The potential of AI is unlimited. It continues to alter our behavior and realign our expectations in both our professional and personal lives. In this blog post, we explore five AI trends that will shape the technology landscape in 2026. DocuWare Chief Product & Technology Officer Michael Bochmann also shares his insights on how these advancements will impact businesses in the coming year.
“Outcome and measurable value are the key differentiators that will affect which AI technology will be at the forefront this year. Companies are not buying AI as a technology — they are buying the results it delivers,” Bochmann explains. "In the software industry, the emphasis has shifted from adding features to achieving clear business outcomes and ROI, demonstrating that the value generated by AI justifies its cost.”
Table of Contents
1. Cloud-native and API-first software
Although creating applications appears easy and accessible from a front-end perspective, the reality behind the scenes is far more complex. The back-end landscape is often fragmented and chaotic, which creates complexity and integration challenges. This gap between what seems simple and the development effort that is needed is an important consideration when planning a product strategy.
“Cloud-native software, like DocuWare, is evolving into a modular, API-first platform that scales flexibly and adapts to changing business demands,” Bochmann says. “An open, API-driven architecture enables DocuWare to connect seamlessly with AI workflows, analytics tools and industry platforms, supporting enterprise-wide digital workflows.”
An application programming interface (API) is a set of rules that allows different software systems to communicate. The connection acts as a service agreement that defines how communication will take place through requests and responses when software “calls” the API and asks it to perform a specific function. Developers use APIs to take advantage of pre-built features and tools, saving time and eliminating duplicate work.
2. Generative AI-driven for business process automation
Generative AI (GenAI) is a hot topic these days. This technology includes apps that can create text, images and sounds. However, it’s not magic — GenAI needs to be trained on a large volume of data to work successfully.
With GenAI, you can receive detailed answers to tough questions, quickly summarize large amounts of information and automate tasks that previously required human effort. Gen AI models learn from the data they’re fed. Once trained, a model can spot trends, predict outcomes, make decisions and generate new content.
Some models are trained on massive data sets and learn to grasp the complexities of human language. These are called Large Language Models (LLMs) and power general-purpose AI software like ChatGPT.
AI-based intelligent document processing (IDP)
As companies digitalize operations, outdated capture technologies are a hurdle. This is particularly apparent when it comes to extracting information from unstructured documents and diverse data formats. Examples of unstructured data include things like scans or PDFs of invoices, delivery notes, contracts or handwritten forms.
“AI-driven intelligent document processing has evolved from basic text recognition to true document understanding, enabling context-aware interpretation of both structured and unstructured data within integrated workflows,” Bochmann explains.
DocuWare IDP combines machine learning, natural language processing and the latest in deep OCR technology. Deep OCR uses deep learning and neural networks to improve text recognition accuracy, even with unfamiliar fonts, varying sizes and unusual layouts. Additionally, computer vision algorithms are trained using large amounts of data to identify patterns and determine connections between information. For example, a basic algorithm can learn to tell the difference between invoices and delivery notes.
Examples of DocuWare IDP in action
Law firms
DocuWare IDP helps law firms streamline contract reviews and quickly summarize court documents and legal briefs. The software automatically extracts key details and sends them straight to your document or case management system.
IDP can:
- Quickly create contract summaries for review.
- Pull out important information and set it as metadata, making searches much easier.
- Speed up e-discovery by finding and organizing relevant documents.
Contract management
Many organizations still store contracts in spreadsheets or shared folders without an effective way to track deadlines, renewal dates or contract details. DocuWare IDP automates contract review, ensuring you never miss a renewal again.
Once contracts are classified and indexed, monitoring them is a breeze. Need to see which supplier agreements are ending soon or check if a certain clause is included? DocuWare IDP helps you find those answers quickly.
IDP can:
- Spot incoming contracts.
- Pull out key details, such as who’s involved, important dates, terms and clauses — which makes searching simple.
- Kick off automated tasks such as sending contracts for legal review or electronic signing.
Implementing DocuWare IDP quickly demonstrates the value of an AI investment, whereas other AI initiatives require more time. The software can be purchased as a standalone product or as part of the overall DocuWare system.
3. Agentic AI works independently, sets objectives and adapts as necessary
“In document-centric environments, agentic AI refers to systems that autonomously manage document flows, set goals, interpret context and adapt processing steps dynamically, beyond rule-based automation or prompt-driven interactions,” Bochmann says.
Agentic AI can retain information, learn from past experiences and connect with external tools and data to manage complex workflows. It also suggests content. For example, the way streaming platforms recommends your next watch by using the latest information on your previous likes and dislikes.
By retaining contextual knowledge, user preferences and historical processing data, agentic AI can recommend document-related content, information or next processing steps — applying personalization principles to enterprise document and data workflows.
4. Robust data security and privacy through AI-powered compliance
As regulatory requirements and data risks continue to increase, robust data security and privacy will become core capabilities of modern document management platforms in 2026. AI-powered compliance features will continuously monitor documents and workflows in real time, automatically checking for adherence to regulations such as GDPR, HIPAA and emerging AI governance laws — without slowing down business processes.
Smarter classification and adaptive controls
AI-driven document management systems use machine learning to accurately classify sensitive data, such as personal information or financial records. This enables the automatic application of retention policies, real-time risk detection and dynamic access controls that adapt based on user role and context. Instead of relying on employees to manually follow policies, these systems enforce compliance by design, reducing human error and accelerating remediation when risks arise.
Zero trust security across the document lifecycle
Security frameworks are increasingly built on Zero Trust principles, combining advanced encryption, digital rights management and granular access controls. These measures ensure that sensitive information remains protected throughout its lifecycle. They provide organizations with full visibility and auditability, giving them confidence that their documents are secure — wherever and whenever they are accessed.
5. Intelligent collaboration with human-in-the-loop automation
In 2026, working smarter will become a defining principle of digital transformation, fueled by intelligent collaboration and human-in-the-loop automation. As organizations operate within increasingly connected ecosystems of CRM, ERP, business applications and communication platforms, seamless integration with document management systems will be essential. This allows teams to collaborate efficiently in real time while maintaining full control and compliance through features like co-editing, parallel reviews or approval workflows.
AI handles the routine, humans drive the exceptions
AI-driven workflows take over routine tasks such as document classification, routing and data extraction. When exceptions, risks or policy thresholds are detected, the workflow automatically involves the appropriate team member for review or approval.
Collaboration embedded in workflows
Collaboration occurs within the document and the process itself, rather than across disconnected tools, ensuring that all stakeholders are aligned and informed. This improves coordination between departments, reduces rework and ensures that every action is traceable and auditable.
Human-in-the-loop automation
The true value of this type of automation lies in continuous learning and feedback. AI models improve over time through human input. Organizations benefit from faster decision-making, high-quality outcomes and greater confidence in complex processes. The result? Experts remain the pilots, while AI acts as a copilot, amplifying human intelligence through data, integration and automation.
Embracing AI: the path forward for business success
As we look ahead to 2026, the undeniable impact of AI on technology and business becomes clear. From smarter automation to agentic systems and enhanced security, these advancements will transform how organizations work and innovate.
In this rapidly evolving landscape, embracing AI as a catalyst for measurable outcomes, rather than just a technology, will be key to staying ahead. The future isn’t about what AI can do, it’s about what we can achieve together with it.