Why Governance Is The Make-Or-Break Foundation For AI Agent Success

Forbes

15-07-2025

As Domo's chief design officer and futurist, Chris Willis writes on AI and combining data, technology and emerging trends in innovative.
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Imagine your company just deployed an AI agent to optimize marketing budget allocation. Without a preamble, the agent redirects 70% of your annual budget to one new TikTok influencer—a decision based on a pattern it detected in your historical data.
The problem? That pattern was built on misclassified transactions and was completely nonexistent. By the time a human notices, millions have been committed to a strategy built on faulty conclusions.
This scenario isn't science fiction. It's the reality businesses face when implementing AI agents without proper security and governance frameworks. Cloudera research shows that two of the top three challenges for managing AI's data landscape today are data security and reliability (66%) and compliance and governance issues (38%).
Governance isn't just a nice-to-have; it's what determines whether AI creates value or destroys it. Deloitte found that "organisations with more mature AI governance frameworks report a 28% increase in staff using AI solutions and experience nearly 5% higher revenue growth."
In my previous article, I discussed how conversational AI will kill the dashboard, creating a future where business insights emerge through natural dialogue with AI agents. But for them to be trusted with business-critical decisions, robust governance is essential.
Why Governance Must Precede Agent Creation
The fundamental mistake many organizations make is planning for AI agents as if they were traditional analytics tools. They aren't. Traditional dashboards are passive displays requiring human interpretation. AI agents independently analyze information, make recommendations and increasingly execute decisions without continuous human oversight.
This autonomy creates entirely new stakes when AI agents allocate resources, interact with customers or make operational decisions. Without proper guardrails, the consequences can be severe.
Consider two contrasting approaches to automation implementation at a couple of major retail giants.
The first retailer approached AI pricing and inventory management with a strong governance foundation. The CEO described implementation as using technology to increase speed without compromising quality or supply chain standards. The company's AI-powered inventory system carefully validates data quality before making decisions, showing the thinking behind its recommendations.
In contrast, the second retailer rushed the implementation of its automated inventory and pricing systems, failing to use proper data governance. Its systems were plagued with data errors—product dimensions entered incorrectly, wrong currencies used and incomplete information—leading to empty shelves, inventory chaos and ultimately a multi-billion-dollar failure within two years.
The difference is striking: The first retailer's governed approach has allowed them to steadily expand their AI capabilities across operations while maintaining customer trust. The second retailer's governance failures created a customer experience disaster that severely damaged their brand.
Building Your Agent Governance Framework
Organizations building effective AI agents follow a governed path to success. Rather than viewing governance as red tape, they recognize it as the framework that enables sustainable advancement.
Start by mapping decision types and associated risks. Categorize decisions based on business impact and reversibility. Low-impact, easily reversed decisions may require minimal oversight, while high-impact, difficult-to-reverse decisions demand rigorous controls and a human in the loop.
Next, establish clear data quality protocols. An AI agent is only as good as the data it interprets. Implementing verification processes and data lineage tracking ensures your agent builds intelligence on reliable information.
Build transparency and explainability checkpoints into your agent workflows, for instance, requiring the system to surface its top three data sources and confidence level before any decision above $10,000. "Black box" decision-making is incompatible with responsible business practices. Your governance framework should mandate that agents can articulate their reasoning, provide confidence scores and maintain audit trails.
Create tiered approval systems for different decision types, and implement continuous monitoring processes. Governance is an ongoing discipline that evolves with your AI agent's capabilities and business needs.
The Competitive Advantage Of Governed AI Agents
Far from impeding innovation, thoughtful agent governance creates measurable competitive advantages.
First, just as the Deloitte findings I discussed earlier point out, governance increases trust in AI, which means people use it more frequently and follow its guidance more consistently.
Second, organizations that invest upfront in governance frameworks typically deploy AI capabilities faster. When governance is baked into development, companies avoid the repeated delays caused by discovering data quality issues, compliance concerns and stakeholder objections mid-project.
Third, strong governance enables organizations to identify potential issues before they manifest. Beyond preventing costly mistakes, a proactive approach safeguards against regulatory penalties and reputation damage while freeing resources that would otherwise be spent addressing compliance issues and stakeholder concerns. The time saved can be redirected toward innovation rather than remediation.
The Path Forward
Approaching governance as a bureaucratic obstacle is outdated thinking. With laws like the EU's AI Act and others on the horizon, embracing governance as a function helps companies insulate themselves from regulatory hurdles that are sure to come and makes truly beneficial AI agents possible. Begin by thinking across short-, medium- and long-term initiatives, and then audit your three highest-value automated decisions or establish a cross-functional AI oversight committee.
The most powerful question you can ask before deploying an AI agent isn't 'What can our AI agent do?' but 'How do we ensure our AI agent creates sustainable value?" As the next generation of business intelligence emerges through conversational AI agents, the governance frameworks established today will determine which organizations thrive and which struggle.
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