and vetting, see what to pay an AI consultant.

Here is an illustrative case. Say a 45-person wholesale distributor spends about 12 hours a week turning customer emails into quotes. A big firm proposes a discovery phase and an AI roadmap covering every department. A small consultancy proposes to automate the quote drafts first, train the three people in sales support to check and send them, and measure the hours after six weeks. The second proposal is smaller, and it is the one the owner can actually judge: did the 12 hours go down?

A proposal that names the workflow and the measured result is easier to approve than a cheaper one that leaves both vague.

When is a big consulting firm the right call?

A big consulting firm is the right call when the hard part of the job is something only scale or a recognized name can supply. At 30 to 100 employees, those cases look like this:

• Your AI use touches regulated decisions about customers, credit, health or hiring in several countries, and the legal analysis is harder than the build.
• An auditor, regulator, investor or major client wants an opinion from a firm they already know.
• AI is one part of a larger programme the big firm is already running, such as a new ordering system (ERP) or a merger.
• You need many people working in parallel across sites, which a five-person consultancy cannot staff.

A mixed setup often works better than picking one side. A small consultancy builds the systems and trains the team, and a big firm is hired for one narrow, high-stakes piece, such as a legal review of an automated decision. That way you pay large-firm rates only where they buy something a small firm cannot give you.

Why should an AI consultancy both build the systems and train your team?

An AI consultancy should both build and train because a system that nobody on staff can run stops being used. BCG's 2025 AI at Work survey of more than 10,600 white-collar workers in 11 countries and regions found that regular AI use was sharply higher among employees who got at least five hours of training and had access to in-person training and coaching (BCG). A year later, BCG's 2026 edition found that only 36% of respondents feel that they have received adequate upskilling (BCG). In the EU, training is also a legal duty: Article 4 of the EU AI Act requires companies that deploy AI systems to take measures to support the AI literacy of their staff (European Commission). Our guide to what the EU AI Act requires of a small business covers the other obligations. [BCG] [BCG] [European Commission]

Hands-on training also catches problems that a strategy deck never meets. Early sessions tend to stall on setup: a login that hangs, a connector without the right permission, a file saved where nobody can find it, or a default model that quietly gives weak answers. A firm that only advises never sits through that part, and that part decides whether the tool is still open a month later.

Which criteria should a 30 to 100 person company use to choose an AI consultancy?

Judge the proposed team and the first project, because the size of the firm predicts little on its own. These criteria separate a good fit from a poor one for a company of 30 to 100 people:

• Named people. The proposal lists who will do the work and how many days each, and the senior person from the sales call is on that list.
• One first workflow. The first project fixes one process with a before and after measurement, the same logic as testing AI before spending serious money. A company-wide roadmap can wait.
• Training in the scope. The people who will run the system get hands-on sessions on their own work, with a set point where they run it alone.
• Your accounts. Tools, logins and data sit in your company's name from day one.
• Proof in production. At least one system they built that a client still uses, and a client you can call about it.
• Fit with your tools. Experience with what you already run, such as Microsoft 365, Google Workspace or your CRM.
• Basic risk habits. A plain answer on data protection under the Swiss FADP or the GDPR, and on what happens when the AI gets something wrong. The free NIST AI Risk Management Framework, released in January 2023 for voluntary use, works as a checklist for that conversation (NIST). [NIST]

What should you ask an AI consultancy before you sign?

Send every provider the same questions in writing, so the answers sit side by side. These questions show quickly whether a team builds or mostly presents:

• Which of the people on this call will work on our project in week two?
• What is the smallest first project you would take on, and on what date does it end?
• What will one of our staff be able to do alone after your training that they cannot do today?
• Which system you built still runs at a client without you, and may we speak to that client?
• Who covers when your lead person is sick or on holiday?
• What happens to scope and timeline if our data turns out messier than you expected?
• If we stop after the first project, what exactly do we keep?

Who can help a 30 to 100 person company with AI?

Several kinds of provider serve companies of 30 to 100 people, and the useful test is whether they build and then hand over to a trained team. Small hands-on consultancies fit companies that want working systems and staff who can run them, usually starting with one workflow; Anovis AI, a small Swiss consultancy that builds AI systems inside small companies and trains the people who use them, is one example. Specialist development studios with research-heavy teams fit custom machine learning on your own data, such as image recognition or demand forecasting. Training-only providers fit teams whose tools are already set up and who need confidence using them. Big consulting firms fit the regulated and multi-country cases above. When you search, look for providers whose service pages name both implementation and training, and who show results from companies your size.

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