---
"title": "Do AI adoption yourself or hire an AI consultant: which is better for
  your firm?"
"description": "A fair comparison of rolling out AI on your own versus hiring an
  outside AI consultant, covering cost, speed, risk, internal skills and
  follow-through, plus when each option wins."
"section": "Comparisons"
"sidebarTitle": "Do it yourself vs AI consultant"
"published": "2026-10-29T14:31:50.019Z"
"updated": "2026-10-29T14:31:50.019Z"
---

## Should we figure out AI ourselves or hire an AI consultant?

Doing it yourself fits when you have someone internal with time and curiosity to own it, your goals are modest (better everyday use of one chat tool), and you can afford to learn by trial and error. A consultant fits when nobody has the time, leadership wants results on a deadline, the work touches client data or core workflows, or past attempts have stalled. Many firms land in between: they bring in outside help to set direction and train people, then run it themselves.

The deciding question is whether you have an owner with real time for this, not just interest.

## Who this is for

Owners, managing partners, COOs and operations leads who know their firm should be using AI better and are deciding whether to handle it in-house or pay for outside help.

## The short version

- Doing it yourself costs less in fees and builds skill inside the firm, but it usually takes longer and depends on one or two people finding the time.
- A consultant costs more up front and can move faster, but only pays off if the firm keeps the work going after the engagement ends.
- Either path fails without an owner, a few chosen workflows and clear rules for using AI with client data.

## What each option means

**Doing it yourself** means someone inside the firm, often an operations lead, a tech-minded partner or an early adopter, picks the tools, writes the rules, shows colleagues how to use them and tries automating a few tasks. Free vendor guides, online courses and trial accounts are the usual starting points.

**Hiring an AI consultant** means bringing in an outside person or firm to assess where AI fits, recommend tools, train the team and sometimes build agents or automations. Engagements range from a single workshop to a multi-month program. If you're weighing a consultant against a full-time hire instead, see [hiring an AI lead vs a consultant](/docs/hire-ai-lead-vs-consultant).

## How the two usually compare

These are typical patterns, not rules. Results depend heavily on the people involved on either side.

| Factor | Do it yourself | AI consultant |
|---|---|---|
| Direct cost | Low, mostly tool licenses and staff time | Higher, fees on top of tools |
| Hidden cost | Staff hours pulled from billable or core work | Management time to brief and oversee |
| Speed to first results | Slower, learning as you go | Faster, with a tested process |
| Skill left inside the firm | High, if the owner stays | Depends on how much training is included |
| Risk of bad choices | Higher on data handling and tool selection | Lower, if the consultant is experienced |
| Fit to your workflows | Strong, insiders know the work | Strong only if the consultant learns your work |
| Building agents and automations | Hard without technical skills | Often included or available |
| Best when | Modest goals, a willing owner, time to experiment | Deadlines, sensitive data, stalled efforts |

## When doing it yourself is the better choice

- You have someone who wants to own AI and can protect several hours a week for it.
- Your goal is mostly better daily use of a chat assistant, not new systems.
- The firm is small and the stakes of a slow start are low.
- You want to learn what works before spending on outside help.
- Your team already experiments with AI and mainly needs agreed rules and shared examples.

In these cases a consultant may be more than you need. A good starting point is our guide on [where to start with AI](/docs/where-to-start-with-ai), followed by an [acceptable use policy](/docs/write-an-ai-acceptable-use-policy) and a short list of [workflows worth automating](/docs/find-workflows-to-automate-with-ai).

## When hiring a consultant is the better choice

- Nobody internal has the time, and AI keeps slipping down the list.
- You've tried before and adoption stalled. Our page on [why AI adoption stalls](/docs/why-ai-adoption-stalls) covers the common reasons.
- The work involves client documents, financial data or other sensitive material, and you want the rules and setup right the first time. See [keeping client data safe with AI](/docs/keeping-client-data-safe-with-ai).
- You want custom agents or automations, not just better prompting.
- Leadership wants a clear plan and visible results within a set period.
- Partners or senior staff are skeptical, and an outside voice will carry more weight.

## The honest trade-offs

**Doing it yourself isn't free.** The fees are low, but the hours come from someone who usually has another job. If that person is a senior billable employee, the real cost can exceed a consultant's fee. Track the time honestly.

**A consultant can leave nothing behind.** If the engagement ends with a slide deck and no trained people, the firm is back where it started. Ask up front what your team will be able to do on its own afterward. Our list of [questions to ask an AI consultant](/docs/questions-to-ask-an-ai-consultant) helps here.

**Insiders know the work, outsiders know the patterns.** Your people understand your workflows far better than any consultant. A consultant has seen what works and what fails across many firms. The best results usually come from combining both.

**Tools change quickly.** Whichever path you choose, AI products change their features, names and pricing often. Someone has to keep watching, which is an ongoing job, not a one-time project.

## A combined approach that often works

Many firms use outside help for the parts that are hardest to learn alone, then own the rest. A typical shape: a consultant helps pick the first workflows and tools, writes the rules with you, trains the team on real work and builds one or two automations. An internal owner takes over from there, with occasional check-ins. This keeps fees bounded and leaves the skill inside the firm. Our guide to [building AI capability inside your firm](/docs/build-ai-capability-inside-your-firm) covers the internal side.

## Questions to ask before choosing

1. **Who will own AI here, and how many hours a week can they give it?**
2. **Are our goals about better daily use, or about new systems and automations?**
3. **How sensitive is the data people will use with AI?**
4. **What happens if this takes a year instead of a quarter?**
5. **What should our team be able to do on its own when outside help ends?**

If you're unsure where you stand, see [is my company behind on AI?](/docs/is-my-company-behind-on-ai) and our [AI implementation roadmap](/docs/ai-implementation-roadmap).

## How Ruby helps

Ruby Advisory works with firms that want outside help without becoming dependent on it. Our recommended path for most firms is the combined approach: we help choose the first workflows and tools, set the rules, train your team on its own work and build the automations worth building, then hand ownership to someone inside your firm. We help with:

- **AI strategy:** picking tools, writing the rules and choosing where to start.
- **AI training:** live sessions and cohort programs built around your real work.
- **AI engineering:** agents and automations connected to the systems you already use.

To talk through whether you need outside help at all, [book a call](https://rubyadvisory.com/book-a-call).

## Related pages

- [Hire an AI lead vs a consultant](/docs/hire-ai-lead-vs-consultant)
- [AI consulting team vs solo consultant](/docs/ai-consulting-team-vs-solo-consultant)
- [Questions to ask an AI consultant](/docs/questions-to-ask-an-ai-consultant)
- [Where to start with AI](/docs/where-to-start-with-ai)
- [Run an AI pilot that doesn't stall](/docs/run-an-ai-pilot-that-does-not-stall)