Recent  Jaya Gupta  's  writing on context graphs  has surfaced an important question: where does real enterprise AI value actually come from? The emerging answer is that it won’t come from layering AI on top of systems of record, but from capturing the decision context  that connects data to action. For us at Beacon, this wasn’t a thesis we set out to prove. Context graphs emerged as a by-product of running AI agents in production —first across workflows and support, and lat

The World Economic Forum's  " Scaling AI: Now Comes the Hard Part "  panel at Davos 2026 didn't declare AI startups dead. It did something more significant… it marked the end of the experimentation phase. Enterprises are done with pilots. Production is where most AI strategies are breaking. “Everybody talks about AI, the impact of AI, but where is the value? Where is it in dollar figures? And this is what we are able to establish.” - Amin Nasser, CEO of Aramco It’s Pretty Cle

By 2026, most implementation leaders will agree on the fundamentals. AI will be embedded into delivery. Speed will matter more than activity. Customer experience will start at implementation, not after go-live. But many will still underestimate what AI orchestration actually means. And, where the real gains come from. Because the biggest shift isn’t better visibility, smarter governance, or cleaner reporting. It’s this: AI is beginning to execute the work of implementation i

As we close out 2025, I'm reflecting on what has been an extraordinary year for Beacon. It was defined by rapid growth, strong partnerships, and most importantly, the trust our customers, team, and investors have placed in us. Seeing how far we've come has been humbling, and I'm incredibly grateful to everyone who's been part of this journey. The Numbers Tell a Story We achieved 30% quarter-over-quarter revenue growth this year. But behind that number is something more meanin

For all the noise surrounding enterprise AI, one truth is becoming impossible to ignore: most companies are still trapped in pilot mode . AI experiments linger for months, burning time, money, and credibility, yet failing to deliver the production-level business outcomes executives expect. What separates the organizations that scale AI from those that stall isn’t bigger models, larger datasets, or more ambitious roadmaps. It’s something far more fundamental and far more urgen

The AI Summit New York 2025, held December 10-11, 2025 at the Javits Center, brought together enterprise leaders, innovators, and technologists to explore how AI is reshaping industries and driving real business value. This year's event was a watershed moment, not for the hype around AI, but for the hard-won lessons on turning AI ambition into measurable outcomes. At Beacon.li , we were energized by the conversations, the energy, and the unmistakable realization that enterpri

These aren’t just our client stories. These are survival stories. Because in enterprise implementations… the scariest things don’t hide under the bed, they hide in your backlog. Not all nightmares happen in the dark. Every day, our outreach team walks into calls with brave enterprise teams who swear, “We’ve got our implementation process under control.” But somewhere around slide four of the demo, that's when the confessions begin. Endless onboarding cycles, midnight war room

Customer support in insurance has two sides. Customers want fast and clear resolutions and delays hurt satisfaction and loyalty. Agents, meanwhile, are buried under repetitive queries, often missing critical tickets. Despite claims of being “fully digitized,” many insurers still struggle when a user wants a simple update on a claim or information about their policy ¹ . Portals confuse, chatbots only point the way, and most queries still end up as tickets for human agents. AI

Imagine Sarah, a claims adjuster at a mid-sized insurer. Her day should be simple: review claims, approve eligible ones, and keep policyholders updated. But in reality, automation only takes her so far. A policyholder submits a claim online. Automation flags the submission and routes it to Sarah. Sounds ideal. Until she discovers: Some documents are incomplete, but the system can’t tell which ones. Policy details are spread across three different platforms. She has to verify

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