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

• Feeding AI with real-world knowledge can transform it from a static tool into a problem-solving partner for enterprise service teams.

• Data observability ensures trustworthy, scalable AI systems by proactively monitoring issues like data drift, bias, and pipeline health.

• Explainable AI (XAI) is redefining compliance in banking, increasing transparency, reducing risk, and building trust with stakeholders.

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!

Train Your AI Like You Train Your Team

AI holds incredible promise for service organizations. It can predict failures, boost efficiency, and deliver data-driven insights.

But there's a catch: AI is only as smart as the knowledge it's trained on. Too often, teams expect AI magic while feeding it outdated manuals and static SOPs. To actually unlock its potential, you have to train AI on what's happening in the field.

Here's how to get started:

🛠 Capture Real-Time Field Interactions: Gather videos, photos, and voice notes that reflect real-world fixes, not just textbook solutions.

📂 Organize Unstructured Knowledge: Use tagging and search tools to prevent insights from getting lost in Slack threads or buried in PDFs.

🔄 Give it a Steady Data Diet: Regularly update AI with fresh, relevant, and practical information from the front lines.

Read The Full Article >

The Case For Trustworthy AI: Data Observability

AI models are only as reliable as their data. Yet, a recent BARC study revealed that over 40% of organizations lack trust in their AI inputs and outputs. Data observability, proactively monitoring the health of your data pipelines, holds the key to building resilient AI systems that businesses can trust.

Here’s what effective data observability looks like:

⏳ Freshness: Ensure data is up-to-date and available when needed to avoid pipeline delays.

📊 Distribution: Watch for unexpected data anomalies or outliers caused by errors or drift.

📈 Volume: Significant changes in data volume, like missing records, signal errors upstream.

🗃 Schema: Monitor for unplanned structural changes that could break systems downstream.

🔍 Lineage: Track who changed what and when—end-to-end transparency builds accountability.

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From Black Box To Glass Box: Making AI Transparent For Banking

AI’s potential for reshaping banking is immense, but transparency remains a critical hurdle, particularly in compliance, risk management, and data privacy. Generative AI models can produce unpredictable outcomes, and without clear explainability, banks face significant regulatory and ethical risks.

The solution? A shift toward explainable AI (XAI) systems and responsible governance.

Here’s how banks can navigate these challenges effectively:

📜 Engage Compliance Teams Early: Involve legal and compliance experts during AI system design, not just post-deployment.

🗂 Establish Governance Frameworks: Create clear roles, validation standards, and accountability mechanisms, such as AI risk committees.

🔍 Use Interpretable Models: Opt for simpler models for high-stakes decisions or leverage tools like SHAP and LIME for complex systems.

📈 Continuously Monitor Performance: Track models for bias, drift, ethical outcomes, and regulatory compliance using robust monitoring tools.

⚖️ Conduct Regular Fairness Audits: Incorporate antidiscrimination reviews and ethical oversight to ensure compliance.

Read The Full Article >

Wrapping Up

If these articles sparked your interest, we have a network that you will absolutely love: Forbes Technology Council.

This exclusive, vetted community brings together the brightest minds in technology — founders, CEOs, CIOs, CTOs, CISOs, and other leaders of technology-focused teams.

Put yourself at the forefront of innovation with access to publishing opportunities on Forbes.com, a personalized, SEO-friendly Executive Profile, and the chance to network with other respected leaders in the field.

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