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AI in Finance: Stretch Every Dollar and Lift Valuation in a BANI World

Being Human for a Better Tomorrow in the Age of AI

  • May 16, 2025
  • 46th edition
Innovation AI in Finance Stretch Every Dollar and Lift Valuation in a BANI World

From VUCA to a BANI World for Finance

We’ve all heard the US Army War College term VUCA (Volatility, Uncertainty, Complexity, Ambiguity) used to describe the leadership landscape.
Yet, in 2020, futurist Jamais Cascio proposed an evolution: BANI – Brittle, Anxious, Non-linear, and Incomprehensible – to better capture today’s ‘poly-crisis’ business reality.

This BANI framework starkly defines the gauntlet thrown down before strategic and operational Finance:

  • Brittle: Business environments, rocked by overlapping crises (COVID aftershocks, AI’s rapid ascent, geopolitical tensions, economic and military conflicts), expose deep fragilities in financial planning systems, operational resilience, and traditional forecasting methods. Plans snap easily under such concurrent pressures.
  • Anxious: This inherent brittleness, amplified by incessant news cycles and instantaneous global reactions, fuels a pervasive anxiety. Fear and indecision can take hold, leading to an intense market premium on predictability and severe punishment for any missed earnings or guidance.
  • Non-linear: In this world, cause and effect are often disproportionate. Small missteps can cascade into major issues, but conversely, strategic early investments, especially in agility and insight, can yield outsized rewards. Finance must pivot from solely managing risk and stretching the dollar to proactively enabling value-creating investments.
  • Incomprehensible: Like the ancient mariner surrounded by undrinkable seawater, finance teams are awash in vast oceans of internal and external data. Yet, modeling complex variables like FX volatility, forecasting interest rates, or deriving clear, forward-looking strategic insights from this deluge remains incredibly challenging. Secondly, finance teams sit on two unruly oceans of information: live market feeds that swing by the minute, and fragmented internal cost data locked in ERPs, data marts, and spreadsheets.
This BANI environment doesn’t just demand more from finance; it demands a different kind of finance.

Forward Looking Finance in a BANI World: Challenges and Opportunities

In this BANI world, a forward-looking finance function converts challenges to opportunities and could do the following differently:

Overcome Brittleness: Steering Poly-Crises

Challenge #1: Maintaining credible guidance when the macro ground keeps shifting. Investors still reward predictability and punish earnings surprises even more harshly in turbulent periods.

Opportunity #1: Micro-scenario, rolling-forecast disciplines let finance surface upside, not just hedge downside. Companies that systematically stress individual drivers (vs. three big scenarios) move capital faster and communicate confidence.

Keeping Calm: Anxiety and Non-Linearity Challenge with Speed and Smarts to Stretch the Dollar

Challenge #2: €1.56 trn in excess working capital still trapped on global balance sheets, per PwC’s 19,000-company study (PwC).

Opportunity #2: McKinsey research confirms that early working-capital optimisation in transformations signals pace and can self-fund broader change programmes and focused efforts on accounts receivable and payable can free up substantial cash (30% or more of AR/AP balance) within weeks. These early wins provide not just liquidity but also critical momentum and can effectively “self-fund broader change programmes.”

Overcoming Inertia in Resource Allocation, Budgeting Inertia, Long Record-to-Report Cycles

Challenge #3: Annual budgeting inertia traps cash in legacy lines starving strategic initiatives.

Opportunity #3: Treating capital allocation as a monthly exec-level process (with 5–20% funds held in reserve) builds resilience and lets firms pounce on disruptive openings. Many organizations struggle to translate strategy into agile resource deployment. McKinsey’s research (“Tying short-term decisions to long-term strategy,” McKinsey on Finance No. 86, July 2024) found only about half of companies effectively align budgets with strategy, and just 53% fully fund their identified priorities.

Understand the Incomprehensible: Obscured Cost & Profitability Insights and Disintegrated External and Internal Data

Challenge #4: Many finance departments operate with outdated Activity-Based Costing (ABC) models. An IMA Network article (Pedro San Martin, April 2025, citing a 2021 IMA survey) highlights that 47% of CFOs admit their ABC models rely on stale, annual assumptions, leading to mispriced products and strategic blind spots.

Opportunity #4: Achieving True Cost Intelligence with “ABC 4.0”. The evolution to real-time, data-driven “ABC 4.0,” integrating with operational systems, offers profound clarity. The same article provides examples where this shift led to uncovering hidden losses and achieving significant margin lifts (9–18%) through data-informed process redesign and strategic portfolio adjustments.

Challenge #5: Disconnected ERPs, spreadsheets, and third-party feeds produce conflicting “versions of the truth,” leading to mis-timed hedges, inventory swings, and stalled investment decisions.

Opportunity #5: A single, governed data spine—linking transactional, operational, and external variables—gives CFOs faster signal detection, tighter hedging, and clearer unit-economics for resource allocation.

Addressing these fundamental strategic and operational challenges transforms finance from a perceived cost center into an indispensable strategic partner, ready to leverage the full potential of AI.

AI in Finance: Custom Built Solutions Are the Norm

Unlike Sales and Marketing, Operational HR, AI solutions in Finance tend to be custom built for the following reasons:
  • Data gravity & entanglement – Every finance workflow (actuals, sub-ledgers, tax, controls) already lives inside an ERP or close-coupled treasury/close/FP&A suite. Re-platforming the “source of truth” is a far bigger lift than bolting AI onto stand-alone marketing or HR point tool.
  • Regulation & auditability – Finance outputs feed statutory filings, SOX controls, Basel/IFRS rules. Black-box predictions that can’t be reconciled back to journal-entry level are non-starters.
  • High switching cost – FP&A and treasury processes are sticky; once a model lives in Excel or an EPM cube, replacing it risks business disruption.

AI First Solutions in Finance: It’s Early Days

Having said that, there are a few illustrative examples of promising AI First products in Finance and early stage implementations:
  • Micro-scenario planning / rolling forecast – Pigment: A cloud model built around in-memory graph, then infused with LLM “Planner Agent” to generate hundreds of what-ifs and plain-language variance narratives.
  • Working-capital & AR optimisation – HighRadius: Autonomous Finance which uses ML risk scoring + GenAI collection dunning; treasury bot recommends hedge trades.
  • Cash-flow forecasting / treasury Tesorio (mid-market) – Pure SaaS cash twin that stitches bank + ERP feeds; LLM chat. Kyriba AI Forecast Models (upper-mid/enterprise) using gen-AI time-series models.
  • Short-cycle budgeting co-pilots – Mosaic ArcAI, Causal: FP&A bots living inside Sheets/Excel to pull actuals nightly and rewrite forecast drivers.

AI in Finance: Closing the Books, Opening New Chapters

‘Scenario planning is getting a stress test ‘is a good FT article by Anjali Raval. In this article she refers to great quotes from Mike Tyson “Everyone has a plan until they get punched in the face.” ; A UK board chair : “The one scenario everyone seems to have forgotten to plan for is the one where all the scenarios are wrong.”and a CFO , “In this environment, it’s easier to identify risks than it is to see the opportunities.”

This BANI world doesn’t just demand more from finance—it demands a fundamentally different kind of finance. One that stretches beyond traditional boundaries to become both guardian of capital and catalyst for growth.

Finance has always been the trusted keeper of enterprise value. Now, in the Age of AI, it stands at a pivotal inflection point that enables it to ask transformative questions:

  • 1. What if finance teams could move from quarterly forecasts to continuous, adaptive planning that responds in real-time to market signals?
  • 2. What if working capital optimization became so seamless that companies could self-fund their most ambitious transformation initiatives?
  • 3. What if capital allocation evolved beyond annual budgeting cycles to become a dynamic, data-driven process that redirects resources monthly toward the highest-value opportunities?
  • 4. What if cost intelligence became so granular and real-time that every decision maker could understand the true economics of each product, customer, and channel?
  • 5. What if the finance function, once viewed primarily as a control mechanism, became recognized as the strategic nerve center driving enterprise value creation?

The finance function has always been about numbers—but its future will be defined by insights.

That’s how we ensure finance remains not just relevant but indispensable in the Age of AI—by embracing tools that amplify our uniquely human abilities to interpret, prioritize, and connect financial outcomes to business strategy.

I’d love to hear about your observations, thoughts and experiences of using AI in finance:

  • 1. How is your organization currently using AI in financial functions?
  • 2. What's your biggest challenge in moving toward more strategic, AI-enabled finance?
  • 3. Which of the reimagined finance approaches described above excites you most?

Share your thoughts in the comments below!

As always, I welcome your comments, insights, and ideas.

Explore, engage, share and stay tuned for more.

Best wishes

“AI is a language. Treat it like one: practice, iterate, and mind your grammar prompts, assumptions, and verification.”

II. Questioning / Asking

Good conversations flow from well‑sequenced questions—topical, simple, coherent, cohesive.
LLM Conversation Example 1
Q: What are empirical judgments?

A: Empirical judgments are based on observation, experience, or experimentation.
Q: What are moral judgments?

A: Moral judgments are based on ethical principles and values.

IV. Judgment

“The structure of a language affects its speakers’ worldview and cognition.”
— Henry Hazlitt
“The art of questioning is the source of all knowledge.”
— Thomas Berger
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