✅ Final thoughts: Deploying a custom GPT is not just plug-and-play. It involves data strategy, governance, change management, training users and ongoing monitoring. But done right, it becomes a strategic asset, not just a toy.

📈 Impacts & metrics to track: Reduction in support tickets, faster response times (ex: a deployment cut HR/IT tickets by ~42%). Improved employee productivity, higher utilisation of internal knowledge. ROI: fewer hours spent on routine tasks → focus on higher-value work.

🔐 Key risks & governance: Monitoring and versioning, preventing drift or misinformation. Security vulnerabilities: e.g., a recent study found > 95% of custom GPTs lacked adequate safeguards.

🔐 Key risks & governance: Data privacy and ensuring internal documents aren’t exposed. Model behaving according to brand and compliance rules.

Choose approach: • RAG (Retrieval-Augmented Generation) – embed your data, GPT retrieves at runtime. • Fine-tuning (if you have large structured dataset). Monday Labs Create instructions/prompts, test rigorously, monitor accuracy and bias.

How to build one (simplified steps): Define the goal: what tasks this GPT should handle. Matrik Gather and clean your data: docs, transcripts, policies. Matrik

📚 Why companies build them: Better accuracy & relevance when model knows your domain. Brand voice («sounding like your team») and internal knowledge-reuse. Automation of repetitive tasks (support, onboarding, summarising) that generic bots can't handle as well.

🔍 What is a "Custom GPT"? It’s a version of a GPT-model (for example from OpenAI) tailored for a specific business by feeding it proprietary documents, workflows, policies or tone.

🚀 Companies are increasingly deploying custom GPTs trained on internal data—transforming generic LLMs into domain-specific assistants for HR, sales, support and more. (Source: OpenAI Help & industry case studies)

3/ If you’re evaluating your data/AI architecture, consider how scalable your platform is for agentic workflows, not just analytics.

2/ For organizations: having the right data platform is no longer “nice to have” — it’s a strategic enabler for the kind of AI that actually acts.

1/ Snowflake is clearly betting that the next wave of enterprise AI will involve agentic systems — AI agents that act, reason, and integrate deeply with business workflows.

Snowflake launches a suite of new developer tools to supercharge enterprise-grade agentic AI development, positioning itself as a key platform player in the AI data stack. (Snowflake Press Release)

5/🌍 Regulators urge stronger transparency rules and human review for automated financial decisions affecting millions of EU citizens.

4/📉 The gap exposes consumers to bias, discrimination, and opaque decision-making — eroding trust in digital finance.

3/⚖️ While the EU AI Act classifies many financial tools as high-risk, several retail-finance use cases still escape strict oversight.

2/🚫 Some algorithms flag customers as “risky” and automatically close or restrict accounts, often without explanation or appeal.

1/🧵 AI is no longer a future concept in finance — it’s shaping everyday retail-banking and insurance decisions across the EU right now.

AI Risks for EU Retail Finance Consumers 💳AI systems in EU retail finance are already triggering real consumer harm — from unexplained account freezes to biased credit scoring. (EU Observer) https://t.co/I1kkXw6QXt

4/🧵 Bottom line: Hype is fading—businesses now need measurable outcomes, not just pilots