Every year, the compliance technology market introduces more platforms, more features, and more certifications for practitioners to adopt and manage. For governance professionals, starting with GCs and Company Secretaries all the way governance managers and legal operators, it sometimes feels as f the tools designed to reduce their workload keep adding to it. The wave of Agentic AI washing rolling into SaaS this year is not making things any better.
This is not unique to entity management. Across regulated industries, the same pattern is playing out. In financial services, FINRA’s 2026 Regulatory Oversight Report found that AI adoption is outpacing governance frameworks, with firms deploying tools without the controls or supervision to manage them responsibly.
In healthcare, a CHIME Foundation survey found that while 84% of organisations have established AI governance committees, most lack the operational tools to govern effectively. The technology arrives faster than the infrastructure to make it useful.
Entity management systems and the reality of governance work
For governance practitioners, the pressure from this increased market noise is compounded by personal accountability, and how it come up against tools that promise completely automated, entirely AI-powered processes.
A missed filing or a compliance failure in a jurisdiction they manage carry real liability. The last thing they need is another grey area in their processes and audit trails generated by the introduction of AI for the sake of AI, without a true understanding of the reality of their role.
As we explored in Why It’s Time to Eliminate Spreadsheets for Entity Management, the old ways of working were unsustainable, yes, but replacing spreadsheets with technology that automates parts of the work but makes other parts more complicated does not solve the underlying problem.
AI agents and their potential applications in every business function almost never fail to provoke enthusiasm, and that is understandable. That said, the measure of good entity management AI software should not be how sophisticated or impressive it is. In our opinion, it should be how little the practitioner has to actively manage it.
Ask any operations professional: the best infrastructure is invisible. It works, it stays accurate, and it stays out of the way. That principle — which has shaped the evolution of the corporate secretary function over the past decade — should also shape what comes next.
From system of record to system of work
We built the Kube entity management platform on a few core principles:
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A foundational system of record has to come first.
A single source of truth for entity data that governance teams can rely on, without constantly checking, updating, or reconciling. That is the system of record. As we have written about in detail, why data architecture matters in entity management software is not an abstract technical question. It determines whether every downstream capability, from reporting to automation to AI, can actually be trusted.
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Then the intelligence layer can be built.
Data that sits in a system, however clean and well-structured, still requires practitioners to extract it, interpret it, act on it, and chase it across teams and jurisdictions. That is the gap where new technology can provide the most value, and augment what experienced governance professionals accomplish every day.
Entity management AI software should close that gap by simplifying and automating without requiring users to jump through new accreditation hoops or changing how they manage accountability and governance trails.
This is the shift from system of record to system of work.
It is also the idea at the heart of the “Salesforce moment” argument for legal entity data: entity data has been one of the last untapped data assets in the enterprise. The organisations that structure it properly and build intelligence on top of it will fundamentally change how governance is delivered.
What that looks like in practice
At Kuberno, we believe the application of AI-powered automation, or even AI agents further down the road, can’t be at the expense of a team’s clarity. And that’s reflected in the focus of our roadmap for the year to come:
Kube, and the AI layer underpinning it that we call Kaia, is being built along three main directions of growth; we translate those internally into three ‘pain buckets’. All the features and capabilities prioritised for next year’s roadmap fall under one of the three, as can be read below.
“I can never find what I need when I need it.” Our direction here is to make entity data and documents retrievable in seconds, through plain language queries, configurable reports, or filtered dashboards. No digging through shared drives, no chasing colleagues across departments. This builds on capabilities already live in Kube, including KAIA’s smarter document search and recent updates to grids, global search, and in-app notifications.
“I’m personally on the hook for compliance across dozens of jurisdictions.” This is the direction where most of our 2026 roadmap investment sits. The vision is compliance workflows handled end to end: from understanding the intent of a request, to compiling the data, to preparing and submitting filings to the relevant registrar. Human sign-off is built in at the decision points that matter. The admin gets handled; the practitioner’s expertise is applied where it counts. This is the practical application of what we described in legal entity governance automation: structuring oversight in a way that works every day, in every jurisdiction.
“I have all this data but no way to see what’s actually going on.” Our ambition for this bucket is a platform that flags regulatory changes before they become problems, verifies entity data against registry records across more than 190 jurisdictions, and surfaces gaps and inconsistencies before anyone else finds them. Compliance data becoming intelligence that feeds back into daily operations and continuous data quality. This is the bucket that carries the most weight for practitioners who live with the fear that something has slipped through the cracks.
AI operated, human controlled
Across every regulated industry, the same conclusion is emerging: human oversight is non-negotiable. A Moody’s study on AI in risk and compliance found that 42% of practitioners believe human oversight is mandatory, not optional, and that the prevailing model is evolving towards humans handling high-risk, complex decisions while automation takes on repetitive execution. Gallagher’s 2026 AI Adoption and Risk Survey found that personal accountability has overtaken legal and compliance functions as the primary ownership model for AI use — reflecting a growing recognition that the people using these tools need to carry out their own due diligence.
This is exactly the dynamic governance practitioners live with every day. They carry personal liability. They cannot hand control to a system they do not know inside out. As the DMJ and Kuberno survey on GenAI adoption in governance found, the profession is not resistant to technology, but it is deeply sceptical of hype. Practitioners want to see things work, not hear about them.
The right approach uses intelligent automation to handle the administrative burden (form completion, evidence gathering, filing submission, regulatory monitoring) while keeping professional judgement at the decision points that matter. That is what “AI operated, human controlled” means. Not AI doing everything. Technical experts, both the Kuberno team and the intelligent systems they have built, taking away the noise so governance practitioners can focus on the work that actually requires them.
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