Women in Tech Roundtable with NatWest

Challenges and insights that surround embedding AI in data & operational models.

Embedding AI in Data & Operational Models

At the start of July, a panel of 13 Tech experts gathered in NatWest’s Bishopsgate HQ to explore how AI is being integrated into data and operational strategies.

The morning revealed that no two firms are the same when approaching AI. Some firms are still laying the groundwork, focusing on data ownership, normalisation, and architecture, while others have aligned on AI strategies but haven’t fully embedded them yet.

Many SMEs are still in the early phases of their AI journey. While enthusiasm is high, there’s a clear demand for more structured guidance—particularly around governance and decision-making. Participants noted that without well-defined ownership of AI initiatives across departments, efforts can become fragmented or stall altogether.

This lack of clarity often leads to confusion over who holds the decision rights for AI-related projects. Is it the data team? Operations? IT? The answer varies, but the consensus was that successful AI integration requires cross-functional collaboration and clearly assigned responsibilities.

Core Challenges for AI in Financial Services

Diverging Speeds of AI Adoption

Within many organisations, certain teams are rapidly advancing AI initiatives while others lag behind. This disparity can lead to operational fragmentation, misalignment of goals, and inefficiencies across departments.

Erosion of Long-Term Strategic Vision

A strong focus on short-term deliverables and quick wins can undermine the organisation’s ability to maintain a sustainable, long-term strategy. This may result in missed opportunities for innovation and weakened resilience over time.

Balancing Transformation with Stability

While it is essential to acknowledge that AI will fundamentally reshape the organisation, it is equally important to manage this transformation carefully. Without clear governance and a structured roadmap, rapid change can lead to confusion, loss of control, and strategic drift.

Empowering Middle Management as Change Agents

Driving AI transformation cannot rely solely on top-down directives from the C-suite. Middle management must also be actively engaged in championing the agenda. A collaborative approach—where strategic vision from leadership meets operational insight from mid-level managers—ensures alignment and accelerates meaningful progress.

Do's & Don'ts for AI Integration

Do:

  1. Put Policies in Place – Have clear, customer-facing policies for AI where applicable.
  2. Adopt a Co-Pilot Approach – Use AI to assist, not replace; great for reporting and year-end processes.
  3. Remember, Language Matters – Ensure communications and reporting around AI lead to action, not just documentation.

Don't:

  1. Get Left Behind – Keep up with evolving standards and innovations.
  2. Operate in a Silo – Cross-functional collaboration is critical.
  3. Let Sales/Ops Override Logic – Make sure policy is adhered to even if excitement drives ambition; adjust rather than bypass.

This roundtable underscored a growing consensus: there is no one-size-fits-all approach to integrating AI in financial services. All the more important, then, that industry leaders gather to share insights and ensure productive and compliant use of the emerging technology. If you are interested in exploring this topic further, please please reach out to Megan Sheret, Associate, Wealth, Asset & Data.