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From AI Awareness to AI Application: Working as an Interim L&D Manager in a Big Four Environment

Introduction

Working as an interim manager in a Big Four organisation means working in an environment where pace, quality and client value are always under pressure. Expectations are high. Clients expect relevance, speed and expertise. The firm itself also needs to keep changing. Now, much of that change is driven by AI. Not just basic AI awareness. Not just general knowledge about hallucinations, ethics, data centre costs or risk. Those topics still matter, but they are no longer the full conversation.

The focus has moved.

It is now about application.

• How do we build AI agents?
• How do we connect those agents to business processes?
• How do we create strong client cases?
• How do we help professionals use AI responsibly and effectively in their daily work?

For L&D, this changes the assignment. It is no longer enough to teach people how to prompt. The real challenge is to help people develop new ways of working.

From prompting to process thinking

In the first phase of AI adoption, the focus was often on individual skills. People learned how to write better prompts, how to review AI output and how to recognise potential risks. That phase was useful. But it is not enough anymore. In professional services, AI is increasingly about integration into the work process. A good prompt may still help, but it is only one small part of a much larger change.

The real questions are now:

• How does AI change the way we analyse client needs?
• How can AI speed up the preparation of advice?
• How can agents support recurring processes?
• How do we protect quality, trust and professional judgement?
• How do we help people take ownership of these new ways of working?

This is where L&D becomes highly relevant.

A possible client case: an AI agent for client preparation

Imagine a team preparing for an important client meeting. Consultants need to collect information about the client, the sector, previous engagements, relevant risks, legislation, market developments and internal expertise. This takes time. It is often done manually. Information is spread across systems, documents, client files and external sources.

An AI agent could support this process. The agent collects relevant input, creates a first analysis, identifies possible themes and suggests questions the team can use during the client conversation. The consultant remains responsible for interpretation and decision-making. The agent accelerates the process but does not replace professional judgement. The client case could be described as follows.

Case: AI-supported client preparation

A multidisciplinary team is preparing for a strategic client meeting. In the past, this required several hours of desk research, internal alignment and document analysis. With the support of an AI agent, part of the preparation is automated.

The agent supports the team by:

• collecting relevant client information
• summarising previous interactions
• identifying trends and risks
• formulating deeper client questions
• preparing a first meeting structure
• linking possible services to client needs

The professional reviews, enriches and challenges the output. This creates a combination of speed, quality and human judgement. The value is time saved. The real value lies in better preparation, sharper client questions and more consistency in the way teams prepare for client conversations.

What does this mean for L&D?

For L&D, this creates a clear assignment. Not by developing yet another generic AI training, but by connecting learning directly to work. In this context, an L&D professional needs to take on four roles.

1. Translator of strategy into behaviour

The firm may decide that AI is strategically important. But professionals need to understand what this means in their role, in client conversations and in daily processes. L&D translates strategic ambition into visible behaviour in questions like:

• What does a consultant do differently because of AI?
• What should a manager be able to guide?
• What should a partner be able to assess?
• Where is the boundary between AI support and professional judgement?

2. Designer of practice-based learning

A traditional AI training course is not enough. Professionals learn best when they work with realistic cases. This requires learning interventions built around client dilemmas, process examples and practical exercises. These may include AI labs, simulations, guided practice, peer review and reflection on real client cases. The key question is not: “Did you complete the training?” The better question is: “Can you apply AI responsibly in a client process?”

3. Guardian of quality and trust

In a Big Four environment, trust is central. AI may bring speed, but not at the expense of quality, compliance or client confidence. L&D therefore needs to work closely with risk, legal, quality, technology and the business. Not as a final checkpoint, but as a partner in the design of responsible adoption. A strong learning solution makes clear:

• when AI can be used
• which data can and cannot be used
• how output must be validated
• who remains accountable
• how professional judgement remains visible

4. Driver of adoption

Technology only creates value when people change their behaviour. That requires more than communication and training. L&D can strengthen adoption by involving leaders, developing champions, sharing practice examples and helping teams make AI part of their client preparation, quality reviews and regular team routines. AI adoption is therefore not only a technology challenge. It is also a change challenge.

The new standard for L&D

AI does not only change the work of consultants. It also changes the work of L&D. The standard is rising. Learning solutions need to be faster, more relevant and more closely connected to the business. There is less room for generic programmes that do not directly support performance. For L&D, this means:

• less standalone training
• more learning in the flow of work
• less focus on knowledge transfer
• more focus on application and behaviour
• less supply-driven learning
• more co-creation with business, technology and risk

The modern L&D professional needs to understand how AI works, but more importantly, how AI changes work.

Conclusion

Working as an interim manager in a Big Four organisation requires pace, focus and adaptability. This is especially true now that AI is moving from a general knowledge topic to concrete client application. The conversation is no longer mainly about prompting. It is about agents, processes, client value, quality and responsible use.

For L&D, this creates a major opportunity. By treating AI not as a separate training topic, but as part of professional excellence. By connecting learning to real client cases. By helping people use new technology with confidence, quality and responsibility. That is where L&D can make a real difference. Not by following the development from a distance. But by helping shape the way professionals will work next.