AI Agents Become the New Enterprise “User” and They’ll Transform How Companies Automate
Enterprise software won’t just serve humans, it will also support a new class of users: AI agents acting on behalf of teams, partners, and customers. These agents will configure, trigger, and monitor automations with minimal human involvement, shifting organizations toward a true “set it and forget it” model. As companies deploy their own agents to boost productivity and customer experience, platforms like Adeptia will become the behind-the-scenes infrastructure these digital workers rely on to execute data tasks instantly and accurately.
First-Mile Data Becomes AI’s New Power Source in 2026
Enterprises will realize that AI’s real leverage point isn’t the model—it’s the First-Mile Data flowing into it: the messy, inconsistent information arriving from customers, partners, brokers, and legacy systems. As this scattered data becomes the biggest obstacle to automation and AI accuracy, organizations will shift attention upstream. The priority will be normalizing and enriching incoming data before it hits AI workflows. And companies that get it right will see faster operations, more dependable AI outputs, and a dramatically smoother path to true AI-driven transformation.
Enterprises Hit Pause on Legacy Migrations and Unlock Value Through AI-Ready Integrations
Enterprises will rethink costly “lift-and-shift” migrations and instead focus on modernizing legacy systems through smarter integration layers. As AI-native tools collide with non-native, decades-old systems and processes, the industry will realize that modernization doesn’t require ripping anything out. By wrapping legacy platforms with APIs and AI-ready connectors, organizations can expose hard-to-reach data, preserve proven business logic, and experiment with AI and automation faster, bridging the gap between legacy reliability and next-generation intelligence.
Industry-Specific AI and Vertical SaaS Will Outpace Horizontal Platforms in 2026
Enterprises will accelerate their shift away from broad, horizontal SaaS tools toward verticalized platforms and AI models built for the nuances of their industry. These specialized systems will deliver smarter automation, faster performance, and lower compute costs by focusing on tightly defined data patterns and workflows, far outperforming one-size-fits-all solutions. As proprietary data and domain-specific intelligence become competitive differentiators, companies will favor platforms that understand their business out of the box rather than forcing customization on generic tools.
Insurers Will Replace Traditional iPaaS Tools with Intelligent Data Automation
The era of simple data movement is ending. Across health, life, and other insurance lines, insurers will recognize that integration without intelligence can’t keep up with the complexity of eligibility rules, plan variations, and regulatory demands. Rather than relying on traditional iPaaS tools that simply shuttle files around, insurers will shift toward platforms that can understand, validate, and act on data in real time—reducing rework, compliance risk, and IT dependency while strengthening the entire enrollment and policy administration lifecycle.
Enrollment Accuracy Will Become an AI Benchmark for Group Health Providers
As AI becomes embedded across healthcare operations, enrollment data quality will emerge as a defining benchmark for automation success. Group health providers will begin treating “Enrollment Data Accuracy” as a formal KPI, on par with reconciliation accuracy in finance or defect rates in manufacturing. Organizations that continue relying on legacy file transfers and script-based oversight will see costs spike as data complexity grows, while those who adopt intelligent automation will set the standard for operational resilience, compliance, and AI-ready infrastructure.
Financial Institutions Will Turn to Hybrid Data Fabric Platforms to Modernize Integration Without Compromising Control
Financial institutions will increasingly adopt hybrid data fabric architectures as the new foundation for integration and automation. Rather than pushing all data into the cloud, an approach often at odds with regulatory, sovereignty, and security requirements, these platforms create a virtualized, intelligent layer that unifies on-prem and cloud data without physically relocating it. This shift will enable financial institutions to automate complex data flows, apply AI-driven intelligence, and accelerate digital transformation while maintaining tight governance over their most sensitive information.
