Insights from the A-Team Group’s AI in Capital Markets Summit

The inaugural AI in Capital Markets Summit, hosted by A-Team Insight, from A-Team Group, delivered an excellent day of dialogue, strategy, and pragmatic innovation at the intersection of artificial intelligence and financial services. Industry thought leaders, technologists, and data practitioners convened to share real-world insights on what’s working, what’s challenging, and what’s next. The following outlines several key themes that emerged during the summit.

Agentic AI, The Next Evolution in Financial Intelligence

Experts explored how AI agents can plan and act on behalf of users across workflows. Unlike simple automation, agentic AI drives toward objectives, from investment research to post-trade processing.

Key insights:

  • Agentic AI transforms AI from a passive assistant to an active agent with autonomy in workflow execution.

  • The success of agentic systems requires human-in-the-loop oversight, especially in high-risk domains like client communication or trading.

  • Firms must define ownership models for AI, is it owned by IT, Investments, or Ops? The answer depends on each firm’s culture and strategic alignment. It is vital to start early and embrace a phased rollout with risk-managed experimentation.

From GenAI to POC and Production, Realizing Business Value

This session focused on how to move from experimentation to enterprise deployment:

  • Proof of Concept (POC) does not equal Production – Many AI efforts stall at POC due to gaps in data quality, integration, and governance.

  • Business impact is greater than ROI alone – Strategic outcomes like risk reduction, customer satisfaction, and faster decision-making matter as much as direct returns.

  • Define key metrics and operational KPIs (complaints, inquiry volume, usage) as measurements that demonstrate ROI. “What breaks first?” The data. When data governance isn’t mature, production AI becomes unreliable.

Data Architecture as the Foundation for Trusted AI

The sessions all reinforced a critical theme that AI is only as strong as the data it rests on.

  • Data must be treated as a product, with strong ownership, governance, lineage, and quality controls.

  • A semantic layer and metadata-rich catalogs are essential for AI to understand and operate across complex financial data landscapes.

  • Emerging best practices now include using AI to govern and validate the data itself, a virtuous feedback loop.

AI Integration into Legacy Systems and Workflows

Deploying AI in legacy environments remains a significant challenge. Key takeaways include:

  • Utilize abstraction layers (APIs) to bridge the gap between legacy and modern architecture.

  • Consider « Buy vs Build vs Compose » for AI tools where vendor solutions offer commodity value, focus internal resources on bespoke, high-impact applications.

  • Agentic systems interacting with other internal and external agents raise new questions around workflow governance, permissions, and auditability.

Surveillance, Risk, and AI Governance

AI’s role in holistic surveillance was a hot topic, unifying fragmented signals across trading, communications, and cyber.

  • Effective surveillance depends on interlinked, high-quality data with full historical context.

  • AI helps mitigate alert fatigue by reducing false positives and surfacing only high-value signals. But it also raises new questions: Who owns the data? Who governs agent behavior? How do we ensure explainability?

Real-World Use Cases and Agentic Applications

One of the most compelling sessions showed agentic AI in action:

  • A post-trade settlement agent could autonomously identify root causes for delays and recommend actions for a 15% efficiency improvement, backed by structured + unstructured data integration and full lineage traceability.

  • Fine-grained data classification (down to paragraph or sentence level) is a must to protect confidentiality and ensure precise permissions.

Enterprise AI Strategy and the Microsoft Vision

In the closing keynote, Microsoft shared a vision of a fully-agentic enterprise future with three phases of maturity:

  1. AI as an Assistant

  2. AI as a Team Member

  3. AI as an Operator of Workflows

  • Microsoft indicated that it has already saved $400M+ in support costs and is driving AI adoption across product development, customer service, and marketing.

  • AI skilling is essential for emerging roles that include AI trainers, ROI analysts, data/agent specialists, and AI governance strategists.

  • Enterprise architecture and process reengineering are now essential for AI transformation.

Final Thoughts

The AI in Capital Markets Summit showcased the financial industry’s rapid evolution, not just in technology, but in mindset. From regulatory-grade data governance to production-grade agentic intelligence, we’re moving beyond hype and toward measurable, scalable impact.

In addition, kudos to the A-Team and element22 on their new collaboration, A-Team Research, which delivers research insights and benchmarking across data, analytics, and AI.

Explore more here: https://lnkd.in/eNyk2kMu.

If you’re designing your firm’s AI roadmap, the time to start is now, but start with intention, trust in your data, and humans in the loop.

Learn more about Meradia’s innovation within our Data practice: https://meradia.com/data-technology-management/

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