1. Focus on Business Problems, Not Technology Alone
AI is most valuable when its making your business more efficient by making more money, or saving money or time. Ensuring you apply AI in areas of your business that are highly routine, repetitive, or monotonous is usually the best place to start.
According to Dan Sinowat, AI is “95% business, 5% technical.” Businesses often fail because they emphasize technology over solving real-world problems. AI must directly address business challenges to produce meaningful results.
2. Data Quality is the Foundation of AI Success
Use the garbage in, garbage out approach and make sure your data and sources are solid because if you use data or reference material or inputs that aren’t high quality, your outputs will suffer tremendously.
Stefan Boehmer emphasized, “If you don’t have clean data, AI is challenging to apply.” Organizations need a structured, clean, and accessible data lake. Without quality data, predictive analytics and AI projects struggle to yield valuable insights.
3. Start Small to Achieve Big AI Wins
I really like the approach highlighted in Jim Collin’s book Good to Great in which he writes about how to implement new technologies and strategies by first using bullets and then cannonballs.
The idea is simple: bullets are smaller and less costly and its easier to recalibrate their targeting but once you’ve aligned the target that’s when you use bigger artillery.
Cory Thigpen stressed the importance of a phased approach: “Customers that really struggle tend to be overly ambitious…trying to boil the ocean just to get started.” Starting small with clearly defined pilots helps ensure successful scaling later.
3. Rapid Prototyping Validates AI Investments
Startups are known to race to MVP or minimal viable product – that means the least amount of investment you can put out that works just to gauge product alignment, value, and demand without breaking the bank.
Dan Sinowat explained the critical role of rapid prototyping: “We don’t just give you a slide deck—we actually build you a rapid prototype where you can test that use case.” This approach confirms ROI before committing significant resources, avoiding costly missteps.
4. Human Oversight is Essential
If some portion of your operation or sales process has high value and high touchpoints behind it, keep the human element. If it doesn’t, try implementing AI to enhance the business but make sure to also keep oversight of it.
Dan highlighted that successful AI requires humans to remain actively involved: “We still need humans in the loop.” Companies should integrate AI with human judgment, ensuring accountability, trust, and ethical oversight remain priorities.
5. Change Management and Culture are Crucial to AI Implementation
Your team will use AI and new technologies when the culture is there to support its implementation. Not the other way around. In other words, adaptive and forward-thinking attitudes and beliefs need to be set in place before major change will take hold.
Cory noted, “There’s a huge people component…There always needs to be robust change management.” Organizational culture and leadership attitudes directly influence how effectively AI solutions are embraced and utilized.
6. AI Should Amplify Human Effort, Not Replace It
If ever there were an opportunity to dramatically save time, money, frustration, and make more money as a result, that time is upon us. AI should be boosting all the positives and minimizing the negatives.
Stefan shared practical insights: “We have turnover every six months; labor cost is rising…There’s a need to automate, but not to eliminate people.” AI should supplement human capabilities, addressing labor shortages and enhancing productivity, not replacing the workforce.
7. AI Agents Will Drive Autonomous Business Operations
The future is upon us and one of the next interesting functions of AI is when multiple AI agents can work together (almost like people) to streamline tasks.
Dan discussed how AI agents coordinate complex tasks, stating, “Agentic AI will help you autonomously do many things quicker, faster, and better.” Businesses must prepare for autonomous systems coordinating various specialized roles, significantly enhancing operational efficiency.
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