Are We Using AI the Right Way?
Cofounder and CTO at Beaconcross Technologies
The jury is still out on whether AI adoption is really boosting the bottom line and driving business value. It is visible to all - 95% of generative AI projects are failing, according to an MIT study.
Why? In my view, the rush for shiny demos, shallow objectives, and the lack of clear mapping between business goals and AI use is fueling this misalignment.
Large language models (LLMs) seem simple. You prompt and get results. However, in reality, implementing them in production and integrating within business flows requires both experience and expertise. Starting a generative AI project is easy. Yet, customizing it to business objectives and delivering consistent, predictable outcomes is where things become difficult.
Those who manage to cross these barriers, however, end up with solutions that deliver real and measurable value.
Report copy available here: State of AI in Business 2025 Report (PDF)
A Common Example: When AI Creates Friction Instead of Resolution
Summarization is one of the most basic use cases for generative AI. Yet in real-world implementations, after failing to provide a useful response, systems often redundantly ask users for a description of their issue all over again before redirecting to a human agent! This creates frustration instead of support. Technology should be designed to feel more people-centric, reducing friction rather than adding to it.
I am building Beaconcross Technologies - we help companies with Conversational & Agentic AI/ML projects.
Ready to Build AI That Delivers Real Business Value?
Schedule a 1-on-1 strategy and architecture session directly with our principal AI engineering leads.
Book Consult