Tech Connect Insights on ‘Delivering AI-at-Scale’

At our third and final Tech Connect event for 2025, Professor Alan W. Brown shared insights on ‘Delivering AI-at-Scale’.

Discussion unfolded under the Chatham House Rule and participants were open with their questions, comments and concerns regarding regulation of AI, job displacement and the challenge of implementation.

Professor Brown explored how AI is reshaping industries, challenging traditional business models and redefining the skills needed for success in the digital age.

Read on to explore the key themes.

Welcome to the Future (Again)

Discussions began with a reminder that AI’s rapid evolution has outpaced many organisations’ ability to adapt. While public awareness of AI has grown, perceptions remain divided between optimism about productivity gains and scepticism about risks.

The conversation highlighted that AI’s impact now extends across every sector, company and role. Strategy, technology, policy and budgets are all being rewritten in response.

For financial organisations, this means balancing innovation with trust, compliance and long-term value creation.

The Adoption Challenge

Through example case studies and participant discussion it became evident that while many firms are achieving isolated successes with AI adoption, they continue to face challenges in scaling those gains across the organisation.

According to research cited from McKinsey, while 78% of large organisations use AI in at least one function, fewer than one in five track performance using defined KPIs – limiting their ability to measure real impact.

Another barrier is that while over 75% of senior leaders report using AI tools regularly, frontline adoption has stalled at around 50%. Without widespread employee engagement and training, organisations risk creating a split workforce where AI becomes a source of stress rather than empowerment.

Lessons from AI Leaders

Participants were referred to three case studies, each representing a different stage of AI maturity.

Walmart:
Walmart’s deployment of AI across 11,000 stores has transformed supply chain efficiency and customer experience. Automation has cut costs by 20% and accelerated production by 18 weeks. Data privacy, system integration and user trust remain ongoing challenges. Walmart’s experience shows that scaling AI is as much about change management as it is about technology.

JPMorgan Chase:
JPMorgan’s US$18 billion technology investment, US$1.3 billion of which is dedicated to AI, shows how a major financial institution can leverage AI for both efficiency and innovation. From real-time fraud detection to intelligent customer support, AI is embedded across 200,000 employees. Regulatory compliance, ‘hallucination risks’ and workforce transformation require constant oversight. The case demonstrates that speed must be balanced with accuracy and that governance is a competitive differentiator in regulated industries.

McKinsey:
McKinsey built its own AI platform, Lilli. This bespoke approach emphasised learning before scaling. By starting with 2,500 users, McKinsey prioritised user feedback, risk management and cultural change over rapid deployment.
The lesson: true transformation is not just about building a tool but about rewiring how an organisation operates.

From Experimentation to Implementation

A recurring message throughout the session was that AI success depends on moving beyond pilots. Many organisations are stuck in ‘proof-of-concept paralysis’, unable to transition from experimentation to deployment.

Professor Brown argued that workflow redesign – rethinking processes to integrate AI effectively – has the biggest impact on results, yet fewer than a quarter of firms have undertaken it.

A Framework for Scaling

To help organisations navigate AI implementation, Professor Brown shared a four-step AI risk assessment framework:

1. Prioritise: define clear business cases and success metrics before starting
2. Apply: implement strong data privacy and governance controls including human oversight and training
3. Measure: track performance against KPIs and monitor for risk
4. Assess: review outcomes regularly to determine whether to expand, modify or discontinue an AI initiative. Essentially, ‘fail fast’ but elegantly by assessing and applying learnings.

This structured approach helps mitigate risks and ensures that AI investments remain aligned with business goals and regulatory standards.

Human vs. AI Workforce

A recurrent theme of the discussion was the human dimension of AI transformation. Professor Brown highlighted that while AI automates repetitive tasks and accelerates decision-making, it also reshapes job roles and required skill sets. Upskilling and reskilling remain lagging across industries.

‘By 2030, 70% of job skills will have changed and AI literacy is projected to be the most in-demand skill by 2025.’

Organisations that invest early in training are more likely to see employees who embrace AI as an enabler rather than a threat.

Key Takeaways for Financial Services

For the finance sector, AI has potential to reshape client engagement, operational efficiency and compliance management but the risks are equally significant.

Professor Brown identified five ‘dilemmas’ that every leader must navigate:

• Productivity: balancing automation with the human insight that builds trust
• Value: ensuring that AI creates measurable business value#
• Ethical: embedding fairness, accountability and transparency into AI systems
• Leadership: equipping executives to make informed decisions in a data-driven environment
• Human: supporting workers as roles evolve and ensuring inclusion in the AI future

Becoming an “Intelligent Adopter”

Professor Brown closed with a call to action: ‘You don’t need to be an expert in AI technology, but you must become an intelligent adopter.’

He proposed ten practical questions that every organisation should ask before deploying AI, from defining the specific task AI will improve to establishing how bias will be managed.

AI’s integration into financial services is inevitable, but its success will depend on how thoughtfully it is scaled.

As Jersey continues to position itself as an innovative international finance centre, discussions like this are crucial in shaping a shared understanding of how to deploy AI responsibly, effectively and at scale.

 

Read more about this topic in our Guide to AI

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