Finance gets stronger when AI delivers 📊

The useful signal this week is not that CFOs are getting more interested in AI. That part is settled. What is changing is the standard for what now counts as believable value, because spending is rising, expectations are maturing, and finance is being asked to separate durable operating gains from expensive activity.

That shift is healthy for the function. Once CFOs stop treating AI as a single transformation promise and start tying it to growth design, workflow redesign, and financial line items, the conversation gets closer to how capital actually earns credibility within the business.

This issue covers the value discipline now shaping enterprise AI, the Scale Test system, and three articles worth your time.

The useful signal is not that more channels can now be measured. It is whether the spend can be tied to a clearer operating case before it gets scaled. That is the same discipline finance should keep applying to AI and every other growth line item that wants more budget without enough proof.

That is why Roku Ads Manager is worth a look. It gives teams a more direct way to plan and measure streaming TV campaigns with clearer visibility into how spend is being deployed.

For its first CTV campaign, Jennifer Aniston’s DTC haircare brand LolaVie had a few non-negotiables. The campaign had to be simple. It had to demonstrate measurable impact. And it had to be full-funnel.

LolaVie used Roku Ads Manager to test and optimize creatives — reaching millions of potential customers at all stages of their purchase journeys. Roku Ads Manager helped the brand convey LolaVie’s playful voice while helping drive omnichannel sales across both ecommerce and retail touchpoints.

The campaign included an Action Ad overlay that let viewers shop directly from their TVs by clicking OK on their Roku remote. This guided them to the website to buy LolaVie products.

Discover how Roku Ads Manager helped LolaVie drive big sales and customer growth with self-serve TV ads.

The DTC beauty category is crowded. To break through, Jennifer Aniston’s brand LolaVie, worked with Roku Ads Manager to easily set up, test, and optimize CTV ad creatives. The campaign helped drive a big lift in sales and customer growth, helping LolaVie break through in the crowded beauty category.

Learn more.

83% of CFOs plan to increase their AI budgets by more than 15% over the next two years, according to Bain. That number matters because it shows the capital commitment is no longer tentative. Finance is moving past pilot curiosity and into sustained funding, which means weak use-case discipline will become more expensive with each budget cycle. Bain also notes that speed and cycle-time reduction are the leading early sources of value in finance, ahead of cost savings, which is a useful reminder that faster decisions often show up before cleaner margin expansion does.

If the budget curve is steepening faster than the operating plan, finance is funding momentum before it has fully earned trust.

At one company, the AI portfolio looked active enough to satisfy everyone. The pilots were moving, the budget was in place, and leaders liked the story it told about modernization. But when finance tried to explain what was actually improving, the answers became less clear. Some projects were making work faster. Some were supposed to lift decision quality. Some were really workflow-redesign bets disguised as software deployments. The issue was not effort. There was a weak separation between activity and scale-worthy value.

Finance gets a cleaner line between experimentation and defensible scale. That makes AI funding easier to protect when the board asks what the spending is actually changing.

CFOs are no longer just funding enterprise AI from the sidelines, and a meaningful share of rising AI spend is now being directed into finance itself.

My take: What matters here is that capital is moving before the model is fully settled, which raises the cost of weak use-case discipline. The CFO edge is no longer just access to an AI budget, but the ability to decide which finance workflows deserve real scale and which still belong in controlled experimentation.

Long-term organizational growth depends on linking finance more closely to strategic priorities and supporting investments that may begin with near-term wins but build larger future returns.

My take: The useful signal is that growth discipline is not just about defending margins quarter to quarter. It is also about deciding which investments deserve patient capital because they reduce future cost, strengthen resilience, or open a more durable path to expansion.

Enterprise AI is still falling short on financial returns because many companies are deploying the technology without redesigning workflows, tying pilots to clear value, or building the training and governance needed for real adoption.

My take: The real issue is not that AI cannot create value. It is that finance often sees visible activity before it sees redesigned ownership, workflow changes, or line-item impact, and that gap is exactly where return stories start to break down.

This usually breaks when finance has visibility into AI activity but no clear way to judge whether a workflow has earned the right to expand. The dashboard shows pilots, the teams can point to productivity gains, and the budget has already moved. But when you ask which use cases are tied to a financial line item, which ones require workflow redesign, and which ones are still mostly promises, the picture becomes less stable. That is when finance discovers it has momentum tracking, not proof tracking.

Can you produce, right now, a list of your AI initiatives, the first financial result each one is supposed to improve, and which ones still have activity without enough proof?

The useful signal is not that more AI events are showing up on the calendar. It is whether the conversations inside them are grounded in real operating change, measurable outcomes, and a clearer view of what should actually scale. That is the same filter finance should apply before another wave of AI spending is treated as progress by default.

That is why Gladly Connect Live feels worth a look. It offers a sharper window into how teams are thinking about AI, customer operations, and practical implementation at a level finance leaders can evaluate more seriously.

The room you want to be in. This is where CX leaders are tackling the hard AI questions and sharing what's actually working. For CX and ecommerce leaders. Atlanta, May 4–6. Space is limited — secure your spot now.

Register now

The deeper operating problem this week is not that AI takes time to pay off. It is that many companies are still trying to scale investment before they have established a durable standard for what financial proof should look like. That becomes riskier as budgets rise, because weak assumptions are repeated with more confidence once they are funded.

The common thread is straightforward. Finance gets stronger when it treats AI as a set of operating claims that must earn belief, rather than as a category that automatically deserves more money. The teams that handle this phase well will not be the ones spending most aggressively. They will be the ones who know which use cases deserve trust, which still need redesign, and which have not yet produced enough evidence to scale.

Until next edition. — Marcus Reid

P.S. If your team has found a clean way to distinguish AI initiatives that deserve scale from the ones that still need workflow redesign or tighter value proof, reply directly to this email. I am collecting examples of the standards that finance leaders are using before pilot energy becomes permanent spend.