In many organisations today, we’re still dragging the horse to water when it comes to AI. I can relate; the default is to stay buried in the weeds of our day-to-day tasks.
Often, the mere idea of stepping back to take a bigger perspective feels like a luxury we can’t afford. But we must be brutally honest. In an era where the pace of change is accelerating, staying in your comfort zone is the greatest risk of all. We need to move from being task-performers to being builders.
The fear of productive play
There is a specific kind of fear that comes with experimentation. It’s the ‘What if I spend the whole day playing around with a new tool and have nothing to show for it by 5pm?’ thought.
Yet it is precisely this playfulness that leads to breakthroughs. Innovation doesn’t always start with a corporate mandate; it often starts in your personal life. Innovation does not keep business hours.
Ask yourself:
- How are you using AI at home?
- Are your children using it to explore the world?
- Are you using it to augment your own studies or hobbies?
We see this beautifully with the younger generation. For a 14-year-old today, using a Large Language Model (LLM) seems so effortless because their minds aren’t set like ours. They don’t even think about it, they just get on with it.
Many students of today already use AI as an exoskeleton for learning and building. Used right, it’s a phenomenal tool to help them reach higher, faster.
The goal isn't just to use AI. The goal is to cultivate a team of builders who use AI to automate the routine so they can focus on the remarkable.
Innovating under pressure
But let’s get back to the work environment. We often hear about the security risk or regulatory red tape as a reason for stagnation. Especially in financial services. These are valid concerns, of course. Guardrails need to be in place, but they shouldn’t be dead ends.
I picked up some great inspiration in a recent conversation. Steve works in a highly locked-down environment where getting new software approved feels like moving a mountain. Instead of giving up, Steve built a Microsoft 365 agent for himself. Every morning, before his team’s status call, this agent summarises the last 48 hours of incoming emails and system tickets.
He’s better prepared, saves hours of manual checking, and provides more value to his team. He didn’t wait for a company-wide AI roll-out; he built his own solution. Many others would have just complained about the workload.
The next level: Agentic orchestration
Beyond individual efforts like Steve’s, I’m seeing some of our more visionary clients move toward multi-agent orchestration. Think of this as a ‘parallel agent factory.’ Instead of one person talking to one chatbot, they are setting up systems where a lead orchestrator agent manages a “swarm” of specialised worker agents.
One agent might pull the data, another validates it against compliance rules, and a third drafts the commentary; all happening simultaneously in the background. It turns the reporting process from a linear assembly line into a high-speed, parallel engine. This is the post-workload era in action; where the focus shifts entirely from doing the task to orchestrating the solution.
You’ll want to hire people who, like Steve, aren’t afraid to start building the first version of that engine.
In client reporting, the goal is to move from a linear assembly line to a parallel agent factory. That is the power of agentic orchestration: high-speed, multi-step problem solving at scale.
Hiring for the post-workload era
This shift changes everything about how we should lead and hire. We are entering an era where it is becoming impossible to hide behind a heavy workload. If the machines can handle the grunt work, the only question left is: Can you solve the problem?
As we look across industries, the skills becoming most valuable aren’t just technical. What matters now is creativity and critical thought.
Now, we want to upskill for:
- Strategic thinking: Identifying which ‘weeds’ can be cleared by AI.
- Creative problem solving: Using tools to bridge gaps, even within constraints.
- Experimental mindsets: A willingness to ‘fail’ for a day to gain a week of efficiency.
Conclusion
The goal is to liberate your people. When we automate the routine, we can reclaim headspace.
The question is: Are you curious? Are you building an environment with guardrails that allows your team to play? Does your culture reward experimentation? Are you brave and ready to build the future?
Our next article by Brad Burgunder will focus on the insights gained from conversations with Heads of Client Reporting at the PMCR conference and at the dinner that we hosted in New York last week. We were keen to find out how they are upskilling and adapting to the new challenges and opportunities with this shift towards AI.
Frequently asked questions:
- How is AI changing client reporting in asset management? AI is shifting the focus from manual data aggregation and task-performing to high-level analysis. By automating the routine aspects of reporting, like summarising tickets or clearing data anomalies, teams can transition into creators who focus on enhancing the client experience and strategic problem-solving.
- What are the most important AI skills for asset management teams today? Beyond technical coding, the most valuable skills are strategic thinking, creative problem-solving, and an experimental mindset. Firms are increasingly hiring for critical thought, i.e. the ability to identify which manual processes can be cleared by AI and how to use tools to bridge gaps within regulatory constraints.
- How can financial services firms innovate with AI despite strict regulatory guardrails? Innovation in a locked-down environment starts with productive play within approved frameworks. As seen in our Steve example, using existing enterprise tools like Microsoft 365 to build internal agents can save hours of manual work without bypassing security protocols. Guardrails should be viewed as boundaries for safe experimentation, not as dead ends for progress.
- What is a builder mindset in the context of AI adoption? A builder mindset is the shift from just using a tool to actively creating solutions. Instead of waiting for corporate mandates, builders experiment with AI to automate their own workloads. This culture of experimentation leads to breakthroughs in efficiency and allows teams to focus on value-add tasks rather than the grunt work of reporting.
- How does agentic orchestration differ from standard AI use in reporting? Standard AI use typically involves a human giving a single prompt to a chatbot to get a single output. Agentic orchestration is the next level where a lead orchestrator agent manages a swarm of specialised AI agents to complete complex, multi-step workflows. In client reporting, this allows for parallel processing. Where data extraction, compliance checking, and commentary drafting happen simultaneously, a linear manual process turns into a high-speed, automated factory.
- How do I build an AI-ready culture in my reporting team? Building an AI-ready culture requires leadership to reward experimentation and allow for failing during the learning phase. It involves encouraging the use of AI in personal life to normalise the technology and creating a safe environment where team members feel empowered to use AI as an exoskeleton for learning and building.
