Cedario Analytics

The AI consulting market has exploded. From boutique firms to large integrators to software vendors calling themselves consultants, there’s no shortage of people willing to help you “implement AI.” The problem is that the quality, honesty, and actual expertise behind these offerings varies dramatically.

If you’re a Canadian business evaluating AI consulting partners, this guide will help you understand what a legitimate engagement looks like, what red flags to watch for, and what questions to ask before signing anything.

What a Legitimate AI Consulting Engagement Looks Like

Discovery and feasibility come first

A credible AI consultant will not propose a solution before understanding your problem. The first engagement should involve deep discovery — understanding your workflows, your data, your team’s capabilities, and the specific outcome you want to achieve. Any firm that skips this and goes straight to pitching a platform or product is prioritizing their revenue over your success.

Custom solutions, not off-the-shelf tools

While commercial AI tools have their place, a consulting firm that primarily resells or implements third-party platforms isn’t doing consulting — they’re doing implementation. Real AI consulting involves building or customizing solutions to your specific data, problem, and environment. If the recommendation always seems to point toward one vendor’s product, ask why.

Clear deliverables and timelines

You should know exactly what you’re getting, when you’re getting it, and what success looks like at each stage. Vague statements like “we’ll explore AI opportunities” are not deliverables. Look for written scopes of work with specific outputs: feasibility reports, working prototypes, integration plans, performance benchmarks.

Red Flags When Evaluating AI Consultants

• They skip feasibility and go straight to building. Moving directly to development without assessing data readiness, ROI, and risk is a sign that the firm is more interested in billing hours than delivering outcomes.
• They can’t explain the methodology. Ask them how they’ll build the model. Ask what happens if the first approach doesn’t work. If the answers are vague or overly technical without substance, that’s a warning sign.
• No named team members or verifiable credentials. AI consulting requires deep technical expertise. If a firm can’t tell you who specifically will work on your project — and what their background is — you have no way to evaluate whether they’re capable of delivering.
• They guarantee results. Machine learning is an empirical process. Good consultants will give you honest probability ranges, not guarantees. Anyone promising a specific accuracy percentage or ROI figure before seeing your data is not being truthful.

Questions to Ask Before Hiring an AI Consultant

• “Who specifically will work on my project?” Get names and credentials. Understand whether the people who sell you the engagement are the same people who will execute it.
• “What happens if the prototype doesn’t perform as expected?” The answer reveals how they handle failure — which is an inevitable part of any real ML project. Good consultants have a plan for iteration and honest communication when something isn’t working.
• “Do you have experience in my industry?” Domain knowledge matters. An AI consultant who understands manufacturing, healthcare, or financial services will build more relevant solutions than one who applies generic approaches across every sector.
• “What do you deliver if we decide not to proceed after the assessment?” A good feasibility assessment has standalone value — you should walk away with a document that helps you make an informed decision, regardless of whether you hire the firm to build anything.

Why Transparent, Research-Led Consulting Produces Better Results

The firms that consistently deliver results in AI consulting share a few traits: they start with honesty about what’s feasible, they’re led by people with genuine technical depth, and they prioritize your long-term outcomes over short-term revenue.

At Cedario Analytics, our team is composed of PhD researchers and engineers who have worked on real ML problems across manufacturing, finance, healthcare, and cybersecurity. We lead every engagement with a feasibility assessment because we know that building the wrong thing is worse than building nothing — and because our reputation depends on giving you the truth.