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AI makes zero-based budgeting a practical finance tool

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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.

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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.

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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. 

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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.

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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.

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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.

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Finance

Google Cloud Pursues Financial Markets in FactSet Alliance | PYMNTS.com

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Google Cloud Pursues Financial Markets in FactSet Alliance | PYMNTS.com

Google Cloud and FactSet, a provider of data and artificial intelligence solutions to the financial markets, plan to jointly develop AI agents designed to assist with portfolio operations, deal advisory and corporate finance.

The agents are one of three areas of focus the companies will pursue in a new partnership that will bring new AI-powered solutions to the financial industry, FactSet said in a Tuesday (June 30) press release.

The partnership brings together FactSet’s data, analytics and workflows with Google Cloud’s agentic AI capabilities and infrastructure, according to the release.

The new jointly designed agents will be built using Google Cloud’s Gemini Enterprise Agent Platform.

Another area of focus will be FactSet AI enhanced with Gemini models. FactSet is embedding Google’s enterprise Search and Gemini model capabilities in the FactSet Workstation to launch the new agents for finance; leveraging Google Cloud’s AI capabilities to accelerate the development of new Workstation products with deep research functionality and multi-modal experiences; and directly integrating with Google grounding to improve FactSet’s AI-enhanced insights.

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The partnership’s third area of focus is deeper financial intelligence in Gemini Enterprise, which is Google Cloud’s AI platform for building, governing and deploying agents. FactSet’s MCP and agent sharing functionality will deepen the platform’s financial intelligence and provide financial professionals with seamless interoperability between the FactSet Workstation and Gemini Enterprise, per the release.

FactSet CEO Sanoke Viswanathan said in the release: “AI is fundamentally shifting how financial professionals access data, derive insights and make decisions. Together with Google Cloud, we are putting trusted financial data and advanced AI capabilities to work, empowering our clients with more intuitive, connected and intelligent agents.”

Google Cloud Chief Product and Business Officer Karthik Narain said in the release: “By combining Google Cloud’s agentic AI capabilities with FactSet’s deep financial expertise, we are enabling investment professionals to surface insights faster, automate complex workflows, and realize commercial value from AI.”

The PYMNTS Intelligence report “Financial Services Pulls Ahead in the Enterprise AI Race” found that 85% of financial services and insurance firms are increasing their AI budgets over the next 12 months.

The top justifications for these investments are productivity and efficiency gains, cited by 65% of the firms, and strategic or competitive positioning, also cited by 65%, according to the report.

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What the Supreme Court’s campaign finance ruling means for the 2026 election

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What the Supreme Court’s campaign finance ruling means for the 2026 election

Tuesday’s Supreme Court ruling changing certain federal campaign finance limits could make a big difference in the battle for control of Congress this fall, giving Republican candidates who have been getting outraised by opponents direct access to more party cash.

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World Bank drops climate finance target amid US pressure

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World Bank drops climate finance target amid US pressure

The World Bank is ditching its commitment to steer 45 percent of its spending toward projects with climate benefits, after facing pressure from the Trump administration.

The move, announced Monday following a meeting of the bank’s board of directors last week, marks a victory in President Donald Trump’s effort to purge climate policies from U.S. foreign policy. His administration has described the target as “distortionary” and “nonsensical.”

The bank preserved its broader Climate Change Action Plan — of which the 45 percent target was a key metric — just days before it was set to expire at the end of June. In addition to directing money toward climate projects, the plan provides technical support for helping countries reduce their greenhouse gas pollution and adapt to rising temperatures.

“We will retire the 45% climate co-benefits target,” the World Bank Group said in a statement, noting that it had “done significant work in answering client demand and needs.”

The bank’s work on climate “is and will remain firmly client driven, supporting them in delivering on their own ambitions as set out in their national plans and NDCs,” the statement added, referring to the nationally determined contributions countries submit under the Paris Agreement.

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The decision to drop the climate finance target follows months of pressure from the Trump administration. People with knowledge of the negotiations said the U.S. was firm that the target must go despite other countries indicating their support for the bank’s climate goal. The U.S. has sway over the bank’s decisions as its largest shareholder.

Beyond the finance target, the Climate Change Action Plan also provides diagnostic reports on countries’ climate and development goals and aims to align lending with the Paris Agreement, which calls for preventing temperature rise from surpassing 2 degrees Celsius since the Industrial Revolution.

The bank said it would honor a board request to undertake an independent evaluation of the climate plan to determine if it’s helping countries grapple with rising temperatures. The decision effectively extends the plan beyond its expiration at the end of June.

The climate target was supported by many of the bank’s shareholders. It’s also been a prominent signal of the bank’s support for climate action at a time when the impacts of rising temperatures are accelerating.

“This is way, way away from where we should be for a responsible financial architecture,” said one official from a developed country who was directly involved in the negotiations and was granted anonymity to describe internal discussions.

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The bank will continue to track and report on the amount of money going to projects with climate co-benefits. It exceeded its own target last year by directing 48 percent of its financing to climate-related projects.

Other climate targets embedded in agreements that govern different arms of the bank will remain, including one for the International Development Association, the bank’s fund for the poorest countries.

Multilateral development banks play a key role in global climate negotiations, where wealthy countries have committed to helping provide $300 billion a year for poorer countries by 2035. That no longer includes the United States, which has left the Paris Agreement and will exit the underlying United Nations Framework Convention on Climate Change early next year.

“Targets send enormous signals about an institution’s direction of travel,” said Clemence Landers, a senior fellow at the Center for Global Development. “At the same time, it’s a sign of the times and the World Bank is doing its level best to not rankle its largest shareholder.”

She believes the bank will continue financing renewable energy projects in countries that want them, despite having dropped its climate target.

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“I wouldn’t be shocked if the bank continued to have an extremely robust clean pipeline with or without this target,” said Landers.

The bank says retiring the 45 percent target is part of its shift from a focus on “inputs to outcomes.” It will continue to monitor and report net greenhouse gas emissions across its projects and countries’ ability to withstand climate risks.

“We will continue to report to the Board on progress, including on climate co-benefits, and to contribute to our related joint MDB efforts,” the statement said, referring to its role as a multilateral development bank. “We will explore and discuss ways to better structure our engagement on adaptation, nature and pollution.”

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