Stop Building AI Projects. Start Solving Problems with AI.

Last month, a VP of Product told me their team had spent six months and $200K building an AI-powered feature. Customer feedback? "This is interesting, but I still do it the old way because it's faster."

They'd built a technically impressive solution to a problem nobody actually had.

This isn't an isolated case. I've watched dozens of companies rush to add AI to their products, not because it solves a meaningful problem, but because they feel pressure to "do something with AI." The result is a graveyard of pilot projects, abandoned experiments, and features users ignore.

The pattern is always the same: they started with the technology instead of the problem.

The "AI for AI's Sake" Trap

Here's what happens when companies put AI first:

1. The Mandate: Leadership decides "we need AI in our product" or "we need an AI strategy"
2. The Scramble: Teams brainstorm ways to incorporate AI into existing features
3. The Build: Engineers create something technically interesting that uses AI
4. The Launch: The feature ships with fanfare about "AI-powered" capabilities
5. The Silence: Usage is disappointing. The feature adds complexity without meaningful benefit

The problem isn't the technology. AI is genuinely powerful. The problem is starting with the solution and working backward to find problems it might solve.

It's like buying a chainsaw and then looking around your house for things to cut. Sure, you could technically use it to slice bread, but that doesn't mean you should.

The Difference Between AI as a Goal vs. AI as a Tool

When AI is the goal, conversations sound like this:

• "How can we add AI to this feature?"
• "What can we do with GPT-4?"
• "Our competitors have AI, we need it too"
• "Let's run an AI pilot project"

When AI is a tool, conversations sound like this:

• "Our support team spends 4 hours a day categorizing tickets. Can we automate that?"
• "Engineers waste 30 minutes per day writing repetitive code. What if we could generate it?"
• "Customers abandon the form because they don't know how to answer. Could we help them?"
• "We're losing deals because proposals take 2 weeks to write. How do we speed that up?"

Notice the difference? One starts with capabilities looking for applications. The other starts with pain points looking for solutions.

AI should be boring. It should disappear into the background of tools that just work better. The best AI implementations are the ones where users don't even realize AI is involved—they just notice their problem is solved.

If you're exploring where AI can add real value to your business, our AI consulting services can help you identify high-impact use cases and avoid common pitfalls.

How to Identify Problems Worth Solving with AI

Not every problem needs AI. Some problems need better processes. Some need simpler tools. Some need to be eliminated entirely rather than automated.

AI makes sense when you have:

1. High-volume repetitive tasks where humans currently provide judgment or pattern recognition. Support ticket routing. Document classification. Data entry with context-dependent decisions.

2. Problems where "good enough" is valuable and perfection isn't required. Drafting responses that humans will edit. Suggesting code that engineers will review. Generating initial analyses that analysts will refine.

3. Scale problems that are economically constrained. You'd love to give every customer personalized onboarding, but you can't afford 50 customer success managers. You want code review on every PR, but your team is too small.

4. Expertise bottlenecks. One person knows how to do something critical, and they're overwhelmed. AI can help distribute that expertise without hiring 10 more specialists.

AI doesn't make sense when:

• A simple rule or workflow would solve it
• The task requires real accountability or legal liability
• Errors are expensive or dangerous
• The process is the problem (automating broken processes makes them worse faster)
• You're solving for "looking innovative" rather than outcomes

Start with the Outcome, Not the Technology

Here's a framework that works:

Step 1: Identify the pain What is actually costing you time, money, or opportunities? Where are humans spending hours on work that feels mechanical? What are customers complaining about?

Be specific. "Communication is inefficient" is not specific enough. "Our sales team spends 3 hours per deal writing proposals that are 80% similar to previous proposals" is specific.

Step 2: Define success in business terms What would it mean to solve this problem? Don't say "we implement AI." Say "sales team spends 30 minutes instead of 3 hours on proposals" or "support costs drop by 20% while CSAT stays flat or improves."

If you can't articulate the business outcome, you're not ready to build anything. Learn more about defining KPIs and success metrics for your initiatives.

Step 3: Explore solutions without prejudging the technology Maybe AI is the answer. Maybe it's a better template system. Maybe it's changing the process so proposals aren't needed. Maybe it's hiring a specialist.

Be honest about whether AI is actually the best tool for this job, or just the most exciting one.

Step 4: Prototype narrow and fast If AI seems promising, build the smallest possible version that tests the core assumption. Can AI actually do the task well enough to be useful? Will people actually use it if it works?

One week and one use case. Not six months and a platform.

Step 5: Measure the outcome you defined Did proposals actually get faster? Did support costs actually drop? Did the thing you said mattered actually improve?

If not, kill it. Quickly. Don't let it become a zombie project that limps along because you've invested so much.

Real Examples of Purposeful AI

Let me show you what this looks like in practice.

Internal Support Ticket Routing A mid-sized SaaS company had 12 support agents manually categorizing and routing 500 tickets per day. It took 5-10 minutes per ticket and was mind-numbing work. New agents took weeks to learn the routing rules.

They built a simple classifier that looked at ticket content and suggested the right team and priority. Agents could accept or override. Cost to build: one week. Result: routing time dropped from 8 minutes to 30 seconds. New agent ramp time cut in half.

Nobody wrote a press release. Customers never noticed. But the support team was thrilled, and the company saved about $150K annually in time costs.

Code Review Assistance An engineering team at a fintech company had a bottleneck: senior engineers were spending 10+ hours per week reviewing pull requests for common issues (naming conventions, missing tests, security patterns). Junior engineers waited days for feedback.

They built a PR bot that left comments on obvious issues before human review. Not sophisticated. Not flashy. But it caught about 60% of the feedback senior engineers would have given, and it did it instantly.

Senior engineers spent less time on rote feedback and more time on architecture. Junior engineers got faster feedback and learned faster. Cycle time improved by 2 days.

Customer Onboarding Assistant A B2B product had complex setup that required 3-4 calls with a solutions engineer. The company couldn't afford to hire more SEs, so growth was constrained by onboarding capacity.

They built an in-app assistant that answered setup questions and walked customers through configuration. It couldn't handle everything, but it could handle the straightforward 70%. Complex cases still went to humans.

Result: onboarding capacity tripled without hiring. SE time focused on high-value customers and complex situations. Sales could close more deals without worrying about downstream capacity.

Proposal Generation for Consulting A consulting firm wrote 80% similar proposals for every engagement. Partners spent hours per week tweaking previous proposals instead of selling or delivering work.

They built a tool that took basic engagement details (industry, scope, size) and generated a first-draft proposal from their previous work. Partners spent 30 minutes editing instead of 3 hours writing.

Proposals got better because they drew from the best examples across the firm. Response time dropped from a week to a day. Win rates improved because they could respond while momentum was hot.

How to Avoid the Pilot Graveyard

Most AI experiments die not because the technology doesn't work, but because they never had a real path to adoption. Here's how to avoid that:

1. Pick problems that people actively complain about If nobody is asking for it to be better, they won't use it when you build it. Start with the painful stuff that people actually want solved.

2. Build for people who will use it daily Internal tools for your team are better first projects than customer-facing features. Your team will give you brutal, immediate feedback. They'll actually use it if it helps. And if it fails, it fails privately.

3. Make it 10x better at one thing, not 20% better at ten things Narrow scope. Solve one problem completely rather than touching ten problems superficially. Users will tolerate limitations if the core value is undeniable.

4. Ship it to real users in week one Don't spend months building in isolation. Get it in front of users immediately, even if it's embarrassingly simple. Fast feedback loops are how you learn what actually matters.

For more practical examples of AI delivering value for small businesses, check out our guide on AI use cases for SMBs.

5. Measure the behavior change, not the sentiment "This is cool" means nothing. "I used this instead of the old way 47 times this week" means everything. Are people actually changing their behavior because your tool is better?

6. Kill it fast if it's not working Failed experiments are fine. Zombie projects that nobody uses but nobody kills are waste. Set a clear bar (usage, time savings, cost reduction) and kill it if you don't hit it within a month.

The Framework: Problem → Solution → Technology

Here's the decision tree that works:

1. What specific problem costs us time, money, or opportunity?
2. What would success look like in measurable business terms?
3. What solutions might address this problem? (Consider AI, but also everything else)
4. If AI seems promising, what's the smallest test we can run?
5. Did it work? Is anyone using it? Did the business outcome improve?
6. If yes, scale it. If no, kill it or pivot.

Technology is step 3, not step 1. Starting with AI and looking for problems is backward. Starting with problems and evaluating whether AI is the right solution is forward.

Most AI initiatives fail because they skip straight to step 4 without doing the hard work of steps 1-3. They build technically impressive solutions to problems that don't matter, or use AI to solve problems that didn't need it.

How to Actually Start

If you're serious about using AI purposefully, here's what to do this week:

Monday: Make a list of the most painful, time-consuming, repetitive work your team does. Be specific. Hours wasted, not vague frustrations.

Tuesday: Pick the one problem where solving it would have the most obvious business impact. Define what success looks like in numbers, not feelings.

Wednesday: Brainstorm solutions. Is AI actually the best tool for this job? Or is there a simpler way?

Thursday: If AI makes sense, sketch out the smallest possible version that would test whether it actually works. One use case. One week to build.

Friday: Decide if you're going to build it. If yes, start. If no, repeat with a different problem.

Don't start with "we need an AI strategy." Start with "here's a problem that's costing us $X per month, and here's how we'll know if we've solved it."

The companies winning with AI aren't the ones with the most sophisticated models or the biggest AI teams. They're the ones solving real problems with whatever technology makes sense, and sometimes that's AI.

The Bottom Line

AI is a tool, not a trophy. The goal isn't to have AI in your product. The goal is to solve problems that matter.

Stop looking for ways to add AI to your roadmap. Start looking at your biggest problems and evaluating whether AI is the right tool to solve them. Often it is. Sometimes it's not.

When you start with the problem, you build things people actually want. When you start with the technology, you build things that sound impressive in board meetings but die in production.

The best AI project you'll ever ship is the one where users don't care that it's AI. They just care that it works.

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Want help identifying where AI actually makes sense for your product? I work with B2B SaaS companies to find the AI opportunities that drive real business outcomes—not the ones that just look good on slides. Let's talk about what's actually slowing your team down.