Growth & Strategy
Personas
SaaS & Startup
Time to ROI
Medium-term (3-6 months)
I spent six months watching startups blow through budgets on AI projects that never saw production. One client burned $50K on an AI recommendation engine that performed worse than their existing rule-based system. Another tried to automate their entire customer service with AI chatbots, only to create more support tickets than they solved.
The problem? Everyone's treating AI like a magic solution instead of what it actually is: a very powerful pattern-matching tool that needs the right job to be useful. While everyone's rushing to implement AI everywhere, smart businesses are asking a different question: "Which specific business problem can AI actually solve better than what we're doing now?"
After working with dozens of clients on AI implementations – some spectacular successes, others expensive failures – I've learned that choosing the right AI use case isn't about following the latest trends. It's about understanding your business constraints, your data reality, and where AI's strengths actually align with your problems.
Here's what you'll learn from my experiments:
Why most AI projects fail before they start (and how to avoid this trap)
My framework for evaluating AI use cases based on actual business impact
The data quality reality check that saves months of wasted effort
When to choose AI vs. when to stick with simpler solutions
A step-by-step process for piloting AI use cases without breaking your budget
Let's skip the hype and focus on what actually works. Check out our AI implementation strategies for more practical guidance.
Reality Check
What the AI consultants won't tell you
Walk into any tech conference or scroll through LinkedIn, and you'll hear the same AI advice repeated everywhere:
"Start with your biggest business problem" – Usually followed by a recommendation to automate everything
"AI will transform your entire business" – With promises of 10x productivity gains
"You need an AI strategy now or you'll be left behind" – Creating FOMO-driven decision making
"Start small with pilot projects" – Without explaining how to actually choose what to pilot
"Focus on customer-facing applications first" – Because they're more visible, even if they're harder to implement
This conventional wisdom exists because AI vendors and consultants make money when you buy their solutions, regardless of whether they actually solve your problems. The industry has created a narrative that every business needs AI immediately, without addressing the fundamental question: "AI for what, exactly?"
The reality is that most businesses already have solutions that work for their current problems. The question isn't "How can we use AI?" but "Where specifically would AI be significantly better than what we're doing now, and is that improvement worth the complexity and cost?"
Here's where this approach falls short: it treats AI as a solution looking for a problem, rather than a tool that's exceptionally good at specific types of tasks. It also ignores the infrastructure, data quality, and organizational requirements that determine whether an AI project will actually succeed.
After watching too many failed implementations, I developed a different approach focused on business constraints and realistic expectations rather than futuristic promises.
Consider me as your business complice.
7 years of freelance experience working with SaaS and Ecommerce brands.
Last year, I started working with a B2B SaaS client who was convinced they needed AI for everything. They'd seen competitors launching AI features and felt the pressure to "innovate or die." Their initial wish list included AI-powered customer support, predictive analytics, automated content generation, and intelligent user onboarding.
The reality check came when I asked them to walk me through their current processes. Their customer support was already handled efficiently by a small team using help desk software. Their biggest pain point wasn't customer service – it was actually content creation for their marketing team, who were spending 20+ hours per week writing blog posts, social media content, and email campaigns.
But here's where it gets interesting: when I dug deeper, I discovered they had been manually categorizing and tagging thousands of customer support tickets for two years. This data was sitting unused in their system, but it represented a goldmine for understanding customer behavior patterns that could drive product development decisions.
My first instinct was to focus on the obvious content creation use case – it seemed like a perfect fit for AI automation. But when I analyzed their existing content performance, I realized something important: their best-performing content came from deep industry expertise and personal experiences that AI couldn't replicate. The generic content they could automate with AI wasn't driving results anyway.
The breakthrough came when I shifted focus to that unused support ticket data. Instead of trying to automate customer-facing processes, we looked at using AI to analyze patterns in customer issues, feature requests, and churn indicators. This wasn't glamorous AI – no flashy chatbots or recommendation engines – but it could provide insights that would actually change how they built their product.
This experience taught me that the best AI use cases often aren't the obvious ones, and they're rarely found by asking "What can AI do for us?" Instead, they emerge from asking "What data do we have that could tell us something valuable if we could analyze it at scale?"
Here's my playbook
What I ended up doing and the results.
Based on this experience and several similar client projects, I developed a systematic approach for choosing AI use cases that actually deliver value. Here's my exact framework:
Step 1: The Data Audit
Before considering any AI application, I catalog what data the business actually has – not what they wish they had. For the SaaS client, this revealed they had been collecting detailed support ticket data, user behavior analytics, and customer feedback surveys for years. The key insight: they had structured data with clear patterns that could be analyzed, not just unstructured text that would require complex processing.
Step 2: The 10x Better Test
I only consider AI use cases where the potential solution would be at least 10x better than the current approach – not just marginally better. For content creation, AI might be 2x faster but would require extensive editing. For analyzing thousands of support tickets to identify churn patterns, AI could process in hours what would take humans weeks.
Step 3: The Business Impact Mapping
Rather than starting with AI capabilities, I map business problems to potential impact. Customer churn analysis could directly influence product roadmap decisions worth hundreds of thousands in retained revenue. Content automation might save a few hours per week. The choice becomes obvious when you quantify the business outcomes.
Step 4: The Infrastructure Reality Check
Most AI projects fail because of infrastructure constraints, not algorithm performance. I evaluate whether the company has clean, accessible data, technical resources for implementation, and processes for acting on AI insights. If they can't reliably export their customer data, they're not ready for predictive analytics.
Step 5: The Pilot Design
Instead of building comprehensive AI systems, I design 2-week pilots that test the core hypothesis with minimal investment. For the support ticket analysis, we started by manually categorizing 100 recent tickets, then used a simple AI tool to categorize 1000 more, comparing results to validate the approach before investing in custom development.
The key insight from this process: AI works best when you're trying to find patterns in large amounts of data that humans can't process efficiently, not when you're trying to replace human creativity or judgment. The most successful implementations focused on augmenting human decision-making with data insights, not automating human tasks.
Pattern Recognition
AI excels at finding patterns in large datasets that humans would miss or take too long to discover manually.
Data Infrastructure
Clean, accessible data is more important than sophisticated algorithms for successful AI implementation.
Business Impact
Focus on use cases where AI provides 10x improvement over current solutions, not marginal gains.
Pilot Testing
Start with small experiments that validate your hypothesis before investing in full-scale AI systems.
The support ticket analysis pilot exceeded expectations. Within two weeks, we identified three major customer pain points that hadn't been visible through traditional reporting. The AI categorization revealed that 40% of support requests were related to a specific onboarding step that users consistently struggled with.
More importantly, we discovered that customers who submitted certain types of feature requests within their first 30 days had a 3x higher lifetime value than average users. This insight directly influenced their product roadmap and customer success outreach strategy.
The project took 3 weeks from conception to actionable insights, cost less than $5,000 in tools and implementation, and provided data that influenced decisions worth over $200,000 in product development resources. Compare that to the $50,000 recommendation engine that never made it to production.
Six months later, they're still using the same analysis framework to guide product decisions, and it's become one of their most valuable business intelligence tools. The lesson: sometimes the most powerful AI applications are the least sexy ones.
What I've learned and the mistakes I've made.
Sharing so you don't make them.
Here are the key lessons from multiple AI implementation experiments:
Start with your data, not your dreams – The best use case is determined by what data you already have, not what you wish you could do
Boring AI often works better than flashy AI – Data analysis and pattern recognition provide more business value than customer-facing chatbots
Infrastructure beats algorithms – Clean data pipelines and reliable processes matter more than the latest AI models
Pilot everything before scaling – Two-week experiments save months of development time and prevent expensive failures
Measure business impact, not technical metrics – Model accuracy is meaningless if it doesn't drive better business decisions
Human-AI collaboration works better than AI replacement – Use AI to augment human decision-making, not replace human judgment
Simple solutions often outperform complex ones – A basic analysis that provides actionable insights beats a sophisticated system that nobody uses
The biggest mistake is choosing AI use cases based on what's technically possible rather than what's actually valuable for your specific business context.
How you can adapt this to your Business
My playbook, condensed for your use case.
For your SaaS / Startup
For SaaS companies, focus on data analysis and user behavior insights. Use AI to analyze support tickets, feature usage patterns, and churn indicators. Start with customer success workflows where you have clean data and clear business outcomes.
For your Ecommerce store
For e-commerce stores, prioritize inventory and customer behavior analysis. Use AI to identify purchasing patterns, optimize pricing strategies, and predict demand. Begin with conversion optimization where you can measure ROI directly.