--- "title": "AI training vs AI consulting vs AI engineering: which does your firm need?" "description": "A plain decision guide for leaders choosing between AI training, AI strategy consulting and AI engineering, with the symptoms that point to each and the order that usually works." "section": "Concepts" "sidebarTitle": "AI training vs consulting vs engineering" "published": "2026-10-16T18:27:37.371Z" "updated": "2026-10-16T18:46:00.622Z" --- You need AI training when people don't know how to use AI in their own work, AI consulting when you don't yet know where AI should go or what to do first, and AI engineering when a piece of work should run as a system across your tools instead of through individual prompts. Many firms eventually need some of all three, but rarely all at once and rarely in equal measure. Getting the order right saves money and goodwill. ## Who this is for CEOs, COOs and managing partners who have been told they "need to do something about AI" and are trying to work out what kind of help to look for. ## The short version - Match the help to the problem. Skills problems need training, direction problems need strategy, and repetitive multi-system work needs engineering. - Training usually comes first, because it makes every later decision better informed. - Engineering without trained users and a clear target workflow is the most expensive way to learn. ## What each one actually is **AI training** builds skill and habit. People learn how to give useful context, check output, break work into steps and apply AI to tasks they already own. The output is a team that uses AI well, plus shared prompts, checklists and examples. **AI strategy and consulting** builds direction. Someone looks across the firm's work, finds where AI is likely to help most, weighs risk and cost, and recommends what to do first, what to buy, what to build and what to leave alone. The output is a short list of priorities and decisions. **AI engineering** builds systems. Engineers connect AI to your email, CRM, documents or databases, build agents that carry out multi-step work, and make those systems secure, observable and maintainable. The output is software that runs. ## A symptom-based decision guide Read the statements below and notice which group sounds most like your firm. **Signals you need training** - We have licenses, but only a few people use them regularly. - People tried AI, got a mediocre answer and gave up. - Partners or senior staff are skeptical because they haven't seen it work on their own tasks. - Everyone uses AI differently and nobody shares what works. **Signals you need strategy** - We have lots of ideas and no agreed priority. - Different teams are buying different tools. - We don't know which workflows would actually benefit, or how to judge success. - Leadership wants a plan it can explain to the board, investors or partners. - Nobody has written down what data is allowed where. **Signals you need engineering** - The same multi-step task happens every week and people re-prompt it by hand each time. - Information has to be copied between several systems to get one output. - The work needs access to internal data that a chat window can't safely reach. - A process has a clear, checkable output and enough volume to justify automating it. If you recognize yourself in several groups, that is common. The next question is order. ## The order that usually works 1. **Train a first group on real work.** This produces quick, visible value and gives you people who understand what AI is good and bad at. 2. **Use what they learn to set priorities.** Trained people are far better at spotting good use cases than anyone working from a slide. 3. **Engineer only the workflows that earn it.** By this point you know which tasks recur, which data they need and who will own the result. There are sensible exceptions. If you face a pressing data-handling question, a short strategy and policy step should come before broad training. If one painful, well-understood process is costing real time every week, a small engineering project can run in parallel with training. ## How the three fit together Think of it as people, plan and plumbing. Training changes what people can do. Strategy decides where to point that capability. Engineering removes the repetitive work that shouldn't need a person at all. Each one is weaker on its own: a plan nobody can execute, a skilled team with no priorities, or a system nobody trusts or uses. ## Common mistakes - **Buying engineering to fix a skills problem.** A custom tool won't help a team that doesn't yet trust or understand AI output. - **Commissioning a strategy no one acts on.** A long document without trained owners tends to sit on a shelf. - **Training once and stopping.** A single session raises interest. Follow-up turns it into habit. - **Treating the three as separate vendors with no handoff.** Lessons from training should feed priorities, and priorities should shape what gets built. - **Over-scoping the first step.** A small, well-chosen start beats a firm-wide program that takes months to begin. ## When you do not need us If your team already uses AI confidently, you have a clear short list of priorities, and you have an internal engineer who can own a small build, you may only need occasional advice or nothing at all. Likewise, if your main need is generic tool onboarding, the vendor's own materials may be enough to start. ## How Ruby helps We do all three, and we will tell you which one you need now: - **AI training:** hands-on, role-specific sessions built around your firm's actual tasks. - **AI strategy:** a short, honest view of where AI belongs in your work and what to do first. - **AI engineering:** secure agents and integrations for the workflows that should run as systems. The simplest way to find out where you stand is [one practical hour of AI training](https://rubyadvisory.com/book-a-call) with your team. ## Related pages - [Why giving your team ChatGPT licenses isn't an AI strategy](/docs/licenses-are-not-a-strategy) - [Where should your firm start with AI?](/docs/where-to-start-with-ai) - [Custom AI agents for your business: what they are and who should build them](/docs/custom-ai-agents-for-your-business) - [Hiring an AI lead vs an outside consultant vs a consulting team](/docs/hire-ai-lead-vs-consultant)