Why Your AI Deployment Is Solving the Wrong Problem, and How to Fix It Before You Go Live
After a long, long meeting, your company might choose and deploy an AI tool based on what the “clearest” problem was.
But that might not be the actual problem, the one you need to solve because it’s costing you money and sanity. (Sometimes, higher-ups are trying to find a problem they can throw AI at, whether it needs AI or not.)
Max Kolden, our SVP of Solutions Engineering, has seen that play out a number of times: Companies are fighting imaginary enemies while the real evil (cost, churn, and operational drag) keeps on snickering in the background.
You should be starting by building for an outcome, not just by picking a tool.
And before you deploy anything, Max says, you should be sure that solving that problem will have the biggest impact on your company.
His framework helps you make sure you’re fixing the right problem and setting yourself up for success, whether you’re working with a vendor or taking on the task in-house.
Key takeaways:
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The Problem With Letting an AI Novice Define the Problem
When Max started having serious AI deployment conversations with enterprise clients, he noticed the leaders who were accountable for implementation were usually being given that mandate from above. CEOs would come forward with a specific goal like “reduce cost,” “fix cancellations,” or “improve margin.”
But that rarely came with real diagnostic work that confirmed those AI mandates would address actual operational breakdowns.
“Sure, some folks are deploying AI and it’s making their team more efficient, but they’re not necessarily seeing more output because of that,” Max says. So are they really more efficient?
According to MIT, 95% of companies have a neutral or negative ROI with AI deployments. A lot of them don’t have the necessary in-house expertise to see positive ROI, which Max has noticed too.
When you’re missing internal expertise
An enterprise tech company decided to provide small and medium businesses with an AI-enabled concierge/business advisor service, which helps those businesses stay on top of tax deadlines and regulatory filings.
Max came in to consult when that concierge, which had strong upfront revenue, had a 50-55% cancellation rate.
He discovered that customers were churning because they expected a strategic financial advisor, and what the concierge service provided was essentially a calendar reminder and form-filler.
While there was demand for an AI-enabled support system to help address compliance tasks (the company actually passed their revenue expectations), what they built didn’t match what customers expected.
Without a dedicated in-house AI team, they couldn’t solve real operational or client problems.
Solve the First Problem. Then, Solve the One That Matters.
Max says that a successful AI deployment happens in two stages:
In stage one, you prove the AI investment was worth it. Scope your pilot tightly around a KPI you already own, and hit it. Then, you gain internal trust.
Stage two is where the real leverage lives. You can find the root cause of the problem that was sitting under the surface.
In the example of the enterprise tech company above, churn numbers didn’t tell the whole story. Customer expectations vs. the product reality did. Stage one in their AI deployment would give them the data, trust, and vantage point to see that problem clearly. In stage two, they could address it.
You can, too.
A 4-Step Framework for Bespoke AI Deployment
Step 1: Validate the stated problem and stress-test it against operational reality
You’re going to feel the urge to design right away, but you’re getting ahead of yourself. Take a beat.
Start instead by surfacing your self-identified pain point and pressure-testing whether it’s the right problem to build against. That way, you can see if the rest of the deployment will stick.
Map the stated pain point to 1 of 3 measurable levers:
- Revenue growth
- Margin improvement
- Operational cost reduction
If no one can say which lever this deployment is pulling, then your scope is too broad to get started, and you’re not going to see measurable results.
It’s also time to check in on your data readiness, Max says: If you’ve got a bunch of data that’s incorrect or duplicative, you will fail from the very beginning.
Data should be digitized, reasonably clean, and accessible. If it isn’t, build data remediation into your timeline. That’s something you can do with AI, and your vendor can help, too.
Step 2: Map the exact workflow and find the intervention point
A common mistake companies make: selecting the AI platform first, then trying to find applications for it.
You should be asking: Where in the workflow should AI intervene?
Go deep on the specific operational workflow where the problem is occurring, find the moment where AI intervention would change the outcome, define the outcome you want, and build the solution around that.
Now, you’ll have a defined intervention point: a specific, time-bound moment where AI can fulfill a purpose: generate an output, surface an insight, or remove friction.
Another problem you may run into is identifying one workflow you want to improve, and finding an AI tool specifically for it. A tool that works for one problem might not be the right tool for other workflows. Your chosen platform should be able to address as many relevant workflows as possible.
Step 3: Build deterministic guardrails around the AI’s output
Most operations leaders underestimate this step, and that’s why it’s where most pilots fall apart.
The main issue: large language models are probabilistic. They give you the most likely answer based on available information. That means the same prompt can give different responses at different times.
It can lead to hallucinations; it’s a liability.
Hallucinations can only be fixed by building agents that have guardrails: what you want the output to be, what sources you want the model to use, and the format and tone in which you want the output. (These guardrails can also help with compliance if you’re in a regulated industry like legal, healthcare, or finance.)
Of course, you still want a person signing off on outputs. The guardrails just cut down the time and thinking you’d do otherwise.
Step 4: Stand up a human orchestration layer and staff it correctly
Max knows that human oversight matters: It’s where unpredicted errors are found. Humans are the ones who set up those all-important guardrails within the agentic framework, and they’re the ones to identify edge cases, in turn improving the knowledge base for similar occurrences in the future.
The team needs 3 capabilities:
- Enough technical fluency to work with the system’s documentation and tooling
- Enough domain knowledge to know whether an output is operationally correct
- The judgment to distinguish between the need for a patch and an escalation
The orchestration team can trace an error in the knowledge base, identify the upstream cause, and update the agent logic or underlying data before it happens again.
A team that’s actively watching your system (the orchestration layer) is positioned to see the next operational problem before you do.
What Success Looks Like
Most leaders kick off an AI deployment naming what they want: lower costs, higher margins, more revenue.
Those are usually the right targets, but achieving those goals should only be the start.
Once you drive positive change on the metrics your team is looking to improve, you begin uncovering larger opportunities to improve service delivery. Ones that you didn’t think possible before because your systems were too limited.
The hard work is architecting the right systems to improve the metrics executive teams care about. The real fun begins when those systems start to work, opening up opportunities to create value you didn’t even realize was there.
FAQs
How do I know if I’m solving the right problem before I start building?
Map your stated pain point to one of three measurable levers: revenue growth, margin improvement, or operational cost reduction, then you can start to identify specific workflows to optimize. If you can’t clearly connect your problem to one of those levers, you don’t have a defined problem.
Do I need a dedicated team to manage an AI deployment long-term?
A human orchestration layer isn’t a nice-to-have. It makes sure your deployment stays successful in the long run. Your team needs three things: enough technical fluency to work with the system, enough domain knowledge to evaluate whether outputs are correct, and the judgment to know when to patch something versus when to escalate it.
What should I do if my pilot hits its KPI but nothing changes at the company level?
A pilot that delivers results without driving broader organizational change usually means one of two things: either the problem you solved wasn’t connected to a lever that leadership actually cares about, or the right people weren’t looped in from the beginning. Use the pilot data to reopen the conversation upstream.