Generative AI may have been the most transformative leap since the advent of the internet, reshaping how content is created, tasks are automated, and data is analysed.
A more profound shift, that many are already reading about, is now underway with Agentic AI.
Agentic AI goes beyond content generation. Agentic AI systems can pursue a defined goal, plan steps, take action, and adjust course in real time, much like a human would. This advancement will fundamentally redefine how people and organisations engage with data, services, and, increasingly, with each other.
The potential is enormous. Businesses across sectors are already exploring agentic AI to accelerate decisions, streamline operations, and unlock productivity. Yet, despite the enthusiasm, adoption is lagging. The limiting factor isn’t capability — it’s trust.
When enthusiasm meets uncertainty
According to IDC’s Understanding Agentic AI Technology Adoption in Asia/Pacific report, about seven out of 10 organisations in the region expect agentic AI to disrupt their business models in the next 18 months. Still, apprehension remains, largely because agentic systems act autonomously.
When sensitive data and critical decisions are involved, that autonomy can feel risky, especially when the rationale behind an AI-generated decision isn’t clear.
Historically, working with data has been a reactive process: building pipelines, running queries, and analysing dashboards. That manual approach offers transparency. In contrast, delegating such tasks to a system that doesn’t readily explain its logic creates what many leaders perceive as a “black box”, creating a confidence gap that’s hard to bridge.
What organisations still lack, and why governance is critical
Agentic AI transforms data interaction. Rather than reactively seeking insights, users gain conversational assistants that surface to them proactively, recommending next steps, and even executing complex workflows autonomously. This vision, however, depends on real-time access to reliable enterprise data—something many organisations still lack.
Without a proper data infrastructure, competing demands for the same resources can create bottlenecks, fragment workflows, and dilute value. The result: friction between the promise of Agentic AI and the reality of implementation.
- Sean Stauth, Global Director, AI and ML, Qlik
To scale safely and effectively, Agentic AI requires rigorous governance. Without it, even the most advanced systems can become unreliable — or worse, harmful. Trust, in this context, comes from explainability. Organisations must demand more than just answers; they need AI systems to show their work.
Opaque large language models that generate outputs without traceable logic aren’t sufficient in enterprise settings. Agentic systems must cite sources and offer source-level traceability to support auditability and accountability.
Additionally, process changes are inevitable. Realising long-term value requires more than regulatory compliance. The pace of innovation will outstrip policy, making self-regulation essential.
Data quality is non-negotiable for Agentic AI to work
No AI system can function effectively without high-quality data. Poor lineage, fragmented governance, and data silos undermine outcomes. Organisations must unify structured and unstructured data — often through lakehouse architectures — and apply consistent taxonomies and governance standards.
Take SAPPORO Holdings, for example. By implementing a real-time change data capture solution, the Japanese conglomerate was able to modernise its integration platform, achieving a 75 percent boost in planning cycles and 80 percent lower integration costs. The company was able to get real-time, trusted insights across departments, and agentic systems capable of context-aware, reasoned action.
Start with low-risk, high impact to show value and get buy-in
Deploying agentic AI isn’t a race — it’s a marathon. A phased approach, starting with low-risk, high-impact use cases, allows organisations to demonstrate value and build buy-in. Let real business needs lead and then apply the right AI tools to meet them, not the other way around.
Ultimately, Agentic AI should not replace human decision-making. Instead, it will augment it, enabling organisations to act faster and more strategically with trusted, real-time insights.
It is important to know that trust is the currency of scale with Agentic AI. Without confidence in how these systems reason, organisations will remain hesitant, even if the technology is ready. The winners in this next era of AI will be those who lead with governance, invest in data quality, and build explainable systems from the ground up.
Sean Stauth is Global Director, AI and ML, Qlik
