“What’s one challenge you faced when implementing an AI analytics tool in your business? How did you overcome it, and what would you do differently?”
Here is what 6 thought leaders had to say.
Data Quality Issues Derail AI Implementation
One big challenge was handling messy and inconsistent data across different departments. The AI tool was only as good as the data fed into it, and early outputs were misleading—false patterns, irrelevant insights, wrong KPIs being flagged.
The fix wasn’t technical at first—it started with aligning teams on what data mattered and cleaning historical records. Built a basic data dictionary and had weekly syncs to iron out edge cases. Only after that did the model start delivering actual value.
If doing it again, the better move would be starting with a pilot on one clean dataset rather than trying to plug everything in at once. Small wins build trust and give space to tune the system without chaos.
Vipul Mehta, Co-Founder & CTO, WeblineGlobal
Building Team Trust Overcomes AI Adoption Resistance
One challenge we faced was getting the team to trust and understand the insights from the AI analytics tool. Initially, many were skeptical because the recommendations sometimes conflicted with their gut instincts or past habits. To overcome this, we ran parallel tests comparing AI suggestions with traditional methods and shared transparent results with the team. We also held training sessions to explain how the AI works and what data it uses. If I could do it differently, I’d involve the team earlier in the tool selection process and set clearer expectations about the learning curve. Building trust takes time, but making the AI part of a collaborative process speeds adoption and maximizes impact.
Georgi Petrov, CMO, Entrepreneur, and Content Creator, AIG MARKETER
Legacy System Integration Complicates AI Analytics Deployment
One challenge I faced when implementing an AI analytics tool in my business was integrating it with our existing systems. We had a mix of legacy software and newer platforms, which made the data transfer and synchronization more complex than expected. The AI tool didn’t easily communicate with everything, and there were compatibility issues. To overcome this, I worked closely with our IT team to map out the data flow and make necessary adjustments to both the tool and our systems. We also brought in external consultants to help with the integration. Looking back, I would have allocated more time to research and select a tool with better integration capabilities. While we eventually succeeded, the extra effort upfront could have saved us from a few hiccups in the process.
Nikita Sherbina, Co-Founder & CEO, AIScreen
Human Expertise Enhances AI Analytics Limitations
One of the biggest challenges we faced implementing an AI analytics tool was aligning its automated insights with the real nuances of our clients’ diverse markets. The tool churned out data and patterns quickly, but often missed context—like cultural trends or sudden local shifts—that aren’t easily captured by algorithms. At first, this led to some recommendations that felt off or irrelevant. To overcome this, we created a hybrid approach: we let the AI handle heavy data crunching, but paired it with human review from our regional experts to validate and interpret the findings before acting on them. It’s a blend of tech speed and human intuition.
If I could do it again, I’d invest more time upfront training the team on interpreting AI outputs and setting clearer parameters for the tool. AI is powerful, but it’s not a plug-and-play magic bullet—human insight still needs to be front and center.
Eugene Leow Zhao Wei, Director, Marketing Agency Singapore
AI Misclassification Solved Through Human Verification
The biggest challenge was false confidence—assuming the AI insights were always accurate. Initially, we utilized an AI tool to analyze call transcripts and assess lead quality. It turns out that it misclassified key terms like “follow-up” or “quote” and flagged good leads as junk. That skewed our cost-per-lead data.
We resolved the issue by combining machine scoring with human review. We now audit a sample weekly to catch patterns that the AI misses. If I could do it over, I’d treat AI as a decision-support tool, not a decision-maker. Trust the output, but verify before you act.
Andrew Peluso, Founder, What Kind Of Bug Is This
AI Tools Support Decisions, Not Replace Judgment
One of the biggest challenges was learning to trust what the AI analytics tool was telling me. At first, it looked impressive with clean dashboards and detailed breakdowns. But the recommendations didn’t always line up with what was actually driving results. The models were picking up on short-term spikes and treating them like long-term trends. So that led to over-optimizing campaigns based on misleading signals, especially in channels with limited visibility into the full customer journey.
Things started to shift when I stopped expecting the tool to give final answers. I began using it to flag patterns and surface outliers. These were things that needed a second look. I started validating its suggestions against other sources like GA4, CRM data, and even call transcripts. If multiple signals pointed in the same direction, I’d dig in. If not, I’d move on.
Looking back, I would’ve set clearer boundaries from the start. Because AI analytics works better when it supports decisions instead of trying to make them. It’s solid at narrowing down where to look. But it can’t replace context or judgment. Once I adjusted that mindset, it actually saved time and helped cut wasted spend. Because it pointed to what really moved the needle on CAC.
Josiah Roche, Fractional CMO, JRR Marketing
