In the current debate around AI, authenticity, and professional credibility, a fascinating case study has emerged: a direct comparison between two very different approaches to the same accounting task—bank reconciliation. One is a slickly produced video from Excel influencer and Microsoft MVP Mark Proctor, and the other is an AI-powered presentation based on lived experience and field-tested innovation. But the real contest isn’t visual style. It’s substance.
And it raises some important questions.
Processed Ideas vs Polished Packaging
The spark for this reflection came during a series of video recordings intended for internal reference. But I realised: these weren’t just rough thoughts. They were authentic, shaped by real-world insight. And like food, ideas too need preparation—made digestible, palatable, structured. Not fake. Just cooked.
What’s at stake here is how we value authenticity in knowledge. When a piece of content is produced by AI—scripted, narrated, explained—it may lack the “voice” of a familiar influencer. But if the ideas come from real-world problems and proven field experience, isn’t that a deeper kind of authenticity?
So let’s test that idea. Let’s consider the example of Mark Proctor’s Power Query-based reconciliation video—a polished, widely viewed, and well-received tutorial. It’s got the audience. It’s got the praise. Job done, it seems.
But is that all that matters?
A Contest of Ideas, Not Cosmetics
Let’s put aside the packaging: ignore the AI-generated voiceover, the production gloss, the YouTube likes. Strip it all down. Focus on just the methodologies—mine versus Mark’s.
Proctor’s video shows a Power Query-driven process involving 45 steps. It’s a method that assumes a trained userbase, one that companies must be willing to invest in. Yet in the same breath, Mark notes that businesses are not willing to invest in such training—leading to knowledge decay, process failure, and abandonment of the method.
This contradiction exposes the flaw.
Instead of insisting that managers change their behavior to suit a fragile process, why not offer a solution that aligns with existing constraints—one that doesn’t require an army of Power Query-trained staff to operate?
From the Podium to the Process
Let me tell you where my method came from. It wasn’t in a classroom. It wasn’t from YouTube. It was at a bar in Stringfellows, of all places, in 1997. While watching dancers on a podium, the penny dropped: reconcile by reversing one set, stacking it with two others, and isolating the unmatched items. No academic paper taught me that. It came from weeks of lived chaos—seeing manual reconciliations fail, watching finance staff tick boxes blindly and still miss the big picture.
Back at the office, I implemented it. We imported data from three sources: the bank statement, the cash book, and the Sun accounts ledger. Using Excel, we stacked them, added signs to reverse direction, and built a matching and elimination model. The result? Reconciliations that previously took days became manageable. Structured. Trackable. Auditable. And adaptable to real enterprise changes—new banks, new systems, new staff.
This wasn’t a hack. It was sustainable.
Lived Experience vs Theoretical Authority
I don’t know whether Mark’s method is based on his own enterprise experience, or if it’s a product designed for social media appeal. I do know that mine was born in the chaos of real-world implementation—and later re-implemented alongside Oracle Financials when their system proved incapable of handling reconciliation reliably.
In fact, Edexcel, my client at the time, spent £2 million on an Oracle implementation. And when it failed to deliver on basic reconciliation needs, they brought back my Excel-based system. It wasn’t flashy. But it worked.
The irony? Oracle consultants insisted their solution couldn’t be beaten by Excel. Until it was.
Popularity vs Performance
Let’s be clear: if this were judged by YouTube popularity, Mark wins. If judged by sustainability in the field, tested under pressure, integrated into financial governance processes and surviving multiple system changes, I believe my methodology wins.
So why hasn’t it gone viral?
Because we conflate visibility with validity. AI voices aren’t trusted. Slick editing is. But the bosses—the people who have to live with the results—care about different things: reliability, efficiency, continuity. If given the choice, with all context stripped away, I believe most of them would pick the method that works—not the one that looks good.
Time for a Recall?
If this were the auto industry and we found that a highly-promoted method was, in fact, less effective or efficient than a quietly successful alternative, there’d be a recall. A public acknowledgement. A fix.
That’s what I’m calling for now—not a takedown, but a genuine review. A public challenge: put both methods side by side. Let managers—not influencers—decide.
Final Thought: Let the Bosses Decide
In the end, it’s not about Mark or me. It’s about giving the people responsible for enterprise outcomes a genuine choice. A level playing field. One where substance wins over surface. One where lived experience can stand alongside social media metrics and ask: which solution truly serves your business?
If the answer is mine, then let’s stop calling it a hack—and start calling it what it is: better.
Hiran de Silva
Excel enterprise consultant. Advocate of authentic, field-tested methodologies. Still reconciling more than just bank accounts.
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