Save time before you switch on Agent mode. Sceptical? See in video how an average Analyst can build an institutional DCF model in 3 minutes.
Excel modelling has changed—permanently and office colleagues remain unaware.
Since the 2020–2022 Excel upgrades, most institutional Analysts are not equipped with game-changing benefits available to your business today.
Following a wave of Microsoft Excel upgrades involving a soup of “dynamic arrays”, LET, LAMBDA, MAP/REDUCE/SCAN, BYROW/BYCOL, TAKE/DROP, &tc, Excel isn’t just a grid of cells—it’s a first-class functional programming environment that analysts can use without writing VBA or macros.
Most teams haven’t caught up. Ask your in-house spreadsheet guru about “functional modelling” and you’ll often get a blank look. Meanwhile, analysts are still burning hours down at the cellular level—hunting through thousands of buried formulas across a forest of tabs.
It’s time to move up a level.
From the cell to the grid (and then to policy)
Instead of 5,000+ fragile cell formulae, a modern institutional model can be driven by a simple dozen or so reusable functions—clean, DCF commands that flow across the spreadsheet grid. Think of these commands as Excel LEGO Blocks:
Reusable: one well-tested LAMBDA replaces hundreds of copy-pasted formulas.
Auditable: logic lives in named functions you can read, explain, and unit-test.
Composable: assemble models quickly from a shared catalogue (revenues, OpEx, debt stacks, waterfalls, flex-office / OpRE modules, etc.).
Performance-oriented: fewer recalcs, fewer errors, clearer intent.
Too much? Let’s simplify with a video to save a few more thousand words:
Example: how your Analyst can build a financial Excel DCF model in under 3 minutes manually (just Excel, no macros, no VBA, no Agent AI mode).
Once you’re off the spreadsheet cell treadmill, you unlock the policy level:
“How should we model flex offices? What is our firm-wide view on operational real estate categories? How do we standardise debt monitoring reporting rules?”
Instead of wrestling with legacy templates, you codify policy once, agree a shared function library and reuse it everywhere. This is a classic example of how innovation and new technology does not replace jobs but enhances the quality and level of work that your Analyst can focus on, which increases productivity levels and dismantles typical analytical bottlenecks and delays.
Why this matters to senior leaders
Speed-to-underwrite: deal screens in minutes, not days; faster bid cycles. Build new corporate templates for new products / assets in minutes based on in-house existing policies and expertise.
Governance: standard logic, versioning, and lightweight unit tests mean fewer surprises and cleaner audits.
Cost & risk: less rework, fewer external rebuilds, and dramatically lower error rates.
Talent leverage: your best modellers ship reusable logic; your whole team benefits.
And to the sceptical analyst (yes, you!)
This isn’t about an AI bot taking your seat. It’s about eliminating low-value, error-prone grunt work so you can do the good stuff: structure, scenarios, insight.
You’ll still live in Excel—just with power tools:
Build a module once (RevenueSchedule, DebtAmortisation, WaterfallTier), then reuse it forever.
Swap brittle, hidden formulas for readable, named functions you can step through and test.
When AI agents arrive, your models are agent-friendly: clear inputs/outputs, strict interfaces, and fewer edge-case failure points.
Avoid the genAI trap (generative AI)
There’s a rush to “automate underwriting” with generic gen-AI. In practice, prescriptive institutional requirements don’t pair well with free-form prompting. Teams spend heavily and end up with brittle one-offs.
Better path: structure your Excel first. Make logic explicit and modular. Then let AI assist—surfacing checks, generating scenarios, or scaffolding new functions—on top of a rock-solid model architecture.
What a modern setup looks like
Catalogue of ‘Excel LEGO Blocks’: a central team online repository of your business custom commands (with descriptions, expected inputs/outputs, and unit checks).
Lightweight testing: sample inputs + expected outputs to sanity-check each function.
Versioning & review: simple change logs; peer review for new/updated modules.
On-ramp playbooks: 3–5 starter templates (single-asset DCF, OpRE, development, portfolio rollup) assembled from the same blocks.
Try this tomorrow morning (15-minute challenge)
Identify one noisy sheet (e.g., OpEx).
Create a named LAMBDA for a core calculation (inputs: start period, growth rule, seasonality; output: dynamic array of monthly OpEx).
Replace hundreds of row formulas with a single spill.
Add a quick check: total vs. expected.
Write a one-paragraph docstring in the name manager. Congratulations—you just moved from cell to grid.
Where Dashflow for CRE team helps
Whether you’re a BlackRock/Blackstone-type team or a one-person former institutional analyst burning the hours at a boutique family office, our consultancy and upskilling workshops accelerate the shift:
Blueprint session: map your policy choices and target catalogue of in-house models.
Module sprint: convert a legacy template into composable functions.
Governance light: versioning, review, and testing that analysts actually use.
AI-ready foundations: models structured for Co-Pilot/agent assist—no rewrites.
It’s free to start—you already own Excel. The payoff is measured in days saved per deal and risk you no longer carry.
Curious? Let’s run a morning workshop with your team and make a plan to rebuild one of your investment templates using this ground-breaking approach with ‘Excel LEGO Blocks’. You’ll leave with a reusable catalogue methodology, a cleaner model approach, and a clear path to AI-enhanced underwriting—without the hype.