Making LLMs Work for the Enterprise
by Ulf Persson, Chief Executive Officer
No technology has inspired the c-suite more since the boom of the internet than large language models (LLMs). With their ease of accessibility and open models, we knew it was a potential game changer with the power to revolutionize how we transform our business to better serve customers and adapt to market changes faster and more efficiently.
This optimism was evident by looking at the enterprise rates of adoption during 2025. For example, the McKinsey State of AI 2025 report found that 78% of respondents are now using genAI, up from 55% in 2023.
A pivotal moment in 2025: Opportunities and challenges with genAI
Like most new technologies, enterprise applications started at low-bearing fruit opportunities such as content summarization and productivity hacks using tools like OpenAI ChatGPT and Microsoft Copilot, largely driven by employees.
As organizations move to build on the excitement and opportunity of AI, the next question is how to harness its vast potential. MIT found in their report The GenAI Divide State of AI In Business 2025 that a majority of the early adopters struggled with ROI and that few projects reached actual production. This shouldn’t be a surprise, as technological breakthroughs need time to bed in, and the AI revolution brings much more than just a technological inflection point. It requires changes across the organization.
The ABBYY State of Intelligent Automation: GenAI Confessions 2025 report sheds light on some of the key components that must be part and parcel of a comprehensive AI strategy. Nearly a third of business leaders surveyed found training genAI models harder than expected, and that the tools were difficult to integrate into their business processes. Proper governance was raised by a fourth of the respondents, and the risk of model misuse by staff. Clear objectives, project oversight, cost control, compliance, and governance are important parts for successful implementation. Training staff is another one. Importantly, successful early adopters have found a combination of new AI technology and existing proven technologies to be a winning formula.
According to the study, significant improvements were achieved when adding other solutions to LLMs, including process intelligence, Document AI, and retrieval augmented generation (RAG).
ABBYY plus genAI yields stronger outcomes
We at ABBYY have made it our mission to empower enterprises to use and maximize the value of LLMs, when it makes sense. Our platform is flexible, supports compliance, and keeps costs under control using small language models (SLMs) that are purpose-built and optimized for specific complex business processes and unstructured documents. This hybrid approach is how we continue to challenge the status quo and get consistently recognized as a leader in Document AI.
Additionally, at the ABBYY AI Summits held globally throughout 2025 and at the ABBYY Ascend virtual events, we showcased success stories with customers and partners using Document AI and Process AI to solve real-world business challenges such as streamlining processes before implementing AI, improving accuracy of LLMs, accelerating KYC compliance, and mitigating AI model risks. Above all, we’ve provided clarity over the AI hype.
And we’re just getting started.
In this latest edition of The Intelligent Enterprise, we share practical tips on how you can leverage intelligent document processing (IDP) to improve LLM results within enterprise applications, such as invoice processing, and to enable agentic automation. We also recap our last stop of 2025 at the ABBYY AI Summit Singapore and take out our crystal ball on what we expect to happen with enterprise AI in 2026.
The year 2025 was one of transformation, as organizations experimented with and saw early success of genAI for personal productivity and improving customer engagements. We look forward to 2026 being the year for execution across the enterprise, where we’ll see more sophisticated deployments of LLMs that deliver measurable value.
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