How Can Banks Build AI Assistants That Last Beyond Copilot and Other External Solutions?

The conversation around AI assistants in banking has shifted. It’s no longer about whether to use them, but which kind of solution to rely on. Should banks embrace ready-made tools like Microsoft Copilot and ChatGPT, or invest in building their own internal platforms?
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At first glance, the off-the-shelf option looks irresistible. Copilot slots neatly into Microsoft 365, becoming an extra “pair of hands” for daily office tasks. ChatGPT has become the “universal workspace,” capable of answering questions and drafting content on demand.
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As seen below from our AI Barometer 2025, Copilot is the first AI chatbot deployed internally across 13 major Belgian financial institutions (77%). However, more than half have also deployed their own internal chatbot for employees (54%).
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My main question is: Why does having an internal chatbot make sense with all the solutions and platforms already on the market? And what would an ideal internal solution for banks look like?


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While off-the-shelf tools bring instant convenience, banks know a hard truth: convenience is not the same as control. Like renting an apartment instead of owning a home, you enjoy the space but the landlord decides when to renovate, what you can change, and whether your needs come first.
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Convenience vs. Control: Navigating the Strategic Trade-Off
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When adopting AI assistants, banks face a core dilemma: should they prioritize convenience or control?

External solutions like Microsoft Copilot or ChatGPT offer instant productivity gains. Employees can start using them today, without lengthy deployment cycles. These tools integrate seamlessly into familiar ecosystems like Microsoft 365 and benefit from ongoing innovation delivered automatically by global providers.

But this convenience comes at a cost.

For regulated institutions like banks, control is non-negotiable. Internal AI assistants built in-house or on top of open-source LLMs allow for tight alignment with business processes, compliance requirements, and risk management frameworks. They can be tailored to sector-specific needs, such as CEFR B2-compliant rewrites or internal policy interpretation. More importantly, they give banks ownership over their platform’s evolution.
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This creates a familiar tension:
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• External tools promise agility, speed, and ease of use.
  
  
• Internal platforms offer resilience, customization, and strategic alignment.
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Banks want both. But rarely can they have both at least, not without a clear architectural vision.

The winning approach isn’t to reject external tools outright. It’s to embed them into a broader AI strategy that ensures banks remain in control of their AI journey, not passengers on someone else’s roadmap.

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The Hidden Risk of Provider Dependency
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While external AI tools offer an attractive starting point, full reliance on a single provider like Microsoft or OpenAI introduces a strategic vulnerability: dependency on someone else’s roadmap.
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Banks operating in regulated, long-horizon environments have very different needs than global tech giants. Yet when using off-the-shelf tools, they surrender influence over:
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• What features are prioritized
  
  
• When bugs get fixed
  
  
• Whether new capabilities align with the financial sector’s unique demands
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This creates a dangerous misalignment. While providers optimize for mass-market productivity, banks require domain-specific precision especially when it comes to compliance, customer experience, and internal governance.
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That’s why forward-looking banks are increasingly turning to open source large language models (LLMs) and soon, more agile small language models (SLMs) as a hedge against vendor lock-in. These models offer:
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• Flexibility to adapt as AI technology evolves
  
  
• Customization using internal, proprietary datasets
  
  
• The ability to switch out models without overhauling entire systems
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In effect, strategic freedom becomes the true competitive edge. Control isn’t just about protecting sensitive data or meeting regulatory expectations, it's about owning the direction, timing, and purpose of your AI transformation.
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Why Banks Need Their Own AI Roadmap
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The real question facing banking leaders today isn’t whether tools like Copilot or ChatGPT work, it's whether they support the bank in building lasting, adaptable capabilities.

AI technology is evolving at a pace that far outstrips most IT planning cycles. New models are released every quarter. Interfaces and features change monthly. What looks cutting-edge today may become obsolete tomorrow.

For banks, this velocity creates a dilemma. Chasing the “latest AI tool” may deliver quick wins, but it rarely leads to sustainable transformation. In fact, it often results in fragmented pilots, disjointed experiences, and internal fatigue.

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That’s why leading institutions are reframing the conversation—from tool adoption to platform design.

They’re asking:

“What kind of AI assistant infrastructure do we need to stay relevant—not just this year, but five years from now?”
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This mindset shift moves the focus from reactive tech adoption to strategic enablement designing systems, interfaces, and internal workflows that evolve with the AI landscape, rather than being disrupted by it.

The banks that succeed will be the ones that stop chasing hype and start building AI roadmaps aligned with business outcomes.

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Building the Foundations of an Internal AI Platform
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Rather than chasing every new update from big providers, banks should prioritize building a stable foundation:

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1. A Secure Chat Interface
   
   
   
   • A central entry point for employees to interact with AI in a safe, consistent way.
     
     
2. A Prompt Gallery
   
   
   
   • Standardized, reusable instructions for common tasks: summarization, extraction, translations, drafting content.
     
     
   • Reduces fragmentation and prevents “prompt chaos” across the organization.
     
     
3. An Assistant Gallery
   
   
   
   • Specialized assistants tailored to business lines (e.g., compliance FAQs, credit policy guidance).
     
     
   • Knowledge-augmented for reliable, department-level support.
     
     
4. An Agent Gallery
   
   
   
   • Multi-step workflows automating end-to-end processes performing tasks (e.g., onboarding, claims, portfolio monitoring).
     
     
5. Automation Mini-Apps
   
   
   
   • Purpose-built, high-scale tools (e.g., customer feedback analysis across thousands of records).
     
     

This layered approach provides a roadmap for scaling from simple productivity boosts to enterprise-level AI automation.

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The Real Challenge: Staying Relevant in a Fast-Moving AI Landscape
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Banks shouldn’t aim to outpace giants like OpenAI, Google or Microsoft on core model innovation. That’s not where their competitive edge lies. Instead, the real advantage for financial institutions is in building solutions that fit their business, not just the technology trends.
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What matters most is how AI is applied internally, securely, and strategically.

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• Product–Business Fit
  Success comes from deploying AI assistants that are tightly aligned with banking workflows—whether it’s navigating compliance rules, supporting relationship managers, or handling complex credit decisions. Generic office tools don’t meet these needs.
  
  
• A Sustainable Roadmap
  The most effective banks take an iterative approach—building capability layer by layer. First prompts, then assistants, then agents. Each step reinforces the last, creating a platform that scales without fragmentation.
  
  
• Resilience to Change
  A well-structured internal platform gives banks the flexibility to swap between LLMs (whether open source, proprietary, or hybrid) as technology and regulation evolve without disrupting internal workflows or user experience.
  
  

In other words, winning in banking AI is not about chasing novelty, it's about designing durability. The goal isn’t to react to every new model launch, but to create an adaptable ecosystem that delivers business value today and can evolve with tomorrow’s innovations.

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A Vision of Maturity
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Imagine this:
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• Every employee has access to a bank-wide AI workspace.
  
  
• Prompts and assistants are standardized, governed, and reusable.
  
  
• Agents handle entire workflows behind the scenes, freeing employees for higher-value work.
  
  
• When new LLMs emerge, the bank can plug them in without starting over.
  
  

That’s not about being first to adopt the latest AI feature, it's about creating a resilient, evolving platform aligned with the bank’s strategy.

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Looking Ahead: The Role of Standards like MCP
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One of the biggest hurdles banks face with AI assistants is managing context safely and consistently across systems (many different ones). Emerging standards such as the Model Context Protocol (MCP) are designed to address exactly this challenge, by defining how LLMs exchange context with tools and applications in a transparent, auditable way. While still in its early stages, MCP hints at a future where AI assistants in banking can be both interoperable and governed by design, reducing the risk of fragmented solutions while making it easier to scale securely.

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Final Thought
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External AI tools offer quick wins. But if banks stop there, they risk dependency without differentiation. The bigger opportunity lies in designing internal AI assistants as a long-term product, balancing short-term productivity with long-term control.

The banks that succeed won’t be those who adopt AI the fastest but those who build a platform that endures change while serving business needs today.