Intelligent Signals: What Data Leaders Are Really Talking About in 2025

In the past month, I’ve had the opportunity to participate in three powerful events that each tackled data and AI from different angles: the Data Management Summit 2025, the FIX EMEA Trading Conference, and the GovTech Summit in London.

Together, they offered a 360-degree view of how institutions—financial and public sector alike—are adapting their strategies to turn information into action. What I heard across the board wasn’t just hype about GenAI or shiny new tools. It was something more grounded and more compelling: a real, systemic shift in how we treat data, not just as a compliance burden, but as a core asset for insight, value creation, and impact.

This blog isn’t a recap. It’s a reflection on the themes that stood out to me and how they align with the work we’re doing at ipushpull to help our clients navigate this transition.

1. Data and AI Are Strategic Now—Not Experimental

Across financial services and government alike, there’s a visible shift from isolated experiments to enterprise-scale thinking. It’s no longer enough to improve processes at the margins. The question is: what’s going to move the dial?

This means:

• Starting with outcomes, not technology

• Prioritising use cases that can scale and create repeatable value

• Ensuring continuity—picking champions who will still be around 12 months later

We’re seeing this in conversations with ipushpull clients too: a strong appetite for real-time delivery, automation, and embedded intelligence. Not just better tools—better workflows.

2. Governance Is Now a Growth Enabler

The most forward-thinking organisations are treating data and AI governance as an accelerator, not a constraint.

That means:

• Building layered frameworks (strategic, operational, technical)

• Ensuring transparent approval processes for use cases and vendors

• Embedding human-in-the-loop oversight where needed

• Establishing data controls, bias checks, and auditability from day one

This theme also came through at the GovTech Summit, where policymakers and regulators stressed the need for practical governance that enables innovation, not just checklists, but sector-specific, agile guardrails that don’t stifle experimentation.

3. From Data Swamps to Data Products

One theme that kept resurfacing was the idea of treating data as a product—well-documented, quality-assured, and reusable.

This mindset is helping teams:

• Reduce redundancy

• Build scalable pipelines

• Empower non-technical users through marketplaces and no-code access

• Surface “signal” from unstructured data using AI enrichment

It’s not just about centralising data—it’s about curating and activating it.

At ipushpull, this is exactly what we focus on: making high-value data discoverable, contextualised, and live, across any system or messaging platform our clients already use.

4. Intelligence That Moves at Market Speed

At the FIX EMEA Trading Conference, I joined a panel on The Future of Information and Research. A major theme was the changing nature of the distribution of trading and investment insights.

The takeaway? Latency kills value. Insights need to be:

• Real-time

• Embedded in execution platforms

• Tailored to specific personas and roles

It’s not just about producing information. It’s about delivering insight at the exact point of decision-making—whether that’s a trader responding to a market signal, or a client service team managing a relationship.

That’s what ipushpull enables: structured delivery of intelligence in real-time, across any channel.

5. AI That Delivers—Not Just Demos

There was a healthy level of realism across all three events about the limits of AI labs. Many organisations admitted they’ve built impressive prototypes that never scaled beyond their sandbox.

The common blockers?

• Lack of data readiness

• No governance model

• Talent gaps or turnover

• Disconnected from business outcomes

What’s working instead is a shift toward production-grade thinking: governance built in from the start, data that’s clean and traceable, and a clear line from use case to value.

6. It All Starts with (Good) Data

No surprise here, but it’s worth repeating: bad data breaks everything. Whether you’re training models, powering dashboards, or producing regulatory reports, if you don’t know where your data came from, who touched it, or how trustworthy it is, you’re flying blind.

Solutions discussed included:

• Data meshes

• Unified models

• Metadata-first design

• Investment in lineage and observability tools

At ipushpull, we’re increasingly helping clients tackle this head-on by making data delivery smarter, safer, and easier to track.

Final Thought: Turning Complexity Into Confidence

What connects the financial sector with government? A shared challenge: making data usable and trusted at scale.

What connects every successful AI initiative? Structured intelligence is delivered to the right people, at the right moment, and under the right controls.

That’s the future we’re building toward at ipushpull. Whether it’s in the front office, back office, or policy desk, we believe the most valuable insight is the one that arrives before you know you need it.

If you're working through these challenges too, or exploring how to make your data strategy more actionable, I'd love to continue the conversation.