AI makes zero-based budgeting a practical finance tool
7 mins read

AI makes zero-based budgeting a practical finance tool


Experts in exploring how to use nuclear fusion will assure you that the technology is coming – according to their estimates, only 30 years away.

The joke is that if you wait three decades and ask them where it is- they will say the same thing,

In finance and purchasing, the concept of zero based budgeting This has long been something like the discovery of fusion power: more of an aspiration than something that could actually be implemented today by any real-world corporation.

Which is unfortunate. Like the idea of ​​a world using free, non-polluting energy provided by a fusion plant, on paper ZBB promises an objective, data-based baseline for every budget phase that will give decision makers only real and Will allow to work with the present, not what happened last year or even before.

The proposition with ZBB is that by mandating comprehensive justification and verification of each spend, rather than relying on historical spending patterns, organizations can remove potential blockers within their procurement processes. The purpose of this approach is to ensure that what you are doing is the numerically proven best case for the specific circumstances at hand.

This approach is certainly highly attractive, so much so that Jimmy Carter Attempts were made to enforce this discipline by the federal government in the late 1970s and failed., However, ZBB never really gained traction or was widely adopted, and so its aspirations were largely relegated to the realm of “theory taught in business schools but lacking practical feasibility”.

Factors that brought ZBB back to the table

History and controversy aside, the basic idea of ​​ZBB is clear – it presents the CFO with an approach that mandates comprehensive justification and explicit approval for all expenditures during each new budgetary cycle, usually at the beginning of the fiscal year. Does. This process clearly provided a way for the CFO to make relevant decisions based on a real picture of the company's cash flows.

But ZBB never really went away. In fact, it is experiencing a resurgence. Consulting firms like McKinsey have reminded us If we can measure the value of every dollar and start fresh with each budget cycle, we can reduce the risks associated with acting on outdated information and boost overall performance results.

ZBB idealism is also happening on a micro level with social media influencers Jumping on the ZBB Bandwagon, Influencers like Beth Fuller have credited their ability to pay off credit card debt to following online content creators who advocate ZBB principles.

The question is how will we make ZBB, which has long been an ideal but has proven very difficult to implement, work at the enterprise level? It turns out, a viable path 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 ZBB door

Currently, the main focus in the artificial intelligence field is on finding use cases for AI to solve real business problems. Organizations have been at the forefront of this effort for many years, what we call “autonomous sourcing.”

Specifically, organizations using the 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 based not only on real and updated market data, but also on the benefits of the corporate knowledge bank. This knowledge base facilitates multidimensional comparisons, enabling organizations to evaluate purchasing not only longitudinally (compared to previous periods) but also orthogonally, across different business units within the enterprise.

This may not be the exact dictionary definition of ZBB. But it represents a radical change from the lack of data and visibility that CFOs are struggling with and goes a way to open the door to ZBB's underlying vision: data-driven financial accuracy.

This autonomous spend management approach aligns with organizations that want to rationalize and optimize their budgeting processes, often starting with their procurement functions. These forward-thinking organizations naturally understand 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 provides organizations with the ability to realize the ZBB ideal approximately 90% of the time. This is happening through organizations that consciously and rigorously try to rationalize every purchase using autonomous sourcing and make data-driven decisions at every vendor relationship.

A commitment to data-driven evaluation of vendor relationships is critically important on the path to truly zero-based decision making of any kind. Why? Because this is your best way to ensure that you are not locked into any partnerships or contractual arrangements that are not continuing to add value.

Even starting to explore this area of ​​spend with the proper data and analytical tools can help pull organizations out of the proverbial quicksand of inefficiencies. For example, last year Mace Business School published Research This led to the conclusion that the simple act of tracking one category of expenditure could catalyze a reduction in overall expenditure.

The exciting potential lies in the ability to capitalize on significant cost-savings opportunities through AI-powered procurement solutions for modern businesses with diverse spend categories such as marketing, HR, sales, IT, finance and others, for example, precision Supplier sourcing and matching, e-negotiation and automated awarding capabilities.

ZBB's future is now, not in 30 years

President Carter's administration wanted to achieve such objectives and probably could have done so on paper—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 would not have worked – because without the efficiency provided by AI, ZBB would have required manual sourcing, selection, bidding, negotiation and awarding for every procurement and vendor relationship in the business.

The truth is, completing every aspect of ZBB manually, as envisioned by its originator, peat fire, is an insurmountable task for man. However, harnessing the power of AI to automate many processes as well as giving individual business units the autonomy to source and fulfill their own purchases through autonomous sourcing means that ZBB can thrive in today's dynamic business landscape. Not only practical, but also necessary.

Considering all this, perhaps we can eliminate the notion that ZBB is the accounting industry's version of fusion.

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


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