The AI Playbook: 5 Rules to Build Smarter, Scalable AI Initiatives
Here’s the hard truth about AI: nearly 80% of AI initiatives still fail to scale. Why? According to Eric Prugh, Chief Product Officer at Authenticx, it often comes down to misaligned goals, poor implementation, and unrealistic expectations.
In our recent webinar, Eric pulled back the curtain on what it really takes to turn AI into business value in our recent webinar. Whether you're just dipping your toe into AI or looking to get a stalled initiative back on track, these 5 takeaways are for you.
Watch the recording of the webinar and view the top 5 takeaways below:
1. AI Is More Than Just ChatGPT
Let’s get this out of the way: AI ≠ ChatGPT. Generative AI may be dominating headlines, but it's only one slice of the broader AI pie. Eric emphasized that AI is any technology that helps you automate, categorize, or surface issues—often without human input. That includes everything from natural language processing (NLP) to predictive analytics. The real magic happens when you match the right AI approach to the right business challenge.
2. Don’t Chase Shiny Objects—Start With Strategic Alignment
The number one reason AI projects fail? Teams chasing “AI for AI’s sake.” Instead of asking “How can we use AI?”, start by asking “What problem are we trying to solve?” Eric was clear: AI needs to be mapped to a clear business objective. It should accelerate goals your organization already cares about—not distract from them.
3. Remember: AI Is Probabilistic, Not Perfect
AI doesn’t give you hard truths. It gives you likely truths. That nuance matters. As Eric pointed out, AI is inherently probabilistic—especially in subjective or context-rich scenarios. Expecting 100% accuracy will lead to disappointment. Instead, put validation processes in place and educate teams on how to interpret and act on AI outputs responsibly.
4. Layering Models = Smarter Results
One of the most insightful takeaways? Don’t rely on just one type of AI. At Authenticx, Eric’s team layers multiple models—like pairing NLP with large language models (LLMs). This hybrid approach lets them extract structure from data first, then apply generative models to deliver insights more efficiently (and cost-effectively). The result? Better accuracy, scalability, and performance.
5. You Don’t Have to Go It Alone
AI is evolving fast—and getting it right often requires niche expertise. Eric shared that Authenticx is even exploring hiring full-time prompt engineers to fine-tune interactions with AI agents. Whether it’s partnering with an expert team or hiring specialized talent, the takeaway is clear: bringing in help can make the difference between stalled and successful.
Final Thoughts: Scaling AI Starts With Smarter Strategy
This session was packed with practical insight for any business navigating the fast-moving world of AI. Eric’s message? AI is full of promise—but it’s not plug-and-play. It requires strategy, structure, and the right people behind the wheel.