Why CFOs Are Cutting AI Budgets (And The 3 Metrics That Save Them) via @sejournal, @purnavirji

Every AI vendor pitch follows the same script: “Our tool saves your team 40% of their time on X task.”

The demo looks impressive. The return on investment (ROI) calculator backs it up, showing millions in labor cost savings. You get budget approval. You deploy.

Six months later, your CFO asks: “Where’s the 40% productivity gain in our revenue?”

You realize the saved time went to email and meetings, not strategic work that moves the business forward.

This is the AI measurement crisis playing out in enterprises right now.

According to Fortune’s December 2025 report, 61% of CEOs report increasing pressure to show returns on AI investments. Yet most organizations are measuring the wrong things.

There’s a problem with how we’ve been tracking AI’s value.

Why ‘Time Saved’ Is A Vanity Metric

Time saved sounds compelling in a business case. It’s concrete, measurable, and easy to calculate.

But time saved doesn’t equal value created.

Anthropic’s November 2025 research analyzing 100,000 real AI conversations found that AI reduces task completion time by approximately 80%. Sounds transformative, right?

What that stat doesn’t capture is the Jevons Paradox of AI.

In economics, the Jevons Paradox occurs when technological progress increases the efficiency with which a resource is used, but the rate of consumption of that resource rises rather than falls.

In the corporate world, this is the Reallocation Fallacy. Just because AI completes a task faster doesn’t mean your team is producing more value. It means they’re producing the same output in less time, but then filling that saved time with lower-value work. Think more meetings, longer email threads, and administrative drift.

Google Cloud’s 2025 ROI of AI report, surveying 3,466 business leaders, found that 74% report seeing ROI within the first year, most commonly through productivity and efficiency gains rather than outcome improvements.

But when you dig into what they’re measuring, it’s primarily efficiency gains, and not outcome improvements.

CFOs understand this intuitively. That’s why “time saved” metrics don’t convince finance teams to increase AI budgets.

What does convince them is measuring what AI enables you to do that you couldn’t do before.

The Three Types Of AI Value Nobody’s Measuring

Recent research from Anthropic, OpenAI, and Google reveals a pattern: The organizations seeing real AI ROI are measuring expansion.

Three types of value actually matter:

Type 1: Quality Lift

AI can make work faster, and it makes good work better.

A marketing team using AI for email campaigns can send emails quicker. And they also have time to A/B test multiple subject lines, personalize content by segment, and analyze results to improve the next campaign.

The metric isn’t “time saved writing emails.” The metric is “15% higher email conversion rate.”

OpenAI’s State of Enterprise AI report, based on 9,000 workers across almost 100 enterprises, found that 85% of marketing and product users report faster campaign execution. But the real value shows up in campaign performance, not campaign speed.

How to measure quality lift:

• Conversion rate improvements (not just task completion speed).
• Customer satisfaction scores (not just response time).
• Error reduction rates (not just throughput).
• Revenue per campaign (not just campaigns launched).

One B2B SaaS company I talked to deployed AI for content creation.

• Their old metric was “blog posts published per month.”
• Their new metric became “organic traffic from AI-assisted content vs. human-only content.”

The AI-assisted content drove 23% more organic traffic because the team had time to optimize for search intent, not just word count.

That’s quality lift.

Type 2: Scope Expansion (The Shadow IT Advantage)

This is the metric most organizations completely miss.

Anthropic’s research on how their own engineers use Claude found that 27% of AI-assisted work wouldn’t have been done otherwise.

More than a quarter of the value AI creates isn’t from doing existing work faster; it’s from doing work that was previously impossible within time and budget constraints.

What does scope expansion look like? It often looks like positive Shadow IT.

The “papercuts” phenomenon: Small bugs that never got prioritized finally get fixed. Technical debt gets addressed. Internal tools that were “someday” projects actually get built because a non-engineer could scaffold them with AI.

The capability unlock: Marketing teams doing data analysis they couldn’t do before. Sales teams creating custom materials for each prospect instead of using generic decks. Customer success teams proactively reaching out instead of waiting for problems.

Google Cloud’s data shows 70% of leaders report productivity gains, with 39% seeing ROI specifically from AI enabling work that wasn’t part of the original scope.

How to measure scope expansion:

• Track projects completed that weren’t in the original roadmap.
• Ratio of backlog features cleared by non-engineers.
• Measure customer requests fulfilled that would have been declined due to resource constraints.
• Document internal tools built that were previously “someday” projects.

One enterprise software company used this metric to justify its AI investment. It tracked:

• 47 customer feature requests implemented that would have been declined.
• 12 internal process improvements that had been on the backlog for over a year.
• 8 competitive vulnerabilities addressed that were previously “known issues.”

None of that shows up in “time saved” calculations. But it showed up clearly in customer retention rates and competitive win rates.

Type 3: Capability Unlock (The Full-Stack Employee)

We used to hire for deep specialization. AI is ushering in the era of the “Generalist-Specialist.”

Anthropic’s internal research found that security teams are building data visualizations. Alignment researchers are shipping frontend code. Engineers are creating marketing materials.

AI lowers the barrier to entry for hard skills.

A marketing manager doesn’t need to know SQL to query a database anymore; she just needs to know what question to ask the AI. This goes well beyond speed or time saved to removing the dependency bottleneck.

When a marketer can run their own analysis without waiting three weeks for the Data Science team, the velocity of the entire organization accelerates. The marketing generalist is now a front-end developer, a data analyst, and a copywriter all at once.

OpenAI’s enterprise data shows 75% of users report being able to complete new tasks they previously couldn’t perform. Coding-related messages increased 36% for workers outside of technical functions.

How to measure capability unlock:

• Skills accessed (not skills owned).
• Cross-functional work completed without handoffs.
• Speed to execute on ideas that would have required hiring or outsourcing.
• Projects launched without expanding headcount.

A marketing leader at a mid-market B2B company told me her team can now handle routine reporting and standard analyses with AI support, work that previously required weeks on the analytics team’s queue.

Their campaign optimization cycle accelerated 4x, leading to 31% higher campaign performance.

The “time saved” metric would say: “AI saves two hours per analysis.”

The capability unlock metric says: “We can now run 4x more tests per quarter, and our analytics team tackles deeper strategic work.”

Building A Finance-Friendly AI ROI Framework

CFOs care about three questions:

• Is this increasing revenue? (Not just reducing cost.)
• Is this creating competitive advantage? (Not just matching competitors.)
• Is this sustainable? (Not just a short-term productivity bump.)

How to build an AI measurement framework that actually answers those questions:

Step 1: Baseline Your “Before AI” State

Don’t skip this step, or else it will be impossible to prove AI impact later. Before deploying AI, document current throughput, quality metrics, and scope limitations.

Step 2: Define Leading Vs. Lagging Indicators

You need to track both efficiency and expansion, but you need to frame them correctly to Finance.

• Leading Indicator (Efficiency): Time saved on existing tasks. This predicts potential capacity.
• Lagging Indicator (Expansion): New work enabled and revenue impact. This proves the value was realized.

Step 3: Track AI Impact On Revenue, Not Just Cost

Connect AI metrics directly to business outcomes:

• If AI helps customer success teams → Track retention rate changes.
• If AI helps sales teams → Track win rate and deal velocity changes.
• If AI helps marketing teams → Track pipeline contribution and conversion rate changes.
• If AI helps product teams → Track feature adoption and customer satisfaction changes.

Step 4: Measure The “Frontier” Gap

OpenAI’s enterprise research revealed a widening gap between “frontier” workers and median workers. Frontier firms send 2x more messages per seat.

This means identifying the teams extracting real value versus the teams just experimenting.

Step 5: Build The Measurement Infrastructure First

PwC’s 2026 AI predictions warn that measuring iterations instead of outcomes falls short when AI handles complex workflows.

As PwC notes: “If an outcome that once took five days and two iterations now takes fifteen iterations but only two days, you’re ahead.”

The infrastructure you need before you deploy AI involves baseline metrics, clear attribution models, and executive sponsorship to act on insights.

The Measurement Paradox

The organizations best positioned to measure AI ROI are the ones who already had good measurement infrastructure.

According to Kyndryl’s 2025 Readiness Report, most firms aren’t positioned to prove AI ROI because they lack the foundational data discipline.

Sound familiar? This connects directly to the data hygiene challenge I’ve written about previously. You can’t measure AI’s impact if your data is messy, conflicting, or siloed.

The Bottom Line

The AI productivity revolution is well underway. According to Anthropic’s research, current-generation AI could increase U.S. labor productivity growth by 1.8% annually over the next decade, roughly doubling recent rates.

But capturing that value requires measuring the right things.

Forget asking: “How much time does this save?”

Instead, focus on:

• “What quality improvements are we seeing in output?”
• “What work is now possible that wasn’t before?”
• “What capabilities can we access without expanding headcount?”

These are the metrics that convince CFOs to increase AI budgets. These are the metrics that reveal whether AI is actually transforming your business or just making you busy faster.

Time saved is a vanity metric. Expansion enabled is the real ROI.

Measure accordingly.

More Resources:

Featured Image: SvetaZi/Shutterstock

The New AI Marketplace: How ChatGPT’s Native Shopping Could Rewrite Digital Commerce via @sejournal, @gregjarboe

When OpenAI quietly added native shopping to ChatGPT – alongside a partnership with Walmart – it marked more than another AI feature rollout. It signaled a fundamental shift in how consumers discover, compare, and purchase products online.

For the first time, shoppers can browse and buy directly inside an AI conversation – no search results, no scrolling, and no marketplace middleman.

To understand what this means for the future of search, marketplaces, and digital marketing, I spoke with Tim Vanderhook, CEO of Viant Technology, who recently shared his perspective on LinkedIn. Vanderhook believes this move could redefine the entire digital commerce ecosystem, breaking down the “gatekeeper dynamic” that platforms like Amazon and Google have long relied on.

In this direct conversation, he explains why LLM-powered shopping could reshape the funnel, rewrite the rules of attribution, and open the door to a new kind of AI-native marketplace.

The Beginning Of A New Marketplace

Greg Jarboe: You called this “the beginning of an exciting new kind of marketplace.” How do you see LLM-powered commerce evolving over the next few years, and what will make it fundamentally different from search- or marketplace-driven models like Google or Amazon?

Tim Vanderhook: We see LLM-powered commerce as a foundational shift, not just in how people discover, but in how they interact with products, services, and brands. Traditionally, platforms like Google, Amazon, or Walmart served as digital commerce gatekeepers, where visibility is controlled by rankings, algorithms, or marketplace dynamics. In an LLM-powered future, the interface becomes conversational, personalized, and far more dynamic.

This model re-centers discovery around intent, not just keywords. Rather than a one-size-fits-all search result, consumers will have AI-driven shopping assistants that understand context, including where, when, why, and for whom they’re buying. This collapses the “search → click → checkout” funnel into a single, intelligent conversation.

For marketers, that means success will be driven by the quality of engagement and product fit, not just ad spend or ranking. In many ways, it’s the inverse of the search economy: Instead of bidding for space, brands will need to earn their way into relevance via storytelling, brand-building, and trust.

Breaking Down The Gatekeepers

Greg Jarboe: You wrote that OpenAI’s move could “break down the gatekeeper dynamic” that Amazon, Walmart, and others rely on. Is this the start of a real power shift in digital commerce? Or will the incumbents adapt fast enough through partnerships and integrations to maintain their dominance?

Tim Vanderhook: Absolutely, and it’s already underway. Legacy players like Amazon have long benefited from their control of both inventory and discovery. That changes when the discovery interface shifts from their search bars to independent, intelligent LLMs like ChatGPT.

That said, don’t count them out. These incumbents have built massive infrastructure and trust. Many will adapt – and fast – by integrating with LLMs or embedding their services into new ecosystems. But the power dynamic will shift: from owning the funnel to participating in a more open, orchestrated marketplace.

In that new environment, the advantage goes to whoever can deliver the best outcome, not just whoever owns the shelf.

The New Role Of Brands And Marketers

Greg Jarboe: If the LLM becomes the new interface for discovery and transactions, what does that mean for brands and marketers? How should they rethink SEO, paid media, and retail media strategies when product visibility depends on conversational AI rather than rankings or ad placements?

Tim Vanderhook: It’s a seismic change. When product discovery becomes conversational and personalized – not driven by static rankings or paid placements – traditional media strategies need a new playbook. Brands must optimize not just for keywords, but for context. That will elevate the importance of full funnel advertising, tailoring paid media strategies around intent and ensuring retail media campaigns can be activated, optimized, and measured in real time.

And in an LLM-driven world, one of the only ways to guarantee visibility is to be the brand consumers ask for by name. Most marketers still spend nearly 70% of their paid ad budgets on channels like search and social that harvest existing intent or “Demand Capture” and only 30% ad spend on long-term brand-building channels like Connected TV and streaming audio that drive real “Demand Generation” and new business growth. That ratio made sense in a keyword-driven world. But in an AI-driven one, marketers have the power to shape the very conversations that define their brands.

The brands people already know and trust are the ones most likely to appear in an LLM’s response. The companies that win in the LLM era will flip that script, and invest MORE in brand, in CTV, in storytelling, the work that generates demand before the consumer ever types (or prompts) a query. In this new landscape, brand storytelling becomes a visibility strategy.

Partnerships Now, Disintermediation Later

Greg Jarboe: You mentioned that in the short term, marketplaces will partner with OpenAI, but in the long term, OpenAI won’t need them. What incentives or business models could sustain those partnerships – and what happens when smaller retailers can plug in directly to ChatGPT?

Tim Vanderhook: In the short term, it’s symbiotic. Marketplaces provide supply, fulfillment, and customer trust – things LLMs need to deliver on the last mile. OpenAI provides access to intent at scale. Both sides benefit.

But long-term, LLMs could grow to be able to connect directly with retailers, cutting out the middle layers. That creates new business models. Think “preferred placement” fees in conversations, affiliate commissions, or verified product data partnerships.

Smaller retailers especially stand to benefit. They’ve historically lacked the ability to compete on page one of Amazon or Google. In a conversational model, they can plug into the system via APIs and win on merit, product value, or relevance – not just ad spend.

The Future Of Attribution And Advertising

Greg Jarboe: How does AI-native commerce change the way marketers should approach attribution, targeting, and customer acquisition when the “search” and “purchase” phases collapse into one step?

Tim Vanderhook: In an AI-native model, the traditional funnel collapses. Search and purchase happen in the same moment, so attribution must evolve. Brands need systems that can measure the full path from prompt to purchase, across channels and devices.

In this new world, marketers must stop chasing last-click metrics and start optimizing for true incrementality. What drove the purchase intent in the first place? How can we replicate that upstream influence? That’s the future, and we’re building for it now.

Trust, Transparency, And Brand Safety

Greg Jarboe: If ChatGPT becomes a transactional interface, how will issues like brand safety, product authenticity, and trust be handled? Will consumers rely on AI-driven recommendations the same way they currently rely on ratings and reviews?

Tim Vanderhook: They will, if and only if, the system earns that trust. That’s why brand safety, transparency, and authenticated data will be non-negotiable.

LLMs will need accountability controls: where the product came from, how it was vetted, and whether it’s real. They’ll need to show their reasoning, not just “what,” but “why.” Consumers are already skeptical of black-box recommendations. AI must be explainable and accountable.

For brands, this means owning your presence in the AI ecosystem. Provide structured data. Ensure your offers and inventory are verifiable. And align with partners who take identity, measurement, and integrity seriously.

As AI reshapes the interface of commerce, I believe those values will only become more essential.

What Marketers Should Do Next

As Vanderhook points out, the rise of LLM-driven shopping doesn’t just introduce another channel – it redefines how intent, discovery, and conversion intersect. For marketers, that means preparing for a world where visibility depends less on search rankings or ad placements and more on how effectively your data, product information, and brand trust are integrated into AI ecosystems.

The winners in this new landscape won’t be those who chase algorithms, but those who make their brands intelligible – and indispensable – to intelligent systems.

More Resources:

Featured Image: SvetaZi/Shutterstock