Business Implementation
4 min read

AI Agent for Learning at BNY

I remember when developing learning content at BNY used to take a month. Now, with our AI agent, it's down to an hour. This is a game changer that goes way beyond theory. With 20,000 employees, BNY People is transforming learning and development through AI. We're talking about drastic reductions in content development time and opening doors to innovation and employee engagement at every level. I connected our AI strategy with solid partnerships and orchestrated a learning-by-doing approach. But watch out, it's not without challenges. Here's how we did it.

Modern illustration depicting AI agent development for learning, BNY's AI strategy, employee engagement, idea generation

I remember when developing learning content at BNY used to take a month – an eternity in business terms. Now, with our AI agent, we've cut that down to an hour. We've turned a marathon into a sprint. So, how did we orchestrate this transformation? With 20,000 employees, BNY People is in the midst of a practical overhaul of learning and development through AI. This is not just theory; it's a drastic reduction in content development time and a newfound energy for engagement and innovation at every level. I connected our AI strategy with cutting-edge tech partnerships and embraced a learning-by-doing approach. But watch out, it wasn't without its bumps (I got burned a few times before finding the right balance). We had to juggle speed and quality while avoiding the pitfalls of over-automation. Here's how we did it, with the challenges and wins along the way.

Creating the AI Agent for Learning

In the fast-paced world of content development, identifying needs is critical. First, I realized we needed to speed up our production. Imagine waiting a whole month for each piece of educational content... it was inefficient. That's why we built an AI agent focused on learning by doing. And that's when things changed.

Modern illustration of an AI learning agent with geometric shapes and violet gradients, symbolizing innovation and efficient content development.
A modern AI agent for learning, symbolizing innovation.

Our agent doesn't just generate content. It rebrands and corrects existing learning collateral, keeping us aligned with our brand values. But watch out, it's crucial to ensure the AI's outputs are consistent with the company's identity. I've seen errors slip through when not closely monitored.

Reducing Content Development Time

Previously, creating content took a month. With AI, we've cut that down to just an hour. Yes, an hour! It's a real game changer. Imagine the time and resource savings. But it's not magic. Initial setup requires careful planning. I had to ensure every variable was optimized to avoid costly pitfalls.

Modern illustration on reducing content development time with AI, cutting from a month to an hour, featuring geometric shapes.
AI significantly reduces content development time.

Key takeaways include:

  • Moving from a month to an hour frees up valuable time for other tasks.
  • Human resources are better utilized, improving overall efficiency.
  • But remember not to overlook the preparation and testing phase.

Empowering Employees to Contribute Ideas

We've made it so every employee can engage with our AI tools. This has turned idea generation into a collective responsibility. It boosts not only morale but also innovation. I've seen brilliant ideas emerge from all levels of the organization.

But balance is key. Not every idea can be pursued.

BNY's AI Strategy and Partnerships

Our strategy emphasizes strategic partnerships. By collaborating with AI industry leaders, we've enhanced our capabilities. These partnerships keep us at the forefront of innovation. However, the trade-off is the time and negotiations these collaborations demand. It's a constant balancing act.

The strengths of our strategy include:

  • Increased capacity to integrate the latest AI technologies.
  • A network of partners enriching our ecosystem.
  • A strong commitment to continuous innovation.

Learning by Doing: Engaging Employees

We've adopted a learning by doing approach. This has significantly increased employee engagement and aligns with our time management goals. But watch out, this method can lead to burnout if not properly managed.

Modern illustration of learning by doing engaging employees with geometric shapes and violet gradients.
Learning by doing enhances employee engagement.

The lessons learned are clear:

  • Involving everyone improves team dynamics.
  • A hands-on approach accelerates the learning process.
  • It's crucial to monitor for signs of fatigue.

So here's what I got from BNY's AI journey. First off, cutting down content development time from a month to just a few days is huge. That's tangible impact. Secondly, with 20,000 employees, AI really boosted creativity and engagement across the board. Third, BNY's AI strategy is a real lesson in efficiency and engagement. But watch out, you need to orchestrate partnerships and tools properly to avoid performance hiccups. Looking ahead, AI is a real game changer, but you need to be aware of the limits and hidden costs. I suggest you think about how AI could revolutionize your organization's processes. Start small, iterate, and empower your team. For a deeper dive, check out the video "BNY People uses OpenAI" on YouTube. It's practical stuff, not just theory!

Frequently Asked Questions

BNY uses an AI agent to speed up content development and correct learning materials.
AI has reduced content development time from a month to an hour.
BNY encourages all levels to use AI tools to generate ideas.
BNY collaborates with AI leaders to enhance its capabilities.
This approach increases employee engagement and improves time management.
Thibault Le Balier

Thibault Le Balier

Co-fondateur & CTO

Coming from the tech startup ecosystem, Thibault has developed expertise in AI solution architecture that he now puts at the service of large companies (Atos, BNP Paribas, beta.gouv). He works on two axes: mastering AI deployments (local LLMs, MCP security) and optimizing inference costs (offloading, compression, token management).

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