AI Masterclass 3

Organizations of all sizes are rushing to implement AI, but many stumble due to common and preventable missteps. In the third installment of BeTechly's AI Masterclass series, "Which Pitfalls Will Sidetrack Your AI Success Fastest?", DJ Singley (Chief Technologist & Enterprise Architect, MAPSYS) and Dan Greenleaf (CEO, AI Rising) broke down the biggest traps teams fall into—and more importantly, how to avoid them.

 

1. Lack of Training is Stalling AI Adoption

One of the most consistent pitfalls: companies aren’t training their teams. According to the survey graph below, respondents said a lack of ongoing training was a key blocker. Greenleaf emphasized that many employees don’t even know tools like ChatGPT or CoPilot are available, let alone how to use them securely.

Instead of pushing self-paced online courses, he recommends brief, recurring expert-led sessions that introduce practical applications. Singley added that vendors like MAPSYS often revisit clients after platform updates to train them on new capabilities.

 

2. Misaligned Objectives Are Derailing Projects

Another 18% of participants cited misaligned project goals as a major issue. Many organizations want to "use AI" without clarifying the problem it should solve. Singley advised aligning AI initiatives with specific business objectives—such as cost savings, process efficiency, or new revenue streams—before diving into implementation.

Greenleaf compared this to using a power drill. It’s a great tool, but only if you’re using it for the right job.

 

3. Don’t Build What You Can Buy

Greenleaf stressed that unless you're a cutting-edge tech company, you likely don’t need to build custom AI models. A vast majority of clients, he noted, can succeed using pre-trained models or tools built on top of them. Singley added that building your own solution may seem cost-effective until it scales.

 

4. Integration is the Top Priority for Improvement

When asked what they'd most like to improve with AI, 30% of attendees pointed to integrating AI with legacy systems. Singley noted that this is one of the most common and realistic goals for AI right now. Many customers are exploring ways to enhance pricing tools or CRM systems using AI for greater efficiency.

Greenleaf added that as long as systems are built with modern APIs, integrating AI is often easier than expected for most companies. However, in critical areas like hearthcare, AI should only assist—not make final decisions.

 

5. You Don't Need to Start Big

Finally, Singley urged teams not to "boil the ocean" with AI projects. Start small with proof-of-concept use cases, like automating password resets or simple analytics tasks, and build from there.

Greenleaf reinforced this approach: "…it's like investing for your retirement. Yeah, it costs some money now, but it’s going to cost you more later if you’re not making the investment…"

 

Watch the full replay of AI Masterclass Part 3 here, and check out AI Masterclass Part 1 and Part 2 using the links below!

AI Masterclass Part 1: Replay | Blog

AI Masterclass Part 2: Replay | Blog