This month, we’re getting into the future of AI interfaces, where voice and chat don’t just talk, they understand. A world where technology doesn’t just respond but reasons, blending conversation with document comprehension to turn information into action. Want to figure out if this is just SciFi? Keep reading! Meanwhile, JPMorgan is proving that AI-first banking isn’t a pipe dream, it’s a $18B reality, with leadership and knowledge management driving transformation at scale. And as we step into the agentic era, one thing is clear: trust isn’t optional. Governance is the secret sauce turning AI ambition into lasting value.
All that in a single newsletter! And it will only take you 3 minutes to be fully up to speed with what is going in the world of AI 😉
The Next Interface Shift Won’t Replace Document Reading—It Will Reinvent It
Voice and chat are fast becoming the natural front doors to technology, aligning with how humans think and communicate—through conversation. Yet true intelligence demands more than speaking; it requires understanding. The most capable systems will not only converse, but also interpret, retrieve, and reason over the written word—just as humans combine dialogue with reading to make sense of complex information.
The future of conversational AI will be multimodal—integrating voice, text, and visual context into one seamless experience. As interfaces evolve, they’ll blend spoken interaction with the ability to absorb documents, visualize data, and act on knowledge. In this world, voice and chat serve as the gateway, while document comprehension remains the engine—the foundation of trust, accuracy, and insight.
The systems that master both won’t just talk; they’ll understand—turning information into informed action and interaction that finally feels human. .
If you think banks are slow to change, think again: JPMorgan is investing $18B in tech next year, not to experiment—but to rewire how the business runs. The focus isn’t GenAI for GenAI’s sake. It’s reengineering the workflows that matter most—credit, fraud, marketing, ops—using the right tool for the job. Sometimes that’s RPA. Sometimes agentic AI.
What matters is the outcome.
One insight stands out: knowledge management is becoming a bona fide discipline—because AI is only as smart as the context and content it learns from. And none of this scales without leadership. Business execs must guide operating model change, not just approve AI pilots.
A thought-provoking insight at how the largest bank in the U.S. is envisioning and navigating real AI adoption—at scale, and for the long term.
Shaping the Agentic Era with Purpose-Built AI Governance
You can’t unlock AI’s full potential without trust. Real governance is what turns agentic ambition into lasting business value.
Organizations that fail to infuse transparency and security could see a 50% decline in model adoption, business goal attainment and user acceptance by 2026.
Organizations must allocate specific resources to build resilient governance capabilities that can evolve with complex AI applications. For firms that successfully capture AI value, the benchmark is often allocating at least 5% of total AI investment to governance infrastructure.
Did you know we’ve had three AI winters already? Just like overhyped tech bubbles, AI has crashed before—big promises, underwhelming delivery. But here’s what’s interesting: each “cool down” helped reset expectations and laid the groundwork for further progress. Today’s GenAI gold rush might cool off, but it’s not the end—it’s the cycle. Smart execs and investors are planning past the hype, focusing on durable value, not viral demos. AI winter isn’t failure—it’s recalibration.
AI models trained to “read” documents like humans—by understanding structure, hierarchy, and references—outperform traditional chatbots by up to 40% on complex reasoning tasks, researchers recently found. It’s a reminder that the future of conversational AI isn’t just about how machines talk, but how well they read.
Continuous improvement now happens in real-time, powered by ongoing, data-rich feedback loops. See why ABBYY is the trusted partner for LLM and agentic AI success.
IDP or LLMs? A Dive into Document Processing Strategies
Explore pros and cons of using general-purpose large language models (LLMs) for processing business documents. Curious how you can amplify each of their respective strengths?
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