Industry executives and experts share their predictions for 2026. Read them in this 18th annual VMblog.com series exclusive.
By Mike Bachman, Head of Research and Emerging Technology, and Mani Gill, SVP, Product, at Boomi
The rapid evolution of intelligent automation and AI-driven systems brings both exciting opportunities and new challenges. As we look ahead, key trends are set to define the landscape, including an increase in agentic protocols, the resurgence of decentralized ledgers, the critical role of data liquidity, and the growing demand for explainable data lineage. For organizations, these will be key 2026 priorities, as they highlight the pressing need of increased security, transparency, and trust to thrive in an automated world.
Agentic protocols are a security and bloat ticking timebomb. By 2026, the rapid rise of autonomous agentic protocols is set to move from the fringes to the mainstream, creating a dramatically fast-growing and complex attack surface across enterprise operations. This rush to adopt autonomous technologies without robust oversight is leading to “security blindspots,” multiplying potential entry points for cyberattacks as agents dynamically connect and delegate without constant human supervision. Additionally, tool bloat will cause agents to be overwhelmed with choice when selecting the right tools to respond to a prompt. Current governance frameworks are lagging, which means accountability suffers, as tracing the origin of incidents in the complex web of machine decisions is almost impossible. IT leaders who ignore these looming architectural challenges risk opening their organizations to serious harm and being blindsided by costly breaches and compliance failures.
To manage this, we will see the return of decentralized ledgers as the audit trail for the AI economy. The blockchain is meant for a cause far greater than cryptocurrency and NFTs. The value of decentralized ledgers will be realized between agentic systems. As AI agents increasingly handle complex enterprise operations, the need for trustworthy, real-time auditing of machine-to-machine transactions will become critical, as traditional logs are too slow and fragmented. The solution lies in the adoption of private, verifiable decentralized ledgers, serving not for currency, but as immutable records of every action taken by AI systems across workflows. These ledgers provide the necessary hard proof against data manipulation, ensuring accountability and making regulatory compliance possible in highly automated environments. Further, smart contracts will act as automated digital guardrails, enforcing business rules like access control by embedding policy directly into the agent-to-agent transaction process.
As such, explainable data lineage will become a business imperative. In 2026, data governance will evolve from a compliance requirement to a source of competitive advantage. As AI becomes embedded in daily operations, executives will need to show not just what decisions were made, but how the data behind them was used and transformed. Metadata will make that possible. Continuous lineage tracking from Agent to source will evolve into explainable lineage, where every step in the data’s journey can be visualized and understood in plain language. Companies that can explain their data will build more trust with regulators, customers, and employees. In a world driven by intelligent automation, transparency will be the new standard for accountability.
Data liquidity will become the currency of intelligent automation. The companies leading the AI race will be the ones that can move data easily and trust it completely. Speed alone won’t be enough. Information has to flow with clarity and control. Metadata will play a central role in this transformation. It will act as a guide, carrying the instructions and permissions that tell systems how to use data responsibly. True data liquidity means more than fast movement. It means movement with meaning. Organizations that make metadata part of their integration and automation fabric will gain the agility to connect data, applications, and AI agents without losing oversight or accuracy.
As we move into the coming year, the path to successful intelligent automation will hinge on strong governance, clear accountability, and the ability to integrate data seamlessly. Organizations that tackle challenges such as agentic security, decentralized auditing, and data clarity will be better positioned to unlock the full potential of AI while building trust and resilience in a rapidly evolving digital landscape in 2026 and beyond.
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ABOUT THE AUTHOR
Mike Bachman
Mike Bachman, affectionately dubbed “Bachman” by peers, is Head of Architecture and AI Strategy for the Boomi Technology Office. With over two decades of expertise, he has assisted executives and their teams in overcoming complex obstacles through innovative tech solutions. Bachman’s background encompasses AI R&D, data pipeline development, infrastructure planning, application integration, performance analysis, and the exploration of nascent technologies.
Mani Gill
Mani Gill is a software executive and innovator with a passion for driving employee and customer success, pioneering solutions that allows customers to capitalize on the opportunity of big data and analytics. Mani has served as the General Manager responsible at SAP and Business Objects for the flagship Business Intelligence and Enterprise Information Management solutions. He successfully introduced numerous new business models, and emerging technology concepts including creating and launching the first business intelligence SaaS solution in 2006. Excels at scaling businesses leveraging partner networks and driving the acquisition and integration of strategic software companies.
