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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.

Finance

Should investors have bought gold or the S&P 500 5 years ago?

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Should investors have bought gold or the S&P 500 5 years ago?
Image source: Getty Images

Remember 2020/21, when Covid-19 crashed stock markets? At their 2020 lows, the UK FTSE 100 and US S&P 500 indexes had collapsed by 35%. Nevertheless, 2020/21 was a great time to buy shares, because returns have been outstanding since.

But would I done better five years ago buying the S&P 500 or investing in gold, one of the world’s oldest stores of value?

Over the past five years, the S&P 500 has leapt by 70.4%. However, this capital gain excludes cash dividends — regular cash returns paid by some companies to shareholders.

Adding dividends, the S&P 500’s return jumps to 81.8%, turning $10,000 into $10,818. That works out at a compound yearly growth rate of 12.7%.

Then again, as a British investor, I buy US assets using pounds sterling. The US index’s return in GBP terms over five years is 13.6% a year. This equates to a five-year total return of 89.2% — still a handsome result for UK buyers of US shares.

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For many, gold is the ideal asset in times of trouble. First, it has several uses: as a store of value (often in bank vaults), for jewellery, and as an excellent conductor of electricity in electronics. Second, it is scarce: all the gold ever mined would fit into a cube with sides of under 23m.

As I write, the gold price stands at £3,484.50. This is up an impressive 178.5% over the past five years. That works out at a compound yearly growth rate of 22.7% a year — thrashing the S&P 500’s returns.

Of course, gold pays no income, but these bumper returns can more than make up for this omission. Then again, with the S&P 500 worth around $60trn, its gains have been enjoyed by a much larger cohort of investors

Thus, over the past five years, investors have made more money owning gold than investing in the S&P 500. And speaking of high-performing investments, here’s another hidden gem from spring 2021…

As an older investor (I turned 58 this month), my family portfolio is packed with boring, old-school FTSE 100 and FTSE 250 shares that pay generous dividends.

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For example, my family owns shares in Lloyds Banking Group (LSE: LLOY), whose stock has soared since 2021. As I write, Lloyds shares trade at 96.68p, valuing the Black Horse bank at £56.7bn.

Over one year, the shares are up 37.8%, easily beating major market indexes. Over five years, this stock has soared by 135.6% — comfortably beating most UK and US shares over this timescale.

Again, the above returns exclude dividends, which Lloyds stock pays out generously. Right now, its dividend yield is 3.8% a year, beating the wider FTSE 100’s yearly cash yield of 3.1%.

Earlier this year, Lloyds shares were riding high, peaking at 114.6p on 4 February. They have since fallen by 15.6%, driven down by the US-Iran war, soaring energy prices, and fears of an economic slowdown. Of course, if the UK endures another recession, banking revenues, profits, and cash flow could take a nasty hit.

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That said, sticky, above-target inflation hinders the Bank of England from cutting interest rates. This boosts Lloyds’ net interest margin, boosting its 2026 earnings. And that’s why we will keep holding tightly onto our Lloyds shares!

The post Should investors have bought gold or the S&P 500 5 years ago? appeared first on The Motley Fool UK.

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The Motley Fool UK has recommended Lloyds Banking Group. Cliff D’Arcy has an economic interest in Lloyds Banking Group shares. Views expressed on the companies mentioned in this article are those of the writer and therefore may differ from the official recommendations we make in our subscription services, such as Share Advisor, Hidden Winners and Pro. Here at The Motley Fool, we believe that considering a diverse range of insights makes us better investors.

Motley Fool UK 2026

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4 Smart Ways to Use Your Tax Return for Financial Planning

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4 Smart Ways to Use Your Tax Return for Financial Planning

(Image credit: Getty Images)

In my work helping people think through retirement planning decisions, I often see people focus heavily on preparing their tax return but spend very little time reviewing it afterward.

By the time tax season ends, most people treat the document like a receipt: They file it, save a copy somewhere and move on.

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The CFO who turned Adobe’s finance department into an AI lab | Fortune

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The CFO who turned Adobe’s finance department into an AI lab | Fortune

Finance chief Dan Durn is turning Adobe’s finance organization into an early proving ground for agentic AI—using autonomous software agents to forecast results, scan contracts, and even answer hundreds of thousands of emails.

The push mirrors Adobe’s broader strategy around agentic AI. For customers, the company lets them choose models, combine them with their own data and Adobe’s, and point agents at specific business outcomes.

Internally, Durn, who is also in charge of technology, security and operations, has taken a similar approach to finance: pairing a rules-based, data-heavy function with AI, within a structure where finance, IT, and security report to one leader so pilots can move to production quickly. “Accuracy is non-negotiable,” he adds; that’s why Adobe is investing in structured data and governance so it can move fast without sacrificing precision, he says. 

The rise of AI is rapidly reshaping corporate leadership, accelerating turnover and elevating executives who can deliver fast, tangible results. Even long-tenured leaders face increasing pressure from investors to move aggressively on AI. Recent leadership changes, including the announced retirement of Adobe CEO Shantanu Narayen, highlight how little patience markets now have for perceived hesitation. At the same time, Adobe reported that annualized revenue from its AI-first products more than tripled year over year in its first quarter of fiscal 2026, which ended Feb. 27. Across Fortune 500 companies, this dynamic is creating a new internal proving ground where executives are judged by how effectively, and how quickly, they deploy AI to drive growth, efficiency, and innovation.

Using AI in finance

Inside finance, Durn groups AI use into three buckets: forecasting, anomaly detection, and general productivity.

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For forecasting, AI uncovers patterns and signals in data that would be difficult for humans to detect quickly, he explains. Anomaly-detection agents flag performance that’s unexpectedly strong or weak—“things that can get lost in the sea of data”—so finance can intervene faster, he says.

However, Durn says the best examples now sit in productivity, citing three use cases:

1. Extracting information from PDFs

One of the most developed use cases involves “containers” of information—collections of PDFs such as investor transcripts, quarterly reports, and analyst research. Finance teams use Adobe’s PDF Spaces to load documents into a shared digital workspace and use an agentic AI assistant to surface themes, insights, and messaging cues in minutes rather than hours.

A recent Forrester TEI study found Acrobat’s agentic AI Assistant increases efficiencies in document summarization and analysis by 45%. Durn says that matters because “the world’s information lives in PDF,” and AI that turns static content into insights that can be used.

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2. Cutting contract review time in half

Adobe is also using agentic AI to overhaul contract reviews across finance and procurement functions including revenue assurance, contract operations, product fulfillment, and vendor management. Instead of finance professionals combing through every clause, an AI assistant scans thousands of contracts, highlights provisions relevant to each function, and flags non-standard terms.

The system has cut review time roughly in half, speeding individual reviews and allowing teams to query the entire contract repository—for example, identifying which contracts include auto-cancellation features or foreign-exchange adjustment windows, Durn says. Adobe built its first prototype by April 2024 and began onboarding teams in January 2025.

3. Automating “common” inboxes

A third area is the “common inboxes” that handle high-volume internal and external email—shared addresses for sales, treasury, finance, and supplier questions. Adobe deployed an agentic AI assistant that auto-tags, prioritizes, routes, and, when criteria are met, auto-responds to emails. Typical queries include supplier billing issues or standard credit-quality questions coming into the treasury from Salesforce.

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“In 2025 alone, the system auto-responded to about 300,000 emails across 19 inboxes, saving more than 5,000 hours of manual work and freeing teams to focus on more complex issues,” he says. The tool took about six months to build; beta teams began using it around August 2024, with full rollout in January 2025.

The payoff, he stresses, isn’t headcount cuts but the ability to scale more efficiently as Adobe grows.

Grassroots ideas, decade-long build

Durn traces these finance use cases to Adobe’s long AI journey and a bottom-up idea pipeline. The company has invested in machine learning and AI for more than a decade, initially to understand customer usage patterns and embed intelligence into products—work that laid the groundwork for generative and agentic AI.

Many of the best applications come from “reaching down into the organization” and asking employees where AI could remove friction or make their jobs easier, he says. There are more ideas than capacity, so the team prioritizes those with the greatest impact.

When deciding whether to green-light AI investments, Durn focuses on organizational velocity—the ability of back-office functions to keep pace with faster product innovation. If finance doesn’t adopt AI, he argues, it risks becoming a “rate limiter of growth.”

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The actual spend is modest, he adds; much of the work involves change management and process redesign layered onto Adobe’s technology.

Durn’s perspective on change management coincides with new research from McKinsey. To capture the full value of AI, organizations need to go beyond “a piecemeal approach and push for a double transformation—both technical and organizational—that includes reimagining how work gets done across functions and workflows,” according to the report. While 88% of organizations surveyed are now experimenting with AI, fewer than 20% report tangible bottom-line results,, the research finds.

How AI is changing his own job

For his own workflow, Durn relies on AI primarily for insight generation. Ahead of earnings, his team loads pre-earnings research reports, Adobe filings, and peer transcripts into an AI-powered workspace to surface themes and likely investor questions.

Scripts and Q&A preparation are then run through models with guardrails to test whether messaging addresses those themes and to ask, “If I were an investor, what are my key takeaways?”

He sees it as a useful check on clarity and consistency—using AI to validate instincts and sharpen how Adobe communicates with the market.

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