AI makes zero-based budgeting a practical finance tool

Experts in the pursuit of harnessing nuclear fusion will assure you that the technology is coming — just 30 years away, according to their projections.

The joke is that if you wait three decades and ask them where it is— they’ll say the same thing.

In finance and procurement, the concept of zero-based budgeting has long been a bit like the pursuit of fusion power: more of an aspiration rather than something any real-world corporation can actually implement today. 

Which is unfortunate. Like the idea of the world utilizing the free, non-polluting energy that a fusion plant would offer, on paper ZBB promises objective, data-based baselines for every budgeting phase that would allow decision-makers to only work with what’s real and current, not what happened last year, or even farther back.

The proposal with ZBB is that by mandating a comprehensive justification and validation of each expense, rather than relying on historical spending patterns, organizations can remove possible blockers within their procurement processes. This approach aims to ensure that what you’re doing is the numerically provable best case for the specific circumstances at hand.

This approach certainly holds immense appeal, so much so that Jimmy Carter tried and failed to make federal government adhere to this discipline in the second half of the 1970s. However, ZBB never really gained traction or widespread adoption, and so its aspirations were largely relegated to the realm of “theory taught in business schools but lacking practical viability.”

The factors putting ZBB back on the table

History and controversy aside, the core idea of ZBB is clear — it presents CFOs with an approach that mandated comprehensive justification and explicit approval for all expenditures during each new budgetary cycle, typically at the outset of the financial year. This process ostensibly offered CFOs a way to make relevant decisions against a true picture of the company’s cash flow.

But ZBB never truly went away. In fact, it is experiencing a resurgence. Consulting firms like McKinsey have reminded us that if we could weigh the value of every dollar and start afresh with every budget cycle we could mitigate the risks associated with operating on outdated information and boost overall performance outcomes.

ZBB idealism is also happening at the micro-level, with social media influencers hopping on the ZBB bandwagon. Influencers like Beth Fuller have attributed their ability to pay off credit card debts to following online content creators who advocate for ZBB principles.

The question then becomes how would we make ZBB, long an ideal but one that proved too difficult to implement, work at the enterprise level? It turns out, a viable way exists, or at least we can start the process to get there. 

And you won’t be surprised to learn that the game-changer here is AI.

A way to open the door to ZBB

Currently, the spotlight within the artificial intelligence domain is on finding use cases for AI to solve real business problems. Organizations have been at the forefront of this endeavor for several years through an approach we term “autonomous sourcing.”

Specifically, organizations using an autonomous spend management approach source can purchase as many new services and vendors as they need within a given budgetary cycle. However, this process is underpinned by not just genuine and up-to-date market data, but also with the benefit of a corporate knowledge bank.  This knowledge base facilitates multidimensional comparisons, enabling organizations to evaluate purchases not only longitudinally (against previous periods) but also orthogonally, meaning across different business units within the enterprise. 

This may not be the precise dictionary definition of ZBB. But it represents a radical change from the lack of data and visibility CFOs have struggled with and a way to open the door to the underlying vision of ZBB: data-driven financial accuracy.

This autonomous spend management approach resonates with organizations seeking to rationalize and optimize their budgeting processes, often commencing with their procurement operations. These forward-thinking entities inherently grasp the transformative potential of leveraging machine learning and generative AI capabilities to tackle the sourcing problem.

And the convergence of machine learning, generative AI and autonomous sourcing platforms presents organizations with the ability to realize approximately 90% of the ZBB ideal in the present day. That’s happening via organizations using autonomous sourcing to consciously and strictly seek to rationalize every purchase and make data-driven decisions on every vendor relationship.

The commitment to data-driven evaluation of vendor relationships is actually super-important on the path to any form of zero-based decision-making basis. Why? Because it’s your best way of ensuring that you’re not locked into any partnerships or contractual arrangements that aren’t continuing to add value.

Even starting to explore this area of spend with proper data and analytical tools can move organizations off the proverbial sandbar of inefficiency. Last year, for instance, the Mays Business School published research that concluded the simple act of tracking a single category of expenditure can catalyze a reduction in overall spending.

The exciting prospect lies in the potential for modern businesses with diverse spending categories like marketing, HR, sales, IT, finance, and others to capitalize on significant cost-saving opportunities through AI-powered procurement solutions, e.g., accurate supplier sourcing and matching, e-negotiation and automated awarding capabilities.

ZBB’s future is now, not 30 years off

President Carter’s administration wanted to achieve such objectives and possibly on paper could have done — if they had all the time in the world, and exclusive access to the entire computing power of the United States at the time.

But even under those circumstances ZBB might not have worked — as without the efficiencies afforded by AI, ZBB would require manual sourcing, selecting, bidding, negotiating and awarding for every single purchase and vendor relationship in the business. 

The truth is, fulfilling every aspect of ZBB manually, as envisioned by its originator, Pete Phyrr, is an insurmountable task for humans. However, using the power of AI to automate numerous processes, alongside giving  individual business units the autonomy to source and complete their own purchases through autonomous sourcing, means ZBB becomes not just practicable, but essential in today’s dynamic business landscape.

Weighing it all up, maybe we can retire the notion that ZBB is the accounting industry’s version of fusion.

Instead, we can use the power of autonomous sourcing to perform the equivalent of fusion in the back office.