Hiring an AI consultant may seem easy: identify an expert and assign them a problem. However, many organizations struggle with what to ask before signing a contract. The difference between successful and wasted AI partnerships often lies not in the technology, but in clear expectations, alignment, and asking the right questions upfront.
So before you bring someone in, here’s what to ask, what to watch for, and what a successful partnership actually looks like.
Summary
Most AI consulting engagements fail before they start because the right questions never got asked. Before signing a contract, you should know what deliverables to expect, what red flags to watch for, and what your organization actually needs to change internally for AI to ever stick.
“What Does an AI Consultant Actually Do?”
The title gets thrown around loosely, creating mismatched expectations before the engagement even starts.
“An AI consultant should be someone who’s helping an organization really redesign how work is getting done using AI tools,” said Sarah Bhatia, Director of AI Product Innovation at Slingshot. “It’s not someone who’s a prompt engineer or a developer creating slide decks. It should really be somebody who’s embedding themselves into your process and stakeholder conversations to give you tangible output.”
Chris Howard, CIO of Slingshot, put it more bluntly: “An AI consultant should be somewhat of an expert; they’ve solved previous problems using artificial intelligence. They have experience with when it makes sense to use AI, when it doesn’t, what its strengths and weaknesses are, and its risks.”
There’s a meaningful difference between someone who can demo the latest model and someone who has navigated the messy reality of integrating AI into a business. Technical leaders should be looking for lived experience, not just fluency with tools.
“What Should I Expect to Walk Away With?”
Too often, the answer defaults to a slide deck or a proof of concept that never makes it past the demo. A meaningful engagement should leave your organization measurably stronger than when it started.
“Your engagement should be about getting into your workflows and understanding how you can compress timelines and increase internal capabilities,” Sarah said. “If none of those change, you’re falling into the same trap we see over and over again: bolting AI onto something and not transforming your company.”
If you hire an AI consultant, you should be coming out of the other side of that feeling like you’re more capable than when you started.
Chris took it a step further: “There should be real organizational impact; people should be learning from this experience. If I were in this CIO’s shoes, I’d have an internal discussion about who your AI champions are today, and who you want walking away from this as your next ones.”
He also flagged something that often gets overlooked: staying current after the engagement ends. “It takes a lot of work to stay up to speed with AI. A good consultant should be passing that along, too. How do we keep learning? What should we be paying attention to?”
If the consultant does all the work and walks away without transferring knowledge or a plan to keep learning, the engagement was a rental, not an investment.
“What Will This Require From My Organization?”
Technical leaders will naturally probe into data security, regulatory compliance, and expected outcomes. But the deeper questions are the ones that get skipped.
“One thing CIOs aren’t asking that they should be is more around what behaviors need to change for AI to be impactful,” Sarah said.
She referenced a recent talk by Daniel Montgomery at the Louisville AI Exchange, where he called AI ‘corporate Ozempic,’ alluding to the idea that companies think they can buy a tool and see results without committing to a real lifestyle change. “That’s where long-term change comes in: updated to your behavior and your culture as a company.”
If your AI consultant doesn’t discuss organizational behavior in the first conversation, that should raise a red flag.
“How Do I Spot the Wrong Fit Early?”
This one is an internal question for yourself. But the warning signs tend to show up early if you know where to look.
“If they lead with a bunch of tools, if they say they specialize in this tool or that tool without understanding what your workflow is, I think that’s a real problem,” Sarah warned. “If they’re trying to solve problems with tools, that shows me they’re not really diving into your organization and truly understanding problems.”
Chris introduced another red flag: the AI cheerleader. “You’d want to avoid a consultant that’s an AI cheerleader: somebody that thinks AI can solve everything. Because there are definitely problems where AI really isn’t a great fit. You want someone focused on just telling you the truth, being realistic about it.”
You want to avoid a consultant who’s all-AI, all-the-time; someone who thinks it solves everything. Because there are definitely problems AI can’t solve.
“What Should the First Few Weeks Look Like?”
Once the contract is signed, the shape of the work matters just as much as the questions you asked before kickoff.
Sarah laid out a practical arc: “The first conversations are around understanding the problems. Getting with the executive stakeholders and key members of the team to understand what we’re trying to change and where the pain points are.
“Instead of prioritizing features, you should be prioritizing use cases. Where could we make an impact? From there, you should expect some sort of working prototype; something to prove out a way to implement this use case.”
Getting to a working prototype is a milestone, not a finish line. A prototype proves that a use case has legs, but the real value of a consulting engagement is in what your organization is equipped to do after the consultant leaves the room.
And that ending shouldn’t just be a demo. Sarah finished with: “By the end, you should really understand how to identify opportunities for AI, how to prototype solutions quickly, and how to evaluate risk and measure impact. And hopefully you’ve converted a few internal AI champions along the way.”
“What Happens if I Choose the Wrong Partner?”
Again, this is more of an internal question, but an important one: what could go wrong if I don’t make a smart choice about an AI consultant?
Chris pointed to the most immediate risk: “The worst case is something going wrong with data security and regulatory compliance. If the consultant you hired gave no thought to risk, they can really make some bad decisions that negatively impact your business.”
But Sarah highlighted a risk that’s arguably more damaging: cultural failure. “The biggest risk is that it fails culturally. If your first attempt at AI feels chaotic or performative, or people don’t feel like the AI worked, then you’re telling your organization that AI will never work. At that point, you stop growing, you stop exploring, and you ultimately get left behind in your industry.”
A botched first engagement creates organizational scar tissue that makes every future AI initiative harder to get off the ground.
“Is My Organization Even Ready for This?”
That question might be the most honest one a leader can ask.
Chris was direct: “When it comes to any AI initiative, your job as a CIO is to make sure your organization is ready. Have people who are ready to work with the consultant: engage them and collaborate with them. Because if you don’t have anybody within your team that’s ready to learn and explore, then your partnership isn’t going to go very far.”
That means identifying a small internal team before the engagement starts. They don’t need to be AI experts; they need to be ready to learn. But readiness doesn’t mean your team has to have all the answers already. Even organizations with internal AI momentum can benefit from an outside perspective.
Sarah put it this way: “There’s no shame in looking ‘out of house’ to get input. Even if you’re successfully building prototypes internally, there’s value in looking outside at people who have helped other companies implement this, to see what lessons they’ve learned. Most importantly, it can give you an outside perspective.”
Start With the Right Questions, Not the Right Tools
The best AI consulting engagements don’t start with a tool demo or a flashy proposal. They start with the right questions, asked by leaders who are clear on what they need and ready to do the hard work alongside their partners.
Ask what the consultant will leave behind when they’re gone. Ask where AI isn’t a good fit. Ask what your organization needs to change, not just what it needs to buy. And ask yourself whether your team is truly ready to engage.
Because the real risk of getting this wrong isn’t wasted budget. It’s the scar tissue. One bad engagement can convince an entire organization that AI doesn’t work, and that belief is far harder to undo than any technical mistake.
The leaders who get this right won’t just be the ones who picked the best consultant. They’ll be the ones who asked the hardest questions before the engagement ever started.
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Written by: Savannah Cherry
Savannah is our one-woman marketing department. She posts, writes, and creates all things Slingshot. While she may not be making software for you, she does have a minor in Computer Information Systems. We’d call her the opposite of a procrastinator: she can’t rest until all her work is done. She loves playing her switch and meal-prepping.
Expert: Chris Howard
Chris has been in the technology space for over 20 years, including being Slingshot’s CIO since 2017. He specializes in lean UX design, technology leadership, and new tech with a focus on AI. He’s currently involved in several AI-focused projects within Slingshot.
Expert: Sarah Bhatia
Sarah Bhatia brings people together. In her decade plus of product and product-adjacent experience, her focus has been on cross-functional collaboration, asking lots of questions, and getting big results. She excels at strategy development, and getting the right brains in the room to solve big problems. Sarah would describe herself as a daredevil, because she’s not afraid to ask “dumb“ questions, get smart answers, and take (calculated) risks.
Frequently Asked Questions
A strong AI consultant embeds themselves into your workflows and stakeholder conversations to redesign how work gets done. They are not just prompt engineers or deck builders. They bring lived experience knowing when AI makes sense, when it does not, and what the real risks are.
You should walk away more capable than when you started. That means compressed timelines, stronger internal skills, identified AI champions, and a plan for staying current after the engagement ends. If the consultant does all the work and leaves without transferring knowledge, you rented expertise instead of building it.
Watch out for consultants who lead with specific tools before understanding your workflow, or who position AI as a solution for every problem. A good consultant will be honest about where AI is not a great fit and will ask about your organizational behavior, not just your tech stack.
Early conversations should focus on understanding pain points with executive stakeholders and key team members. From there, the work shifts to prioritizing use cases over features and building a working prototype to prove out the approach. A prototype is a milestone, not the finish line.
The immediate risks include data security and compliance failures. But the deeper risk is cultural. A chaotic or ineffective first engagement can convince your entire organization that AI does not work, making every future initiative harder to launch. That kind of organizational scar tissue is far harder to undo than any technical mistake.
