For a moment, it looked like the AI race had only one direction: bigger models and more compute. But reality is starting to catch up. As AI moves from demos into business-critical workflows, new bottlenecks are emerging—messy data, non-deterministic outputs, and the growing need for governance. This month’s stories highlight a shift happening across the industry: from chasing model capabilities to building the infrastructure, controls, and data foundations that make AI reliable in the real world.
For years, the AI race focused on bigger models and more compute. But the industry is starting to realize that the real bottleneck is often data. The shift from model-centric to data-centric AI is gaining momentum as companies invest in automated data pipelines, dataset benchmarking, quality scoring, and domain-specific training sets.
Even the most advanced models can only perform as well as the information they learn from. In reality, much of the world’s knowledge still lives in messy, fragmented formats—documents, images, forms, and complex workflows that machines struggle to interpret.
Technologies that extract, structure, and validate this information are becoming a critical layer in the AI stack. They turn raw information into AI-ready data and help bridge the gap between the messy world of enterprise data and the structured datasets modern models require. In the next phase of AI, data infrastructure may matter as much as the models themselves.
Expecto Patronum: Conjuring Determinism to Protect Business-Critical Workflows
A recent experiment confirms what many teams are starting to observe in production: LLMs don’t just read inputs, they interpret them through the lens of their training. Sometimes that prior knowledge overrides what’s actually on the page. While fascinating in a lab, this becomes risky in finance, insurance, or government workflows.
The warning is straightforward:
“Blind reliance on AI without recognizing its shortcomings can lead to serious consequences, both professionally and personally.”
In document processing, those consequences show up as compliance gaps, swapped values, or decisions made on confident but misplaced assumptions.
This is not to diminish the power of LLMs, but it sure does clarify their role. They are exceptional at flexibility and context. But they are not a replacement for deterministic AI. In document processing, this is the structured extraction, the validation rules, and audit trails. The discipline that binds output to evidence.
If generative AI is the spell, IDP is the Patronus. Not to suppress the magic, but to ensure it protects rather than harms.
Good AI governance is hard. Three out of four organizations report having a dedicated AI governance process in place, but only 12% describe their efforts as mature in a recent Cisco survey.
And 93% of organizations plan further investment to keep up with the complexity of AI systems and the expectations of customers and regulators.
Effective AI governance requires broad, cross-functional participation. It depends on structured operating practices such as documenting model limitations, conducting bias and security audits, and establishing review and oversight workflows.
ChatGPT, the popular chatbot from OpenAI, reported 900 million weekly users in February. It remains the fastest-growing consumer application in history, reaching 100 million monthly active users just two months after its 2022 launch. For reference, TikTok took about nine months after its global launch to reach 100 million users, Instagram 2 1/2 years and Facebook nearly 5 years.
AI by the Numbers
According to Deloitte's latest State of AI in the Enterprise report, 25% of leaders now report that AI is having a transformative effect on their companies. That's more than double from 12% a year ago.
Agentic AI can plan and act autonomously, but risks scaling hidden biases. The article stresses reliable data, balanced AI methods, diverse design, and human oversight to ensure automation remains fair, accurate, and accountable.
AI hype often drives investments, but real value comes from solving clear business problems. Companies succeed by integrating tools, partnering with experts, and balancing build-vs-buy decisions for scalable, cost-effective AI.
ABBYY's CFO Brian Unruh shares practical advice for bank executives on avoiding AI investment mistakes, prioritising KYC capabilities, and building defensible returns through purpose-built AI.
ABBYY in Action: Innovation Spotlight
Agentic Meets IDP: Smarter Automation for Business Success
We've entered a new era of automation: agentic artificial intelligence, AI that doesn’t just assist but makes decisions and takes action. Paired with IDP, it’s transforming workflows with precision and autonomy, redefining how businesses streamline complexity.
AI in Practice: Real-World Applications & Case Studies
Logistics Co Turns AP Complexity into Simplicity with ABBYY
Vinmar Group wanted to minimize the complexity of their account payable processes. Their primary goal was clear: alleviate the manual handling of huge volumes of invoices by transforming these documents into actionable data.
Proservartner’s Hackathon-Winning Solution Powered by ABBYY
Proservartner’s KYC solution, built by ABBYY MVP Vivek Kumar and his team, uses ABBYY IDP to automate data capture, validation, and integration with precision. It delivers faster onboarding, stronger compliance, and seamless experiences.
ABBYY Ascend 2026 is our premier global event series, designed to showcase the latest advancements in purpose-built AI and automation.
This year’s theme, “Turning Complexity into Clarity,” focuses on transforming enterprise workflows and unlocking new opportunities. Watch this video to learn more and register. Also check out the updated agenda highlights on the registration pages.
This premier event brings together industry leaders to explore the rapidly evolving healthcare landscape, with a focus on:
Health Plan Strategy & Innovation
Population Health & Value-Based Care
AI & Digital Transformation
ABBYY and partner Naviant are co-sponsoring this event. If you are in the Chicago area on April 13–14, be sure to visit our exhibit and attend our panel session “How Health Plans Can Lead the Next Wave of Change” on Tuesday, April 14 from 1:20 - 2:00 PM.
Une IA de confiance pour automatiser les documents
Discover how trusted AI can automate the processing of sensitive documents such as IDs, contracts, and compliance records, while ensuring data accuracy, regulatory compliance, and full auditability.
ABBYY recently co-sponsored the Healthcare 2040 Expo in Sydney together with our partner Tecala. The event brought together healthcare leaders and technology innovators to explore the future of healthcare, including digital transformation, AI adoption, and smarter operational models. During the event, ABBYY shared how healthcare organizations can unlock greater value from their data and automation initiatives through intelligent document processing and process intelligence.
Asian Banking & Finance and Insurance Asia Summit – Philippines
ABBYY participated in the Asian Banking & Finance and Insurance Asia Summit in the Philippines. The summit brought together banking and insurance leaders across the region to discuss the evolving financial services landscape, with a focus on digital transformation, operational efficiency, and AI-driven innovation. We shared how ABBYY supports financial institutions in accelerating intelligent automation.
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