A Changing World

Two weeks ago, I had to create and present a proposal to leadership on GenAI in Analytics. The ask was simple enough in theory: show us what GenAI-powered analytics could look like in our organisation. In practice, it meant building a story, a solution, and a demo for a future that is still unfolding. The timeline was tight, the expectations high, and the territory unfamiliar.

I spent the first couple of days in a quiet battle with myself. I oscillated between reluctance and resolve, my mind clouded with questions I had no answers to. I have a decade of analytical experience that has made me comfortable in the knowns. I was hesitant to begin, worried that I wouldn’t be able to learn fast enough to meet the expectations. The scope felt wide, the stakes real. But eventually, I did what I’ve learnt to do in uncertain moments. I asked for help.

I reached out to my data engineer, someone whose strength lies in technical experimentation and speed of learning. Together, we combined his fluency in systems with my experience in business storytelling and organisational change. By the second day of our collaboration, we had mapped out a script and a narrative arc. We had a solution that allowed users to interact with data in natural language by running reports, diving deeper into metrics, and asking open-ended questions. It was functional, but it didn’t yet feel exciting.

The breakthrough came just hours before the meeting. Out of desperation, I read through the product guide, page by page. Buried deep in the guide, I found what we were missing. The tool could be enriched with context repositories, and the chat interface could serve as the primary user front-end. This meant that instead of merely querying data, we could offer context-aware, conversational insights. This product would be exactly what the leadership had been hoping to see.

We quickly integrated a ready-made demo video into our flow, adjusted our presentation, and walked into the meeting with a quiet confidence. What followed was an energised discussion, clear alignment, and an enthusiastic green light to proceed with a P0 launch later this year.

What we already know still matters

Change is rarely comfortable. But in this case, what allowed me to move forward wasn’t new knowledge, but old habits and trusted allies. I leaned on what I already had which was my data engineer I could collaborate with, a method for structuring a narrative, and a mental model for audience-first communication.

We were solving a problem we understood well already, which is to democratise and self-serve insights.Only the interface had changed.

What’s unfamiliar about GenAI is often not the goal, but the path. The methods are evolving, the tools unfamiliar, and the expectations shifting. But the underlying needs are not new; there are still analytical problems to solve, decisions to support, and workflows to improve. What’s changing is how we engage with those needs.

Why AI feels different

Many of us have navigated waves of transformation: from paper to digital, local to cloud, batch to real-time. But GenAI feels different and there is a collective sense that something fundamental is shifting.

My own leaders have started asking for a BI assistant they can simply talk to. There is an emerging need for unstructured data to offer context-relevant insights. We might be approaching a world where a leader never opens a Tableau dashboard or runs a SQL query. With integrations to Slack or Outlook, they might interact with analytics entirely through conversation alone.

These aren’t minor adjustments and are structural reimaginings of how analytics integrates into the organisation.

Yet, amidst this change, the known remains. Stakeholders still look to us for clarity. Teams still require upskilling. Problems still need framing. The value chain of ‘define, deliver, refine’ continues to apply. In that sense, GenAI doesn’t replace our responsibilities but it reframes them.

Progress without speed

The pressure to become an expert overnight is real. There is an unspoken assumption that because the technology is moving fast, we must move just as fast to stay relevant. But that’s neither realistic nor sustainable.

The reality is that slow, deliberate engagement is still progress. In fact, it is often the most durable form of learning. Start by delegating one task. Let AI draft a weekly update, generate a summary, or audit a query. In our team, for instance, Copilot now maintains data pipelines based on agreed standards. We act as the human-in-the-loop, reviewing and approving it’s output. The repetitive groundwork has been eased, leaving bandwidth to focus on high-value tasks.

Ask for help

Curiosity, I’ve found, is a quiet but powerful response to uncertainty. When you don’t know something but still want to move forward, the best thing you can say is, “I’m not sure, but I’m curious.” That simple mindset opens doors to unexpected clarity.

A small habit I’ve adopted is to keep a personal list of unfamiliar terms, tools, and references I come across. Later, I look them up to ensure I am in the know. It helps me identify where I need to dig deeper, and where I can rely on others.

In truth, no one knows everything. But those who remain curious, and asks the next question tend to find their footing sooner.

Learning is a shared project

In our organisation, the CTO has become an informal mentor for AI adoption. He has created space for debate, for knowledge-sharing, and admitting what we don’t yet know. That openness has made all the difference.

We learn faster when we learn together. And maybe for the first time, we have the opportunity of being early adopters alongside most of our organisations. Find someone whose work overlaps with yours. Someone whose approach to AI is different, but adjacent. Observe how they explore, what they ask, how they adjust. Make it easier for the next person by sharing when something works for you. We are all building the bridge as we walk it.

Depth over volume

There’s no shortage of content on AI with articles, webinars, podcasts, and newsletters flooding our inboxes daily. But more information rarely means more clarity.

Last month, I enrolled in a short training focused on generative AI for business leaders. It was practical, focused, and immediately useful for me. I summarised my learnings and shared them with stakeholders across our org in the hopes that they can benefit from the notes. Since then, I’ve been slowly working through more technical courses that will help us build our internal BI assistant. Time is limited, but building depth that unblocks me to deliver on my priorities is what matters to me right now.

When you’re overwhelmed, narrow the scope. Choose one trusted voice. One course. One podcast. Go deep before you go broad.

Know where you add value

In the early stages of adopting a new system, one of the hardest things to establish is where your work ends and someone else’s begins. That clarity matters both for focus and for collaboration.

If your expertise is analytics, then your role is evolving to enable your stakeholders to use GenAI and accelerate insight generation. This can look like building prompt kits, validating GenAI outputs, and modelling topics on data collections. But the design and implementation of infrastructure for GenAI analytics is someone else’s contribution.

Knowing your circle of competence i.e., what you own, what you influence, and what you need to collaborate on, is essential. It helps focus your learnings and manage expectations. It ensures effective teams operations across evolving landscapes.

The time you take is the time you need

GenAI will continue to evolve. New tools will emerge and with them, expectations will rise. But your response need not be hurried. The time you take to learn, reflect, fail, and try again is not wasted time. That is the path forward.

To lead with curiosity is to embrace your knowledge gap without self-judgment. It is to ask, to listen, to iterate.

So start with one small thing this week. Pick one task. Have one conversation. Collaborate on one experiment.

And then, do it all again.

Thank you for reading. I’d love to hear how AI is entering your world, and how you’re navigating its challenges. Please share your reflections in the comments.