CMO Digest
Why AI Isn’t Delivering for B2B Financial Marketers - And What You Can Do About It
In this article, Financial Marketing Insights' Founder, Jacob Howard, reflects on a recent blog by Oracle and discusses how B2B Financial Services Marketers can get ahead with AI.
AI as a concept promises a lot to B2B financial marketers - faster content creation, better targeting, smarter personalization, and predictive insights. But in practice, many marketers find themselves stuck between ambition and infrastructure. Inspired by a recent blog by Oracle, Why Your AI Solutions May Not Be Working as Expected, here I'll offer 5 thoughts on why the promise of AI might not be meeting expectations - and how marketers can still make progress even within complex, siloed organizations.
1. Focus on What You Can Control
In large financial institutions, marketers rarely own the full data ecosystem. But you likely have access to key platforms - the CRM, email, web content, push notifications, and social media. Start by identifying AI use cases that live within those systems. For example:
Using in-built AI systems to predict email engagement based on past campaign behaviour
AI to research and personalise web content for known and unknown client segments
Using AI to optimize and test social media timing, formats and messaging
These are achievable, high-impact areas that don’t require enterprise-wide integration.
2. Work with the Data You Have - But Make It Smarter
You may not be able to unify every data source, but you can improve the quality and consistency of the data you do control. That means:
Cleaning and tagging data & CRM records for better segmentation
Standardizing campaign metadata across platforms
Ensuring consistent naming conventions and taxonomy in your CMS
Even small improvements in data hygiene can dramatically improve performance.
3. Collaborate Across Silos - Strategically
You might not be able to break down every wall, but you can build bridges. Partner with sales, IT, or compliance teams to identify shared goals - like improving client retention or onboarding. These collaborations can unlock access to additional data or systems without requiring full integration.
Frame AI initiatives as pilots or proofs of concept that support broader business objectives. That often makes it easier to get buy-in and resources.
4. Make AI Output Actionable for Your Channels
AI is only useful if it leads to action. In B2B financial marketing, that means translating insights into campaigns, content, and client communications. For example:
Use predictive scoring to prioritize leads for nurture
Feed AI-driven insights into email personalisation
Surface relevant content based on client behavior patterns
Research and experiment easily and trial new approaches
Make sure outputs are clear, usable, and aligned with your existing workflows and business goals.
5. Audit and Adjust - Continuously
AI models can drift or optimize for the wrong outcomes. Regularly review performance and look for unintended consequences. For example, is your model favouring one product line too heavily? Is your lead scoring missing key client types?
Even if you’re working within constraints, you can still refine and improve over time.
Final Thought
AI in B2B financial marketing isn’t about having perfect data or full control - it’s about making smart, strategic moves within your sphere of influence. Oracle’s insights remind us that success starts with clarity, collaboration, and continuous improvement.
You don’t need to overhaul your entire tech stack to get value from AI. You just need to start where you are - and build from there.