In today’s Tech Pulse, gain insight into how:

• Organizations are shifting from traditional risk management toward AI-powered systems of governance and trust to enhance transparency and resilience.

• A strategic overhaul of comprehensive data lineage practices can provide the clarity and end-to-end visibility to drive ethical and effective AI governance.

• Indirect tax departments can adopt GenAI by embedding robust AI governance frameworks, prioritizing transparency, and defining accountability.

Each of these articles is penned by members of Forbes Technology Council, key luminaries shaping the future of technology leadership.

Grab your coffee, and let's dive in!

AI-Driven Risk Management: From Reactive Records to Resilient Trust Systems

As AI transforms businesses, traditional risk management models relying solely on reactive “systems of record” are becoming increasingly outdated.

Organizations now embrace proactive governance strategies, leveraging advanced analytics and machine learning to predict and prevent risks in real time. This shift changes risk management, embedding ethics and trust into operational decisions.

Here's what to consider as you modernize your risk approach:

🔍 Domain-Specific Intelligence: Use specialized AI models tailored for your industry, such as BloombergGPT for financial insights or sector-specific LLMs, to deliver precise, actionable risk assessments.

🔄 Dynamic, Adaptive Controls: Replace static controls with AI-supported continuous feedback loops, real-time anomaly detection, and evolving cybersecurity protocols to keep pace with rapidly shifting threats.

📊 Transparent, Data-Driven Decisions: Adopt predictive analytics, scenario simulations, and transparent reporting for proactive decision-making, aligning actions with corporate ethics and regulatory requirements.

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Why Traditional Data Lineage Won't Cut It for AI Governance

As companies lean into AI, traditional data lineage methods, tracking information only at basic table-and-column levels, can’t deliver the transparency and context necessary today. Effective AI governance calls for an overhaul toward a comprehensive, end-to-end data lineage strategy, improving compliance, trust, and innovation capabilities.

To effectively manage your AI risk, adopt these four pillars in your data lineage approach:

🌐 Ecosystem Context: Trace data flows across integrated applications, APIs, and external sources for improved compliance and rapid issue resolution.

💡 Business Logic & Context: Clearly document the rationale behind data usage, bridging technical tracking with real business decisions and enhancing regulatory defense and transparency.

📈 Consumption Visibility: Map out precisely how AI models leverage data and influence outcomes—essential for ethical AI usage, accountability, and understanding decision pathways.

⚖️ Compliance & Governance Integration: Integrate proactive governance into data processes, turning compliance from a bottleneck into a strategic accelerator.

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How AI Governance Will Define the Future of Tax Technology

GenAI presents enormous potential for indirect tax departments, automating tasks, speeding analysis, and enhancing productivity. But the stakes are high—a clear framework for AI governance is essential to responsibly leverage AI while protecting sensitive data, adhering to regulations, and maintaining client trust.

Here's how your tax organization can responsibly adopt GenAI:

👥 Human Oversight Is Essential: Employ a human-in-the-loop model, verifying AI-generated outputs for compliance, accuracy, and appropriateness, ensuring ethical use and accountability.

⚖️ Actively Address & Minimize Bias: Choose transparent AI vendors, regularly audit AI recommendations, and ensure underlying data is current, accurate, and comprehensive.

🔍 Transparency Boosts Trust: Clearly communicate how AI is used in tax procedures. Share how AI contributes to specific outcomes transparently, maintaining client confidentiality and industry standards.

📜 Define Responsibility Clearly: Develop explicit guidelines within your organization on AI liability, ensuring employees understand responsibilities, risks, usage rules, and institutional policies.

🚦 Know AI Limitations & Capabilities: Explore AI tools within safe "sandbox" environments first, clearly delineating what AI can and cannot do reliably. Stay agile as these tools advance rapidly.

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Wrapping Up

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