The Model Context Protocol (MCP) frenzy is alive and kicking in the world of finance and accounting platforms, it’s almost as if offering MCP access to your platform is table stakes in the industry. This applies from the industry incumbents of NetSuite and Intuit’s Quickbooks all the way through to tech-forward platforms such as Aleph, Numeric, Pigment, Rillet, and Tabs. This has all happened in the last few months, following on the heels of the Claude and agentic AI releases in late 2025.

Anyone writing about MCPs feels the need to include a definition—Anthropic created the standard so I’ll just reference their text, “It provides a universal, open standard for connecting AI systems with data sources, replacing fragmented integrations with a single protocol. The result is a simpler, more reliable way to give AI systems access to the data they need.” Your AI (powered currently by LLMs) can talk directly with external platforms, it can understand how the data is organized and what it means, and it can interact with the data using functions provided by the platform.

Might it be that the world up till now, where platforms delivered primarily a visual interface to interact with their data model and workflows is on its way out? It does seem that with the uptake of AI and agents that we are moving toward an empowered user experience, able to architect your own workflows and ultimately natural language interaction with data storage and compute environments.

I’ve even seen commentary on how command line interfaces (CLIs) are re-emerging as a work medium for non-software developers. Of course, chat interfaces, common in AI since the first ChatGPT release, are in a way light-CLI environments too.

New world of substrate access

Sivulka says in his article In Defense of Vertical Software “The value of enterprise software comes from understanding the process and the organization well enough to make the software do exactly the right thing.” and continues on to call this “last mile” the critical component, which I think is a great way to describe the value.

Vertical software capitalized on owning the information delivery and control mechanisms in products, enabling sticky relationships with users and pricing models to match. A different world is emerging, where custom interface and workflow design is almost entirely within reach of the user. Driving this new world are foundational AI models, constantly increasing in capability and usability, that have given users the ability to create one-off and repeatable solutions, do work more efficiently and agentic frameworks in which autonomous agents, provided with scope and direction, can work alone or together in teams to tackle much larger scale tasks.

The trend we’ve seen as a result of this is finance and accounting platforms providing access at the core substrate level, so that the underlying utility of the platform is exposed and more transparent. Vendors may not yet have dispensed with front-end UIs but they have provided the option to bypass UIs for those teams that are sufficiently skilled to use these new developer paradigms.

I feel this does lead to the question though: What characteristics will successful platforms in this new world embody?

Two positions that will survive the trend

I think Gamble in her article The Two Positions that Create a Durable Moat captures two key concepts that will help differentiate success from failure in the new platform wars; the mint position, where the platform owns the data or context at the time an event happens, and the accountability position, where the platform is responsible whenever something goes wrong.

With mint Gamble says it clearly as “the vault stores, the ledger records, the mint creates”—at the very instant a transaction occurs or knowledge or other value is built your platform captures the context. And with accountability it’s the party that underwrites the outcome and absorbs the downside when things fail. These I think will exist outside of any foundation model or environment in which a user customizes their experience and workflow.

I think the concurrent trends in AI right now are strengthening the case that these two moats or value positions will define which platforms secure longevity in the future. With the mint position, the external user or system interaction generates the ground-truth record—foundation models will interact with systems that store that record. The accountability position is closely related to this growing sense of outcomes-based commitment in products and services. You need the vertical integration to achieve a service-level guarantee that requires significant expertise and understanding of the task at hand but built upon a solid, dependable and auditable data substrate.

Accountability and the knowledge worker

The future operating paradigm could be a merger of data platforms that expose base-level constructs along with functions to operate on and transform that data (back to our starting topic of MCP), with general utility AI and with application expertise sufficient to provide a service-level guarantee. Such a guarantee may, for example in accounting, be auditability and/or accuracy.

Interestingly, the very resource thought to be at most risk of replacement with AI may instead be the most fundamentally valuable resource that could make the whole stack work: The knowledge-based worker, who has an intricate understanding of what a service-level guarantee represents and how to coordinate the technology to deliver. Of course, the guarantee is only as good as the person who understands what it promises. As Andrej Karpathy highlighted recently in the context of AI agents “you can outsource your thinking, but you cannot outsource your understanding.”

The reframe: CCO as revenue de-risking partner

There’s a real contrast in scaling a company at $50MM ARR versus $1B: at $50MM you can lose 20% of your customers and grow successfully through it, the acquisition is achievable. At $1B you can’t. The customer success function that worked through the first hundred million breaks at an order of magnitude more. As part of the Speaking C-Suite podcast series I interviewed Harini Gokul who spoke about how redesign of the customer success function is critical as the company scales. This article is the first of a series of three covering the concepts and insights that Harini shared in the discussion.

Harini’s philosophy is fundamentally that the CCO is the CFO’s revenue de-risking partner and should see themselves in a pseudo-CFO role, which she calls the Closet CFO. The CCO brings the most reliable, most forecast-able substrate of next year’s revenue. It’s way more impactful than just stating a retention rate would imply. (check out Harini’s interview with Churn.FM as well)

It is cheaper to sell to your existing customers as is shown in the chart from benchmarkit below. Said more technically, expansion CAC is lower than new CAC. It is clearly important to CFOs: lower acquisition investment leads to increased revenue from upsell and margin from uplift. With the right data its more predictable and a better overall capital return. A balance is of course essential, but it also helps identify issues with companies. For example, companies focusing on net-new CAC and, while still seeing top-line growth, what’s going on behind the scenes are declining retention metrics and a failing product and market vision overall. Net-new is a temporary band-aid.

The customer success team is the function closest to the customer. They see the expansion triggers and contraction signals way before anyone else. Integrate that contextual knowledge with solid data and usage monitoring and you have a very powerful customer evaluation framework. The recommendation then is for CCOs to run their function as a forecasting input, not a retention cost-center, as that is what is of most value to the CFO (or as Maxio outlines, companies should seek alignment between CS and the CFO).

The math: how the install base shrinks the net-new problem

Quantifying this in an example: Assume $1B target revenue, with a currently installed base of $400MM. A 90%+ GRR commitment de-risks $350MM before sales even has to touch a new logo. Or, every 1 percent of GRR at this scale represents $4MM of net-new sales the company doesn’t have to find and book. That increased retention leads directly to lower AE and SDR resourcing requirements—it directly affects net profitability. As a result GRR becomes a very powerful metric with indicators for both revenue growth and profitability.

Where this really shows up is in how the public markets value companies that vary across the NRR and GRR dimension (SaaSMag covers this well). You can distill things down to: A dollar of de-risked revenue is worth more than a probability weighted dollar of net-new revenue, even at the same nominal amount. Predictable revenue necessarily leads to a higher valuation multiple, it represents a more powerful company on paper. In the public markets revenue quality is priced explicitly, NRR and GRR are high up on the topic list in earnings materials and comps treat a 115% NRR business and a 100% NRR business as different categories of company. For private companies, this manifests itself in returns projections and working-capital planning. You can run a leaner company when the revenue base is reliable.

And at the board level, the CFO’s challenge is reduced, with a large segment of the revenue locked in at high confidence then the board and executives can focus on the more uncertain and variable component of net-new. Guaranteed to make you friends with your CFO.

Why every renewal must be a growth event

Every renewal must achieve expansion. Consider NRR and the magical pivot point of 100%. Below 100% and the company needs to add in new recurring revenue just to stay flat, you’re fighting to stay in the same place and as we cover in more detail below you’re spending more on acquisition per dollar of ARR when supplementing with new versus expansion. Above 100% and that company is successfully growing its existing customer base, and retaining that performance is compound growth. This is why Harini states the importance of renewal ARR increase “a flat renewal is a failed renewal”. Powerhouse growth comes from deepening the relationship and impact with your customers, demonstrating the product’s value continually over time and expanding scope of usage within the customer.

There’s a tendency to conflate the three main factors in renewal value: uplift, upsell and cross-sell. The first, uplift, is an increase in price for the same product or service—no change in what the customer is experiencing, just a capture of additional monetary value. The second, upsell, is an expansion of the product within the customer—additional seats, increased service capacity, more transaction volume etc. The customer is expanding their usage of the product. The third, cross-sell, is layering additional products or services into the relationship. New SKUs, services lines or similar to support different use cases.

A product with established foot-hold has pricing power. Even in this world of AI revolution it is still the case that enterprise environments have switching costs, and it is the imperative of the seller to monetize that latent value. Renewals have to be seen not as maintaining the status-quo, but as furthering the commitment of the customer to the product. Therein lies the secret to increased revenue and margin growth.

The cheapest dollar of growth on the P&L

It is striking how many companies only quantify and track new CAC and essentially ignore expansion CAC. As my discussion with Harini demonstrated, the margin growth opportunity with existing customers is a fundamental track to growth out-performing the market. There’s also no or proportionally lower integration or cost to deliver the expanded service. The customer is already on the product, using it and gaining the value-add experience.

Moreover, the expansion is forecast-able. Data available from customer usage gives clear indication on adoption and user dynamics that with the right assessment rubric can inform renewal and expansion selling conversations. Net-new by contrast relies on pipeline closing performance and is subject to sales-cycle timelines that can vary between prospects and also over time as the market evolves. Renewal timelines are transparent and very much under control of the company. CFOs can treat renewals as high-confidence whereas new-logo falls into wide-variance. This translates directly to working-capital planning and resourcing and eventually external valuation of the business model.

On every dimension relevant to the CFO, whether CAC, gross margin, predictability or capital efficiency, expansion is the preferable and better dollar for dollar. Companies as they scale past the product validation stage need to build out the customer success infrastructure to capitalize on existing customer dynamics.

The Closet CFO mindset

Underlying the whole thread here is a unique value represented by customer success and CCOs to CFOs: they bring the most secure and reliable source of revenue for upcoming periods. They are in a way, a CFOs greatest friend. And healthy companies will demonstrate good relationships between their CFO and CCO.

Aside from the technical details, and metrics recommendations embedded in here, that certainly should be a general takeaway: CFOs should look to CCOs as a key source of recurring revenue and cashflow, which provides the foundation on which to plan for upcoming periods. And CCOs should understand the value they present to the CFO and invest in that relationship to secure appropriate support and investment for maximizing the company’s growth potential.

In wrapping up, Harini suggests CCOs become “Closet CFOs”: the CCO should think and act like a CFO and in that way will build the most productive relationship with their CFO. Companies that achieve this integration in their exec team will capture the increased growth and margin opportunity and see that manifested in capital efficiency and valuation.

Look out soon for the second and third installments in this series on leadership in customer success!

AI’s Contribution

I wrote every word in this article. I like em-dashes. I do use Claude Code to help refine topic flow and structure plus research for articles.

Title and subtitle into ChatGPT for a “radical image”:

• Bitter Lesson, Rich Sutton, 2019
  It's bitter because the lesson keeps getting re-taught. Attempts to embed our interpretation of discovery and understanding into AI are quickly surpassed in performance by generalized approaches to search and learning with increased computing power. Otherwise summarized: "the only thing that matters in the long run is the leveraging of computation". In the field of finance and accounting, where we have a tendency to hold in high esteem the contribution of human experience, I wonder how this'll play out in application of AI and agentic AI. It's compelling (and comforting) to think our current methods and processes contain permanent value, but maybe it's all rather irrelevant in the not too distant future? In which case, our current problem solving approaches aren’t useful in training models, but our ongoing interactions are critical for inference.

• Roadmap: AI systems of action, Bessemer Venture Partners, 2025
  An important thread in the disruption of legacy ERP is the application of AI to the implementation process, which "means the time and cost to move to a new system are significantly less." This has big (ooh, huge) potential: Current incumbents' moats rely upon high switching costs, remove that and you have a much more open playing field. And not to mention the increased ease of building features in platforms using AI, this means—as I think has been said most succinctly by Franz Farber of Everest Systems—the true value of platforms will be in the guarantee of an outcome (which is really the value the customer actually needs), e.g. a guarantee your financials are accurate and you will be audit ready…

• Future of Work with AI Agents, Stanford University, 2025
  "Domain workers want automation for low-value and repetitive tasks." People naturally want to spend less time doing the boring stuff and it means more time is available for higher-value work, which should result in higher profit margins. Win-win all around then.
  Though I wonder: Sometimes we enjoy not having to turn our brains on, especially the morning after the night before... do we switch AI off at that point?
  The paper also highlights the three most prominent concerns about AI:
  1. lack of trust in accuracy
  2. fear of job replacement
  3. absence of human qualities or capabilities (see above and the Bitter Lesson)
  Then in conclusion "From an incentive perspective, some workers may also withhold honest feedback due to concerns about job security or surveillance." Key takeaway for executives in companies investing in AI usage—bring the team along and focus on reskilling, they are part of the solution not a consequence of it.

• How CFOs are navigating growth, pricing, and forecasting in an AI world, a16z, July 2025
  Annual subscription licenses and revenue recognition often mask the underlying business health—customers may not even be using the platform, but yet the P&L shows revenue recognition on an amortized basis. This happens more often than you'd think. Consumption based pricing (and consequently revenue recognition) would better tie reported financial performance to actual customer value delivered. Finance processes to efficiently implement this have to depend on automation.
  Approaches to ARR then need refined to include annualized estimates of consumption revenue. And then there’s margin: Optimizing margin in operations becomes a fine-grain task of managing costs with usage, requiring clarity on financial impact on a much shorter timeframe than usual month close cycles. Systems need to reliably link activity to predicted financial consequences so optimizations can be made throughout each month.

  I agree with this: "You can't achieve the precision required in consumption forecasting using Microsoft Excel — you have to use advanced analytics, machine learning, and AI to build those predictions."

  But, the unending flexibility of Excel is going to be very difficult to unseat as the principle work medium for finance, FP&A etc. It's a question in the nearer term then of how Excel is augmented (time to highlight Aleph as a key player) by the increased ease of data access and all that brings to the challenge. And then we're into how AI can help make sense of that data—will be fascinating to see how interfaces will evolve to enable natural language direction.

• Blub's Paradox in Finance, Looks Gerat!, March 2025
  Great perspective on why Excel users tend to stick with Excel. Before LLMs and the advent of easy code generation it took a lot more brain power and time commitment to wrangle Python for data processing, Now it's remarkably easy to craft up a script for manipulating and visualizing data—training LLMs benefits from copious amounts of Python code in the ether and so the resulting models can produce pretty useful code straight off the bat. However, even though complex Excel formulae represent many coding constructs (and so Excel power users should be able to pick up more powerful languages) the whole environment of coding is vastly different to that of spreadsheets. Yes, stuff like version control becomes a built-in feature rather than hacked using filenames, but the user needs to build familiarity with projects, libraries, version control and repos, managing virtual environments, not to mention unit testing—which is snother of the massive benefits of coding vs spreadsheets--and so on to actually access the power. I'm a big proponent of moving large portions of what we deal with in financial projections, modeling and analysis to coding—it's just that the complexity of doing that and maintaining reliability and integrity in the output shouldn't be underestimated.

In the Glass…

Glen Scotia 7 Year, 1st Fill Ruby Port Hogshead

Exclusive cask, bottled for Loch Fyne Whiskies

Picked this up on a recent trip back home, from Loch Fyne Whiskies in Inverary, an almost guaranteed stop for me on the drive west. Glen Scotia is one of my absolute favourite distilleries and I never fail to enjoy what they release (code #93 with SMWS by the way, and I have a few of those bottles). The exclusives that LFW get are always excellent and sell out very quickly. This one is no different, the port notes clear and distinct, and the slight smoke that just trickles through on the palate. Terrific stuff.

Last Thought…

I used em dashes long before ChatGPT got into the game but I take no responsibility for correct or incorrect usage, or indeed for the mixed up British vs US spelling above. However, I do strongly recommend checking out ellecordova’s Instagram for some fun with grammar and planets.

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The potential for AI to automate functions like finance is touted by legacy platforms, emerging technology solutions, investors and even some finance leaders. But it's clear to me that the solutions most likely to succeed all feature "Human in the Loop" (HITL).

The amount of marketing collateral and online discourse around AI in finance is bewildering. Yes, there will unquestionably be huge benefits over time but it can be very difficult to separate wheat from the chaff in understanding what these platforms currently use AI to do, where they actually use AI (you are perfectly within your rights to ask this of vendors and require a detailed response) and then what it means for the user.

Focusing on an HITL approach in a product is pretty sensible–done right it serves both as a practical necessity for accuracy and a psychological bridge for user adoption. Has struck me recently, though, as to whether this is singularly a technical requirement for success or just a clever strategy to better ensure adoption by users.

Is HITL playing it safe or a perfectly honest marketing message?

Walk through any finance technology conference or browse vendor websites, and you'll notice a pattern. Nearly every AI solution emphasizes how it "empowers finance professionals" rather than replaces them. The message is carefully crafted: AI handles the mundane while humans focus on "strategic work." It's a compelling narrative that addresses the elephant in every room–will AI take my job?

This positioning is brilliant from a go-to-market perspective. Finance teams are naturally conservative and risk-averse. They're also the ones who, importantly for the vendor, control the purchasing decisions. By promoting HITL, vendors transform from threats into partners. The AI becomes a helpful copilot or assistant rather than a replacement. The fear factor disappears, replaced by curiosity about efficiency gains.

Perhaps there’s even a pricing element that, paradoxically, it’s possible to charge more for a platform that requires human oversight than one without. This plays to the conspiracy believers that it’s a marketing ploy.

Or maybe this messaging is actually honest.

When humans actually matter: connecting the dots

Working in finance, whether in accounting, FP&A or at the leadership level, frequently requires judgment and subjective analysis. So much of what makes up finance is based on assumptions around known unknowns (eek), topics lacking clarity or application of accounting guidelines to situations that don't easily fit into one box or the other.

Take for example an unusual or limited-information transaction in the month close process–should it be expensed, accrued, or flagged as an issue? A reliable answer to this usually is guided by accountant knowledge, years of finance experience, and contextual information perhaps only available at the business level.

What's also interesting here is how platforms will likely evolve to include a greater degree of business activity and data for AI to review. It's compelling to think of AI accessing vendor contracts, email threads about ongoing negotiations, Slack messages about project status, even calendar entries about customer meetings–all to better determine the nature of a transaction and how to handle it in accounting. The AI could theoretically triangulate from multiple data sources to understand context the way a senior accountant does through experience and conversation–and with much greater speed.

But until we're there–and we're definitely not there yet–it's really only feasible for a human to step in and close the loop. The gap between available data and required judgment remains too wide for AI to bridge alone.

The adoption game hinges on understanding accountants (not just accounting)

But, being honest, the HITL emphasis is also shrewd business strategy. I've seen enough technology implementations to know that the human element often is the determining factor between success and failure. Sometimes there are change-resisters who just throw up roadblocks, or at an extreme people who feel that their job is at risk then deliberately sabotage the project. You’d be amazed at what can happen.

HITL flips this dynamic. When the finance manager can review and override AI suggestions, they feel in control. When they see their corrections training the system, they become invested in its success. Each month, as they trust the AI with more routine decisions, they're actively participating in their own role evolution rather than being dragged along and eventually snuffed out.

This psychological element might be even more important than the technical requirements. A solution that's 90% accurate with enthusiastic users will succeed where a 95% accurate solution with resistant users fails. Vendors who understand this truth position HITL not as a limitation but as truly as a differentiating feature.

Finance as judgement not calculation

Yet still it's tempting to view HITL as primarily a marketing strategy–a temporary comfort blanket until AI gets good enough to fully automate finance functions. There's certainly some truth to this. Basic transaction coding and invoice matching will likely need less human oversight over time. The "human in the loop" for these tasks is partly about building trust during the transition.

But this cynical view misses something important. The aspects of finance that require judgment, context, and accountability aren't edge cases–they're core to the function. Financial reporting isn't just about processing transactions; it's about telling the business story accurately and defensibly. FP&A isn't just about financial metrics and drawing lines on a chart; it's about understanding strategy and market dynamics.

The vendors emphasizing HITL might be solving for adoption, but they're also acknowledging a fundamental truth: finance is as much about judgment as calculation. Correspondingly, it can be asserted that the most successful AI implementations will be those that enhance human judgment rather than attempt to replace it.

So where does this leave us…

I can’t see any other (partial) conclusion than: HITL serves dual purposes, technical necessity and adoption catalyst. Smart vendors recognize both aspects and design their solutions accordingly. They're not being dishonest when they emphasize human empowerment–they're being realistic about both current AI capabilities and the nature of finance work.

I think that for finance leaders evaluating AI solutions, understanding this duality is really helpful. Yes, some vendors probably do oversell HITL to quell fears about job displacement. But I think the best solutions genuinely need human expertise to function effectively in the complex, judgment-heavy world of finance. Going beyond that statement right now risks getting drowned in untethered hyperbole about AI taking over the world and replacing everyone except plumbers (which of course are separately at risk from the robots).

The interesting question isn't whether HITL is necessary or just clever marketing (it's really kind of both). After all, in a field where "it depends" is often a critical component in developing an accurate answer, keeping humans in the loop ensures a better outcome for the user and customer.

And AI’s contribution:

I chucked the title and subtitle into ChatGPT 5 and asked it to generate a radical image. Not bad, and the words are all correct.